Merge branch 'master' into master

44 files changed, 5320 insertions, 823 deletions

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index e62c6657..737e1a76 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -8,15 +8,13 @@ import torch
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
 from modules.bsrgan_model_arch import RRDBNet
-from modules.paths import models_path
 
 
 class UpscalerBSRGAN(modules.upscaler.Upscaler):
     def __init__(self, dirname):
         self.name = "BSRGAN"
-        self.model_path = os.path.join(models_path, self.name)
         self.model_name = "BSRGAN 4x"
         self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
         self.user_path = dirname
@@ -44,13 +42,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         model = self.load_model(selected_file)
         if model is None:
             return img
-        model.to(shared.device)
+        model.to(devices.device_bsrgan)
         torch.cuda.empty_cache()
         img = np.array(img)
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(shared.device)
+        img = img.unsqueeze(0).to(devices.device_bsrgan)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index a29f3855..e6d9fa4f 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -69,10 +69,14 @@ def setup_model(dirname):
 

                 self.net = net

                 self.face_helper = face_helper

-                self.net.to(devices.device_codeformer)

 

                 return net, face_helper

 

+            def send_model_to(self, device):

+                self.net.to(device)

+                self.face_helper.face_det.to(device)

+                self.face_helper.face_parse.to(device)

+

             def restore(self, np_image, w=None):

                 np_image = np_image[:, :, ::-1]

 

@@ -82,6 +86,8 @@ def setup_model(dirname):
                 if self.net is None or self.face_helper is None:

                     return np_image

 

+                self.send_model_to(devices.device_codeformer)

+

                 self.face_helper.clean_all()

                 self.face_helper.read_image(np_image)

                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)

@@ -113,8 +119,10 @@ def setup_model(dirname):
                 if original_resolution != restored_img.shape[0:2]:

                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)

 

+                self.face_helper.clean_all()

+

                 if shared.opts.face_restoration_unload:

-                    self.net.to(devices.cpu)

+                    self.send_model_to(devices.cpu)

 

                 return restored_img

 

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
new file mode 100644
index 00000000..4ad334a1
--- /dev/null
+++ b/modules/deepbooru.py
@@ -0,0 +1,173 @@
+import os.path
+from concurrent.futures import ProcessPoolExecutor
+import multiprocessing
+import time
+import re
+
+re_special = re.compile(r'([\\()])')
+
+def get_deepbooru_tags(pil_image):
+    """
+    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
+    """
+    from modules import shared  # prevents circular reference
+
+    try:
+        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
+        return get_tags_from_process(pil_image)
+    finally:
+        release_process()
+
+
+OPT_INCLUDE_RANKS = "include_ranks"
+def create_deepbooru_opts():
+    from modules import shared
+
+    return {
+        "use_spaces": shared.opts.deepbooru_use_spaces,
+        "use_escape": shared.opts.deepbooru_escape,
+        "alpha_sort": shared.opts.deepbooru_sort_alpha,
+        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
+    }
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
+    model, tags = get_deepbooru_tags_model()
+    while True: # while process is running, keep monitoring queue for new image
+        pil_image = queue.get()
+        if pil_image == "QUIT":
+            break
+        else:
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
+
+
+def create_deepbooru_process(threshold, deepbooru_opts):
+    """
+    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
+    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
+    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
+    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
+    the tags.
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_manager = multiprocessing.Manager()
+    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
+    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
+    shared.deepbooru_process.start()
+
+
+def get_tags_from_process(image):
+    from modules import shared
+
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process_queue.put(image)
+    while shared.deepbooru_process_return["value"] == -1:
+        time.sleep(0.2)
+    caption = shared.deepbooru_process_return["value"]
+    shared.deepbooru_process_return["value"] = -1
+
+    return caption
+
+
+def release_process():
+    """
+    Stops the deepbooru process to return used memory
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_queue.put("QUIT")
+    shared.deepbooru_process.join()
+    shared.deepbooru_process_queue = None
+    shared.deepbooru_process = None
+    shared.deepbooru_process_return = None
+    shared.deepbooru_process_manager = None
+
+def get_deepbooru_tags_model():
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
+    this_folder = os.path.dirname(__file__)
+    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
+    if not os.path.exists(os.path.join(model_path, 'project.json')):
+        # there is no point importing these every time
+        import zipfile
+        from basicsr.utils.download_util import load_file_from_url
+        load_file_from_url(
+            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+            model_path)
+        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
+            zip_ref.extractall(model_path)
+        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
+
+    tags = dd.project.load_tags_from_project(model_path)
+    model = dd.project.load_model_from_project(
+        model_path, compile_model=False
+    )
+    return model, tags
+
+
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
+
+    alpha_sort = deepbooru_opts['alpha_sort']
+    use_spaces = deepbooru_opts['use_spaces']
+    use_escape = deepbooru_opts['use_escape']
+    include_ranks = deepbooru_opts['include_ranks']
+
+    width = model.input_shape[2]
+    height = model.input_shape[1]
+    image = np.array(pil_image)
+    image = tf.image.resize(
+        image,
+        size=(height, width),
+        method=tf.image.ResizeMethod.AREA,
+        preserve_aspect_ratio=True,
+    )
+    image = image.numpy()  # EagerTensor to np.array
+    image = dd.image.transform_and_pad_image(image, width, height)
+    image = image / 255.0
+    image_shape = image.shape
+    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
+
+    y = model.predict(image)[0]
+
+    result_dict = {}
+
+    for i, tag in enumerate(tags):
+        result_dict[tag] = y[i]
+
+    unsorted_tags_in_theshold = []
+    result_tags_print = []
+    for tag in tags:
+        if result_dict[tag] >= threshold:
+            if tag.startswith("rating:"):
+                continue
+            unsorted_tags_in_theshold.append((result_dict[tag], tag))
+            result_tags_print.append(f'{result_dict[tag]} {tag}')
+
+    # sort tags
+    result_tags_out = []
+    sort_ndx = 0
+    if alpha_sort:
+        sort_ndx = 1
+
+    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
+    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
+    for weight, tag in unsorted_tags_in_theshold:
+        # note: tag_outformat will still have a colon if include_ranks is True
+        tag_outformat = tag.replace(':', ' ')
+        if use_spaces:
+            tag_outformat = tag_outformat.replace('_', ' ')
+        if use_escape:
+            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+        if include_ranks:
+            tag_outformat = f"({tag_outformat}:{weight:.3f})"
+
+        result_tags_out.append(tag_outformat)
+
+    print('\n'.join(sorted(result_tags_print, reverse=True)))
+
+    return ', '.join(result_tags_out)
diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..eb422583 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,8 +1,10 @@
+import contextlib
+
 import torch
 
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
 
+# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
@@ -32,10 +34,9 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-
-device = get_optimal_device()
-device_codeformer = cpu if has_mps else device
-
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+dtype = torch.float16
+dtype_vae = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
@@ -58,3 +59,14 @@ def randn_without_seed(shape):
 
     return torch.randn(shape, device=device)
 
+
+def autocast(disable=False):
+    from modules import shared
+
+    if disable:
+        return contextlib.nullcontext()
+
+    if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+        return contextlib.nullcontext()
+
+    return torch.autocast("cuda")
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ea91abfe..46ad0da3 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -5,10 +5,8 @@ import torch
 from PIL import Image

 from basicsr.utils.download_util import load_file_from_url

 

-import modules.esrgam_model_arch as arch

-from modules import shared, modelloader, images

-from modules.devices import has_mps

-from modules.paths import models_path

+import modules.esrgan_model_arch as arch

+from modules import shared, modelloader, images, devices

 from modules.upscaler import Upscaler, UpscalerData

 from modules.shared import opts

 

@@ -73,11 +71,10 @@ def fix_model_layers(crt_model, pretrained_net):
 class UpscalerESRGAN(Upscaler):

     def __init__(self, dirname):

         self.name = "ESRGAN"

-        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"

-        self.model_name = "ESRGAN 4x"

+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"

+        self.model_name = "ESRGAN_4x"

         self.scalers = []

         self.user_path = dirname

-        self.model_path = os.path.join(models_path, self.name)

         super().__init__()

         model_paths = self.find_models(ext_filter=[".pt", ".pth"])

         scalers = []

@@ -97,7 +94,7 @@ class UpscalerESRGAN(Upscaler):
         model = self.load_model(selected_model)

         if model is None:

             return img

-        model.to(shared.device)

+        model.to(devices.device_esrgan)

         img = esrgan_upscale(model, img)

         return img

 

@@ -112,7 +109,7 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))

             return None

 

-        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)

+        pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)

 

         pretrained_net = fix_model_layers(crt_model, pretrained_net)

@@ -127,7 +124,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]

     img = np.moveaxis(img, 2, 0) / 255

     img = torch.from_numpy(img).float()

-    img = img.unsqueeze(0).to(shared.device)

+    img = img.unsqueeze(0).to(devices.device_esrgan)

     with torch.no_grad():

         output = model(img)

     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()

diff --git a/modules/esrgam_model_arch.py b/modules/esrgan_model_arch.py
index e413d36e..e413d36e 100644
--- a/modules/esrgam_model_arch.py
+++ b/modules/esrgan_model_arch.py
diff --git a/modules/extras.py b/modules/extras.py
index 6a0d5cb0..f2f5a7b0 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+import math

 import os

 

 import numpy as np

@@ -19,7 +20,7 @@ import gradio as gr
 cached_images = {}

 

 

-def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):

+def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):

     devices.torch_gc()

 

     imageArr = []

@@ -29,7 +30,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
     if extras_mode == 1:

         #convert file to pillow image

         for img in image_folder:

-            image = Image.fromarray(np.array(Image.open(img)))

+            image = Image.open(img)

             imageArr.append(image)

             imageNameArr.append(os.path.splitext(img.orig_name)[0])

     else:

@@ -67,8 +68,13 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"

             image = res

 

+        if resize_mode == 1:

+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)

+            crop_info = " (crop)" if upscaling_crop else ""

+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"

+

         if upscaling_resize != 1.0:

-            def upscale(image, scaler_index, resize):

+            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):

                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))

                 pixels = tuple(np.array(small).flatten().tolist())

                 key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels

@@ -77,15 +83,19 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                 if c is None:

                     upscaler = shared.sd_upscalers[scaler_index]

                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)

+                    if mode == 1 and crop:

+                        cropped = Image.new("RGB", (resize_w, resize_h))

+                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))

+                        c = cropped

                     cached_images[key] = c

 

                 return c

 

             info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"

-            res = upscale(image, extras_upscaler_1, upscaling_resize)

+            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)

 

             if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:

-                res2 = upscale(image, extras_upscaler_2, upscaling_resize)

+                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)

                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"

                 res = Image.blend(res, res2, extras_upscaler_2_visibility)

 

@@ -98,8 +108,14 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                           no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,

                           forced_filename=image_name if opts.use_original_name_batch else None)

 

+        if opts.enable_pnginfo:

+            image.info = existing_pnginfo

+            image.info["extras"] = info

+

         outputs.append(image)

 

+    devices.torch_gc()

+

     return outputs, plaintext_to_html(info), ''

 

 

@@ -143,48 +159,52 @@ def run_pnginfo(image):
     return '', geninfo, info

 

 

-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):

-    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)

-    def weighted_sum(theta0, theta1, alpha):

+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):

+    def weighted_sum(theta0, theta1, theta2, alpha):

         return ((1 - alpha) * theta0) + (alpha * theta1)

 

-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)

-    def sigmoid(theta0, theta1, alpha):

-        alpha = alpha * alpha * (3 - (2 * alpha))

-        return theta0 + ((theta1 - theta0) * alpha)

-

-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)

-    def inv_sigmoid(theta0, theta1, alpha):

-        import math

-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)

-        return theta0 + ((theta1 - theta0) * alpha)

+    def add_difference(theta0, theta1, theta2, alpha):

+        return theta0 + (theta1 - theta2) * alpha

 

     primary_model_info = sd_models.checkpoints_list[primary_model_name]

     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]

+    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)

 

     print(f"Loading {primary_model_info.filename}...")

     primary_model = torch.load(primary_model_info.filename, map_location='cpu')

+    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)

 

     print(f"Loading {secondary_model_info.filename}...")

     secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')

-   

-    theta_0 = primary_model['state_dict']

-    theta_1 = secondary_model['state_dict']

+    theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)

+

+    if teritary_model_info is not None:

+        print(f"Loading {teritary_model_info.filename}...")

+        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')

+        theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)

+    else:

+        theta_2 = None

 

     theta_funcs = {

-        "Weighted Sum": weighted_sum,

-        "Sigmoid": sigmoid,

-        "Inverse Sigmoid": inv_sigmoid,

+        "Weighted sum": weighted_sum,

+        "Add difference": add_difference,

     }

     theta_func = theta_funcs[interp_method]

 

     print(f"Merging...")

+

     for key in tqdm.tqdm(theta_0.keys()):

         if 'model' in key and key in theta_1:

-            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint

+            t2 = (theta_2 or {}).get(key)

+            if t2 is None:

+                t2 = torch.zeros_like(theta_0[key])

+

+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)

+

             if save_as_half:

                 theta_0[key] = theta_0[key].half()

-    

+

+    # I believe this part should be discarded, but I'll leave it for now until I am sure

     for key in theta_1.keys():

         if 'model' in key and key not in theta_0:

             theta_0[key] = theta_1[key]

@@ -193,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
 

     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path

 

-    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'

+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'

     filename = filename if custom_name == '' else (custom_name + '.ckpt')

     output_modelname = os.path.join(ckpt_dir, filename)

 

@@ -203,4 +223,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
     sd_models.list_models()

 

     print(f"Checkpoint saved.")

-    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]

+    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index ac1ba7f4..c27826b6 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,5 +1,8 @@
+import os

 import re

 import gradio as gr

+from modules.shared import script_path

+from modules import shared

 

 re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"

 re_param = re.compile(re_param_code)

@@ -61,6 +64,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 

 def connect_paste(button, paste_fields, input_comp, js=None):

     def paste_func(prompt):

+        if not prompt and not shared.cmd_opts.hide_ui_dir_config:

+            filename = os.path.join(script_path, "params.txt")

+            if os.path.exists(filename):

+                with open(filename, "r", encoding="utf8") as file:

+                    prompt = file.read()

+

         params = parse_generation_parameters(prompt)

         res = []

 

diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..a9452dce 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -21,7 +21,7 @@ def gfpgann():
     global loaded_gfpgan_model

     global model_path

     if loaded_gfpgan_model is not None:

-        loaded_gfpgan_model.gfpgan.to(shared.device)

+        loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)

         return loaded_gfpgan_model

 

     if gfpgan_constructor is None:

@@ -37,22 +37,32 @@ def gfpgann():
         print("Unable to load gfpgan model!")

         return None

     model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)

-    model.gfpgan.to(shared.device)

     loaded_gfpgan_model = model

 

     return model

 

 

+def send_model_to(model, device):

+    model.gfpgan.to(device)

+    model.face_helper.face_det.to(device)

+    model.face_helper.face_parse.to(device)

+

+

 def gfpgan_fix_faces(np_image):

     model = gfpgann()

     if model is None:

         return np_image

+

+    send_model_to(model, devices.device_gfpgan)

+

     np_image_bgr = np_image[:, :, ::-1]

     cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)

     np_image = gfpgan_output_bgr[:, :, ::-1]

 

+    model.face_helper.clean_all()

+

     if shared.opts.face_restoration_unload:

-        model.gfpgan.to(devices.cpu)

+        send_model_to(model, devices.cpu)

 

     return np_image

 

@@ -97,11 +107,7 @@ def setup_model(dirname):
                 return "GFPGAN"

 

             def restore(self, np_image):

-                np_image_bgr = np_image[:, :, ::-1]

-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)

-                np_image = gfpgan_output_bgr[:, :, ::-1]

-

-                return np_image

+                return gfpgan_fix_faces(np_image)

 

         shared.face_restorers.append(FaceRestorerGFPGAN())

     except Exception:

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
new file mode 100644
index 00000000..a2b3bc0a
--- /dev/null
+++ b/modules/hypernetworks/hypernetwork.py
@@ -0,0 +1,347 @@
+import datetime

+import glob

+import html

+import os

+import sys

+import traceback

+import tqdm

+import csv

+

+import torch

+

+from ldm.util import default

+from modules import devices, shared, processing, sd_models

+import torch

+from torch import einsum

+from einops import rearrange, repeat

+import modules.textual_inversion.dataset

+from modules.textual_inversion import textual_inversion

+from modules.textual_inversion.learn_schedule import LearnRateScheduler

+

+

+class HypernetworkModule(torch.nn.Module):

+    multiplier = 1.0

+

+    def __init__(self, dim, state_dict=None):

+        super().__init__()

+

+        self.linear1 = torch.nn.Linear(dim, dim * 2)

+        self.linear2 = torch.nn.Linear(dim * 2, dim)

+

+        if state_dict is not None:

+            self.load_state_dict(state_dict, strict=True)

+        else:

+

+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)

+            self.linear1.bias.data.zero_()

+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)

+            self.linear2.bias.data.zero_()

+

+        self.to(devices.device)

+

+    def forward(self, x):

+        return x + (self.linear2(self.linear1(x))) * self.multiplier

+

+

+def apply_strength(value=None):

+    HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength

+

+

+class Hypernetwork:

+    filename = None

+    name = None

+

+    def __init__(self, name=None, enable_sizes=None):

+        self.filename = None

+        self.name = name

+        self.layers = {}

+        self.step = 0

+        self.sd_checkpoint = None

+        self.sd_checkpoint_name = None

+

+        for size in enable_sizes or []:

+            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))

+

+    def weights(self):

+        res = []

+

+        for k, layers in self.layers.items():

+            for layer in layers:

+                layer.train()

+                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]

+

+        return res

+

+    def save(self, filename):

+        state_dict = {}

+

+        for k, v in self.layers.items():

+            state_dict[k] = (v[0].state_dict(), v[1].state_dict())

+

+        state_dict['step'] = self.step

+        state_dict['name'] = self.name

+        state_dict['sd_checkpoint'] = self.sd_checkpoint

+        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name

+

+        torch.save(state_dict, filename)

+

+    def load(self, filename):

+        self.filename = filename

+        if self.name is None:

+            self.name = os.path.splitext(os.path.basename(filename))[0]

+

+        state_dict = torch.load(filename, map_location='cpu')

+

+        for size, sd in state_dict.items():

+            if type(size) == int:

+                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))

+

+        self.name = state_dict.get('name', self.name)

+        self.step = state_dict.get('step', 0)

+        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)

+        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)

+

+

+def list_hypernetworks(path):

+    res = {}

+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):

+        name = os.path.splitext(os.path.basename(filename))[0]

+        res[name] = filename

+    return res

+

+

+def load_hypernetwork(filename):

+    path = shared.hypernetworks.get(filename, None)

+    if path is not None:

+        print(f"Loading hypernetwork {filename}")

+        try:

+            shared.loaded_hypernetwork = Hypernetwork()

+            shared.loaded_hypernetwork.load(path)

+

+        except Exception:

+            print(f"Error loading hypernetwork {path}", file=sys.stderr)

+            print(traceback.format_exc(), file=sys.stderr)

+    else:

+        if shared.loaded_hypernetwork is not None:

+            print(f"Unloading hypernetwork")

+

+        shared.loaded_hypernetwork = None

+

+

+def find_closest_hypernetwork_name(search: str):

+    if not search:

+        return None

+    search = search.lower()

+    applicable = [name for name in shared.hypernetworks if search in name.lower()]

+    if not applicable:

+        return None

+    applicable = sorted(applicable, key=lambda name: len(name))

+    return applicable[0]

+

+

+def apply_hypernetwork(hypernetwork, context, layer=None):

+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)

+

+    if hypernetwork_layers is None:

+        return context, context

+

+    if layer is not None:

+        layer.hyper_k = hypernetwork_layers[0]

+        layer.hyper_v = hypernetwork_layers[1]

+

+    context_k = hypernetwork_layers[0](context)

+    context_v = hypernetwork_layers[1](context)

+    return context_k, context_v

+

+

+def attention_CrossAttention_forward(self, x, context=None, mask=None):

+    h = self.heads

+

+    q = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)

+    k = self.to_k(context_k)

+    v = self.to_v(context_v)

+

+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

+

+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale

+

+    if mask is not None:

+        mask = rearrange(mask, 'b ... -> b (...)')

+        max_neg_value = -torch.finfo(sim.dtype).max

+        mask = repeat(mask, 'b j -> (b h) () j', h=h)

+        sim.masked_fill_(~mask, max_neg_value)

+

+    # attention, what we cannot get enough of

+    attn = sim.softmax(dim=-1)

+

+    out = einsum('b i j, b j d -> b i d', attn, v)

+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)

+    return self.to_out(out)

+

+

+def stack_conds(conds):

+    if len(conds) == 1:

+        return torch.stack(conds)

+

+    # same as in reconstruct_multicond_batch

+    token_count = max([x.shape[0] for x in conds])

+    for i in range(len(conds)):

+        if conds[i].shape[0] != token_count:

+            last_vector = conds[i][-1:]

+            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])

+            conds[i] = torch.vstack([conds[i], last_vector_repeated])

+

+    return torch.stack(conds)

+

+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+    assert hypernetwork_name, 'hypernetwork not selected'

+

+    path = shared.hypernetworks.get(hypernetwork_name, None)

+    shared.loaded_hypernetwork = Hypernetwork()

+    shared.loaded_hypernetwork.load(path)

+

+    shared.state.textinfo = "Initializing hypernetwork training..."

+    shared.state.job_count = steps

+

+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

+

+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)

+    unload = shared.opts.unload_models_when_training

+

+    if save_hypernetwork_every > 0:

+        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")

+        os.makedirs(hypernetwork_dir, exist_ok=True)

+    else:

+        hypernetwork_dir = None

+

+    if create_image_every > 0:

+        images_dir = os.path.join(log_directory, "images")

+        os.makedirs(images_dir, exist_ok=True)

+    else:

+        images_dir = None

+

+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

+    with torch.autocast("cuda"):

+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)

+

+    if unload:

+        shared.sd_model.cond_stage_model.to(devices.cpu)

+        shared.sd_model.first_stage_model.to(devices.cpu)

+

+    hypernetwork = shared.loaded_hypernetwork

+    weights = hypernetwork.weights()

+    for weight in weights:

+        weight.requires_grad = True

+

+    losses = torch.zeros((32,))

+

+    last_saved_file = "<none>"

+    last_saved_image = "<none>"

+

+    ititial_step = hypernetwork.step or 0

+    if ititial_step > steps:

+        return hypernetwork, filename

+

+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)

+

+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)

+    for i, entries in pbar:

+        hypernetwork.step = i + ititial_step

+

+        scheduler.apply(optimizer, hypernetwork.step)

+        if scheduler.finished:

+            break

+

+        if shared.state.interrupted:

+            break

+

+        with torch.autocast("cuda"):

+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)

+#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)

+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)

+            loss = shared.sd_model(x, c)[0]

+            del x

+            del c

+

+            losses[hypernetwork.step % losses.shape[0]] = loss.item()

+

+            optimizer.zero_grad()

+            loss.backward()

+            optimizer.step()

+

+        pbar.set_description(f"loss: {losses.mean():.7f}")

+

+        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:

+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')

+            hypernetwork.save(last_saved_file)

+

+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

+            "loss": f"{losses.mean():.7f}",

+            "learn_rate": scheduler.learn_rate

+        })

+

+        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:

+            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')

+

+            optimizer.zero_grad()

+            shared.sd_model.cond_stage_model.to(devices.device)

+            shared.sd_model.first_stage_model.to(devices.device)

+

+            p = processing.StableDiffusionProcessingTxt2Img(

+                sd_model=shared.sd_model,

+                do_not_save_grid=True,

+                do_not_save_samples=True,

+            )

+

+            if preview_from_txt2img:

+                p.prompt = preview_prompt

+                p.negative_prompt = preview_negative_prompt

+                p.steps = preview_steps

+                p.sampler_index = preview_sampler_index

+                p.cfg_scale = preview_cfg_scale

+                p.seed = preview_seed

+                p.width = preview_width

+                p.height = preview_height

+            else:

+                p.prompt = entries[0].cond_text

+                p.steps = 20

+

+            preview_text = p.prompt

+

+            processed = processing.process_images(p)

+            image = processed.images[0] if len(processed.images)>0 else None

+

+            if unload:

+                shared.sd_model.cond_stage_model.to(devices.cpu)

+                shared.sd_model.first_stage_model.to(devices.cpu)

+

+            if image is not None:

+                shared.state.current_image = image

+                image.save(last_saved_image)

+                last_saved_image += f", prompt: {preview_text}"

+

+        shared.state.job_no = hypernetwork.step

+

+        shared.state.textinfo = f"""

+<p>

+Loss: {losses.mean():.7f}<br/>

+Step: {hypernetwork.step}<br/>

+Last prompt: {html.escape(entries[0].cond_text)}<br/>

+Last saved embedding: {html.escape(last_saved_file)}<br/>

+Last saved image: {html.escape(last_saved_image)}<br/>

+</p>

+"""

+

+    checkpoint = sd_models.select_checkpoint()

+

+    hypernetwork.sd_checkpoint = checkpoint.hash

+    hypernetwork.sd_checkpoint_name = checkpoint.model_name

+    hypernetwork.save(filename)

+

+    return hypernetwork, filename

+

+

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
new file mode 100644
index 00000000..dfa599af
--- /dev/null
+++ b/modules/hypernetworks/ui.py
@@ -0,0 +1,47 @@
+import html

+import os

+

+import gradio as gr

+

+import modules.textual_inversion.textual_inversion

+import modules.textual_inversion.preprocess

+from modules import sd_hijack, shared, devices

+from modules.hypernetworks import hypernetwork

+

+

+def create_hypernetwork(name, enable_sizes):

+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")

+    assert not os.path.exists(fn), f"file {fn} already exists"

+

+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])

+    hypernet.save(fn)

+

+    shared.reload_hypernetworks()

+

+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""

+

+

+def train_hypernetwork(*args):

+

+    initial_hypernetwork = shared.loaded_hypernetwork

+

+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'

+

+    try:

+        sd_hijack.undo_optimizations()

+

+        hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)

+

+        res = f"""

+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.

+Hypernetwork saved to {html.escape(filename)}

+"""

+        return res, ""

+    except Exception:

+        raise

+    finally:

+        shared.loaded_hypernetwork = initial_hypernetwork

+        shared.sd_model.cond_stage_model.to(devices.device)

+        shared.sd_model.first_stage_model.to(devices.device)

+        sd_hijack.apply_optimizations()

+

diff --git a/modules/images.py b/modules/images.py
index f1aed5d6..b9589563 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
 import datetime

+import io

 import math

 import os

 from collections import namedtuple

@@ -23,6 +24,10 @@ def image_grid(imgs, batch_size=1, rows=None):
             rows = opts.n_rows

         elif opts.n_rows == 0:

             rows = batch_size

+        elif opts.grid_prevent_empty_spots:

+            rows = math.floor(math.sqrt(len(imgs)))

+            while len(imgs) % rows != 0:

+                rows -= 1

         else:

             rows = math.sqrt(len(imgs))

             rows = round(rows)

@@ -287,6 +292,20 @@ def apply_filename_pattern(x, p, seed, prompt):
     if seed is not None:

         x = x.replace("[seed]", str(seed))

 

+    if p is not None:

+        x = x.replace("[steps]", str(p.steps))

+        x = x.replace("[cfg]", str(p.cfg_scale))

+        x = x.replace("[width]", str(p.width))

+        x = x.replace("[height]", str(p.height))

+        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))

+        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))

+

+    x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))

+    x = x.replace("[date]", datetime.date.today().isoformat())

+    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))

+    x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))

+

+    # Apply [prompt] at last. Because it may contain any replacement word.^M

     if prompt is not None:

         x = x.replace("[prompt]", sanitize_filename_part(prompt))

         if "[prompt_no_styles]" in x:

@@ -295,7 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
                 if len(style) > 0:

                     style_parts = [y for y in style.split("{prompt}")]

                     for part in style_parts:

-                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')                        

+                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')

             prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()

             x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))

 

@@ -306,19 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt):
                 words = ["empty"]

             x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))

 

-    if p is not None:

-        x = x.replace("[steps]", str(p.steps))

-        x = x.replace("[cfg]", str(p.cfg_scale))

-        x = x.replace("[width]", str(p.width))

-        x = x.replace("[height]", str(p.height))

-        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]), replace_spaces=False))

-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))

-

-    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)

-    x = x.replace("[date]", datetime.date.today().isoformat())

-    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))

-    x = x.replace("[job_timestamp]", shared.state.job_timestamp)

-

     if cmd_opts.hide_ui_dir_config:

         x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)

 

@@ -347,7 +353,39 @@ def get_next_sequence_number(path, basename):
     return result + 1

 

 

-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):

+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):

+    '''Save an image.

+

+    Args:

+        image (`PIL.Image`):

+            The image to be saved.

+        path (`str`):

+            The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.

+        basename (`str`):

+            The base filename which will be applied to `filename pattern`.

+        seed, prompt, short_filename, 

+        extension (`str`):

+            Image file extension, default is `png`.

+        pngsectionname (`str`):

+            Specify the name of the section which `info` will be saved in.

+        info (`str` or `PngImagePlugin.iTXt`):

+            PNG info chunks.

+        existing_info (`dict`):

+            Additional PNG info. `existing_info == {pngsectionname: info, ...}`

+        no_prompt:

+            TODO I don't know its meaning.

+        p (`StableDiffusionProcessing`)

+        forced_filename (`str`):

+            If specified, `basename` and filename pattern will be ignored.

+        save_to_dirs (bool):

+            If true, the image will be saved into a subdirectory of `path`.

+

+    Returns: (fullfn, txt_fullfn)

+        fullfn (`str`):

+            The full path of the saved imaged.

+        txt_fullfn (`str` or None):

+            If a text file is saved for this image, this will be its full path. Otherwise None.

+    '''

     if short_filename or prompt is None or seed is None:

         file_decoration = ""

     elif opts.save_to_dirs:

@@ -371,10 +409,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:

         pnginfo = None

 

-    save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)

+    if save_to_dirs is None:

+        save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)

 

     if save_to_dirs:

-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)

+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')

         path = os.path.join(path, dirname)

 

     os.makedirs(path, exist_ok=True)

@@ -422,7 +461,29 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")

 

     if opts.save_txt and info is not None:

-        with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:

+        txt_fullfn = f"{fullfn_without_extension}.txt"

+        with open(txt_fullfn, "w", encoding="utf8") as file:

             file.write(info + "\n")

+    else:

+        txt_fullfn = None

+

+    return fullfn, txt_fullfn

 

 

+def image_data(data):

+    try:

+        image = Image.open(io.BytesIO(data))

+        textinfo = image.text["parameters"]

+        return textinfo, None

+    except Exception:

+        pass

+

+    try:

+        text = data.decode('utf8')

+        assert len(text) < 10000

+        return text, None

+

+    except Exception:

+        pass

+

+    return '', None

diff --git a/modules/images_history.py b/modules/images_history.py
new file mode 100644
index 00000000..9260df8a
--- /dev/null
+++ b/modules/images_history.py
@@ -0,0 +1,181 @@
+import os
+import shutil
+
+
+def traverse_all_files(output_dir, image_list, curr_dir=None):
+    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+    try:
+        f_list = os.listdir(curr_path)
+    except:
+        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
+            image_list.append(curr_dir)
+        return image_list
+    for file in f_list:
+        file = file if curr_dir is None else os.path.join(curr_dir, file)
+        file_path = os.path.join(curr_path, file)
+        if file[-4:] == ".txt":
+            pass
+        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+            image_list.append(file)
+        else:
+            image_list = traverse_all_files(output_dir, image_list, file)
+    return image_list
+
+
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    image_list = []
+    image_list = traverse_all_files(dir_name, image_list)
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    num = 48 if tabname != "extras" else 12
+    max_page_index = len(image_list) // num + 1
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num
+    image_list = image_list[idx_frm:idx_frm + num]
+    image_index = int(image_index)
+    if image_index < 0 or image_index > len(image_list) - 1:
+        current_file = None
+        hidden = None
+    else:
+        current_file = image_list[int(image_index)]
+        hidden = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+
+
+def first_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, 1, 0, image_index, tabname)
+
+
+def end_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, -1, 0, image_index, tabname)
+
+
+def prev_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
+
+
+def next_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
+
+
+def page_index_change(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
+
+
+def show_image_info(num, image_path, filenames):
+    # print(f"select image {num}")
+    file = filenames[int(num)]
+    return file, num, os.path.join(image_path, file)
+
+
+def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+    if name == "":
+        return filenames, delete_num
+    else:
+        delete_num = int(delete_num)
+        index = list(filenames).index(name)
+        i = 0
+        new_file_list = []
+        for name in filenames:
+            if i >= index and i < index + delete_num:
+                path = os.path.join(dir_name, name)
+                if os.path.exists(path):
+                    print(f"Delete file {path}")
+                    os.remove(path)
+                    txt_file = os.path.splitext(path)[0] + ".txt"
+                    if os.path.exists(txt_file):
+                        os.remove(txt_file)
+                else:
+                    print(f"Not exists file {path}")
+            else:
+                new_file_list.append(name)
+            i += 1
+    return new_file_list, 1
+
+
+def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+    if opts.outdir_samples != "":
+        dir_name = opts.outdir_samples
+    elif tabname == "txt2img":
+        dir_name = opts.outdir_txt2img_samples
+    elif tabname == "img2img":
+        dir_name = opts.outdir_img2img_samples
+    elif tabname == "extras":
+        dir_name = opts.outdir_extras_samples
+    d = dir_name.split("/")
+    dir_name = "/" if dir_name.startswith("/") else d[0]
+    for p in d[1:]:
+        dir_name = os.path.join(dir_name, p)
+    with gr.Row():
+        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
+        first_page = gr.Button('First Page')
+        prev_page = gr.Button('Prev Page')
+        page_index = gr.Number(value=1, label="Page Index")
+        next_page = gr.Button('Next Page')
+        end_page = gr.Button('End Page')
+    with gr.Row(elem_id=tabname + "_images_history"):
+        with gr.Row():
+            with gr.Column(scale=2):
+                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                with gr.Row():
+                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
+            with gr.Column():
+                with gr.Row():
+                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Row():
+                    with gr.Column():
+                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                        img_file_name = gr.Textbox(label="File Name", interactive=False)
+                with gr.Row():
+                    # hiden items
+
+                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
+                    tabname_box = gr.Textbox(tabname, visible=False)
+                    image_index = gr.Textbox(value=-1, visible=False)
+                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
+                    filenames = gr.State()
+                    hidden = gr.Image(type="pil", visible=False)
+                    info1 = gr.Textbox(visible=False)
+                    info2 = gr.Textbox(visible=False)
+
+    # turn pages
+    gallery_inputs = [img_path, page_index, image_index, tabname_box]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+
+    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+
+    # other funcitons
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
+    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+
+
+def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
+    with gr.Blocks(analytics_enabled=False) as images_history:
+        with gr.Tabs() as tabs:
+            with gr.Tab("txt2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
+            with gr.Tab("img2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
+            with gr.Tab("extras history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+    return images_history
diff --git a/modules/img2img.py b/modules/img2img.py
index 03e934e9..24126774 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -23,13 +23,17 @@ def process_batch(p, input_dir, output_dir, args):
 

     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")

 

+    save_normally = output_dir == ''

+

     p.do_not_save_grid = True

-    p.do_not_save_samples = True

+    p.do_not_save_samples = not save_normally

 

     state.job_count = len(images) * p.n_iter

 

     for i, image in enumerate(images):

         state.job = f"{i+1} out of {len(images)}"

+        if state.skipped:

+            state.skipped = False

 

         if state.interrupted:

             break

@@ -48,7 +52,8 @@ def process_batch(p, input_dir, output_dir, args):
                 left, right = os.path.splitext(filename)

                 filename = f"{left}-{n}{right}"

 

-            processed_image.save(os.path.join(output_dir, filename))

+            if not save_normally:

+                processed_image.save(os.path.join(output_dir, filename))

 

 

 def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):

@@ -103,7 +108,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpaint_full_res_padding=inpaint_full_res_padding,

         inpainting_mask_invert=inpainting_mask_invert,

     )

-    print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

+

+    if shared.cmd_opts.enable_console_prompts:

+        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

 

     p.extra_generation_params["Mask blur"] = mask_blur

 

@@ -124,4 +131,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.samples_log_stdout:

         print(generation_info_js)

 

+    if opts.do_not_show_images:

+        processed.images = []

+

     return processed.images, generation_info_js, plaintext_to_html(processed.info)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index f62a4745..9263d65a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 

 re_topn = re.compile(r"\.top(\d+)\.")

 

+

 class InterrogateModels:

     blip_model = None

     clip_model = None

@@ -54,7 +55,7 @@ class InterrogateModels:
 

         model, preprocess = clip.load(clip_model_name)

         model.eval()

-        model = model.to(shared.device)

+        model = model.to(devices.device_interrogate)

 

         return model, preprocess

 

@@ -64,14 +65,14 @@ class InterrogateModels:
             if not shared.cmd_opts.no_half:

                 self.blip_model = self.blip_model.half()

 

-        self.blip_model = self.blip_model.to(shared.device)

+        self.blip_model = self.blip_model.to(devices.device_interrogate)

 

         if self.clip_model is None:

             self.clip_model, self.clip_preprocess = self.load_clip_model()

             if not shared.cmd_opts.no_half:

                 self.clip_model = self.clip_model.half()

 

-        self.clip_model = self.clip_model.to(shared.device)

+        self.clip_model = self.clip_model.to(devices.device_interrogate)

 

         self.dtype = next(self.clip_model.parameters()).dtype

 

@@ -98,11 +99,11 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]

 

         top_count = min(top_count, len(text_array))

-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device)

+        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)

         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)

         text_features /= text_features.norm(dim=-1, keepdim=True)

 

-        similarity = torch.zeros((1, len(text_array))).to(shared.device)

+        similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)

         for i in range(image_features.shape[0]):

             similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)

         similarity /= image_features.shape[0]

@@ -115,14 +116,14 @@ class InterrogateModels:
             transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),

             transforms.ToTensor(),

             transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))

-        ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)

+        ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)

 

         with torch.no_grad():

             caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)

 

         return caption[0]

 

-    def interrogate(self, pil_image):

+    def interrogate(self, pil_image, include_ranks=False):

         res = None

 

         try:

@@ -139,11 +140,11 @@ class InterrogateModels:
 

             res = caption

 

-            cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)

+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)

 

             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext

             with torch.no_grad(), precision_scope("cuda"):

-                image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)

+                image_features = self.clip_model.encode_image(clip_image).type(self.dtype)

 

                 image_features /= image_features.norm(dim=-1, keepdim=True)

 

@@ -155,7 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:

                     matches = self.rank(image_features, items, top_count=topn)

                     for match, score in matches:

-                        res += ", " + match

+                        if include_ranks:

+                            res += ", " + match

+                        else:

+                            res += f", ({match}:{score})"

 

         except Exception:

             print(f"Error interrogating", file=sys.stderr)

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 1c1070fc..8c4db44a 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from modules.ldsr_model_arch import LDSR
 from modules import shared
-from modules.paths import models_path
 
 
 class UpscalerLDSR(Upscaler):
     def __init__(self, user_path):
         self.name = "LDSR"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = user_path
         self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
         self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 8c862b42..b0f2f33d 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -5,7 +5,6 @@ import importlib
 from urllib.parse import urlparse
 
 from basicsr.utils.download_util import load_file_from_url
-
 from modules import shared
 from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
@@ -43,7 +42,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         for place in places:
             if os.path.exists(place):
                 for file in glob.iglob(place + '**/**', recursive=True):
-                    full_path = os.path.join(place, file)
+                    full_path = file
                     if os.path.isdir(full_path):
                         continue
                     if len(ext_filter) != 0:
@@ -121,16 +120,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
 
 
 def load_upscalers():
+    sd = shared.script_path
+    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
+    # so we'll try to import any _model.py files before looking in __subclasses__
+    modules_dir = os.path.join(sd, "modules")
+    for file in os.listdir(modules_dir):
+        if "_model.py" in file:
+            model_name = file.replace("_model.py", "")
+            full_model = f"modules.{model_name}_model"
+            try:
+                importlib.import_module(full_model)
+            except:
+                pass
     datas = []
+    c_o = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
         module_name = cls.__module__
         module = importlib.import_module(module_name)
         class_ = getattr(module, name)
-        cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
+        cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         opt_string = None
         try:
-            opt_string = shared.opts.__getattr__(cmd_name)
+            if cmd_name in c_o:
+                opt_string = c_o[cmd_name]
         except:
             pass
         scaler = class_(opt_string)
diff --git a/modules/ngrok.py b/modules/ngrok.py
new file mode 100644
index 00000000..7d03a6df
--- /dev/null
+++ b/modules/ngrok.py
@@ -0,0 +1,15 @@
+from pyngrok import ngrok, conf, exception
+
+
+def connect(token, port):
+    if token == None:
+        token = 'None'
+    conf.get_default().auth_token = token
+    try:
+        public_url = ngrok.connect(port).public_url
+    except exception.PyngrokNgrokError:
+        print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
+              f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
+    else:
+        print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
+               'You can use this link after the launch is complete.')
diff --git a/modules/paths.py b/modules/paths.py
index ceb80417..1e7a2fbc 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -1,6 +1,7 @@
 import argparse

 import os

 import sys

+import modules.safe

 

 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

 models_path = os.path.join(script_path, "models")

@@ -12,6 +13,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'),
 for possible_sd_path in possible_sd_paths:

     if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):

         sd_path = os.path.abspath(possible_sd_path)

+        break

 

 assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)

 

@@ -20,7 +22,6 @@ path_dirs = [
     (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),

     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),

     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),

-    (os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),

     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),

 ]

 

diff --git a/modules/processing.py b/modules/processing.py
index 1da753a2..7e2a416d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-import contextlib

 import json

 import math

 import os

@@ -12,9 +11,8 @@ import cv2
 from skimage import exposure

 

 import modules.sd_hijack

-from modules import devices, prompt_parser, masking

+from modules import devices, prompt_parser, masking, sd_samplers, lowvram

 from modules.sd_hijack import model_hijack

-from modules.sd_samplers import samplers, samplers_for_img2img

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

 import modules.face_restoration

@@ -48,6 +46,12 @@ def apply_color_correction(correction, image):
     return image

 

 

+def get_correct_sampler(p):

+    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):

+        return sd_samplers.samplers

+    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):

+        return sd_samplers.samplers_for_img2img

+

 class StableDiffusionProcessing:

     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):

         self.sd_model = sd_model

@@ -56,7 +60,7 @@ class StableDiffusionProcessing:
         self.prompt: str = prompt

         self.prompt_for_display: str = None

         self.negative_prompt: str = (negative_prompt or "")

-        self.styles: str = styles

+        self.styles: list = styles or []

         self.seed: int = seed

         self.subseed: int = subseed

         self.subseed_strength: float = subseed_strength

@@ -85,7 +89,7 @@ class StableDiffusionProcessing:
         self.s_tmin = opts.s_tmin

         self.s_tmax = float('inf')  # not representable as a standard ui option

         self.s_noise = opts.s_noise

-        

+

         if not seed_enable_extras:

             self.subseed = -1

             self.subseed_strength = 0

@@ -111,7 +115,7 @@ class Processed:
         self.width = p.width

         self.height = p.height

         self.sampler_index = p.sampler_index

-        self.sampler = samplers[p.sampler_index].name

+        self.sampler = sd_samplers.samplers[p.sampler_index].name

         self.cfg_scale = p.cfg_scale

         self.steps = p.steps

         self.batch_size = p.batch_size

@@ -123,6 +127,9 @@ class Processed:
         self.denoising_strength = getattr(p, 'denoising_strength', None)

         self.extra_generation_params = p.extra_generation_params

         self.index_of_first_image = index_of_first_image

+        self.styles = p.styles

+        self.job_timestamp = state.job_timestamp

+        self.clip_skip = opts.CLIP_stop_at_last_layers

 

         self.eta = p.eta

         self.ddim_discretize = p.ddim_discretize

@@ -133,7 +140,7 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override

         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]

         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]

-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])

+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1

         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1

 

         self.all_prompts = all_prompts or [self.prompt]

@@ -167,6 +174,9 @@ class Processed:
             "extra_generation_params": self.extra_generation_params,

             "index_of_first_image": self.index_of_first_image,

             "infotexts": self.infotexts,

+            "styles": self.styles,

+            "job_timestamp": self.job_timestamp,

+            "clip_skip": self.clip_skip,

         }

 

         return json.dumps(obj)

@@ -197,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     # enables the generation of additional tensors with noise that the sampler will use during its processing.

     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to

     # produce the same images as with two batches [100], [101].

-    if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:

+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):

         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]

     else:

         sampler_noises = None

@@ -237,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:

             cnt = p.sampler.number_of_needed_noises(p)

 

+            if opts.eta_noise_seed_delta > 0:

+                torch.manual_seed(seed + opts.eta_noise_seed_delta)

+

             for j in range(cnt):

                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))

 

@@ -249,29 +262,49 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x

 

 

+def decode_first_stage(model, x):

+    with devices.autocast(disable=x.dtype == devices.dtype_vae):

+        x = model.decode_first_stage(x)

+

+    return x

+

+

+def get_fixed_seed(seed):

+    if seed is None or seed == '' or seed == -1:

+        return int(random.randrange(4294967294))

+

+    return seed

+

+

 def fix_seed(p):

-    p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed

-    p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed

+    p.seed = get_fixed_seed(p.seed)

+    p.subseed = get_fixed_seed(p.subseed)

 

 

 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):

     index = position_in_batch + iteration * p.batch_size

 

+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)

+

     generation_params = {

         "Steps": p.steps,

-        "Sampler": samplers[p.sampler_index].name,

+        "Sampler": get_correct_sampler(p)[p.sampler_index].name,

         "CFG scale": p.cfg_scale,

         "Seed": all_seeds[index],

         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),

         "Size": f"{p.width}x{p.height}",

         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

+        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),

+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),

         "Batch size": (None if p.batch_size < 2 else p.batch_size),

         "Batch pos": (None if p.batch_size < 2 else position_in_batch),

         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),

         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

         "Denoising strength": getattr(p, 'denoising_strength', None),

-        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

+        "Clip skip": None if clip_skip <= 1 else clip_skip,

+        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,

     }

 

     generation_params.update(p.extra_generation_params)

@@ -290,15 +323,24 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         assert(len(p.prompt) > 0)

     else:

         assert p.prompt is not None

-        

+

+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:

+        processed = Processed(p, [], p.seed, "")

+        file.write(processed.infotext(p, 0))

+

     devices.torch_gc()

 

-    fix_seed(p)

+    seed = get_fixed_seed(p.seed)

+    subseed = get_fixed_seed(p.subseed)

 

-    os.makedirs(p.outpath_samples, exist_ok=True)

-    os.makedirs(p.outpath_grids, exist_ok=True)

+    if p.outpath_samples is not None:

+        os.makedirs(p.outpath_samples, exist_ok=True)

+

+    if p.outpath_grids is not None:

+        os.makedirs(p.outpath_grids, exist_ok=True)

 

     modules.sd_hijack.model_hijack.apply_circular(p.tiling)

+    modules.sd_hijack.model_hijack.clear_comments()

 

     comments = {}

 

@@ -309,33 +351,36 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:

         all_prompts = p.batch_size * p.n_iter * [p.prompt]

 

-    if type(p.seed) == list:

-        all_seeds = p.seed

+    if type(seed) == list:

+        all_seeds = seed

     else:

-        all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]

+        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]

 

-    if type(p.subseed) == list:

-        all_subseeds = p.subseed

+    if type(subseed) == list:

+        all_subseeds = subseed

     else:

-        all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))]

+        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]

 

     def infotext(iteration=0, position_in_batch=0):

         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)

 

     if os.path.exists(cmd_opts.embeddings_dir):

-        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)

+        model_hijack.embedding_db.load_textual_inversion_embeddings()

 

     infotexts = []

     output_images = []

-    precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext

-    ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)

-    with torch.no_grad(), precision_scope("cuda"), ema_scope():

-        p.init(all_prompts, all_seeds, all_subseeds)

+

+    with torch.no_grad(), p.sd_model.ema_scope():

+        with devices.autocast():

+            p.init(all_prompts, all_seeds, all_subseeds)

 

         if state.job_count == -1:

             state.job_count = p.n_iter

 

         for n in range(p.n_iter):

+            if state.skipped:

+                state.skipped = False

+            

             if state.interrupted:

                 break

 

@@ -348,8 +393,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])

             #c = p.sd_model.get_learned_conditioning(prompts)

-            uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)

-            c = prompt_parser.get_learned_conditioning(prompts, p.steps)

+            with devices.autocast():

+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)

+                c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)

 

             if len(model_hijack.comments) > 0:

                 for comment in model_hijack.comments:

@@ -358,16 +404,26 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"

 

-            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)

-            if state.interrupted:

+            with devices.autocast():

+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)

+

+            if state.interrupted or state.skipped:

 

-                # if we are interruped, sample returns just noise

+                # if we are interrupted, sample returns just noise

                 # use the image collected previously in sampler loop

                 samples_ddim = shared.state.current_latent

 

-            x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)

+            samples_ddim = samples_ddim.to(devices.dtype_vae)

+            x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)

             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

 

+            del samples_ddim

+

+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

+                lowvram.send_everything_to_cpu()

+

+            devices.torch_gc()

+

             if opts.filter_nsfw:

                 import modules.safety as safety

                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)

@@ -383,6 +439,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()

 

                     x_sample = modules.face_restoration.restore_faces(x_sample)

+                    devices.torch_gc()

 

                 image = Image.fromarray(x_sample)

 

@@ -408,9 +465,16 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 if opts.samples_save and not p.do_not_save_samples:

                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)

 

-                infotexts.append(infotext(n, i))

+                text = infotext(n, i)

+                infotexts.append(text)

+                if opts.enable_pnginfo:

+                    image.info["parameters"] = text

                 output_images.append(image)

 

+            del x_samples_ddim 

+

+            devices.torch_gc()

+

             state.nextjob()

 

         p.color_corrections = None

@@ -421,7 +485,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)

 

             if opts.return_grid:

-                infotexts.insert(0, infotext())

+                text = infotext()

+                infotexts.insert(0, text)

+                if opts.enable_pnginfo:

+                    grid.info["parameters"] = text

                 output_images.insert(0, grid)

                 index_of_first_image = 1

 

@@ -434,16 +501,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

     sampler = None

-    firstphase_width = 0

-    firstphase_height = 0

-    firstphase_width_truncated = 0

-    firstphase_height_truncated = 0

 

-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):

+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):

         super().__init__(**kwargs)

         self.enable_hr = enable_hr

-        self.scale_latent = scale_latent

         self.denoising_strength = denoising_strength

+        self.firstphase_width = firstphase_width

+        self.firstphase_height = firstphase_height

+        self.truncate_x = 0

+        self.truncate_y = 0

 

     def init(self, all_prompts, all_seeds, all_subseeds):

         if self.enable_hr:

@@ -452,17 +518,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:

                 state.job_count = state.job_count * 2

 

-            desired_pixel_count = 512 * 512

-            actual_pixel_count = self.width * self.height

-            scale = math.sqrt(desired_pixel_count / actual_pixel_count)

+            if self.firstphase_width == 0 or self.firstphase_height == 0:

+                desired_pixel_count = 512 * 512

+                actual_pixel_count = self.width * self.height

+                scale = math.sqrt(desired_pixel_count / actual_pixel_count)

+                self.firstphase_width = math.ceil(scale * self.width / 64) * 64

+                self.firstphase_height = math.ceil(scale * self.height / 64) * 64

+                firstphase_width_truncated = int(scale * self.width)

+                firstphase_height_truncated = int(scale * self.height)

+

+            else:

+                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"

+

+                width_ratio = self.width / self.firstphase_width

+                height_ratio = self.height / self.firstphase_height

+

+                if width_ratio > height_ratio:

+                    firstphase_width_truncated = self.firstphase_width

+                    firstphase_height_truncated = self.firstphase_width * self.height / self.width

+                else:

+                    firstphase_width_truncated = self.firstphase_height * self.width / self.height

+                    firstphase_height_truncated = self.firstphase_height

+

+            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f

+            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f

 

-            self.firstphase_width = math.ceil(scale * self.width / 64) * 64

-            self.firstphase_height = math.ceil(scale * self.height / 64) * 64

-            self.firstphase_width_truncated = int(scale * self.width)

-            self.firstphase_height_truncated = int(scale * self.height)

 

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)

+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

 

         if not self.enable_hr:

             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

@@ -472,46 +555,41 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

 

-        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f

-        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f

+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]

 

-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]

+        decoded_samples = decode_first_stage(self.sd_model, samples)

 

-        if self.scale_latent:

-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

+            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

         else:

-            decoded_samples = self.sd_model.decode_first_stage(samples)

+            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

 

-            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

-            else:

-                lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

-

-                batch_images = []

-                for i, x_sample in enumerate(lowres_samples):

-                    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

-                    x_sample = x_sample.astype(np.uint8)

-                    image = Image.fromarray(x_sample)

-                    image = images.resize_image(0, image, self.width, self.height)

-                    image = np.array(image).astype(np.float32) / 255.0

-                    image = np.moveaxis(image, 2, 0)

-                    batch_images.append(image)

+            batch_images = []

+            for i, x_sample in enumerate(lowres_samples):

+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

+                x_sample = x_sample.astype(np.uint8)

+                image = Image.fromarray(x_sample)

+                image = images.resize_image(0, image, self.width, self.height)

+                image = np.array(image).astype(np.float32) / 255.0

+                image = np.moveaxis(image, 2, 0)

+                batch_images.append(image)

 

-                decoded_samples = torch.from_numpy(np.array(batch_images))

-                decoded_samples = decoded_samples.to(shared.device)

-                decoded_samples = 2. * decoded_samples - 1.

+            decoded_samples = torch.from_numpy(np.array(batch_images))

+            decoded_samples = decoded_samples.to(shared.device)

+            decoded_samples = 2. * decoded_samples - 1.

 

-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

+        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

 

         shared.state.nextjob()

 

-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)

+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

+

         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

 

         # GC now before running the next img2img to prevent running out of memory

         x = None

         devices.torch_gc()

-        

+

         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)

 

         return samples

@@ -540,7 +618,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None

 

     def init(self, all_prompts, all_seeds, all_subseeds):

-        self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)

+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)

         crop_region = None

 

         if self.image_mask is not None:

@@ -647,4 +725,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if self.mask is not None:

             samples = samples * self.nmask + self.init_latent * self.mask

 

+        del x

+        devices.torch_gc()

+

         return samples

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index e811eb9e..919d5d31 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,19 +1,7 @@
 import re

 from collections import namedtuple

-import torch

-

-import modules.shared as shared

-

-re_prompt = re.compile(r'''

-(.*?)

-\[

-    ([^]:]+):

-    (?:([^]:]*):)?

-    ([0-9]*\.?[0-9]+)

-]

-|

-(.+)

-''', re.X)

+from typing import List

+import lark

 

 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"

 # will be represented with prompt_schedule like this (assuming steps=100):

@@ -23,71 +11,117 @@ re_prompt = re.compile(r'''
 # [75, 'fantasy landscape with a lake and an oak in background masterful']

 # [100, 'fantasy landscape with a lake and a christmas tree in background masterful']

 

+schedule_parser = lark.Lark(r"""

+!start: (prompt | /[][():]/+)*

+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*

+!emphasized: "(" prompt ")"

+        | "(" prompt ":" prompt ")"

+        | "[" prompt "]"

+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"

+alternate: "[" prompt ("|" prompt)+ "]"

+WHITESPACE: /\s+/

+plain: /([^\\\[\]():|]|\\.)+/

+%import common.SIGNED_NUMBER -> NUMBER

+""")

 

 def get_learned_conditioning_prompt_schedules(prompts, steps):

-    res = []

-    cache = {}

-

-    for prompt in prompts:

-        prompt_schedule: list[list[str | int]] = [[steps, ""]]

-

-        cached = cache.get(prompt, None)

-        if cached is not None:

-            res.append(cached)

-            continue

-

-        for m in re_prompt.finditer(prompt):

-            plaintext = m.group(1) if m.group(5) is None else m.group(5)

-            concept_from = m.group(2)

-            concept_to = m.group(3)

-            if concept_to is None:

-                concept_to = concept_from

-                concept_from = ""

-            swap_position = float(m.group(4)) if m.group(4) is not None else None

-

-            if swap_position is not None:

-                if swap_position < 1:

-                    swap_position = swap_position * steps

-                swap_position = int(min(swap_position, steps))

-

-            swap_index = None

-            found_exact_index = False

-            for i in range(len(prompt_schedule)):

-                end_step = prompt_schedule[i][0]

-                prompt_schedule[i][1] += plaintext

-

-                if swap_position is not None and swap_index is None:

-                    if swap_position == end_step:

-                        swap_index = i

-                        found_exact_index = True

-

-                    if swap_position < end_step:

-                        swap_index = i

-

-            if swap_index is not None:

-                if not found_exact_index:

-                    prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])

-

-                for i in range(len(prompt_schedule)):

-                    end_step = prompt_schedule[i][0]

-                    must_replace = swap_position < end_step

-

-                    prompt_schedule[i][1] += concept_to if must_replace else concept_from

-

-        res.append(prompt_schedule)

-        cache[prompt] = prompt_schedule

-        #for t in prompt_schedule:

-        #    print(t)

+    """

+    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]

+    >>> g("test")

+    [[10, 'test']]

+    >>> g("a [b:3]")

+    [[3, 'a '], [10, 'a b']]

+    >>> g("a [b: 3]")

+    [[3, 'a '], [10, 'a b']]

+    >>> g("a [[[b]]:2]")

+    [[2, 'a '], [10, 'a [[b]]']]

+    >>> g("[(a:2):3]")

+    [[3, ''], [10, '(a:2)']]

+    >>> g("a [b : c : 1] d")

+    [[1, 'a b  d'], [10, 'a  c  d']]

+    >>> g("a[b:[c:d:2]:1]e")

+    [[1, 'abe'], [2, 'ace'], [10, 'ade']]

+    >>> g("a [unbalanced")

+    [[10, 'a [unbalanced']]

+    >>> g("a [b:.5] c")

+    [[5, 'a  c'], [10, 'a b c']]

+    >>> g("a [{b|d{:.5] c")  # not handling this right now

+    [[5, 'a  c'], [10, 'a {b|d{ c']]

+    >>> g("((a][:b:c [d:3]")

+    [[3, '((a][:b:c '], [10, '((a][:b:c d']]

+    """

 

-    return res

+    def collect_steps(steps, tree):

+        l = [steps]

+        class CollectSteps(lark.Visitor):

+            def scheduled(self, tree):

+                tree.children[-1] = float(tree.children[-1])

+                if tree.children[-1] < 1:

+                    tree.children[-1] *= steps

+                tree.children[-1] = min(steps, int(tree.children[-1]))

+                l.append(tree.children[-1])

+            def alternate(self, tree):

+                l.extend(range(1, steps+1))

+        CollectSteps().visit(tree)

+        return sorted(set(l))

+

+    def at_step(step, tree):

+        class AtStep(lark.Transformer):

+            def scheduled(self, args):

+                before, after, _, when = args

+                yield before or () if step <= when else after

+            def alternate(self, args):

+                yield next(args[(step - 1)%len(args)])

+            def start(self, args):

+                def flatten(x):

+                    if type(x) == str:

+                        yield x

+                    else:

+                        for gen in x:

+                            yield from flatten(gen)

+                return ''.join(flatten(args))

+            def plain(self, args):

+                yield args[0].value

+            def __default__(self, data, children, meta):

+                for child in children:

+                    yield from child

+        return AtStep().transform(tree)

+

+    def get_schedule(prompt):

+        try:

+            tree = schedule_parser.parse(prompt)

+        except lark.exceptions.LarkError as e:

+            if 0:

+                import traceback

+                traceback.print_exc()

+            return [[steps, prompt]]

+        return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]

+

+    promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}

+    return [promptdict[prompt] for prompt in prompts]

 

 

 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])

-ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])

 

 

-def get_learned_conditioning(prompts, steps):

+def get_learned_conditioning(model, prompts, steps):

+    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),

+    and the sampling step at which this condition is to be replaced by the next one.

+

+    Input:

+    (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)

 

+    Output:

+    [

+        [

+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0523,  ..., -0.4901, -0.3066,  0.0674], ..., [ 0.3317, -0.5102, -0.4066,  ...,  0.4119, -0.7647, -1.0160]], device='cuda:0'))

+        ],

+        [

+            ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.0192,  0.3867, -0.4644,  ...,  0.1135, -0.3696, -0.4625]], device='cuda:0')),

+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.7352, -0.4356, -0.7888,  ...,  0.6994, -0.4312, -1.2593]], device='cuda:0'))

+        ]

+    ]

+    """

     res = []

 

     prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)

@@ -101,7 +135,7 @@ def get_learned_conditioning(prompts, steps):
             continue

 

         texts = [x[1] for x in prompt_schedule]

-        conds = shared.sd_model.get_learned_conditioning(texts)

+        conds = model.get_learned_conditioning(texts)

 

         cond_schedule = []

         for i, (end_at_step, text) in enumerate(prompt_schedule):

@@ -110,22 +144,118 @@ def get_learned_conditioning(prompts, steps):
         cache[prompt] = cond_schedule

         res.append(cond_schedule)

 

-    return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)

+    return res

+

+

+re_AND = re.compile(r"\bAND\b")

+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")

+

+def get_multicond_prompt_list(prompts):

+    res_indexes = []

+

+    prompt_flat_list = []

+    prompt_indexes = {}

+

+    for prompt in prompts:

+        subprompts = re_AND.split(prompt)

+

+        indexes = []

+        for subprompt in subprompts:

+            match = re_weight.search(subprompt)

+

+            text, weight = match.groups() if match is not None else (subprompt, 1.0)

+

+            weight = float(weight) if weight is not None else 1.0

+

+            index = prompt_indexes.get(text, None)

+            if index is None:

+                index = len(prompt_flat_list)

+                prompt_flat_list.append(text)

+                prompt_indexes[text] = index

+

+            indexes.append((index, weight))

 

+        res_indexes.append(indexes)

 

-def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):

-    res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)

-    for i, cond_schedule in enumerate(c.schedules):

+    return res_indexes, prompt_flat_list, prompt_indexes

+

+

+class ComposableScheduledPromptConditioning:

+    def __init__(self, schedules, weight=1.0):

+        self.schedules: List[ScheduledPromptConditioning] = schedules

+        self.weight: float = weight

+

+

+class MulticondLearnedConditioning:

+    def __init__(self, shape, batch):

+        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS

+        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch

+

+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:

+    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.

+    For each prompt, the list is obtained by splitting the prompt using the AND separator.

+

+    https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/

+    """

+

+    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)

+

+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)

+

+    res = []

+    for indexes in res_indexes:

+        res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])

+

+    return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)

+

+

+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):

+    param = c[0][0].cond

+    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)

+    for i, cond_schedule in enumerate(c):

         target_index = 0

-        for curret_index, (end_at, cond) in enumerate(cond_schedule):

+        for current, (end_at, cond) in enumerate(cond_schedule):

             if current_step <= end_at:

-                target_index = curret_index

+                target_index = current

                 break

         res[i] = cond_schedule[target_index].cond

 

     return res

 

 

+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):

+    param = c.batch[0][0].schedules[0].cond

+

+    tensors = []

+    conds_list = []

+

+    for batch_no, composable_prompts in enumerate(c.batch):

+        conds_for_batch = []

+

+        for cond_index, composable_prompt in enumerate(composable_prompts):

+            target_index = 0

+            for current, (end_at, cond) in enumerate(composable_prompt.schedules):

+                if current_step <= end_at:

+                    target_index = current

+                    break

+

+            conds_for_batch.append((len(tensors), composable_prompt.weight))

+            tensors.append(composable_prompt.schedules[target_index].cond)

+

+        conds_list.append(conds_for_batch)

+

+    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes

+    # and won't be able to torch.stack them. So this fixes that.

+    token_count = max([x.shape[0] for x in tensors])

+    for i in range(len(tensors)):

+        if tensors[i].shape[0] != token_count:

+            last_vector = tensors[i][-1:]

+            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])

+            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])

+

+    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)

+

+

 re_attention = re.compile(r"""

 \\\(|

 \\\)|

@@ -157,23 +287,26 @@ def parse_prompt_attention(text):
       \\ - literal character '\'

       anything else - just text

 

-    Example:

-

-        'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'

-

-    produces:

-

-    [

-        ['a ', 1.0],

-        ['house', 1.5730000000000004],

-        [' ', 1.1],

-        ['on', 1.0],

-        [' a ', 1.1],

-        ['hill', 0.55],

-        [', sun, ', 1.1],

-        ['sky', 1.4641000000000006],

-        ['.', 1.1]

-    ]

+    >>> parse_prompt_attention('normal text')

+    [['normal text', 1.0]]

+    >>> parse_prompt_attention('an (important) word')

+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]

+    >>> parse_prompt_attention('(unbalanced')

+    [['unbalanced', 1.1]]

+    >>> parse_prompt_attention('\(literal\]')

+    [['(literal]', 1.0]]

+    >>> parse_prompt_attention('(unnecessary)(parens)')

+    [['unnecessaryparens', 1.1]]

+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')

+    [['a ', 1.0],

+     ['house', 1.5730000000000004],

+     [' ', 1.1],

+     ['on', 1.0],

+     [' a ', 1.1],

+     ['hill', 0.55],

+     [', sun, ', 1.1],

+     ['sky', 1.4641000000000006],

+     ['.', 1.1]]

     """

 

     res = []

@@ -215,4 +348,19 @@ def parse_prompt_attention(text):
     if len(res) == 0:

         res = [["", 1.0]]

 

+    # merge runs of identical weights

+    i = 0

+    while i + 1 < len(res):

+        if res[i][1] == res[i + 1][1]:

+            res[i][0] += res[i + 1][0]

+            res.pop(i + 1)

+        else:

+            i += 1

+

     return res

+

+if __name__ == "__main__":

+    import doctest

+    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

+else:

+    import torch  # doctest faster

diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index dc0123e0..3ac0b97a 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer

 

 from modules.upscaler import Upscaler, UpscalerData

-from modules.paths import models_path

 from modules.shared import cmd_opts, opts

 

 

 class UpscalerRealESRGAN(Upscaler):

     def __init__(self, path):

         self.name = "RealESRGAN"

-        self.model_path = os.path.join(models_path, self.name)

         self.user_path = path

         super().__init__()

         try:

diff --git a/modules/safe.py b/modules/safe.py
new file mode 100644
index 00000000..399165a1
--- /dev/null
+++ b/modules/safe.py
@@ -0,0 +1,117 @@
+# this code is adapted from the script contributed by anon from /h/

+

+import io

+import pickle

+import collections

+import sys

+import traceback

+

+import torch

+import numpy

+import _codecs

+import zipfile

+import re

+

+

+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage

+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage

+

+

+def encode(*args):

+    out = _codecs.encode(*args)

+    return out

+

+

+class RestrictedUnpickler(pickle.Unpickler):

+    def persistent_load(self, saved_id):

+        assert saved_id[0] == 'storage'

+        return TypedStorage()

+

+    def find_class(self, module, name):

+        if module == 'collections' and name == 'OrderedDict':

+            return getattr(collections, name)

+        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:

+            return getattr(torch._utils, name)

+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:

+            return getattr(torch, name)

+        if module == 'torch.nn.modules.container' and name in ['ParameterDict']:

+            return getattr(torch.nn.modules.container, name)

+        if module == 'numpy.core.multiarray' and name == 'scalar':

+            return numpy.core.multiarray.scalar

+        if module == 'numpy' and name == 'dtype':

+            return numpy.dtype

+        if module == '_codecs' and name == 'encode':

+            return encode

+        if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':

+            import pytorch_lightning.callbacks

+            return pytorch_lightning.callbacks.model_checkpoint

+        if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':

+            import pytorch_lightning.callbacks.model_checkpoint

+            return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint

+        if module == "__builtin__" and name == 'set':

+            return set

+

+        # Forbid everything else.

+        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")

+

+

+allowed_zip_names = ["archive/data.pkl", "archive/version"]

+allowed_zip_names_re = re.compile(r"^archive/data/\d+$")

+

+

+def check_zip_filenames(filename, names):

+    for name in names:

+        if name in allowed_zip_names:

+            continue

+        if allowed_zip_names_re.match(name):

+            continue

+

+        raise Exception(f"bad file inside {filename}: {name}")

+

+

+def check_pt(filename):

+    try:

+

+        # new pytorch format is a zip file

+        with zipfile.ZipFile(filename) as z:

+            check_zip_filenames(filename, z.namelist())

+

+            with z.open('archive/data.pkl') as file:

+                unpickler = RestrictedUnpickler(file)

+                unpickler.load()

+

+    except zipfile.BadZipfile:

+

+        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle

+        with open(filename, "rb") as file:

+            unpickler = RestrictedUnpickler(file)

+            for i in range(5):

+                unpickler.load()

+

+

+def load(filename, *args, **kwargs):

+    from modules import shared

+

+    try:

+        if not shared.cmd_opts.disable_safe_unpickle:

+            check_pt(filename)

+

+    except pickle.UnpicklingError:

+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)

+        print(traceback.format_exc(), file=sys.stderr)

+        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)

+        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)

+        return None

+

+    except Exception:

+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)

+        print(traceback.format_exc(), file=sys.stderr)

+        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)

+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)

+        return None

+

+    return unsafe_torch_load(filename, *args, **kwargs)

+

+

+unsafe_torch_load = torch.load

+torch.load = load

diff --git a/modules/scripts.py b/modules/scripts.py
index 7c3bd5e7..45230f9a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -162,6 +162,40 @@ class ScriptRunner:
 

         return processed

 

+    def reload_sources(self):

+        for si, script in list(enumerate(self.scripts)):

+            with open(script.filename, "r", encoding="utf8") as file:

+                args_from = script.args_from

+                args_to = script.args_to

+                filename = script.filename

+                text = file.read()

+

+                from types import ModuleType

+

+                compiled = compile(text, filename, 'exec')

+                module = ModuleType(script.filename)

+                exec(compiled, module.__dict__)

+

+                for key, script_class in module.__dict__.items():

+                    if type(script_class) == type and issubclass(script_class, Script):

+                        self.scripts[si] = script_class()

+                        self.scripts[si].filename = filename

+                        self.scripts[si].args_from = args_from

+                        self.scripts[si].args_to = args_to

 

 scripts_txt2img = ScriptRunner()

 scripts_img2img = ScriptRunner()

+

+def reload_script_body_only():

+    scripts_txt2img.reload_sources()

+    scripts_img2img.reload_sources()

+

+

+def reload_scripts(basedir):

+    global scripts_txt2img, scripts_img2img

+

+    scripts_data.clear()

+    load_scripts(basedir)

+

+    scripts_txt2img = ScriptRunner()

+    scripts_img2img = ScriptRunner()

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
new file mode 100644
index 00000000..36a996bf
--- /dev/null
+++ b/modules/scunet_model.py
@@ -0,0 +1,88 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import devices, modelloader
+from modules.scunet_model_arch import SCUNet as net
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_name = "ScuNET GAN"
+        self.model_name2 = "ScuNET PSNR"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pth"])
+        scalers = []
+        add_model2 = True
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            if name == self.model_name2 or file == self.model_url2:
+                add_model2 = False
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        if add_model2:
+            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+            scalers.append(scaler_data2)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+
+        device = devices.device_scunet
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(device)
+
+        img = img.to(device)
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        device = devices.device_scunet
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
+            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
+            return None
+
+        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        model = model.to(device)
+
+        return model
+
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
new file mode 100644
index 00000000..43ca8d36
--- /dev/null
+++ b/modules/scunet_model_arch.py
@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+    """ Self-attention module in Swin Transformer
+    """
+
+    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+        super(WMSA, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.head_dim = head_dim
+        self.scale = self.head_dim ** -0.5
+        self.n_heads = input_dim // head_dim
+        self.window_size = window_size
+        self.type = type
+        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+        self.relative_position_params = nn.Parameter(
+            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+        self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+        trunc_normal_(self.relative_position_params, std=.02)
+        self.relative_position_params = torch.nn.Parameter(
+            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+                                                                                                                 2).transpose(
+                0, 1))
+
+    def generate_mask(self, h, w, p, shift):
+        """ generating the mask of SW-MSA
+        Args:
+            shift: shift parameters in CyclicShift.
+        Returns:
+            attn_mask: should be (1 1 w p p),
+        """
+        # supporting square.
+        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+        if self.type == 'W':
+            return attn_mask
+
+        s = p - shift
+        attn_mask[-1, :, :s, :, s:, :] = True
+        attn_mask[-1, :, s:, :, :s, :] = True
+        attn_mask[:, -1, :, :s, :, s:] = True
+        attn_mask[:, -1, :, s:, :, :s] = True
+        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+        return attn_mask
+
+    def forward(self, x):
+        """ Forward pass of Window Multi-head Self-attention module.
+        Args:
+            x: input tensor with shape of [b h w c];
+            attn_mask: attention mask, fill -inf where the value is True;
+        Returns:
+            output: tensor shape [b h w c]
+        """
+        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+        h_windows = x.size(1)
+        w_windows = x.size(2)
+        # square validation
+        # assert h_windows == w_windows
+
+        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+        qkv = self.embedding_layer(x)
+        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+        # Adding learnable relative embedding
+        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+        # Using Attn Mask to distinguish different subwindows.
+        if self.type != 'W':
+            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+            sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+        probs = nn.functional.softmax(sim, dim=-1)
+        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+        output = rearrange(output, 'h b w p c -> b w p (h c)')
+        output = self.linear(output)
+        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+                                                 dims=(1, 2))
+        return output
+
+    def relative_embedding(self):
+        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+        # negative is allowed
+        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer Block
+        """
+        super(Block, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        assert type in ['W', 'SW']
+        self.type = type
+        if input_resolution <= window_size:
+            self.type = 'W'
+
+        self.ln1 = nn.LayerNorm(input_dim)
+        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.ln2 = nn.LayerNorm(input_dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(input_dim, 4 * input_dim),
+            nn.GELU(),
+            nn.Linear(4 * input_dim, output_dim),
+        )
+
+    def forward(self, x):
+        x = x + self.drop_path(self.msa(self.ln1(x)))
+        x = x + self.drop_path(self.mlp(self.ln2(x)))
+        return x
+
+
+class ConvTransBlock(nn.Module):
+    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer and Conv Block
+        """
+        super(ConvTransBlock, self).__init__()
+        self.conv_dim = conv_dim
+        self.trans_dim = trans_dim
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.drop_path = drop_path
+        self.type = type
+        self.input_resolution = input_resolution
+
+        assert self.type in ['W', 'SW']
+        if self.input_resolution <= self.window_size:
+            self.type = 'W'
+
+        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+                                 self.type, self.input_resolution)
+        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+            nn.ReLU(True),
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+        )
+
+    def forward(self, x):
+        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+        conv_x = self.conv_block(conv_x) + conv_x
+        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+        trans_x = self.trans_block(trans_x)
+        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+        x = x + res
+
+        return x
+
+
+class SCUNet(nn.Module):
+    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+        super(SCUNet, self).__init__()
+        if config is None:
+            config = [2, 2, 2, 2, 2, 2, 2]
+        self.config = config
+        self.dim = dim
+        self.head_dim = 32
+        self.window_size = 8
+
+        # drop path rate for each layer
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+        begin = 0
+        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution)
+                        for i in range(config[0])] + \
+                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[0]
+        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 2)
+                        for i in range(config[1])] + \
+                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[1]
+        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 4)
+                        for i in range(config[2])] + \
+                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[2]
+        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                      'W' if not i % 2 else 'SW', input_resolution // 8)
+                       for i in range(config[3])]
+
+        begin += config[3]
+        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 4)
+                      for i in range(config[4])]
+
+        begin += config[4]
+        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 2)
+                      for i in range(config[5])]
+
+        begin += config[5]
+        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution)
+                      for i in range(config[6])]
+
+        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+        self.m_head = nn.Sequential(*self.m_head)
+        self.m_down1 = nn.Sequential(*self.m_down1)
+        self.m_down2 = nn.Sequential(*self.m_down2)
+        self.m_down3 = nn.Sequential(*self.m_down3)
+        self.m_body = nn.Sequential(*self.m_body)
+        self.m_up3 = nn.Sequential(*self.m_up3)
+        self.m_up2 = nn.Sequential(*self.m_up2)
+        self.m_up1 = nn.Sequential(*self.m_up1)
+        self.m_tail = nn.Sequential(*self.m_tail)
+        # self.apply(self._init_weights)
+
+    def forward(self, x0):
+
+        h, w = x0.size()[-2:]
+        paddingBottom = int(np.ceil(h / 64) * 64 - h)
+        paddingRight = int(np.ceil(w / 64) * 64 - w)
+        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+        x1 = self.m_head(x0)
+        x2 = self.m_down1(x1)
+        x3 = self.m_down2(x2)
+        x4 = self.m_down3(x3)
+        x = self.m_body(x4)
+        x = self.m_up3(x + x4)
+        x = self.m_up2(x + x3)
+        x = self.m_up1(x + x2)
+        x = self.m_tail(x + x1)
+
+        x = x[..., :h, :w]
+
+        return x
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa7eaeb8..c81722a0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -5,245 +5,66 @@ import traceback
 import torch

 import numpy as np

 from torch import einsum

+from torch.nn.functional import silu

 

-from modules import prompt_parser

+import modules.textual_inversion.textual_inversion

+from modules import prompt_parser, devices, sd_hijack_optimizations, shared

 from modules.shared import opts, device, cmd_opts

+from modules.sd_hijack_optimizations import invokeAI_mps_available

 

-from ldm.util import default

-from einops import rearrange

 import ldm.modules.attention

 import ldm.modules.diffusionmodules.model

 

+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward

+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity

+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward

+

+def apply_optimizations():

+    undo_optimizations()

+

+    ldm.modules.diffusionmodules.model.nonlinearity = silu

+

+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):

+        print("Applying xformers cross attention optimization.")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward

+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward

+    elif cmd_opts.opt_split_attention_v1:

+        print("Applying v1 cross attention optimization.")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):

+        if not invokeAI_mps_available and shared.device.type == 'mps':

+            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")

+            print("Applying v1 cross attention optimization.")

+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

+        else:

+            print("Applying cross attention optimization (InvokeAI).")

+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI

+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):

+        print("Applying cross attention optimization (Doggettx).")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward

+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward

 

-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion

-def split_cross_attention_forward_v1(self, x, context=None, mask=None):

-    h = self.heads

-

-    q = self.to_q(x)

-    context = default(context, x)

-    k = self.to_k(context)

-    v = self.to_v(context)

-    del context, x

-

-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

-

-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)

-    for i in range(0, q.shape[0], 2):

-        end = i + 2

-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])

-        s1 *= self.scale

-

-        s2 = s1.softmax(dim=-1)

-        del s1

-

-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])

-        del s2

-

-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)

-    del r1

-

-    return self.to_out(r2)

-

-

-# taken from https://github.com/Doggettx/stable-diffusion

-def split_cross_attention_forward(self, x, context=None, mask=None):

-    h = self.heads

-

-    q_in = self.to_q(x)

-    context = default(context, x)

-    k_in = self.to_k(context) * self.scale

-    v_in = self.to_v(context)

-    del context, x

-

-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))

-    del q_in, k_in, v_in

-

-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

-

-    stats = torch.cuda.memory_stats(q.device)

-    mem_active = stats['active_bytes.all.current']

-    mem_reserved = stats['reserved_bytes.all.current']

-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

-    mem_free_torch = mem_reserved - mem_active

-    mem_free_total = mem_free_cuda + mem_free_torch

-

-    gb = 1024 ** 3

-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()

-    modifier = 3 if q.element_size() == 2 else 2.5

-    mem_required = tensor_size * modifier

-    steps = 1

-

-    if mem_required > mem_free_total:

-        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))

-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "

-        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")

-

-    if steps > 64:

-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64

-        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '

-                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')

-

-    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]

-    for i in range(0, q.shape[1], slice_size):

-        end = i + slice_size

-        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)

-

-        s2 = s1.softmax(dim=-1, dtype=q.dtype)

-        del s1

-

-        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)

-        del s2

-

-    del q, k, v

-

-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)

-    del r1

-

-    return self.to_out(r2)

-

-def nonlinearity_hijack(x):

-    # swish

-    t = torch.sigmoid(x)

-    x *= t

-    del t

-

-    return x

-

-def cross_attention_attnblock_forward(self, x):

-        h_ = x

-        h_ = self.norm(h_)

-        q1 = self.q(h_)

-        k1 = self.k(h_)

-        v = self.v(h_)

-

-        # compute attention

-        b, c, h, w = q1.shape

-

-        q2 = q1.reshape(b, c, h*w)

-        del q1

-

-        q = q2.permute(0, 2, 1)   # b,hw,c

-        del q2

-

-        k = k1.reshape(b, c, h*w) # b,c,hw

-        del k1

-

-        h_ = torch.zeros_like(k, device=q.device)

-

-        stats = torch.cuda.memory_stats(q.device)

-        mem_active = stats['active_bytes.all.current']

-        mem_reserved = stats['reserved_bytes.all.current']

-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

-        mem_free_torch = mem_reserved - mem_active

-        mem_free_total = mem_free_cuda + mem_free_torch

-

-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()

-        mem_required = tensor_size * 2.5

-        steps = 1

-

-        if mem_required > mem_free_total:

-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))

-

-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]

-        for i in range(0, q.shape[1], slice_size):

-            end = i + slice_size

-

-            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]

-            w2 = w1 * (int(c)**(-0.5))

-            del w1

-            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)

-            del w2

-

-            # attend to values

-            v1 = v.reshape(b, c, h*w)

-            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)

-            del w3

 

-            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]

-            del v1, w4

+def undo_optimizations():

+    from modules.hypernetworks import hypernetwork

 

-        h2 = h_.reshape(b, c, h, w)

-        del h_

+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward

+    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity

+    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward

 

-        h3 = self.proj_out(h2)

-        del h2

 

-        h3 += x

+def get_target_prompt_token_count(token_count):

+    return math.ceil(max(token_count, 1) / 75) * 75

 

-        return h3

 

 class StableDiffusionModelHijack:

-    ids_lookup = {}

-    word_embeddings = {}

-    word_embeddings_checksums = {}

     fixes = None

     comments = []

-    dir_mtime = None

     layers = None

     circular_enabled = False

     clip = None

 

-    def load_textual_inversion_embeddings(self, dirname, model):

-        mt = os.path.getmtime(dirname)

-        if self.dir_mtime is not None and mt <= self.dir_mtime:

-            return

-

-        self.dir_mtime = mt

-        self.ids_lookup.clear()

-        self.word_embeddings.clear()

-

-        tokenizer = model.cond_stage_model.tokenizer

-

-        def const_hash(a):

-            r = 0

-            for v in a:

-                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF

-            return r

-

-        def process_file(path, filename):

-            name = os.path.splitext(filename)[0]

-

-            data = torch.load(path, map_location="cpu")

-

-            # textual inversion embeddings

-            if 'string_to_param' in data:

-                param_dict = data['string_to_param']

-                if hasattr(param_dict, '_parameters'):

-                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11

-                assert len(param_dict) == 1, 'embedding file has multiple terms in it'

-                emb = next(iter(param_dict.items()))[1]

-            # diffuser concepts

-            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:

-                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

-

-                emb = next(iter(data.values()))

-                if len(emb.shape) == 1:

-                    emb = emb.unsqueeze(0)

-

-            self.word_embeddings[name] = emb.detach().to(device)

-            self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'

-

-            ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]

-

-            first_id = ids[0]

-            if first_id not in self.ids_lookup:

-                self.ids_lookup[first_id] = []

-            self.ids_lookup[first_id].append((ids, name))

-

-        for fn in os.listdir(dirname):

-            try:

-                fullfn = os.path.join(dirname, fn)

-

-                if os.stat(fullfn).st_size == 0:

-                    continue

-

-                process_file(fullfn, fn)

-            except Exception:

-                print(f"Error loading emedding {fn}:", file=sys.stderr)

-                print(traceback.format_exc(), file=sys.stderr)

-                continue

-

-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")

+    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)

 

     def hijack(self, m):

         model_embeddings = m.cond_stage_model.transformer.text_model.embeddings

@@ -253,12 +74,7 @@ class StableDiffusionModelHijack:
 

         self.clip = m.cond_stage_model

 

-        if cmd_opts.opt_split_attention_v1:

-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1

-        elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):

-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward

-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack

-            ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward

+        apply_optimizations()

 

         def flatten(el):

             flattened = [flatten(children) for children in el.children()]

@@ -286,21 +102,24 @@ class StableDiffusionModelHijack:
         for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:

             layer.padding_mode = 'circular' if enable else 'zeros'

 

+    def clear_comments(self):

+        self.comments = []

+

     def tokenize(self, text):

-        max_length = self.clip.max_length - 2

         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])

-        return remade_batch_tokens[0], token_count, max_length

+        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)

 

 

 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):

     def __init__(self, wrapped, hijack):

         super().__init__()

         self.wrapped = wrapped

-        self.hijack = hijack

+        self.hijack: StableDiffusionModelHijack = hijack

         self.tokenizer = wrapped.tokenizer

-        self.max_length = wrapped.max_length

         self.token_mults = {}

 

+        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]

+

         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]

         for text, ident in tokens_with_parens:

             mult = 1.0

@@ -317,11 +136,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:

                 self.token_mults[ident] = mult

 

-

     def tokenize_line(self, line, used_custom_terms, hijack_comments):

-        id_start = self.wrapped.tokenizer.bos_token_id

         id_end = self.wrapped.tokenizer.eos_token_id

-        maxlen = self.wrapped.max_length

 

         if opts.enable_emphasis:

             parsed = prompt_parser.parse_prompt_attention(line)

@@ -333,48 +149,53 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         fixes = []

         remade_tokens = []

         multipliers = []

+        last_comma = -1

 

         for tokens, (text, weight) in zip(tokenized, parsed):

             i = 0

             while i < len(tokens):

                 token = tokens[i]

 

-                possible_matches = self.hijack.ids_lookup.get(token, None)

-

-                if possible_matches is None:

+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

+

+                if token == self.comma_token:

+                    last_comma = len(remade_tokens)

+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:

+                    last_comma += 1

+                    reloc_tokens = remade_tokens[last_comma:]

+                    reloc_mults = multipliers[last_comma:]

+

+                    remade_tokens = remade_tokens[:last_comma]

+                    length = len(remade_tokens)

+                    

+                    rem = int(math.ceil(length / 75)) * 75 - length

+                    remade_tokens += [id_end] * rem + reloc_tokens

+                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults

+                

+                if embedding is None:

                     remade_tokens.append(token)

                     multipliers.append(weight)

+                    i += 1

                 else:

-                    found = False

-                    for ids, word in possible_matches:

-                        if tokens[i:i + len(ids)] == ids:

-                            emb_len = int(self.hijack.word_embeddings[word].shape[0])

-                            fixes.append((len(remade_tokens), word))

-                            remade_tokens += [0] * emb_len

-                            multipliers += [weight] * emb_len

-                            i += len(ids) - 1

-                            found = True

-                            used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))

-                            break

-

-                    if not found:

-                        remade_tokens.append(token)

-                        multipliers.append(weight)

-                i += 1

-

-        if len(remade_tokens) > maxlen - 2:

-            vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}

-            ovf = remade_tokens[maxlen - 2:]

-            overflowing_words = [vocab.get(int(x), "") for x in ovf]

-            overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))

-            hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")

+                    emb_len = int(embedding.vec.shape[0])

+                    iteration = len(remade_tokens) // 75

+                    if (len(remade_tokens) + emb_len) // 75 != iteration:

+                        rem = (75 * (iteration + 1) - len(remade_tokens))

+                        remade_tokens += [id_end] * rem

+                        multipliers += [1.0] * rem

+                        iteration += 1

+                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))

+                    remade_tokens += [0] * emb_len

+                    multipliers += [weight] * emb_len

+                    used_custom_terms.append((embedding.name, embedding.checksum()))

+                    i += embedding_length_in_tokens

 

         token_count = len(remade_tokens)

-        remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))

-        remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]

+        prompt_target_length = get_target_prompt_token_count(token_count)

+        tokens_to_add = prompt_target_length - len(remade_tokens)

 

-        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))

-        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]

+        remade_tokens = remade_tokens + [id_end] * tokens_to_add

+        multipliers = multipliers + [1.0] * tokens_to_add

 

         return remade_tokens, fixes, multipliers, token_count

 

@@ -391,7 +212,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:

                 remade_tokens, fixes, multipliers = cache[line]

             else:

-                remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)

+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)

+                token_count = max(current_token_count, token_count)

 

                 cache[line] = (remade_tokens, fixes, multipliers)

 

@@ -405,7 +227,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def process_text_old(self, text):

         id_start = self.wrapped.tokenizer.bos_token_id

         id_end = self.wrapped.tokenizer.eos_token_id

-        maxlen = self.wrapped.max_length

+        maxlen = self.wrapped.max_length  # you get to stay at 77

         used_custom_terms = []

         remade_batch_tokens = []

         overflowing_words = []

@@ -431,32 +253,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):

                     token = tokens[i]

 

-                    possible_matches = self.hijack.ids_lookup.get(token, None)

+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

 

                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None

                     if mult_change is not None:

                         mult *= mult_change

-                    elif possible_matches is None:

+                        i += 1

+                    elif embedding is None:

                         remade_tokens.append(token)

                         multipliers.append(mult)

+                        i += 1

                     else:

-                        found = False

-                        for ids, word in possible_matches:

-                            if tokens[i:i+len(ids)] == ids:

-                                emb_len = int(self.hijack.word_embeddings[word].shape[0])

-                                fixes.append((len(remade_tokens), word))

-                                remade_tokens += [0] * emb_len

-                                multipliers += [mult] * emb_len

-                                i += len(ids) - 1

-                                found = True

-                                used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))

-                                break

-

-                        if not found:

-                            remade_tokens.append(token)

-                            multipliers.append(mult)

-

-                    i += 1

+                        emb_len = int(embedding.vec.shape[0])

+                        fixes.append((len(remade_tokens), embedding))

+                        remade_tokens += [0] * emb_len

+                        multipliers += [mult] * emb_len

+                        used_custom_terms.append((embedding.name, embedding.checksum()))

+                        i += embedding_length_in_tokens

 

                 if len(remade_tokens) > maxlen - 2:

                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}

@@ -464,6 +277,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]

                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))

                     hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")

+

                 token_count = len(remade_tokens)

                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))

                 remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]

@@ -476,27 +290,74 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)

             batch_multipliers.append(multipliers)

         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

-

+    

     def forward(self, text):

-

-        if opts.use_old_emphasis_implementation:

+        use_old = opts.use_old_emphasis_implementation

+        if use_old:

             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)

         else:

             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)

 

-

-        self.hijack.fixes = hijack_fixes

-        self.hijack.comments = hijack_comments

+        self.hijack.comments += hijack_comments

 

         if len(used_custom_terms) > 0:

             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))

-

+        

+        if use_old:

+            self.hijack.fixes = hijack_fixes

+            return self.process_tokens(remade_batch_tokens, batch_multipliers)

+        

+        z = None

+        i = 0

+        while max(map(len, remade_batch_tokens)) != 0:

+            rem_tokens = [x[75:] for x in remade_batch_tokens]

+            rem_multipliers = [x[75:] for x in batch_multipliers]

+

+            self.hijack.fixes = []

+            for unfiltered in hijack_fixes:

+                fixes = []

+                for fix in unfiltered:

+                    if fix[0] == i:

+                        fixes.append(fix[1])

+                self.hijack.fixes.append(fixes)

+            

+            tokens = []

+            multipliers = []

+            for j in range(len(remade_batch_tokens)):

+                if len(remade_batch_tokens[j]) > 0:

+                    tokens.append(remade_batch_tokens[j][:75])

+                    multipliers.append(batch_multipliers[j][:75])

+                else:

+                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)

+                    multipliers.append([1.0] * 75)

+

+            z1 = self.process_tokens(tokens, multipliers)

+            z = z1 if z is None else torch.cat((z, z1), axis=-2)

+            

+            remade_batch_tokens = rem_tokens

+            batch_multipliers = rem_multipliers

+            i += 1

+        

+        return z

+        

+    

+    def process_tokens(self, remade_batch_tokens, batch_multipliers):

+        if not opts.use_old_emphasis_implementation:

+            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]

+            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]

+        

         tokens = torch.asarray(remade_batch_tokens).to(device)

-        outputs = self.wrapped.transformer(input_ids=tokens)

-        z = outputs.last_hidden_state

+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)

+

+        if opts.CLIP_stop_at_last_layers > 1:

+            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]

+            z = self.wrapped.transformer.text_model.final_layer_norm(z)

+        else:

+            z = outputs.last_hidden_state

 

         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise

-        batch_multipliers = torch.asarray(batch_multipliers).to(device)

+        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]

+        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)

         original_mean = z.mean()

         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)

         new_mean = z.mean()

@@ -517,14 +378,19 @@ class EmbeddingsWithFixes(torch.nn.Module):
 

         inputs_embeds = self.wrapped(input_ids)

 

-        if batch_fixes is not None:

-            for fixes, tensor in zip(batch_fixes, inputs_embeds):

-                for offset, word in fixes:

-                    emb = self.embeddings.word_embeddings[word]

-                    emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])

-                    tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]

+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:

+            return inputs_embeds

+

+        vecs = []

+        for fixes, tensor in zip(batch_fixes, inputs_embeds):

+            for offset, embedding in fixes:

+                emb = embedding.vec

+                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])

+                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])

+

+            vecs.append(tensor)

 

-        return inputs_embeds

+        return torch.stack(vecs)

 

 

 def add_circular_option_to_conv_2d():

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
new file mode 100644
index 00000000..79405525
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,306 @@
+import math

+import sys

+import traceback

+import importlib

+

+import torch

+from torch import einsum

+

+from ldm.util import default

+from einops import rearrange

+

+from modules import shared

+from modules.hypernetworks import hypernetwork

+

+

+if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:

+    try:

+        import xformers.ops

+        shared.xformers_available = True

+    except Exception:

+        print("Cannot import xformers", file=sys.stderr)

+        print(traceback.format_exc(), file=sys.stderr)

+

+

+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion

+def split_cross_attention_forward_v1(self, x, context=None, mask=None):

+    h = self.heads

+

+    q_in = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)

+    k_in = self.to_k(context_k)

+    v_in = self.to_v(context_v)

+    del context, context_k, context_v, x

+

+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))

+    del q_in, k_in, v_in

+

+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)

+    for i in range(0, q.shape[0], 2):

+        end = i + 2

+        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])

+        s1 *= self.scale

+

+        s2 = s1.softmax(dim=-1)

+        del s1

+

+        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])

+        del s2

+    del q, k, v

+

+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)

+    del r1

+

+    return self.to_out(r2)

+

+

+# taken from https://github.com/Doggettx/stable-diffusion and modified

+def split_cross_attention_forward(self, x, context=None, mask=None):

+    h = self.heads

+

+    q_in = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)

+    k_in = self.to_k(context_k)

+    v_in = self.to_v(context_v)

+

+    k_in *= self.scale

+

+    del context, x

+

+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))

+    del q_in, k_in, v_in

+

+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

+

+    stats = torch.cuda.memory_stats(q.device)

+    mem_active = stats['active_bytes.all.current']

+    mem_reserved = stats['reserved_bytes.all.current']

+    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

+    mem_free_torch = mem_reserved - mem_active

+    mem_free_total = mem_free_cuda + mem_free_torch

+

+    gb = 1024 ** 3

+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()

+    modifier = 3 if q.element_size() == 2 else 2.5

+    mem_required = tensor_size * modifier

+    steps = 1

+

+    if mem_required > mem_free_total:

+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))

+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "

+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")

+

+    if steps > 64:

+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64

+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '

+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')

+

+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]

+    for i in range(0, q.shape[1], slice_size):

+        end = i + slice_size

+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)

+

+        s2 = s1.softmax(dim=-1, dtype=q.dtype)

+        del s1

+

+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)

+        del s2

+

+    del q, k, v

+

+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)

+    del r1

+

+    return self.to_out(r2)

+

+

+def check_for_psutil():

+    try:

+        spec = importlib.util.find_spec('psutil')

+        return spec is not None

+    except ModuleNotFoundError:

+        return False

+

+invokeAI_mps_available = check_for_psutil()

+

+# -- Taken from https://github.com/invoke-ai/InvokeAI --

+if invokeAI_mps_available:

+    import psutil

+    mem_total_gb = psutil.virtual_memory().total // (1 << 30)

+

+def einsum_op_compvis(q, k, v):

+    s = einsum('b i d, b j d -> b i j', q, k)

+    s = s.softmax(dim=-1, dtype=s.dtype)

+    return einsum('b i j, b j d -> b i d', s, v)

+

+def einsum_op_slice_0(q, k, v, slice_size):

+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

+    for i in range(0, q.shape[0], slice_size):

+        end = i + slice_size

+        r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])

+    return r

+

+def einsum_op_slice_1(q, k, v, slice_size):

+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

+    for i in range(0, q.shape[1], slice_size):

+        end = i + slice_size

+        r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)

+    return r

+

+def einsum_op_mps_v1(q, k, v):

+    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096

+        return einsum_op_compvis(q, k, v)

+    else:

+        slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))

+        return einsum_op_slice_1(q, k, v, slice_size)

+

+def einsum_op_mps_v2(q, k, v):

+    if mem_total_gb > 8 and q.shape[1] <= 4096:

+        return einsum_op_compvis(q, k, v)

+    else:

+        return einsum_op_slice_0(q, k, v, 1)

+

+def einsum_op_tensor_mem(q, k, v, max_tensor_mb):

+    size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)

+    if size_mb <= max_tensor_mb:

+        return einsum_op_compvis(q, k, v)

+    div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()

+    if div <= q.shape[0]:

+        return einsum_op_slice_0(q, k, v, q.shape[0] // div)

+    return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))

+

+def einsum_op_cuda(q, k, v):

+    stats = torch.cuda.memory_stats(q.device)

+    mem_active = stats['active_bytes.all.current']

+    mem_reserved = stats['reserved_bytes.all.current']

+    mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)

+    mem_free_torch = mem_reserved - mem_active

+    mem_free_total = mem_free_cuda + mem_free_torch

+    # Divide factor of safety as there's copying and fragmentation

+    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))

+

+def einsum_op(q, k, v):

+    if q.device.type == 'cuda':

+        return einsum_op_cuda(q, k, v)

+

+    if q.device.type == 'mps':

+        if mem_total_gb >= 32:

+            return einsum_op_mps_v1(q, k, v)

+        return einsum_op_mps_v2(q, k, v)

+

+    # Smaller slices are faster due to L2/L3/SLC caches.

+    # Tested on i7 with 8MB L3 cache.

+    return einsum_op_tensor_mem(q, k, v, 32)

+

+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):

+    h = self.heads

+

+    q = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)

+    k = self.to_k(context_k) * self.scale

+    v = self.to_v(context_v)

+    del context, context_k, context_v, x

+

+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

+    r = einsum_op(q, k, v)

+    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))

+

+# -- End of code from https://github.com/invoke-ai/InvokeAI --

+

+def xformers_attention_forward(self, x, context=None, mask=None):

+    h = self.heads

+    q_in = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)

+    k_in = self.to_k(context_k)

+    v_in = self.to_v(context_v)

+

+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))

+    del q_in, k_in, v_in

+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)

+

+    out = rearrange(out, 'b n h d -> b n (h d)', h=h)

+    return self.to_out(out)

+

+def cross_attention_attnblock_forward(self, x):

+        h_ = x

+        h_ = self.norm(h_)

+        q1 = self.q(h_)

+        k1 = self.k(h_)

+        v = self.v(h_)

+

+        # compute attention

+        b, c, h, w = q1.shape

+

+        q2 = q1.reshape(b, c, h*w)

+        del q1

+

+        q = q2.permute(0, 2, 1)   # b,hw,c

+        del q2

+

+        k = k1.reshape(b, c, h*w) # b,c,hw

+        del k1

+

+        h_ = torch.zeros_like(k, device=q.device)

+

+        stats = torch.cuda.memory_stats(q.device)

+        mem_active = stats['active_bytes.all.current']

+        mem_reserved = stats['reserved_bytes.all.current']

+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

+        mem_free_torch = mem_reserved - mem_active

+        mem_free_total = mem_free_cuda + mem_free_torch

+

+        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()

+        mem_required = tensor_size * 2.5

+        steps = 1

+

+        if mem_required > mem_free_total:

+            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))

+

+        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]

+        for i in range(0, q.shape[1], slice_size):

+            end = i + slice_size

+

+            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]

+            w2 = w1 * (int(c)**(-0.5))

+            del w1

+            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)

+            del w2

+

+            # attend to values

+            v1 = v.reshape(b, c, h*w)

+            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)

+            del w3

+

+            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]

+            del v1, w4

+

+        h2 = h_.reshape(b, c, h, w)

+        del h_

+

+        h3 = self.proj_out(h2)

+        del h2

+

+        h3 += x

+

+        return h3

+    

+def xformers_attnblock_forward(self, x):

+    try:

+        h_ = x

+        h_ = self.norm(h_)

+        q1 = self.q(h_).contiguous()

+        k1 = self.k(h_).contiguous()

+        v = self.v(h_).contiguous()

+        out = xformers.ops.memory_efficient_attention(q1, k1, v)

+        out = self.proj_out(out)

+        return x + out

+    except NotImplementedError:

+        return cross_attention_attnblock_forward(self, x)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2539f14c..3aa21ec1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,24 +1,21 @@
-import glob

+import collections

 import os.path

 import sys

 from collections import namedtuple

 import torch

 from omegaconf import OmegaConf

 

-

 from ldm.util import instantiate_from_config

 

-from modules import shared, modelloader

+from modules import shared, modelloader, devices

 from modules.paths import models_path

 

 model_dir = "Stable-diffusion"

 model_path = os.path.abspath(os.path.join(models_path, model_dir))

-model_name = "sd-v1-4.ckpt"

-model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"

-user_dir = None

 

-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])

+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])

 checkpoints_list = {}

+checkpoints_loaded = collections.OrderedDict()

 

 try:

     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

@@ -30,12 +27,10 @@ except Exception:
     pass

 

 

-def setup_model(dirname):

-    global user_dir

-    user_dir = dirname

+def setup_model():

     if not os.path.exists(model_path):

         os.makedirs(model_path)

-    checkpoints_list.clear()

+

     list_models()

 

 

@@ -45,13 +40,13 @@ def checkpoint_tiles():
 

 def list_models():

     checkpoints_list.clear()

-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)

+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])

 

     def modeltitle(path, shorthash):

         abspath = os.path.abspath(path)

 

-        if user_dir is not None and abspath.startswith(user_dir):

-            name = abspath.replace(user_dir, '')

+        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):

+            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')

         elif abspath.startswith(model_path):

             name = abspath.replace(model_path, '')

         else:

@@ -68,14 +63,20 @@ def list_models():
     if os.path.exists(cmd_ckpt):

         h = model_hash(cmd_ckpt)

         title, short_model_name = modeltitle(cmd_ckpt, h)

-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)

-        shared.opts.sd_model_checkpoint = title

+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)

+        shared.opts.data['sd_model_checkpoint'] = title

     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:

         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)

     for filename in model_list:

         h = model_hash(filename)

         title, short_model_name = modeltitle(filename, h)

-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)

+

+        basename, _ = os.path.splitext(filename)

+        config = basename + ".yaml"

+        if not os.path.exists(config):

+            config = shared.cmd_opts.config

+

+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)

 

 

 def get_closet_checkpoint_match(searchString):

@@ -106,8 +107,11 @@ def select_checkpoint():
 

     if len(checkpoints_list) == 0:

         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)

-        print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)

-        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)

+        if shared.cmd_opts.ckpt is not None:

+            print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)

+        print(f" - directory {model_path}", file=sys.stderr)

+        if shared.cmd_opts.ckpt_dir is not None:

+            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)

         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)

         exit(1)

 

@@ -118,33 +122,72 @@ def select_checkpoint():
     return checkpoint_info

 

 

-def load_model_weights(model, checkpoint_file, sd_model_hash):

-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")

+def get_state_dict_from_checkpoint(pl_sd):

+    if "state_dict" in pl_sd:

+        return pl_sd["state_dict"]

+

+    return pl_sd

+

+

+def load_model_weights(model, checkpoint_info):

+    checkpoint_file = checkpoint_info.filename

+    sd_model_hash = checkpoint_info.hash

+

+    if checkpoint_info not in checkpoints_loaded:

+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")

 

-    pl_sd = torch.load(checkpoint_file, map_location="cpu")

-    if "global_step" in pl_sd:

-        print(f"Global Step: {pl_sd['global_step']}")

-    sd = pl_sd["state_dict"]

+        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)

+        if "global_step" in pl_sd:

+            print(f"Global Step: {pl_sd['global_step']}")

 

-    model.load_state_dict(sd, strict=False)

+        sd = get_state_dict_from_checkpoint(pl_sd)

+        model.load_state_dict(sd, strict=False)

 

-    if shared.cmd_opts.opt_channelslast:

-        model.to(memory_format=torch.channels_last)

+        if shared.cmd_opts.opt_channelslast:

+            model.to(memory_format=torch.channels_last)

 

-    if not shared.cmd_opts.no_half:

-        model.half()

+        if not shared.cmd_opts.no_half:

+            model.half()

+

+        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16

+        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16

+

+        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"

+

+        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:

+            vae_file = shared.cmd_opts.vae_path

+

+        if os.path.exists(vae_file):

+            print(f"Loading VAE weights from: {vae_file}")

+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}

+            model.first_stage_model.load_state_dict(vae_dict)

+

+        model.first_stage_model.to(devices.dtype_vae)

+

+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()

+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:

+            checkpoints_loaded.popitem(last=False)  # LRU

+    else:

+        print(f"Loading weights [{sd_model_hash}] from cache")

+        checkpoints_loaded.move_to_end(checkpoint_info)

+        model.load_state_dict(checkpoints_loaded[checkpoint_info])

 

     model.sd_model_hash = sd_model_hash

-    model.sd_model_checkpint = checkpoint_file

+    model.sd_model_checkpoint = checkpoint_file

+    model.sd_checkpoint_info = checkpoint_info

 

 

 def load_model():

     from modules import lowvram, sd_hijack

     checkpoint_info = select_checkpoint()

 

-    sd_config = OmegaConf.load(shared.cmd_opts.config)

+    if checkpoint_info.config != shared.cmd_opts.config:

+        print(f"Loading config from: {checkpoint_info.config}")

+

+    sd_config = OmegaConf.load(checkpoint_info.config)

     sd_model = instantiate_from_config(sd_config.model)

-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)

+    load_model_weights(sd_model, checkpoint_info)

 

     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)

@@ -163,9 +206,14 @@ def reload_model_weights(sd_model, info=None):
     from modules import lowvram, devices, sd_hijack

     checkpoint_info = info or select_checkpoint()

 

-    if sd_model.sd_model_checkpint == checkpoint_info.filename:

+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:

         return

 

+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:

+        checkpoints_loaded.clear()

+        shared.sd_model = load_model()

+        return shared.sd_model

+

     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

         lowvram.send_everything_to_cpu()

     else:

@@ -173,7 +221,7 @@ def reload_model_weights(sd_model, info=None):
 

     sd_hijack.model_hijack.undo_hijack(sd_model)

 

-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)

+    load_model_weights(sd_model, checkpoint_info)

 

     sd_hijack.model_hijack.hijack(sd_model)

 

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 92522214..20309e06 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,37 +7,63 @@ import inspect
 import k_diffusion.sampling

 import ldm.models.diffusion.ddim

 import ldm.models.diffusion.plms

-from modules import prompt_parser

+from modules import prompt_parser, devices, processing

 

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

 

 

-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])

+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])

 

 samplers_k_diffusion = [

-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),

-    ('Euler', 'sample_euler', ['k_euler']),

-    ('LMS', 'sample_lms', ['k_lms']),

-    ('Heun', 'sample_heun', ['k_heun']),

-    ('DPM2', 'sample_dpm_2', ['k_dpm_2']),

-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),

-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),

-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),

+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),

+    ('Euler', 'sample_euler', ['k_euler'], {}),

+    ('LMS', 'sample_lms', ['k_lms'], {}),

+    ('Heun', 'sample_heun', ['k_heun'], {}),

+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),

+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),

+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),

+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),

+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),

+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),

+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),

 ]

 

 samplers_data_k_diffusion = [

-    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)

-    for label, funcname, aliases in samplers_k_diffusion

+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)

+    for label, funcname, aliases, options in samplers_k_diffusion

     if hasattr(k_diffusion.sampling, funcname)

 ]

 

-samplers = [

+all_samplers = [

     *samplers_data_k_diffusion,

-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),

-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),

+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),

+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),

 ]

-samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]

+

+samplers = []

+samplers_for_img2img = []

+

+

+def create_sampler_with_index(list_of_configs, index, model):

+    config = list_of_configs[index]

+    sampler = config.constructor(model)

+    sampler.config = config

+    

+    return sampler

+

+

+def set_samplers():

+    global samplers, samplers_for_img2img

+

+    hidden = set(opts.hide_samplers)

+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])

+

+    samplers = [x for x in all_samplers if x.name not in hidden]

+    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]

+

+

+set_samplers()

 

 sampler_extra_params = {

     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

@@ -57,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
 

 

 def sample_to_image(samples):

-    x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]

+    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]

     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)

     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

     x_sample = x_sample.astype(np.uint8)

@@ -77,8 +103,10 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
     state.sampling_steps = len(sequence)

     state.sampling_step = 0

 

-    for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs):

-        if state.interrupted:

+    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)

+

+    for x in seq:

+        if state.interrupted or state.skipped:

             break

 

         yield x

@@ -102,14 +130,28 @@ class VanillaStableDiffusionSampler:
         self.step = 0

         self.eta = None

         self.default_eta = 0.0

+        self.config = None

 

     def number_of_needed_noises(self, p):

         return 0

 

     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):

-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)

+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

 

+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'

+        cond = tensor

+

+        # for DDIM, shapes must match, we can't just process cond and uncond independently;

+        # filling unconditional_conditioning with repeats of the last vector to match length is

+        # not 100% correct but should work well enough

+        if unconditional_conditioning.shape[1] < cond.shape[1]:

+            last_vector = unconditional_conditioning[:, -1:]

+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])

+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])

+        elif unconditional_conditioning.shape[1] > cond.shape[1]:

+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]

+

         if self.mask is not None:

             img_orig = self.sampler.model.q_sample(self.init_latent, ts)

             x_dec = img_orig * self.mask + self.nmask * x_dec

@@ -125,7 +167,7 @@ class VanillaStableDiffusionSampler:
         return res

 

     def initialize(self, p):

-        self.eta = p.eta or opts.eta_ddim

+        self.eta = p.eta if p.eta is not None else opts.eta_ddim

 

         for fieldname in ['p_sample_ddim', 'p_sample_plms']:

             if hasattr(self.sampler, fieldname):

@@ -139,7 +181,7 @@ class VanillaStableDiffusionSampler:
 

         self.initialize(p)

 

-        # existing code fails with cetain step counts, like 9

+        # existing code fails with certain step counts, like 9

         try:

             self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)

         except Exception:

@@ -162,7 +204,7 @@ class VanillaStableDiffusionSampler:
 

         steps = steps or p.steps

 

-        # existing code fails with cetin step counts, like 9

+        # existing code fails with certain step counts, like 9

         try:

             samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)

         except Exception:

@@ -181,19 +223,42 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0

 

     def forward(self, x, sigma, uncond, cond, cond_scale):

-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)

+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)

 

-        if shared.batch_cond_uncond:

-            x_in = torch.cat([x] * 2)

-            sigma_in = torch.cat([sigma] * 2)

-            cond_in = torch.cat([uncond, cond])

-            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)

-            denoised = uncond + (cond - uncond) * cond_scale

+        batch_size = len(conds_list)

+        repeats = [len(conds_list[i]) for i in range(batch_size)]

+

+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])

+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

+

+        if tensor.shape[1] == uncond.shape[1]:

+            cond_in = torch.cat([tensor, uncond])

+

+            if shared.batch_cond_uncond:

+                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)

+            else:

+                x_out = torch.zeros_like(x_in)

+                for batch_offset in range(0, x_out.shape[0], batch_size):

+                    a = batch_offset

+                    b = a + batch_size

+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])

         else:

-            uncond = self.inner_model(x, sigma, cond=uncond)

-            cond = self.inner_model(x, sigma, cond=cond)

-            denoised = uncond + (cond - uncond) * cond_scale

+            x_out = torch.zeros_like(x_in)

+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size

+            for batch_offset in range(0, tensor.shape[0], batch_size):

+                a = batch_offset

+                b = min(a + batch_size, tensor.shape[0])

+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])

+

+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)

+

+        denoised_uncond = x_out[-uncond.shape[0]:]

+        denoised = torch.clone(denoised_uncond)

+

+        for i, conds in enumerate(conds_list):

+            for cond_index, weight in conds:

+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)

 

         if self.mask is not None:

             denoised = self.init_latent * self.mask + self.nmask * denoised

@@ -207,8 +272,10 @@ def extended_trange(sampler, count, *args, **kwargs):
     state.sampling_steps = count

     state.sampling_step = 0

 

-    for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs):

-        if state.interrupted:

+    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)

+

+    for x in seq:

+        if state.interrupted or state.skipped:

             break

 

         if sampler.stop_at is not None and x > sampler.stop_at:

@@ -246,6 +313,7 @@ class KDiffusionSampler:
         self.stop_at = None

         self.eta = None

         self.default_eta = 1.0

+        self.config = None

 

     def callback_state(self, d):

         store_latent(d["denoised"])

@@ -291,28 +359,43 @@ class KDiffusionSampler:
         steps, t_enc = setup_img2img_steps(p, steps)

 

         if p.sampler_noise_scheduler_override:

-          sigmas = p.sampler_noise_scheduler_override(steps)

+            sigmas = p.sampler_noise_scheduler_override(steps)

+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)

         else:

-          sigmas = self.model_wrap.get_sigmas(steps)

-

-        noise = noise * sigmas[steps - t_enc - 1]

-        xi = x + noise

-

-        extra_params_kwargs = self.initialize(p)

+            sigmas = self.model_wrap.get_sigmas(steps)

 

         sigma_sched = sigmas[steps - t_enc - 1:]

+        xi = x + noise * sigma_sched[0]

+        

+        extra_params_kwargs = self.initialize(p)

+        if 'sigma_min' in inspect.signature(self.func).parameters:

+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last

+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]

+        if 'sigma_max' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigma_max'] = sigma_sched[0]

+        if 'n' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['n'] = len(sigma_sched) - 1

+        if 'sigma_sched' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigma_sched'] = sigma_sched

+        if 'sigmas' in inspect.signature(self.func).parameters:

+            extra_params_kwargs['sigmas'] = sigma_sched

 

         self.model_wrap_cfg.init_latent = x

 

-        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

+        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

+

 

     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

         steps = steps or p.steps

 

         if p.sampler_noise_scheduler_override:

-          sigmas = p.sampler_noise_scheduler_override(steps)

+            sigmas = p.sampler_noise_scheduler_override(steps)

+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)

         else:

-          sigmas = self.model_wrap.get_sigmas(steps)

+            sigmas = self.model_wrap.get_sigmas(steps)

+

         x = x * sigmas[0]

 

         extra_params_kwargs = self.initialize(p)

diff --git a/modules/shared.py b/modules/shared.py
index ac968b2d..aa69bedf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -12,12 +12,13 @@ import modules.interrogate
 import modules.memmon

 import modules.sd_models

 import modules.styles

-from modules.devices import get_optimal_device

-from modules.paths import script_path, sd_path

+import modules.devices as devices

+from modules import sd_samplers, sd_models

+from modules.hypernetworks import hypernetwork

+from modules.paths import models_path, script_path, sd_path

 

 sd_model_file = os.path.join(script_path, 'model.ckpt')

 default_sd_model_file = sd_model_file

-model_path = os.path.join(script_path, 'models')

 parser = argparse.ArgumentParser()

 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)

 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)

@@ -25,26 +26,36 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))

 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)

 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")

+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")

 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")

 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")

 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")

+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")

 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")

 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")

 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")

+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")

 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")

 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")

 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")

 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")

-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))

-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))

-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))

-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))

-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))

-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))

-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))

-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")

-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)

+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))

+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))

+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))

+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))

+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))

+parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))

+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))

+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))

+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")

+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")

+parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")

+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")

+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")

 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)

 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)

 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)

@@ -53,21 +64,44 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))

 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")

 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)

+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")

 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")

 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))

 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)

 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)

+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)

+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)

+parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)

+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)

+

 

 cmd_opts = parser.parse_args()

-device = get_optimal_device()

+

+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \

+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])

+

+device = devices.device

+weight_load_location = None if cmd_opts.lowram else "cpu"

 

 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)

 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram

-

+xformers_available = False

 config_filename = cmd_opts.ui_settings_file

 

+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)

+hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)

+loaded_hypernetwork = None

+

+

+def reload_hypernetworks():

+    global hypernetworks

+

+    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)

+    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)

+

 

 class State:

+    skipped = False

     interrupted = False

     job = ""

     job_no = 0

@@ -78,6 +112,10 @@ class State:
     current_latent = None

     current_image = None

     current_image_sampling_step = 0

+    textinfo = None

+

+    def skip(self):

+        self.skipped = True

 

     def interrupt(self):

         self.interrupted = True

@@ -88,7 +126,7 @@ class State:
         self.current_image_sampling_step = 0

         

     def get_job_timestamp(self):

-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")

+        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?

 

 

 state = State()

@@ -101,8 +139,6 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")

 

 face_restorers = []

-# This was moved to webui.py with the other model "setup" calls.

-# modules.sd_models.list_models()

 

 

 def realesrgan_models_names():

@@ -111,18 +147,19 @@ def realesrgan_models_names():
 

 

 class OptionInfo:

-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):

+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):

         self.default = default

         self.label = label

         self.component = component

         self.component_args = component_args

         self.onchange = onchange

         self.section = None

+        self.refresh = refresh

 

 

-def options_section(section_identifer, options_dict):

+def options_section(section_identifier, options_dict):

     for k, v in options_dict.items():

-        v.section = section_identifer

+        v.section = section_identifier

 

     return options_dict

 

@@ -140,6 +177,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_format": OptionInfo('png', 'File format for grids'),

     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),

     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),

+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),

     "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),

 

     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),

@@ -150,6 +188,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
 

     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),

     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),

+    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),

 }))

 

 options_templates.update(options_section(('saving-paths', "Paths for saving"), {

@@ -165,9 +204,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
 

 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {

     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),

-    "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),

+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),

+    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),

     "directories_filename_pattern": OptionInfo("", "Directory name pattern"),

-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),

+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),

 }))

 

 options_templates.update(options_section(('upscaling', "Upscaling"), {

@@ -177,7 +217,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),

     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),

     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),

-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),

+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),

 }))

 

 options_templates.update(options_section(('face-restoration', "Face restoration"), {

@@ -189,50 +229,75 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 options_templates.update(options_section(('system', "System"), {

     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),

     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),

-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),

+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),

+}))

+

+options_templates.update(options_section(('training', "Training"), {

+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),

+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),

 }))

 

 options_templates.update(options_section(('sd', "Stable Diffusion"), {

-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),

+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),

+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),

+    "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),

+    "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),

     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),

     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),

     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),

     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),

-    "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),

+    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),

     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),

     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),

+    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),

     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),

+    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),

     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),

+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),

 }))

 

 options_templates.update(options_section(('interrogate', "Interrogate Options"), {

     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),

     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),

+    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),

     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),

     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),

     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),

-    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),

+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),

+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),

+    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),

+    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),

+    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),

 }))

 

 options_templates.update(options_section(('ui', "User interface"), {

     "show_progressbar": OptionInfo(True, "Show progressbar"),

-    "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),

+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),

     "return_grid": OptionInfo(True, "Show grid in results for web"),

+    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),

     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),

+    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),

     "font": OptionInfo("", "Font for image grids that have text"),

     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),

-    "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),

+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),

+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),

 }))

 

 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

-  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

-  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),

-  's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

-  's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

-  's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),

+    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),

+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),

 }))

 

+

 class Options:

     data = None

     data_labels = options_templates

@@ -289,6 +354,8 @@ class Options:
         item = self.data_labels.get(key)

         item.onchange = func

 

+        func()

+

     def dumpjson(self):

         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}

         return json.dumps(d)

@@ -318,14 +385,14 @@ class TotalTQDM:
         )

 

     def update(self):

-        if not opts.multiple_tqdm:

+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:

             return

         if self._tqdm is None:

             self.reset()

         self._tqdm.update()

 

     def updateTotal(self, new_total):

-        if not opts.multiple_tqdm:

+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:

             return

         if self._tqdm is None:

             self.reset()

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 41fda5a7..baa02e3d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -5,11 +5,12 @@ import numpy as np
 import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
 
 from modules import modelloader
-from modules.paths import models_path
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
+from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
 precision_scope = (
@@ -24,7 +25,6 @@ class UpscalerSwinIR(Upscaler):
                          "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
                          "-L_x4_GAN.pth "
         self.model_name = "SwinIR 4x"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = dirname
         super().__init__()
         scalers = []
@@ -58,22 +58,42 @@ class UpscalerSwinIR(Upscaler):
             filename = path
         if filename is None or not os.path.exists(filename):
             return None
-        model = net(
+        if filename.endswith(".v2.pth"):
+            model = net2(
             upscale=scale,
             in_chans=3,
             img_size=64,
             window_size=8,
             img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-            embed_dim=240,
-            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+            depths=[6, 6, 6, 6, 6, 6],
+            embed_dim=180,
+            num_heads=[6, 6, 6, 6, 6, 6],
             mlp_ratio=2,
             upsampler="nearest+conv",
-            resi_connection="3conv",
-        )
+            resi_connection="1conv",
+            )
+            params = None
+        else:
+            model = net(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+                embed_dim=240,
+                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="3conv",
+            )
+            params = "params_ema"
 
         pretrained_model = torch.load(filename)
-        model.load_state_dict(pretrained_model["params_ema"], strict=True)
+        if params is not None:
+            model.load_state_dict(pretrained_model[params], strict=True)
+        else:
+            model.load_state_dict(pretrained_model, strict=True)
         if not cmd_opts.no_half:
             model = model.half()
         return model
@@ -122,18 +142,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
     W = torch.zeros_like(E, dtype=torch.half, device=device)
 
-    for h_idx in h_idx_list:
-        for w_idx in w_idx_list:
-            in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-            out_patch = model(in_patch)
-            out_patch_mask = torch.ones_like(out_patch)
-
-            E[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch)
-            W[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch_mask)
+    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+        for h_idx in h_idx_list:
+            for w_idx in w_idx_list:
+                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+                out_patch = model(in_patch)
+                out_patch_mask = torch.ones_like(out_patch)
+
+                E[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch)
+                W[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch_mask)
+                pbar.update(1)
     output = E.div_(W)
 
     return output
diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py
index 461fb354..863f42db 100644
--- a/modules/swinir_model_arch.py
+++ b/modules/swinir_model_arch.py
@@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module):
 
     Args:
         dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
+        input_resolution (tuple[int]): Input resolution.
         num_heads (int): Number of attention heads.
         window_size (int): Window size.
         shift_size (int): Shift size for SW-MSA.
diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py
new file mode 100644
index 00000000..0e28ae6e
--- /dev/null
+++ b/modules/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------

+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/

+# Written by Conde and Choi et al.

+# -----------------------------------------------------------------------------------

+

+import math

+import numpy as np

+import torch

+import torch.nn as nn

+import torch.nn.functional as F

+import torch.utils.checkpoint as checkpoint

+from timm.models.layers import DropPath, to_2tuple, trunc_normal_

+

+

+class Mlp(nn.Module):

+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):

+        super().__init__()

+        out_features = out_features or in_features

+        hidden_features = hidden_features or in_features

+        self.fc1 = nn.Linear(in_features, hidden_features)

+        self.act = act_layer()

+        self.fc2 = nn.Linear(hidden_features, out_features)

+        self.drop = nn.Dropout(drop)

+

+    def forward(self, x):

+        x = self.fc1(x)

+        x = self.act(x)

+        x = self.drop(x)

+        x = self.fc2(x)

+        x = self.drop(x)

+        return x

+

+

+def window_partition(x, window_size):

+    """

+    Args:

+        x: (B, H, W, C)

+        window_size (int): window size

+    Returns:

+        windows: (num_windows*B, window_size, window_size, C)

+    """

+    B, H, W, C = x.shape

+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)

+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)

+    return windows

+

+

+def window_reverse(windows, window_size, H, W):

+    """

+    Args:

+        windows: (num_windows*B, window_size, window_size, C)

+        window_size (int): Window size

+        H (int): Height of image

+        W (int): Width of image

+    Returns:

+        x: (B, H, W, C)

+    """

+    B = int(windows.shape[0] / (H * W / window_size / window_size))

+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)

+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)

+    return x

+

+class WindowAttention(nn.Module):

+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.

+    It supports both of shifted and non-shifted window.

+    Args:

+        dim (int): Number of input channels.

+        window_size (tuple[int]): The height and width of the window.

+        num_heads (int): Number of attention heads.

+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True

+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0

+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0

+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.

+    """

+

+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,

+                 pretrained_window_size=[0, 0]):

+

+        super().__init__()

+        self.dim = dim

+        self.window_size = window_size  # Wh, Ww

+        self.pretrained_window_size = pretrained_window_size

+        self.num_heads = num_heads

+

+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)

+

+        # mlp to generate continuous relative position bias

+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),

+                                     nn.ReLU(inplace=True),

+                                     nn.Linear(512, num_heads, bias=False))

+

+        # get relative_coords_table

+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)

+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)

+        relative_coords_table = torch.stack(

+            torch.meshgrid([relative_coords_h,

+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2

+        if pretrained_window_size[0] > 0:

+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)

+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)

+        else:

+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)

+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)

+        relative_coords_table *= 8  # normalize to -8, 8

+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(

+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)

+

+        self.register_buffer("relative_coords_table", relative_coords_table)

+

+        # get pair-wise relative position index for each token inside the window

+        coords_h = torch.arange(self.window_size[0])

+        coords_w = torch.arange(self.window_size[1])

+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww

+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww

+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww

+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0

+        relative_coords[:, :, 1] += self.window_size[1] - 1

+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1

+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww

+        self.register_buffer("relative_position_index", relative_position_index)

+

+        self.qkv = nn.Linear(dim, dim * 3, bias=False)

+        if qkv_bias:

+            self.q_bias = nn.Parameter(torch.zeros(dim))

+            self.v_bias = nn.Parameter(torch.zeros(dim))

+        else:

+            self.q_bias = None

+            self.v_bias = None

+        self.attn_drop = nn.Dropout(attn_drop)

+        self.proj = nn.Linear(dim, dim)

+        self.proj_drop = nn.Dropout(proj_drop)

+        self.softmax = nn.Softmax(dim=-1)

+

+    def forward(self, x, mask=None):

+        """

+        Args:

+            x: input features with shape of (num_windows*B, N, C)

+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None

+        """

+        B_, N, C = x.shape

+        qkv_bias = None

+        if self.q_bias is not None:

+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))

+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)

+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)

+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

+

+        # cosine attention

+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))

+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()

+        attn = attn * logit_scale

+

+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)

+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(

+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH

+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww

+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)

+        attn = attn + relative_position_bias.unsqueeze(0)

+

+        if mask is not None:

+            nW = mask.shape[0]

+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)

+            attn = attn.view(-1, self.num_heads, N, N)

+            attn = self.softmax(attn)

+        else:

+            attn = self.softmax(attn)

+

+        attn = self.attn_drop(attn)

+

+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)

+        x = self.proj(x)

+        x = self.proj_drop(x)

+        return x

+

+    def extra_repr(self) -> str:

+        return f'dim={self.dim}, window_size={self.window_size}, ' \

+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'

+

+    def flops(self, N):

+        # calculate flops for 1 window with token length of N

+        flops = 0

+        # qkv = self.qkv(x)

+        flops += N * self.dim * 3 * self.dim

+        # attn = (q @ k.transpose(-2, -1))

+        flops += self.num_heads * N * (self.dim // self.num_heads) * N

+        #  x = (attn @ v)

+        flops += self.num_heads * N * N * (self.dim // self.num_heads)

+        # x = self.proj(x)

+        flops += N * self.dim * self.dim

+        return flops

+

+class SwinTransformerBlock(nn.Module):

+    r""" Swin Transformer Block.

+    Args:

+        dim (int): Number of input channels.

+        input_resolution (tuple[int]): Input resulotion.

+        num_heads (int): Number of attention heads.

+        window_size (int): Window size.

+        shift_size (int): Shift size for SW-MSA.

+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

+        drop (float, optional): Dropout rate. Default: 0.0

+        attn_drop (float, optional): Attention dropout rate. Default: 0.0

+        drop_path (float, optional): Stochastic depth rate. Default: 0.0

+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU

+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

+        pretrained_window_size (int): Window size in pre-training.

+    """

+

+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,

+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,

+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):

+        super().__init__()

+        self.dim = dim

+        self.input_resolution = input_resolution

+        self.num_heads = num_heads

+        self.window_size = window_size

+        self.shift_size = shift_size

+        self.mlp_ratio = mlp_ratio

+        if min(self.input_resolution) <= self.window_size:

+            # if window size is larger than input resolution, we don't partition windows

+            self.shift_size = 0

+            self.window_size = min(self.input_resolution)

+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

+

+        self.norm1 = norm_layer(dim)

+        self.attn = WindowAttention(

+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,

+            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,

+            pretrained_window_size=to_2tuple(pretrained_window_size))

+

+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

+        self.norm2 = norm_layer(dim)

+        mlp_hidden_dim = int(dim * mlp_ratio)

+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

+

+        if self.shift_size > 0:

+            attn_mask = self.calculate_mask(self.input_resolution)

+        else:

+            attn_mask = None

+

+        self.register_buffer("attn_mask", attn_mask)

+        

+    def calculate_mask(self, x_size):

+        # calculate attention mask for SW-MSA

+        H, W = x_size

+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1

+        h_slices = (slice(0, -self.window_size),

+                    slice(-self.window_size, -self.shift_size),

+                    slice(-self.shift_size, None))

+        w_slices = (slice(0, -self.window_size),

+                    slice(-self.window_size, -self.shift_size),

+                    slice(-self.shift_size, None))

+        cnt = 0

+        for h in h_slices:

+            for w in w_slices:

+                img_mask[:, h, w, :] = cnt

+                cnt += 1

+

+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1

+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)

+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)

+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

+

+        return attn_mask        

+

+    def forward(self, x, x_size):

+        H, W = x_size

+        B, L, C = x.shape

+        #assert L == H * W, "input feature has wrong size"

+

+        shortcut = x

+        x = x.view(B, H, W, C)

+

+        # cyclic shift

+        if self.shift_size > 0:

+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))

+        else:

+            shifted_x = x

+

+        # partition windows

+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C

+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

+

+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size

+        if self.input_resolution == x_size:

+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

+        else:

+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))

+            

+        # merge windows

+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)

+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

+

+        # reverse cyclic shift

+        if self.shift_size > 0:

+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))

+        else:

+            x = shifted_x

+        x = x.view(B, H * W, C)

+        x = shortcut + self.drop_path(self.norm1(x))

+

+        # FFN

+        x = x + self.drop_path(self.norm2(self.mlp(x)))

+

+        return x

+

+    def extra_repr(self) -> str:

+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \

+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

+

+    def flops(self):

+        flops = 0

+        H, W = self.input_resolution

+        # norm1

+        flops += self.dim * H * W

+        # W-MSA/SW-MSA

+        nW = H * W / self.window_size / self.window_size

+        flops += nW * self.attn.flops(self.window_size * self.window_size)

+        # mlp

+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio

+        # norm2

+        flops += self.dim * H * W

+        return flops

+

+class PatchMerging(nn.Module):

+    r""" Patch Merging Layer.

+    Args:

+        input_resolution (tuple[int]): Resolution of input feature.

+        dim (int): Number of input channels.

+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

+    """

+

+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):

+        super().__init__()

+        self.input_resolution = input_resolution

+        self.dim = dim

+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)

+        self.norm = norm_layer(2 * dim)

+

+    def forward(self, x):

+        """

+        x: B, H*W, C

+        """

+        H, W = self.input_resolution

+        B, L, C = x.shape

+        assert L == H * W, "input feature has wrong size"

+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

+

+        x = x.view(B, H, W, C)

+

+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C

+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C

+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C

+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C

+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C

+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

+

+        x = self.reduction(x)

+        x = self.norm(x)

+

+        return x

+

+    def extra_repr(self) -> str:

+        return f"input_resolution={self.input_resolution}, dim={self.dim}"

+

+    def flops(self):

+        H, W = self.input_resolution

+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim

+        flops += H * W * self.dim // 2

+        return flops    

+

+class BasicLayer(nn.Module):

+    """ A basic Swin Transformer layer for one stage.

+    Args:

+        dim (int): Number of input channels.

+        input_resolution (tuple[int]): Input resolution.

+        depth (int): Number of blocks.

+        num_heads (int): Number of attention heads.

+        window_size (int): Local window size.

+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

+        drop (float, optional): Dropout rate. Default: 0.0

+        attn_drop (float, optional): Attention dropout rate. Default: 0.0

+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0

+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None

+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

+        pretrained_window_size (int): Local window size in pre-training.

+    """

+

+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,

+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,

+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,

+                 pretrained_window_size=0):

+

+        super().__init__()

+        self.dim = dim

+        self.input_resolution = input_resolution

+        self.depth = depth

+        self.use_checkpoint = use_checkpoint

+

+        # build blocks

+        self.blocks = nn.ModuleList([

+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,

+                                 num_heads=num_heads, window_size=window_size,

+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,

+                                 mlp_ratio=mlp_ratio,

+                                 qkv_bias=qkv_bias,

+                                 drop=drop, attn_drop=attn_drop,

+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,

+                                 norm_layer=norm_layer,

+                                 pretrained_window_size=pretrained_window_size)

+            for i in range(depth)])

+

+        # patch merging layer

+        if downsample is not None:

+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)

+        else:

+            self.downsample = None

+

+    def forward(self, x, x_size):

+        for blk in self.blocks:

+            if self.use_checkpoint:

+                x = checkpoint.checkpoint(blk, x, x_size)

+            else:

+                x = blk(x, x_size)

+        if self.downsample is not None:

+            x = self.downsample(x)

+        return x

+

+    def extra_repr(self) -> str:

+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

+

+    def flops(self):

+        flops = 0

+        for blk in self.blocks:

+            flops += blk.flops()

+        if self.downsample is not None:

+            flops += self.downsample.flops()

+        return flops

+

+    def _init_respostnorm(self):

+        for blk in self.blocks:

+            nn.init.constant_(blk.norm1.bias, 0)

+            nn.init.constant_(blk.norm1.weight, 0)

+            nn.init.constant_(blk.norm2.bias, 0)

+            nn.init.constant_(blk.norm2.weight, 0)

+            

+class PatchEmbed(nn.Module):

+    r""" Image to Patch Embedding

+    Args:

+        img_size (int): Image size.  Default: 224.

+        patch_size (int): Patch token size. Default: 4.

+        in_chans (int): Number of input image channels. Default: 3.

+        embed_dim (int): Number of linear projection output channels. Default: 96.

+        norm_layer (nn.Module, optional): Normalization layer. Default: None

+    """

+

+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):

+        super().__init__()

+        img_size = to_2tuple(img_size)

+        patch_size = to_2tuple(patch_size)

+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]

+        self.img_size = img_size

+        self.patch_size = patch_size

+        self.patches_resolution = patches_resolution

+        self.num_patches = patches_resolution[0] * patches_resolution[1]

+

+        self.in_chans = in_chans

+        self.embed_dim = embed_dim

+

+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

+        if norm_layer is not None:

+            self.norm = norm_layer(embed_dim)

+        else:

+            self.norm = None

+

+    def forward(self, x):

+        B, C, H, W = x.shape

+        # FIXME look at relaxing size constraints

+        # assert H == self.img_size[0] and W == self.img_size[1],

+        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."

+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C

+        if self.norm is not None:

+            x = self.norm(x)

+        return x

+

+    def flops(self):

+        Ho, Wo = self.patches_resolution

+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])

+        if self.norm is not None:

+            flops += Ho * Wo * self.embed_dim

+        return flops           

+

+class RSTB(nn.Module):

+    """Residual Swin Transformer Block (RSTB).

+

+    Args:

+        dim (int): Number of input channels.

+        input_resolution (tuple[int]): Input resolution.

+        depth (int): Number of blocks.

+        num_heads (int): Number of attention heads.

+        window_size (int): Local window size.

+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

+        drop (float, optional): Dropout rate. Default: 0.0

+        attn_drop (float, optional): Attention dropout rate. Default: 0.0

+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0

+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None

+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

+        img_size: Input image size.

+        patch_size: Patch size.

+        resi_connection: The convolutional block before residual connection.

+    """

+

+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,

+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,

+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,

+                 img_size=224, patch_size=4, resi_connection='1conv'):

+        super(RSTB, self).__init__()

+

+        self.dim = dim

+        self.input_resolution = input_resolution

+

+        self.residual_group = BasicLayer(dim=dim,

+                                         input_resolution=input_resolution,

+                                         depth=depth,

+                                         num_heads=num_heads,

+                                         window_size=window_size,

+                                         mlp_ratio=mlp_ratio,

+                                         qkv_bias=qkv_bias, 

+                                         drop=drop, attn_drop=attn_drop,

+                                         drop_path=drop_path,

+                                         norm_layer=norm_layer,

+                                         downsample=downsample,

+                                         use_checkpoint=use_checkpoint)

+

+        if resi_connection == '1conv':

+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)

+        elif resi_connection == '3conv':

+            # to save parameters and memory

+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),

+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),

+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),

+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))

+

+        self.patch_embed = PatchEmbed(

+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,

+            norm_layer=None)

+

+        self.patch_unembed = PatchUnEmbed(

+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,

+            norm_layer=None)

+

+    def forward(self, x, x_size):

+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x

+

+    def flops(self):

+        flops = 0

+        flops += self.residual_group.flops()

+        H, W = self.input_resolution

+        flops += H * W * self.dim * self.dim * 9

+        flops += self.patch_embed.flops()

+        flops += self.patch_unembed.flops()

+

+        return flops

+

+class PatchUnEmbed(nn.Module):

+    r""" Image to Patch Unembedding

+

+    Args:

+        img_size (int): Image size.  Default: 224.

+        patch_size (int): Patch token size. Default: 4.

+        in_chans (int): Number of input image channels. Default: 3.

+        embed_dim (int): Number of linear projection output channels. Default: 96.

+        norm_layer (nn.Module, optional): Normalization layer. Default: None

+    """

+

+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):

+        super().__init__()

+        img_size = to_2tuple(img_size)

+        patch_size = to_2tuple(patch_size)

+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]

+        self.img_size = img_size

+        self.patch_size = patch_size

+        self.patches_resolution = patches_resolution

+        self.num_patches = patches_resolution[0] * patches_resolution[1]

+

+        self.in_chans = in_chans

+        self.embed_dim = embed_dim

+

+    def forward(self, x, x_size):

+        B, HW, C = x.shape

+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C

+        return x

+

+    def flops(self):

+        flops = 0

+        return flops

+

+

+class Upsample(nn.Sequential):

+    """Upsample module.

+

+    Args:

+        scale (int): Scale factor. Supported scales: 2^n and 3.

+        num_feat (int): Channel number of intermediate features.

+    """

+

+    def __init__(self, scale, num_feat):

+        m = []

+        if (scale & (scale - 1)) == 0:  # scale = 2^n

+            for _ in range(int(math.log(scale, 2))):

+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))

+                m.append(nn.PixelShuffle(2))

+        elif scale == 3:

+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))

+            m.append(nn.PixelShuffle(3))

+        else:

+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')

+        super(Upsample, self).__init__(*m)

+        

+class Upsample_hf(nn.Sequential):

+    """Upsample module.

+

+    Args:

+        scale (int): Scale factor. Supported scales: 2^n and 3.

+        num_feat (int): Channel number of intermediate features.

+    """

+

+    def __init__(self, scale, num_feat):

+        m = []

+        if (scale & (scale - 1)) == 0:  # scale = 2^n

+            for _ in range(int(math.log(scale, 2))):

+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))

+                m.append(nn.PixelShuffle(2))

+        elif scale == 3:

+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))

+            m.append(nn.PixelShuffle(3))

+        else:

+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')

+        super(Upsample_hf, self).__init__(*m)        

+

+

+class UpsampleOneStep(nn.Sequential):

+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)

+       Used in lightweight SR to save parameters.

+

+    Args:

+        scale (int): Scale factor. Supported scales: 2^n and 3.

+        num_feat (int): Channel number of intermediate features.

+

+    """

+

+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):

+        self.num_feat = num_feat

+        self.input_resolution = input_resolution

+        m = []

+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))

+        m.append(nn.PixelShuffle(scale))

+        super(UpsampleOneStep, self).__init__(*m)

+

+    def flops(self):

+        H, W = self.input_resolution

+        flops = H * W * self.num_feat * 3 * 9

+        return flops

+    

+    

+

+class Swin2SR(nn.Module):

+    r""" Swin2SR

+        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.

+

+    Args:

+        img_size (int | tuple(int)): Input image size. Default 64

+        patch_size (int | tuple(int)): Patch size. Default: 1

+        in_chans (int): Number of input image channels. Default: 3

+        embed_dim (int): Patch embedding dimension. Default: 96

+        depths (tuple(int)): Depth of each Swin Transformer layer.

+        num_heads (tuple(int)): Number of attention heads in different layers.

+        window_size (int): Window size. Default: 7

+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4

+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True

+        drop_rate (float): Dropout rate. Default: 0

+        attn_drop_rate (float): Attention dropout rate. Default: 0

+        drop_path_rate (float): Stochastic depth rate. Default: 0.1

+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.

+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False

+        patch_norm (bool): If True, add normalization after patch embedding. Default: True

+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False

+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction

+        img_range: Image range. 1. or 255.

+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None

+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'

+    """

+

+    def __init__(self, img_size=64, patch_size=1, in_chans=3,

+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],

+                 window_size=7, mlp_ratio=4., qkv_bias=True, 

+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,

+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,

+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',

+                 **kwargs):

+        super(Swin2SR, self).__init__()

+        num_in_ch = in_chans

+        num_out_ch = in_chans

+        num_feat = 64

+        self.img_range = img_range

+        if in_chans == 3:

+            rgb_mean = (0.4488, 0.4371, 0.4040)

+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)

+        else:

+            self.mean = torch.zeros(1, 1, 1, 1)

+        self.upscale = upscale

+        self.upsampler = upsampler

+        self.window_size = window_size

+

+        #####################################################################################################

+        ################################### 1, shallow feature extraction ###################################

+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

+

+        #####################################################################################################

+        ################################### 2, deep feature extraction ######################################

+        self.num_layers = len(depths)

+        self.embed_dim = embed_dim

+        self.ape = ape

+        self.patch_norm = patch_norm

+        self.num_features = embed_dim

+        self.mlp_ratio = mlp_ratio

+

+        # split image into non-overlapping patches

+        self.patch_embed = PatchEmbed(

+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,

+            norm_layer=norm_layer if self.patch_norm else None)

+        num_patches = self.patch_embed.num_patches

+        patches_resolution = self.patch_embed.patches_resolution

+        self.patches_resolution = patches_resolution

+

+        # merge non-overlapping patches into image

+        self.patch_unembed = PatchUnEmbed(

+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,

+            norm_layer=norm_layer if self.patch_norm else None)

+

+        # absolute position embedding

+        if self.ape:

+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))

+            trunc_normal_(self.absolute_pos_embed, std=.02)

+

+        self.pos_drop = nn.Dropout(p=drop_rate)

+

+        # stochastic depth

+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

+

+        # build Residual Swin Transformer blocks (RSTB)

+        self.layers = nn.ModuleList()

+        for i_layer in range(self.num_layers):

+            layer = RSTB(dim=embed_dim,

+                         input_resolution=(patches_resolution[0],

+                                           patches_resolution[1]),

+                         depth=depths[i_layer],

+                         num_heads=num_heads[i_layer],

+                         window_size=window_size,

+                         mlp_ratio=self.mlp_ratio,

+                         qkv_bias=qkv_bias, 

+                         drop=drop_rate, attn_drop=attn_drop_rate,

+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results

+                         norm_layer=norm_layer,

+                         downsample=None,

+                         use_checkpoint=use_checkpoint,

+                         img_size=img_size,

+                         patch_size=patch_size,

+                         resi_connection=resi_connection

+

+                         )

+            self.layers.append(layer)

+            

+        if self.upsampler == 'pixelshuffle_hf':

+            self.layers_hf = nn.ModuleList()

+            for i_layer in range(self.num_layers):

+                layer = RSTB(dim=embed_dim,

+                             input_resolution=(patches_resolution[0],

+                                               patches_resolution[1]),

+                             depth=depths[i_layer],

+                             num_heads=num_heads[i_layer],

+                             window_size=window_size,

+                             mlp_ratio=self.mlp_ratio,

+                             qkv_bias=qkv_bias, 

+                             drop=drop_rate, attn_drop=attn_drop_rate,

+                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results

+                             norm_layer=norm_layer,

+                             downsample=None,

+                             use_checkpoint=use_checkpoint,

+                             img_size=img_size,

+                             patch_size=patch_size,

+                             resi_connection=resi_connection

+

+                             )

+                self.layers_hf.append(layer)

+                        

+        self.norm = norm_layer(self.num_features)

+

+        # build the last conv layer in deep feature extraction

+        if resi_connection == '1conv':

+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)

+        elif resi_connection == '3conv':

+            # to save parameters and memory

+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),

+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),

+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),

+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),

+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

+

+        #####################################################################################################

+        ################################ 3, high quality image reconstruction ################################

+        if self.upsampler == 'pixelshuffle':

+            # for classical SR

+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

+                                                      nn.LeakyReLU(inplace=True))

+            self.upsample = Upsample(upscale, num_feat)

+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+        elif self.upsampler == 'pixelshuffle_aux':

+            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)

+            self.conv_before_upsample = nn.Sequential(

+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

+                nn.LeakyReLU(inplace=True))

+            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+            self.conv_after_aux = nn.Sequential(

+                nn.Conv2d(3, num_feat, 3, 1, 1),

+                nn.LeakyReLU(inplace=True))            

+            self.upsample = Upsample(upscale, num_feat)

+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+            

+        elif self.upsampler == 'pixelshuffle_hf':

+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

+                                                      nn.LeakyReLU(inplace=True))

+            self.upsample = Upsample(upscale, num_feat)

+            self.upsample_hf = Upsample_hf(upscale, num_feat)

+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),

+                                                      nn.LeakyReLU(inplace=True))

+            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)

+            self.conv_before_upsample_hf = nn.Sequential(

+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

+                nn.LeakyReLU(inplace=True))

+            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+            

+        elif self.upsampler == 'pixelshuffledirect':

+            # for lightweight SR (to save parameters)

+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,

+                                            (patches_resolution[0], patches_resolution[1]))

+        elif self.upsampler == 'nearest+conv':

+            # for real-world SR (less artifacts)

+            assert self.upscale == 4, 'only support x4 now.'

+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

+                                                      nn.LeakyReLU(inplace=True))

+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

+        else:

+            # for image denoising and JPEG compression artifact reduction

+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)

+

+        self.apply(self._init_weights)

+

+    def _init_weights(self, m):

+        if isinstance(m, nn.Linear):

+            trunc_normal_(m.weight, std=.02)

+            if isinstance(m, nn.Linear) and m.bias is not None:

+                nn.init.constant_(m.bias, 0)

+        elif isinstance(m, nn.LayerNorm):

+            nn.init.constant_(m.bias, 0)

+            nn.init.constant_(m.weight, 1.0)

+

+    @torch.jit.ignore

+    def no_weight_decay(self):

+        return {'absolute_pos_embed'}

+

+    @torch.jit.ignore

+    def no_weight_decay_keywords(self):

+        return {'relative_position_bias_table'}

+

+    def check_image_size(self, x):

+        _, _, h, w = x.size()

+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size

+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size

+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')

+        return x

+

+    def forward_features(self, x):

+        x_size = (x.shape[2], x.shape[3])

+        x = self.patch_embed(x)

+        if self.ape:

+            x = x + self.absolute_pos_embed

+        x = self.pos_drop(x)

+

+        for layer in self.layers:

+            x = layer(x, x_size)

+

+        x = self.norm(x)  # B L C

+        x = self.patch_unembed(x, x_size)

+

+        return x

+    

+    def forward_features_hf(self, x):

+        x_size = (x.shape[2], x.shape[3])

+        x = self.patch_embed(x)

+        if self.ape:

+            x = x + self.absolute_pos_embed

+        x = self.pos_drop(x)

+

+        for layer in self.layers_hf:

+            x = layer(x, x_size)

+

+        x = self.norm(x)  # B L C

+        x = self.patch_unembed(x, x_size)

+

+        return x    

+

+    def forward(self, x):

+        H, W = x.shape[2:]

+        x = self.check_image_size(x)

+

+        self.mean = self.mean.type_as(x)

+        x = (x - self.mean) * self.img_range

+

+        if self.upsampler == 'pixelshuffle':

+            # for classical SR

+            x = self.conv_first(x)

+            x = self.conv_after_body(self.forward_features(x)) + x

+            x = self.conv_before_upsample(x)

+            x = self.conv_last(self.upsample(x))

+        elif self.upsampler == 'pixelshuffle_aux':

+            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)

+            bicubic = self.conv_bicubic(bicubic)

+            x = self.conv_first(x)

+            x = self.conv_after_body(self.forward_features(x)) + x

+            x = self.conv_before_upsample(x)

+            aux = self.conv_aux(x) # b, 3, LR_H, LR_W

+            x = self.conv_after_aux(aux)

+            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]

+            x = self.conv_last(x)

+            aux = aux / self.img_range + self.mean

+        elif self.upsampler == 'pixelshuffle_hf':

+            # for classical SR with HF

+            x = self.conv_first(x)

+            x = self.conv_after_body(self.forward_features(x)) + x

+            x_before = self.conv_before_upsample(x)

+            x_out = self.conv_last(self.upsample(x_before))

+            

+            x_hf = self.conv_first_hf(x_before)

+            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf

+            x_hf = self.conv_before_upsample_hf(x_hf)

+            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))

+            x = x_out + x_hf

+            x_hf = x_hf / self.img_range + self.mean

+

+        elif self.upsampler == 'pixelshuffledirect':

+            # for lightweight SR

+            x = self.conv_first(x)

+            x = self.conv_after_body(self.forward_features(x)) + x

+            x = self.upsample(x)

+        elif self.upsampler == 'nearest+conv':

+            # for real-world SR

+            x = self.conv_first(x)

+            x = self.conv_after_body(self.forward_features(x)) + x

+            x = self.conv_before_upsample(x)

+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))

+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))

+            x = self.conv_last(self.lrelu(self.conv_hr(x)))

+        else:

+            # for image denoising and JPEG compression artifact reduction

+            x_first = self.conv_first(x)

+            res = self.conv_after_body(self.forward_features(x_first)) + x_first

+            x = x + self.conv_last(res)

+        

+        x = x / self.img_range + self.mean

+        if self.upsampler == "pixelshuffle_aux":

+            return x[:, :, :H*self.upscale, :W*self.upscale], aux

+        

+        elif self.upsampler == "pixelshuffle_hf":

+            x_out = x_out / self.img_range + self.mean

+            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]

+        

+        else:

+            return x[:, :, :H*self.upscale, :W*self.upscale]

+

+    def flops(self):

+        flops = 0

+        H, W = self.patches_resolution

+        flops += H * W * 3 * self.embed_dim * 9

+        flops += self.patch_embed.flops()

+        for i, layer in enumerate(self.layers):

+            flops += layer.flops()

+        flops += H * W * 3 * self.embed_dim * self.embed_dim

+        flops += self.upsample.flops()

+        return flops

+

+

+if __name__ == '__main__':

+    upscale = 4

+    window_size = 8

+    height = (1024 // upscale // window_size + 1) * window_size

+    width = (720 // upscale // window_size + 1) * window_size

+    model = Swin2SR(upscale=2, img_size=(height, width),

+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],

+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')

+    print(model)

+    print(height, width, model.flops() / 1e9)

+

+    x = torch.randn((1, 3, height, width))

+    x = model(x)

+    print(x.shape)
\ No newline at end of file
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..23bb4b6a
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,121 @@
+import os

+import numpy as np

+import PIL

+import torch

+from PIL import Image

+from torch.utils.data import Dataset

+from torchvision import transforms

+

+import random

+import tqdm

+from modules import devices, shared

+import re

+

+re_numbers_at_start = re.compile(r"^[-\d]+\s*")

+

+

+class DatasetEntry:

+    def __init__(self, filename=None, latent=None, filename_text=None):

+        self.filename = filename

+        self.latent = latent

+        self.filename_text = filename_text

+        self.cond = None

+        self.cond_text = None

+

+

+class PersonalizedBase(Dataset):

+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):

+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

+

+        self.placeholder_token = placeholder_token

+

+        self.batch_size = batch_size

+        self.width = width

+        self.height = height

+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)

+

+        self.dataset = []

+

+        with open(template_file, "r") as file:

+            lines = [x.strip() for x in file.readlines()]

+

+        self.lines = lines

+

+        assert data_root, 'dataset directory not specified'

+

+        cond_model = shared.sd_model.cond_stage_model

+

+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]

+        print("Preparing dataset...")

+        for path in tqdm.tqdm(self.image_paths):

+            try:

+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)

+            except Exception:

+                continue

+

+            text_filename = os.path.splitext(path)[0] + ".txt"

+            filename = os.path.basename(path)

+

+            if os.path.exists(text_filename):

+                with open(text_filename, "r", encoding="utf8") as file:

+                    filename_text = file.read()

+            else:

+                filename_text = os.path.splitext(filename)[0]

+                filename_text = re.sub(re_numbers_at_start, '', filename_text)

+                if re_word:

+                    tokens = re_word.findall(filename_text)

+                    filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)

+

+            npimage = np.array(image).astype(np.uint8)

+            npimage = (npimage / 127.5 - 1.0).astype(np.float32)

+

+            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)

+            torchdata = torch.moveaxis(torchdata, 2, 0)

+

+            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()

+            init_latent = init_latent.to(devices.cpu)

+

+            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)

+

+            if include_cond:

+                entry.cond_text = self.create_text(filename_text)

+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)

+

+            self.dataset.append(entry)

+

+        assert len(self.dataset) > 1, "No images have been found in the dataset."

+        self.length = len(self.dataset) * repeats // batch_size

+

+        self.initial_indexes = np.arange(len(self.dataset))

+        self.indexes = None

+        self.shuffle()

+

+    def shuffle(self):

+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]

+

+    def create_text(self, filename_text):

+        text = random.choice(self.lines)

+        text = text.replace("[name]", self.placeholder_token)

+        text = text.replace("[filewords]", filename_text)

+        return text

+

+    def __len__(self):

+        return self.length

+

+    def __getitem__(self, i):

+        res = []

+

+        for j in range(self.batch_size):

+            position = i * self.batch_size + j

+            if position % len(self.indexes) == 0:

+                self.shuffle()

+

+            index = self.indexes[position % len(self.indexes)]

+            entry = self.dataset[index]

+

+            if entry.cond is None:

+                entry.cond_text = self.create_text(entry.filename_text)

+

+            res.append(entry)

+

+        return res

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
new file mode 100644
index 00000000..898ce3b3
--- /dev/null
+++ b/modules/textual_inversion/image_embedding.py
@@ -0,0 +1,219 @@
+import base64

+import json

+import numpy as np

+import zlib

+from PIL import Image, PngImagePlugin, ImageDraw, ImageFont

+from fonts.ttf import Roboto

+import torch

+

+

+class EmbeddingEncoder(json.JSONEncoder):

+    def default(self, obj):

+        if isinstance(obj, torch.Tensor):

+            return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}

+        return json.JSONEncoder.default(self, obj)

+

+

+class EmbeddingDecoder(json.JSONDecoder):

+    def __init__(self, *args, **kwargs):

+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)

+

+    def object_hook(self, d):

+        if 'TORCHTENSOR' in d:

+            return torch.from_numpy(np.array(d['TORCHTENSOR']))

+        return d

+

+

+def embedding_to_b64(data):

+    d = json.dumps(data, cls=EmbeddingEncoder)

+    return base64.b64encode(d.encode())

+

+

+def embedding_from_b64(data):

+    d = base64.b64decode(data)

+    return json.loads(d, cls=EmbeddingDecoder)

+

+

+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):

+    while True:

+        seed = (a * seed + c) % m

+        yield seed % 255

+

+

+def xor_block(block):

+    g = lcg()

+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)

+    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)

+

+

+def style_block(block, sequence):

+    im = Image.new('RGB', (block.shape[1], block.shape[0]))

+    draw = ImageDraw.Draw(im)

+    i = 0

+    for x in range(-6, im.size[0], 8):

+        for yi, y in enumerate(range(-6, im.size[1], 8)):

+            offset = 0

+            if yi % 2 == 0:

+                offset = 4

+            shade = sequence[i % len(sequence)]

+            i += 1

+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))

+

+    fg = np.array(im).astype(np.uint8) & 0xF0

+

+    return block ^ fg

+

+

+def insert_image_data_embed(image, data):

+    d = 3

+    data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)

+    data_np_ = np.frombuffer(data_compressed, np.uint8).copy()

+    data_np_high = data_np_ >> 4

+    data_np_low = data_np_ & 0x0F

+

+    h = image.size[1]

+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))

+    next_size = next_size + ((h*d)-(next_size % (h*d)))

+

+    data_np_low.resize(next_size)

+    data_np_low = data_np_low.reshape((h, -1, d))

+

+    data_np_high.resize(next_size)

+    data_np_high = data_np_high.reshape((h, -1, d))

+

+    edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]

+    edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)

+

+    data_np_low = style_block(data_np_low, sequence=edge_style)

+    data_np_low = xor_block(data_np_low)

+    data_np_high = style_block(data_np_high, sequence=edge_style[::-1])

+    data_np_high = xor_block(data_np_high)

+

+    im_low = Image.fromarray(data_np_low, mode='RGB')

+    im_high = Image.fromarray(data_np_high, mode='RGB')

+

+    background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))

+    background.paste(im_low, (0, 0))

+    background.paste(image, (im_low.size[0]+1, 0))

+    background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))

+

+    return background

+

+

+def crop_black(img, tol=0):

+    mask = (img > tol).all(2)

+    mask0, mask1 = mask.any(0), mask.any(1)

+    col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()

+    row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()

+    return img[row_start:row_end, col_start:col_end]

+

+

+def extract_image_data_embed(image):

+    d = 3

+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F

+    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)

+    if black_cols[0].shape[0] < 2:

+        print('No Image data blocks found.')

+        return None

+

+    data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)

+    data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)

+

+    data_block_lower = xor_block(data_block_lower)

+    data_block_upper = xor_block(data_block_upper)

+

+    data_block = (data_block_upper << 4) | (data_block_lower)

+    data_block = data_block.flatten().tobytes()

+

+    data = zlib.decompress(data_block)

+    return json.loads(data, cls=EmbeddingDecoder)

+

+

+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):

+    from math import cos

+

+    image = srcimage.copy()

+

+    if textfont is None:

+        try:

+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)

+            textfont = opts.font or Roboto

+        except Exception:

+            textfont = Roboto

+

+    factor = 1.5

+    gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))

+    for y in range(image.size[1]):

+        mag = 1-cos(y/image.size[1]*factor)

+        mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))

+        gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))

+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))

+

+    draw = ImageDraw.Draw(image)

+    fontsize = 32

+    font = ImageFont.truetype(textfont, fontsize)

+    padding = 10

+

+    _, _, w, h = draw.textbbox((0, 0), title, font=font)

+    fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)

+    font = ImageFont.truetype(textfont, fontsize)

+    _, _, w, h = draw.textbbox((0, 0), title, font=font)

+    draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))

+

+    _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)

+    fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)

+    _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)

+    fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)

+    _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)

+    fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)

+

+    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))

+

+    draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))

+    draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))

+    draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))

+

+    return image

+

+

+if __name__ == '__main__':

+

+    testEmbed = Image.open('test_embedding.png')

+    data = extract_image_data_embed(testEmbed)

+    assert data is not None

+

+    data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])

+    assert data is not None

+

+    image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))

+    cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')

+

+    test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}

+

+    embedded_image = insert_image_data_embed(cap_image, test_embed)

+

+    retrived_embed = extract_image_data_embed(embedded_image)

+

+    assert str(retrived_embed) == str(test_embed)

+

+    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)

+

+    assert embedded_image == embedded_image2

+

+    g = lcg()

+    shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()

+

+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177,

+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179,

+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193,

+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28,

+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0,

+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185,

+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82,

+                         204, 86,  73, 222,  44, 198, 118, 240,  97]

+

+    assert shared_random == reference_random

+

+    hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())

+

+    assert 12731374 == hunna_kay_random_sum

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
new file mode 100644
index 00000000..2062726a
--- /dev/null
+++ b/modules/textual_inversion/learn_schedule.py
@@ -0,0 +1,69 @@
+import tqdm

+

+

+class LearnScheduleIterator:

+    def __init__(self, learn_rate, max_steps, cur_step=0):

+        """

+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000

+        """

+

+        pairs = learn_rate.split(',')

+        self.rates = []

+        self.it = 0

+        self.maxit = 0

+        for i, pair in enumerate(pairs):

+            tmp = pair.split(':')

+            if len(tmp) == 2:

+                step = int(tmp[1])

+                if step > cur_step:

+                    self.rates.append((float(tmp[0]), min(step, max_steps)))

+                    self.maxit += 1

+                    if step > max_steps:

+                        return

+                elif step == -1:

+                    self.rates.append((float(tmp[0]), max_steps))

+                    self.maxit += 1

+                    return

+            else:

+                self.rates.append((float(tmp[0]), max_steps))

+                self.maxit += 1

+                return

+

+    def __iter__(self):

+        return self

+

+    def __next__(self):

+        if self.it < self.maxit:

+            self.it += 1

+            return self.rates[self.it - 1]

+        else:

+            raise StopIteration

+

+

+class LearnRateScheduler:

+    def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):

+        self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)

+        (self.learn_rate,  self.end_step) = next(self.schedules)

+        self.verbose = verbose

+

+        if self.verbose:

+            print(f'Training at rate of {self.learn_rate} until step {self.end_step}')

+

+        self.finished = False

+

+    def apply(self, optimizer, step_number):

+        if step_number <= self.end_step:

+            return

+

+        try:

+            (self.learn_rate, self.end_step) = next(self.schedules)

+        except Exception:

+            self.finished = True

+            return

+

+        if self.verbose:

+            tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')

+

+        for pg in optimizer.param_groups:

+            pg['lr'] = self.learn_rate

+

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
new file mode 100644
index 00000000..886cf0c3
--- /dev/null
+++ b/modules/textual_inversion/preprocess.py
@@ -0,0 +1,116 @@
+import os

+from PIL import Image, ImageOps

+import platform

+import sys

+import tqdm

+import time

+

+from modules import shared, images

+from modules.shared import opts, cmd_opts

+if cmd_opts.deepdanbooru:

+    import modules.deepbooru as deepbooru

+

+

+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+    try:

+        if process_caption:

+            shared.interrogator.load()

+

+        if process_caption_deepbooru:

+            db_opts = deepbooru.create_deepbooru_opts()

+            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

+            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

+

+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)

+

+    finally:

+

+        if process_caption:

+            shared.interrogator.send_blip_to_ram()

+

+        if process_caption_deepbooru:

+            deepbooru.release_process()

+

+

+

+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+    width = process_width

+    height = process_height

+    src = os.path.abspath(process_src)

+    dst = os.path.abspath(process_dst)

+

+    assert src != dst, 'same directory specified as source and destination'

+

+    os.makedirs(dst, exist_ok=True)

+

+    files = os.listdir(src)

+

+    shared.state.textinfo = "Preprocessing..."

+    shared.state.job_count = len(files)

+

+    def save_pic_with_caption(image, index):

+        caption = ""

+

+        if process_caption:

+            caption += shared.interrogator.generate_caption(image)

+

+        if process_caption_deepbooru:

+            if len(caption) > 0:

+                caption += ", "

+            caption += deepbooru.get_tags_from_process(image)

+

+        filename_part = filename

+        filename_part = os.path.splitext(filename_part)[0]

+        filename_part = os.path.basename(filename_part)

+

+        basename = f"{index:05}-{subindex[0]}-{filename_part}"

+        image.save(os.path.join(dst, f"{basename}.png"))

+

+        if len(caption) > 0:

+            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:

+                file.write(caption)

+

+        subindex[0] += 1

+

+    def save_pic(image, index):

+        save_pic_with_caption(image, index)

+

+        if process_flip:

+            save_pic_with_caption(ImageOps.mirror(image), index)

+

+    for index, imagefile in enumerate(tqdm.tqdm(files)):

+        subindex = [0]

+        filename = os.path.join(src, imagefile)

+        try:

+            img = Image.open(filename).convert("RGB")

+        except Exception:

+            continue

+

+        if shared.state.interrupted:

+            break

+

+        ratio = img.height / img.width

+        is_tall = ratio > 1.35

+        is_wide = ratio < 1 / 1.35

+

+        if process_split and is_tall:

+            img = img.resize((width, height * img.height // img.width))

+

+            top = img.crop((0, 0, width, height))

+            save_pic(top, index)

+

+            bot = img.crop((0, img.height - height, width, img.height))

+            save_pic(bot, index)

+        elif process_split and is_wide:

+            img = img.resize((width * img.width // img.height, height))

+

+            left = img.crop((0, 0, width, height))

+            save_pic(left, index)

+

+            right = img.crop((img.width - width, 0, img.width, height))

+            save_pic(right, index)

+        else:

+            img = images.resize_image(1, img, width, height)

+            save_pic(img, index)

+

+        shared.state.nextjob()

diff --git a/modules/textual_inversion/test_embedding.png b/modules/textual_inversion/test_embedding.png
new file mode 100644
index 00000000..07e2d9af
--- /dev/null
+++ b/modules/textual_inversion/test_embedding.png
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 00000000..e754747e
--- /dev/null
+++ b/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,363 @@
+import os

+import sys

+import traceback

+

+import torch

+import tqdm

+import html

+import datetime

+import csv

+

+from PIL import Image, PngImagePlugin

+

+from modules import shared, devices, sd_hijack, processing, sd_models

+import modules.textual_inversion.dataset

+from modules.textual_inversion.learn_schedule import LearnRateScheduler

+

+from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,

+                                                       insert_image_data_embed, extract_image_data_embed,

+                                                       caption_image_overlay)

+

+class Embedding:

+    def __init__(self, vec, name, step=None):

+        self.vec = vec

+        self.name = name

+        self.step = step

+        self.cached_checksum = None

+        self.sd_checkpoint = None

+        self.sd_checkpoint_name = None

+

+    def save(self, filename):

+        embedding_data = {

+            "string_to_token": {"*": 265},

+            "string_to_param": {"*": self.vec},

+            "name": self.name,

+            "step": self.step,

+            "sd_checkpoint": self.sd_checkpoint,

+            "sd_checkpoint_name": self.sd_checkpoint_name,

+        }

+

+        torch.save(embedding_data, filename)

+

+    def checksum(self):

+        if self.cached_checksum is not None:

+            return self.cached_checksum

+

+        def const_hash(a):

+            r = 0

+            for v in a:

+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF

+            return r

+

+        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'

+        return self.cached_checksum

+

+

+class EmbeddingDatabase:

+    def __init__(self, embeddings_dir):

+        self.ids_lookup = {}

+        self.word_embeddings = {}

+        self.dir_mtime = None

+        self.embeddings_dir = embeddings_dir

+

+    def register_embedding(self, embedding, model):

+

+        self.word_embeddings[embedding.name] = embedding

+

+        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]

+

+        first_id = ids[0]

+        if first_id not in self.ids_lookup:

+            self.ids_lookup[first_id] = []

+

+        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)

+

+        return embedding

+

+    def load_textual_inversion_embeddings(self):

+        mt = os.path.getmtime(self.embeddings_dir)

+        if self.dir_mtime is not None and mt <= self.dir_mtime:

+            return

+

+        self.dir_mtime = mt

+        self.ids_lookup.clear()

+        self.word_embeddings.clear()

+

+        def process_file(path, filename):

+            name = os.path.splitext(filename)[0]

+

+            data = []

+

+            if filename.upper().endswith('.PNG'):

+                embed_image = Image.open(path)

+                if 'sd-ti-embedding' in embed_image.text:

+                    data = embedding_from_b64(embed_image.text['sd-ti-embedding'])

+                    name = data.get('name', name)

+                else:

+                    data = extract_image_data_embed(embed_image)

+                    name = data.get('name', name)

+            else:

+                data = torch.load(path, map_location="cpu")

+

+            # textual inversion embeddings

+            if 'string_to_param' in data:

+                param_dict = data['string_to_param']

+                if hasattr(param_dict, '_parameters'):

+                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11

+                assert len(param_dict) == 1, 'embedding file has multiple terms in it'

+                emb = next(iter(param_dict.items()))[1]

+            # diffuser concepts

+            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:

+                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

+

+                emb = next(iter(data.values()))

+                if len(emb.shape) == 1:

+                    emb = emb.unsqueeze(0)

+            else:

+                raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

+

+            vec = emb.detach().to(devices.device, dtype=torch.float32)

+            embedding = Embedding(vec, name)

+            embedding.step = data.get('step', None)

+            embedding.sd_checkpoint = data.get('hash', None)

+            embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)

+            self.register_embedding(embedding, shared.sd_model)

+

+        for fn in os.listdir(self.embeddings_dir):

+            try:

+                fullfn = os.path.join(self.embeddings_dir, fn)

+

+                if os.stat(fullfn).st_size == 0:

+                    continue

+

+                process_file(fullfn, fn)

+            except Exception:

+                print(f"Error loading emedding {fn}:", file=sys.stderr)

+                print(traceback.format_exc(), file=sys.stderr)

+                continue

+

+        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")

+

+    def find_embedding_at_position(self, tokens, offset):

+        token = tokens[offset]

+        possible_matches = self.ids_lookup.get(token, None)

+

+        if possible_matches is None:

+            return None, None

+

+        for ids, embedding in possible_matches:

+            if tokens[offset:offset + len(ids)] == ids:

+                return embedding, len(ids)

+

+        return None, None

+

+

+def create_embedding(name, num_vectors_per_token, init_text='*'):

+    cond_model = shared.sd_model.cond_stage_model

+    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings

+

+    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]

+    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)

+    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)

+

+    for i in range(num_vectors_per_token):

+        vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]

+

+    fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")

+    assert not os.path.exists(fn), f"file {fn} already exists"

+

+    embedding = Embedding(vec, name)

+    embedding.step = 0

+    embedding.save(fn)

+

+    return fn

+

+

+def write_loss(log_directory, filename, step, epoch_len, values):

+    if shared.opts.training_write_csv_every == 0:

+        return

+

+    if step % shared.opts.training_write_csv_every != 0:

+        return

+

+    write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True

+

+    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:

+        csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])

+

+        if write_csv_header:

+            csv_writer.writeheader()

+

+        epoch = step // epoch_len

+        epoch_step = step - epoch * epoch_len

+

+        csv_writer.writerow({

+            "step": step + 1,

+            "epoch": epoch + 1,

+            "epoch_step": epoch_step + 1,

+            **values,

+        })

+

+

+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+    assert embedding_name, 'embedding not selected'

+

+    shared.state.textinfo = "Initializing textual inversion training..."

+    shared.state.job_count = steps

+

+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

+

+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)

+

+    if save_embedding_every > 0:

+        embedding_dir = os.path.join(log_directory, "embeddings")

+        os.makedirs(embedding_dir, exist_ok=True)

+    else:

+        embedding_dir = None

+

+    if create_image_every > 0:

+        images_dir = os.path.join(log_directory, "images")

+        os.makedirs(images_dir, exist_ok=True)

+    else:

+        images_dir = None

+

+    if create_image_every > 0 and save_image_with_stored_embedding:

+        images_embeds_dir = os.path.join(log_directory, "image_embeddings")

+        os.makedirs(images_embeds_dir, exist_ok=True)

+    else:

+        images_embeds_dir = None

+        

+    cond_model = shared.sd_model.cond_stage_model

+

+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

+    with torch.autocast("cuda"):

+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)

+

+    hijack = sd_hijack.model_hijack

+

+    embedding = hijack.embedding_db.word_embeddings[embedding_name]

+    embedding.vec.requires_grad = True

+

+    losses = torch.zeros((32,))

+

+    last_saved_file = "<none>"

+    last_saved_image = "<none>"

+

+    ititial_step = embedding.step or 0

+    if ititial_step > steps:

+        return embedding, filename

+

+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)

+

+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

+    for i, entries in pbar:

+        embedding.step = i + ititial_step

+

+        scheduler.apply(optimizer, embedding.step)

+        if scheduler.finished:

+            break

+

+        if shared.state.interrupted:

+            break

+

+        with torch.autocast("cuda"):

+            c = cond_model([entry.cond_text for entry in entries])

+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)

+            loss = shared.sd_model(x, c)[0]

+            del x

+

+            losses[embedding.step % losses.shape[0]] = loss.item()

+

+            optimizer.zero_grad()

+            loss.backward()

+            optimizer.step()

+

+        epoch_num = embedding.step // len(ds)

+        epoch_step = embedding.step - (epoch_num * len(ds)) + 1

+

+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")

+

+        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:

+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')

+            embedding.save(last_saved_file)

+

+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {

+            "loss": f"{losses.mean():.7f}",

+            "learn_rate": scheduler.learn_rate

+        })

+

+        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:

+            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')

+

+            p = processing.StableDiffusionProcessingTxt2Img(

+                sd_model=shared.sd_model,

+                do_not_save_grid=True,

+                do_not_save_samples=True,

+            )

+

+            if preview_from_txt2img:

+                p.prompt = preview_prompt

+                p.negative_prompt = preview_negative_prompt

+                p.steps = preview_steps

+                p.sampler_index = preview_sampler_index

+                p.cfg_scale = preview_cfg_scale

+                p.seed = preview_seed

+                p.width = preview_width

+                p.height = preview_height

+            else:

+                p.prompt = entries[0].cond_text

+                p.steps = 20

+                p.width = training_width

+                p.height = training_height

+

+            preview_text = p.prompt

+

+            processed = processing.process_images(p)

+            image = processed.images[0]

+

+            shared.state.current_image = image

+

+            if save_image_with_stored_embedding and os.path.exists(last_saved_file):

+

+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')

+

+                info = PngImagePlugin.PngInfo()

+                data = torch.load(last_saved_file)

+                info.add_text("sd-ti-embedding", embedding_to_b64(data))

+

+                title = "<{}>".format(data.get('name', '???'))

+                checkpoint = sd_models.select_checkpoint()

+                footer_left = checkpoint.model_name

+                footer_mid = '[{}]'.format(checkpoint.hash)

+                footer_right = '{}'.format(embedding.step)

+

+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)

+                captioned_image = insert_image_data_embed(captioned_image, data)

+

+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)

+

+            image.save(last_saved_image)

+

+            last_saved_image += f", prompt: {preview_text}"

+

+        shared.state.job_no = embedding.step

+

+        shared.state.textinfo = f"""

+<p>

+Loss: {losses.mean():.7f}<br/>

+Step: {embedding.step}<br/>

+Last prompt: {html.escape(entries[0].cond_text)}<br/>

+Last saved embedding: {html.escape(last_saved_file)}<br/>

+Last saved image: {html.escape(last_saved_image)}<br/>

+</p>

+"""

+

+    checkpoint = sd_models.select_checkpoint()

+

+    embedding.sd_checkpoint = checkpoint.hash

+    embedding.sd_checkpoint_name = checkpoint.model_name

+    embedding.cached_checksum = None

+    embedding.save(filename)

+

+    return embedding, filename

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
new file mode 100644
index 00000000..36881e7a
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,42 @@
+import html

+

+import gradio as gr

+

+import modules.textual_inversion.textual_inversion

+import modules.textual_inversion.preprocess

+from modules import sd_hijack, shared

+

+

+def create_embedding(name, initialization_text, nvpt):

+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)

+

+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

+

+    return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""

+

+

+def preprocess(*args):

+    modules.textual_inversion.preprocess.preprocess(*args)

+

+    return "Preprocessing finished.", ""

+

+

+def train_embedding(*args):

+

+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'

+

+    try:

+        sd_hijack.undo_optimizations()

+

+        embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)

+

+        res = f"""

+Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.

+Embedding saved to {html.escape(filename)}

+"""

+        return res, ""

+    except Exception:

+        raise

+    finally:

+        sd_hijack.apply_optimizations()

+

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 5368e4d0..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html

 

 

-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):

+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):

     p = StableDiffusionProcessingTxt2Img(

         sd_model=shared.sd_model,

         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,

@@ -30,11 +30,14 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         restore_faces=restore_faces,

         tiling=tiling,

         enable_hr=enable_hr,

-        scale_latent=scale_latent if enable_hr else None,

         denoising_strength=denoising_strength if enable_hr else None,

+        firstphase_width=firstphase_width if enable_hr else None,

+        firstphase_height=firstphase_height if enable_hr else None,

     )

 

-    print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

+    if cmd_opts.enable_console_prompts:

+        print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

+

     processed = modules.scripts.scripts_txt2img.run(p, *args)

 

     if processed is None:

@@ -46,5 +49,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.samples_log_stdout:

         print(generation_info_js)

 

+    if opts.do_not_show_images:

+        processed.images = []

+

     return processed.images, generation_info_js, plaintext_to_html(processed.info)

 

diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..baf4c397 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -11,6 +11,7 @@ import time
 import traceback

 import platform

 import subprocess as sp

+from functools import reduce

 

 import numpy as np

 import torch

@@ -21,8 +22,11 @@ import gradio as gr
 import gradio.utils

 import gradio.routes

 

+from modules import sd_hijack, sd_models

 from modules.paths import script_path

 from modules.shared import opts, cmd_opts

+if cmd_opts.deepdanbooru:

+    from modules.deepbooru import get_deepbooru_tags

 import modules.shared as shared

 from modules.sd_samplers import samplers, samplers_for_img2img

 from modules.sd_hijack import model_hijack

@@ -32,8 +36,13 @@ import modules.gfpgan_model
 import modules.codeformer_model

 import modules.styles

 import modules.generation_parameters_copypaste

+from modules import prompt_parser

+from modules.images import save_image

+import modules.textual_inversion.ui

+import modules.hypernetworks.ui

+import modules.images_history as img_his

 

-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI

+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI

 mimetypes.init()

 mimetypes.add_type('application/javascript', '.js')

 

@@ -43,6 +52,11 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None

     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'

 

+if cmd_opts.ngrok != None:

+    import modules.ngrok as ngrok

+    print('ngrok authtoken detected, trying to connect...')

+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)

+

 

 def gr_show(visible=True):

     return {"visible": visible, "__type__": "update"}

@@ -64,7 +78,9 @@ random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️

 art_symbol = '\U0001f3a8'  # 🎨

 paste_symbol = '\u2199\ufe0f'  # ↙

-folder_symbol = '\uD83D\uDCC2'

+folder_symbol = '\U0001f4c2'  # 📂

+refresh_symbol = '\U0001f504'  # 🔄

+

 

 def plaintext_to_html(text):

     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"

@@ -93,19 +109,32 @@ def send_gradio_gallery_to_image(x):
     return image_from_url_text(x[0])

 

 

-def save_files(js_data, images, index):

+def save_files(js_data, images, do_make_zip, index):

     import csv

-

-    os.makedirs(opts.outdir_save, exist_ok=True)

-

     filenames = []

+    fullfns = []

+

+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it

+    class MyObject:

+        def __init__(self, d=None):

+            if d is not None:

+                for key, value in d.items():

+                    setattr(self, key, value)

 

     data = json.loads(js_data)

+

+    p = MyObject(data)

+    path = opts.outdir_save

+    save_to_dirs = opts.use_save_to_dirs_for_ui

+    extension: str = opts.samples_format

+    start_index = 0

+

     if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only

+

         images = [images[index]]

-        infotexts = [data["infotexts"][index]]

-    else:

-        infotexts = data["infotexts"]

+        start_index = index

+

+    os.makedirs(opts.outdir_save, exist_ok=True)

 

     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:

         at_start = file.tell() == 0

@@ -113,37 +142,42 @@ def save_files(js_data, images, index):
         if at_start:

             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])

 

-        filename_base = str(int(time.time() * 1000))

-        extension = opts.samples_format.lower()

-        for i, filedata in enumerate(images):

-            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"

-            filepath = os.path.join(opts.outdir_save, filename)

-

+        for image_index, filedata in enumerate(images, start_index):

             if filedata.startswith("data:image/png;base64,"):

                 filedata = filedata[len("data:image/png;base64,"):]

 

             image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))

-            if opts.enable_pnginfo and extension == 'png':

-                pnginfo = PngImagePlugin.PngInfo()

-                pnginfo.add_text('parameters', infotexts[i])

-                image.save(filepath, pnginfo=pnginfo)

-            else:

-                image.save(filepath, quality=opts.jpeg_quality)

 

-            if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):

-                piexif.insert(piexif.dump({"Exif": {

-                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")

-                }}), filepath)

+            is_grid = image_index < p.index_of_first_image

+            i = 0 if is_grid else (image_index - p.index_of_first_image)

+

+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)

 

+            filename = os.path.relpath(fullfn, path)

             filenames.append(filename)

+            fullfns.append(fullfn)

+            if txt_fullfn:

+                filenames.append(os.path.basename(txt_fullfn))

+                fullfns.append(txt_fullfn)

 

         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])

 

-    return '', '', plaintext_to_html(f"Saved: {filenames[0]}")

+    # Make Zip

+    if do_make_zip:

+        zip_filepath = os.path.join(path, "images.zip")

 

+        from zipfile import ZipFile

+        with ZipFile(zip_filepath, "w") as zip_file:

+            for i in range(len(fullfns)):

+                with open(fullfns[i], mode="rb") as f:

+                    zip_file.writestr(filenames[i], f.read())

+        fullfns.insert(0, zip_filepath)

 

-def wrap_gradio_call(func):

-    def f(*args, **kwargs):

+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")

+

+

+def wrap_gradio_call(func, extra_outputs=None):

+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):

         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled

         if run_memmon:

             shared.mem_mon.monitor()

@@ -152,16 +186,31 @@ def wrap_gradio_call(func):
         try:

             res = list(func(*args, **kwargs))

         except Exception as e:

+            # When printing out our debug argument list, do not print out more than a MB of text

+            max_debug_str_len = 131072 # (1024*1024)/8

+

             print("Error completing request", file=sys.stderr)

-            print("Arguments:", args, kwargs, file=sys.stderr)

+            argStr = f"Arguments: {str(args)} {str(kwargs)}"

+            print(argStr[:max_debug_str_len], file=sys.stderr)

+            if len(argStr) > max_debug_str_len:

+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)

+

             print(traceback.format_exc(), file=sys.stderr)

 

             shared.state.job = ""

             shared.state.job_count = 0

 

-            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

+            if extra_outputs_array is None:

+                extra_outputs_array = [None, '']

+

+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

 

         elapsed = time.perf_counter() - t

+        elapsed_m = int(elapsed // 60)

+        elapsed_s = elapsed % 60

+        elapsed_text = f"{elapsed_s:.2f}s"

+        if (elapsed_m > 0):

+            elapsed_text = f"{elapsed_m}m "+elapsed_text

 

         if run_memmon:

             mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}

@@ -176,9 +225,11 @@ def wrap_gradio_call(func):
             vram_html = ''

 

         # last item is always HTML

-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"

+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"

 

+        shared.state.skipped = False

         shared.state.interrupted = False

+        shared.state.job_count = 0

 

         return tuple(res)

 

@@ -187,7 +238,7 @@ def wrap_gradio_call(func):
 

 def check_progress_call(id_part):

     if shared.state.job_count == 0:

-        return "", gr_show(False), gr_show(False)

+        return "", gr_show(False), gr_show(False), gr_show(False)

 

     progress = 0

 

@@ -219,13 +270,19 @@ def check_progress_call(id_part):
         else:

             preview_visibility = gr_show(True)

 

-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image

+    if shared.state.textinfo is not None:

+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)

+    else:

+        textinfo_result = gr_show(False)

+

+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result

 

 

 def check_progress_call_initial(id_part):

     shared.state.job_count = -1

     shared.state.current_latent = None

     shared.state.current_image = None

+    shared.state.textinfo = None

 

     return check_progress_call(id_part)

 

@@ -271,6 +328,11 @@ def interrogate(image):
     return gr_show(True) if prompt is None else prompt

 

 

+def interrogate_deepbooru(image):

+    prompt = get_deepbooru_tags(image)

+    return gr_show(True) if prompt is None else prompt

+

+

 def create_seed_inputs():

     with gr.Row():

         with gr.Box():

@@ -345,11 +407,24 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]

     )

 

-def update_token_counter(text):

-    tokens, token_count, max_length = model_hijack.tokenize(text)

+

+def update_token_counter(text, steps):

+    try:

+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])

+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)

+

+    except Exception:

+        # a parsing error can happen here during typing, and we don't want to bother the user with

+        # messages related to it in console

+        prompt_schedules = [[[steps, text]]]

+

+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)

+    prompts = [prompt_text for step, prompt_text in flat_prompts]

+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])

     style_class = ' class="red"' if (token_count > max_length) else ""

     return f"<span {style_class}>{token_count}/{max_length}</span>"

 

+

 def create_toprow(is_img2img):

     id_part = "img2img" if is_img2img else "txt2img"

 

@@ -358,54 +433,76 @@ def create_toprow(is_img2img):
             with gr.Row():

                 with gr.Column(scale=80):

                     with gr.Row():

-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)

+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 

+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"

+                        )

 

                 with gr.Column(scale=1, elem_id="roll_col"):

                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)

                     paste = gr.Button(value=paste_symbol, elem_id="paste")

                     token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")

-                    hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")

-                    hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])

+                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")

 

                 with gr.Column(scale=10, elem_id="style_pos_col"):

                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)

 

             with gr.Row():

                 with gr.Column(scale=8):

-                    negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)

+                    with gr.Row():

+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 

+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"

+                        )

+

+                with gr.Column(scale=1, elem_id="roll_col"):

+                    sh = gr.Button(elem_id="sh", visible=True)                           

 

                 with gr.Column(scale=1, elem_id="style_neg_col"):

                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)

 

         with gr.Column(scale=1):

             with gr.Row():

+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")

                 interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")

                 submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')

 

+                skip.click(

+                    fn=lambda: shared.state.skip(),

+                    inputs=[],

+                    outputs=[],

+                )

+

                 interrupt.click(

                     fn=lambda: shared.state.interrupt(),

                     inputs=[],

                     outputs=[],

                 )

 

-            with gr.Row():

+            with gr.Row(scale=1):

                 if is_img2img:

-                    interrogate = gr.Button('Interrogate', elem_id="interrogate")

+                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")

+                    if cmd_opts.deepdanbooru:

+                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

+                    else:

+                        deepbooru = None

                 else:

                     interrogate = None

+                    deepbooru = None

                 prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")

                 save_style = gr.Button('Create style', elem_id="style_create")

 

-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste

+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button

 

 

-def setup_progressbar(progressbar, preview, id_part):

+def setup_progressbar(progressbar, preview, id_part, textinfo=None):

+    if textinfo is None:

+        textinfo = gr.HTML(visible=False)

+

     check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)

     check_progress.click(

         fn=lambda: check_progress_call(id_part),

         show_progress=False,

         inputs=[],

-        outputs=[progressbar, preview, preview],

+        outputs=[progressbar, preview, preview, textinfo],

     )

 

     check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)

@@ -413,14 +510,44 @@ def setup_progressbar(progressbar, preview, id_part):
         fn=lambda: check_progress_call_initial(id_part),

         show_progress=False,

         inputs=[],

-        outputs=[progressbar, preview, preview],

+        outputs=[progressbar, preview, preview, textinfo],

     )

 

 

-def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):

+def apply_setting(key, value):

+    if value is None:

+        return gr.update()

+

+    if key == "sd_model_checkpoint":

+        ckpt_info = sd_models.get_closet_checkpoint_match(value)

+

+        if ckpt_info is not None:

+            value = ckpt_info.title

+        else:

+            return gr.update()

+

+    comp_args = opts.data_labels[key].component_args

+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:

+        return

+

+    valtype = type(opts.data_labels[key].default)

+    oldval = opts.data[key]

+    opts.data[key] = valtype(value) if valtype != type(None) else value

+    if oldval != value and opts.data_labels[key].onchange is not None:

+        opts.data_labels[key].onchange()

+

+    opts.save(shared.config_filename)

+    return value

+

+

+def create_ui(wrap_gradio_gpu_call):

+    import modules.img2img

+    import modules.txt2img

+

     with gr.Blocks(analytics_enabled=False) as txt2img_interface:

-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)

+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)

         dummy_component = gr.Label(visible=False)

+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)

 

         with gr.Row(elem_id='txt2img_progress_row'):

             with gr.Column(scale=1):

@@ -446,11 +573,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)

 

                 with gr.Row(visible=False) as hr_options:

-                    scale_latent = gr.Checkbox(label='Scale latent', value=False)

+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)

+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)

                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)

 

-                with gr.Row():

-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)

+                with gr.Row(equal_height=True):

+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)

                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)

 

                 cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)

@@ -475,15 +603,21 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'

                         open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)

 

-                with gr.Group():

-                    html_info = gr.HTML()

-                    generation_info = gr.Textbox(visible=False)

+                    with gr.Row():

+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)

+

+                    with gr.Row():

+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)

+

+                    with gr.Group():

+                        html_info = gr.HTML()

+                        generation_info = gr.Textbox(visible=False)

 

             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)

             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)

 

             txt2img_args = dict(

-                fn=txt2img,

+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),

                 _js="submit",

                 inputs=[

                     txt2img_prompt,

@@ -502,8 +636,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     height,

                     width,

                     enable_hr,

-                    scale_latent,

                     denoising_strength,

+                    firstphase_width,

+                    firstphase_height,

                 ] + custom_inputs,

                 outputs=[

                     txt2img_gallery,

@@ -516,6 +651,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             txt2img_prompt.submit(**txt2img_args)

             submit.click(**txt2img_args)

 

+            txt_prompt_img.change(

+                fn=modules.images.image_data,

+                inputs=[

+                    txt_prompt_img

+                ],

+                outputs=[

+                    txt2img_prompt,

+                    txt_prompt_img

+                ]

+            )

+

             enable_hr.change(

                 fn=lambda x: gr_show(x),

                 inputs=[enable_hr],

@@ -524,13 +670,15 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             save.click(

                 fn=wrap_gradio_call(save_files),

-                _js="(x, y, z) => [x, y, selected_gallery_index()]",

+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",

                 inputs=[

                     generation_info,

                     txt2img_gallery,

+                    do_make_zip,

                     html_info,

                 ],

                 outputs=[

+                    download_files,

                     html_info,

                     html_info,

                     html_info,

@@ -539,6 +687,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             roll.click(

                 fn=roll_artist,

+                _js="update_txt2img_tokens",

                 inputs=[

                     txt2img_prompt,

                 ],

@@ -565,13 +714,29 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (denoising_strength, "Denoising strength"),

                 (enable_hr, lambda d: "Denoising strength" in d),

                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

+                (firstphase_width, "First pass size-1"),

+                (firstphase_height, "First pass size-2"),

+            ]

+

+            txt2img_preview_params = [

+                txt2img_prompt,

+                txt2img_negative_prompt,

+                steps,

+                sampler_index,

+                cfg_scale,

+                seed,

+                width,

+                height,

             ]

-            modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)

+

+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])

 

     with gr.Blocks(analytics_enabled=False) as img2img_interface:

-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True)

+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)

 

         with gr.Row(elem_id='img2img_progress_row'):

+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)

+

             with gr.Column(scale=1):

                 pass

 

@@ -585,7 +750,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:

                     with gr.TabItem('img2img', id='img2img'):

-                        init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")

+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)

 

                     with gr.TabItem('Inpaint', id='inpaint'):

                         init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")

@@ -607,7 +772,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

                     with gr.TabItem('Batch img2img', id='batch'):

                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''

-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.{hidden}</p>")

+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")

                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)

                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)

 

@@ -626,7 +791,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     tiling = gr.Checkbox(label='Tiling', value=False)

 

                 with gr.Row():

-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)

+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)

                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)

 

                 with gr.Group():

@@ -653,13 +818,30 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'

                         open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)

 

-                with gr.Group():

-                    html_info = gr.HTML()

-                    generation_info = gr.Textbox(visible=False)

+                    with gr.Row():

+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)

+

+                    with gr.Row():

+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)

+

+                    with gr.Group():

+                        html_info = gr.HTML()

+                        generation_info = gr.Textbox(visible=False)

 

             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)

             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)

 

+            img2img_prompt_img.change(

+                fn=modules.images.image_data,

+                inputs=[

+                    img2img_prompt_img

+                ],

+                outputs=[

+                    img2img_prompt,

+                    img2img_prompt_img

+                ]

+            )

+

             mask_mode.change(

                 lambda mode, img: {

                     init_img_with_mask: gr_show(mode == 0),

@@ -675,7 +857,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             )

 

             img2img_args = dict(

-                fn=img2img,

+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),

                 _js="submit_img2img",

                 inputs=[

                     dummy_component,

@@ -726,15 +908,24 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 outputs=[img2img_prompt],

             )

 

+            if cmd_opts.deepdanbooru:

+                img2img_deepbooru.click(

+                    fn=interrogate_deepbooru,

+                    inputs=[init_img],

+                    outputs=[img2img_prompt],

+                )

+

             save.click(

                 fn=wrap_gradio_call(save_files),

-                _js="(x, y, z) => [x, y, selected_gallery_index()]",

+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",

                 inputs=[

                     generation_info,

                     img2img_gallery,

-                    html_info

+                    do_make_zip,

+                    html_info,

                 ],

                 outputs=[

+                    download_files,

                     html_info,

                     html_info,

                     html_info,

@@ -743,6 +934,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             roll.click(

                 fn=roll_artist,

+                _js="update_img2img_tokens",

                 inputs=[

                     img2img_prompt,

                 ],

@@ -753,6 +945,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]

             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]

+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]

 

             for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):

                 button.click(

@@ -764,9 +957,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],

                 )

 

-            for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns):

+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):

                 button.click(

                     fn=apply_styles,

+                    _js=js_func,

                     inputs=[prompt, negative_prompt, style1, style2],

                     outputs=[prompt, negative_prompt, style1, style2],

                 )

@@ -788,7 +982,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (seed_resize_from_h, "Seed resize from-2"),

                 (denoising_strength, "Denoising strength"),

             ]

-            modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)

+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])

 

     with gr.Blocks(analytics_enabled=False) as extras_interface:

         with gr.Row().style(equal_height=False):

@@ -800,7 +994,15 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     with gr.TabItem('Batch Process'):

                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")

 

-                upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)

+                with gr.Tabs(elem_id="extras_resize_mode"):

+                    with gr.TabItem('Scale by'):

+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)

+                    with gr.TabItem('Scale to'):

+                        with gr.Group():

+                            with gr.Row():

+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)

+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)

+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)

 

                 with gr.Group():

                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

@@ -827,17 +1029,22 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''

                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)

 

+

         submit.click(

-            fn=run_extras,

+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),

             _js="get_extras_tab_index",

             inputs=[

                 dummy_component,

+                dummy_component,

                 extras_image,

                 image_batch,

                 gfpgan_visibility,

                 codeformer_visibility,

                 codeformer_weight,

                 upscaling_resize,

+                upscaling_resize_w,

+                upscaling_resize_h,

+                upscaling_crop,

                 extras_upscaler_1,

                 extras_upscaler_2,

                 extras_upscaler_2_visibility,

@@ -848,17 +1055,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 html_info,

             ]

         )

-     

+

         extras_send_to_img2img.click(

             fn=lambda x: image_from_url_text(x),

             _js="extract_image_from_gallery_img2img",

             inputs=[result_images],

             outputs=[init_img],

         )

-        

+

         extras_send_to_inpaint.click(

             fn=lambda x: image_from_url_text(x),

-            _js="extract_image_from_gallery_img2img",

+            _js="extract_image_from_gallery_inpaint",

             inputs=[result_images],

             outputs=[init_img_with_mask],

         )

@@ -878,29 +1085,220 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     pnginfo_send_to_img2img = gr.Button('Send to img2img')

 

         image.change(

-            fn=wrap_gradio_call(run_pnginfo),

+            fn=wrap_gradio_call(modules.extras.run_pnginfo),

             inputs=[image],

             outputs=[html, generation_info, html2],

         )

+    #images history

+    images_history_switch_dict = {

+        "fn":modules.generation_parameters_copypaste.connect_paste,

+        "t2i":txt2img_paste_fields,

+        "i2i":img2img_paste_fields

+    }

+

+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)

 

     with gr.Blocks() as modelmerger_interface:

         with gr.Row().style(equal_height=False):

             with gr.Column(variant='panel'):

                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")

-                

+

                 with gr.Row():

-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")

-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")

+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")

+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")

+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")

                 custom_name = gr.Textbox(label="Custom Name (Optional)")

-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)

-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")

-                save_as_half = gr.Checkbox(value=False, label="Safe as float16")

+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)

+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")

+                save_as_half = gr.Checkbox(value=False, label="Save as float16")

                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')

-            

+

             with gr.Column(variant='panel'):

                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)

 

-    def create_setting_component(key):

+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

+

+    with gr.Blocks() as train_interface:

+        with gr.Row().style(equal_height=False):

+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")

+

+        with gr.Row().style(equal_height=False):

+            with gr.Tabs(elem_id="train_tabs"):

+

+                with gr.Tab(label="Create embedding"):

+                    new_embedding_name = gr.Textbox(label="Name")

+                    initialization_text = gr.Textbox(label="Initialization text", value="*")

+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)

+

+                    with gr.Row():

+                        with gr.Column(scale=3):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            create_embedding = gr.Button(value="Create embedding", variant='primary')

+

+                with gr.Tab(label="Create hypernetwork"):

+                    new_hypernetwork_name = gr.Textbox(label="Name")

+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

+

+                    with gr.Row():

+                        with gr.Column(scale=3):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')

+

+                with gr.Tab(label="Preprocess images"):

+                    process_src = gr.Textbox(label='Source directory')

+                    process_dst = gr.Textbox(label='Destination directory')

+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+

+                    with gr.Row():

+                        process_flip = gr.Checkbox(label='Create flipped copies')

+                        process_split = gr.Checkbox(label='Split oversized images into two')

+                        process_caption = gr.Checkbox(label='Use BLIP for caption')

+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

+

+                    with gr.Row():

+                        with gr.Column(scale=3):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')

+

+                with gr.Tab(label="Train"):

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")

+                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))

+                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])

+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")

+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)

+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))

+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    steps = gr.Number(label='Max steps', value=100000, precision=0)

+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)

+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)

+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)

+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)

+

+                    with gr.Row():

+                        interrupt_training = gr.Button(value="Interrupt")

+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')

+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')

+

+            with gr.Column():

+                progressbar = gr.HTML(elem_id="ti_progressbar")

+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)

+

+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)

+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)

+                ti_progress = gr.HTML(elem_id="ti_progress", value="")

+                ti_outcome = gr.HTML(elem_id="ti_error", value="")

+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)

+

+        create_embedding.click(

+            fn=modules.textual_inversion.ui.create_embedding,

+            inputs=[

+                new_embedding_name,

+                initialization_text,

+                nvpt,

+            ],

+            outputs=[

+                train_embedding_name,

+                ti_output,

+                ti_outcome,

+            ]

+        )

+

+        create_hypernetwork.click(

+            fn=modules.hypernetworks.ui.create_hypernetwork,

+            inputs=[

+                new_hypernetwork_name,

+                new_hypernetwork_sizes,

+            ],

+            outputs=[

+                train_hypernetwork_name,

+                ti_output,

+                ti_outcome,

+            ]

+        )

+

+        run_preprocess.click(

+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),

+            _js="start_training_textual_inversion",

+            inputs=[

+                process_src,

+                process_dst,

+                process_width,

+                process_height,

+                process_flip,

+                process_split,

+                process_caption,

+                process_caption_deepbooru

+            ],

+            outputs=[

+                ti_output,

+                ti_outcome,

+            ],

+        )

+

+        train_embedding.click(

+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),

+            _js="start_training_textual_inversion",

+            inputs=[

+                train_embedding_name,

+                learn_rate,

+                batch_size,

+                dataset_directory,

+                log_directory,

+                training_width,

+                training_height,

+                steps,

+                create_image_every,

+                save_embedding_every,

+                template_file,

+                save_image_with_stored_embedding,

+                preview_from_txt2img,

+                *txt2img_preview_params,

+            ],

+            outputs=[

+                ti_output,

+                ti_outcome,

+            ]

+        )

+

+        train_hypernetwork.click(

+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),

+            _js="start_training_textual_inversion",

+            inputs=[

+                train_hypernetwork_name,

+                learn_rate,

+                batch_size,

+                dataset_directory,

+                log_directory,

+                steps,

+                create_image_every,

+                save_embedding_every,

+                template_file,

+                preview_from_txt2img,

+                *txt2img_preview_params,

+            ],

+            outputs=[

+                ti_output,

+                ti_outcome,

+            ]

+        )

+

+        interrupt_training.click(

+            fn=lambda: shared.state.interrupt(),

+            inputs=[],

+            outputs=[],

+        )

+

+    def create_setting_component(key, is_quicksettings=False):

         def fun():

             return opts.data[key] if key in opts.data else opts.data_labels[key].default

 

@@ -920,12 +1318,48 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         else:

             raise Exception(f'bad options item type: {str(t)} for key {key}')

 

-        return comp(label=info.label, value=fun, **(args or {}))

+        if info.refresh is not None:

+            if is_quicksettings:

+                res = comp(label=info.label, value=fun, **(args or {}))

+                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)

+            else:

+                with gr.Row(variant="compact"):

+                    res = comp(label=info.label, value=fun, **(args or {}))

+                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)

+

+            def refresh():

+                info.refresh()

+                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args

+

+                for k, v in refreshed_args.items():

+                    setattr(res, k, v)

+

+                return gr.update(**(refreshed_args or {}))

+

+            refresh_button.click(

+                fn=refresh,

+                inputs=[],

+                outputs=[res],

+            )

+        else:

+            res = comp(label=info.label, value=fun, **(args or {}))

+

+

+        return res

 

     components = []

     component_dict = {}

 

     def open_folder(f):

+        if not os.path.isdir(f):

+            print(f"""

+WARNING

+An open_folder request was made with an argument that is not a folder.

+This could be an error or a malicious attempt to run code on your computer.

+Requested path was: {f}

+""", file=sys.stderr)

+            return

+

         if not shared.cmd_opts.hide_ui_dir_config:

             path = os.path.normpath(f)

             if platform.system() == "Windows":

@@ -939,10 +1373,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         changed = 0

 

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

-            if not opts.same_type(value, opts.data_labels[key].default):

-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"

+            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):

+                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()

 

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

+            if comp == dummy_component:

+                continue

+

             comp_args = opts.data_labels[key].component_args

             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:

                 continue

@@ -960,6 +1397,21 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

         return f'{changed} settings changed.', opts.dumpjson()

 

+    def run_settings_single(value, key):

+        if not opts.same_type(value, opts.data_labels[key].default):

+            return gr.update(visible=True), opts.dumpjson()

+

+        oldval = opts.data.get(key, None)

+        opts.data[key] = value

+

+        if oldval != value:

+            if opts.data_labels[key].onchange is not None:

+                opts.data_labels[key].onchange()

+

+        opts.save(shared.config_filename)

+

+        return gr.update(value=value), opts.dumpjson()

+

     with gr.Blocks(analytics_enabled=False) as settings_interface:

         settings_submit = gr.Button(value="Apply settings", variant='primary')

         result = gr.HTML()

@@ -967,6 +1419,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         settings_cols = 3

         items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)

 

+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]

+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')

+

+        quicksettings_list = []

+

         cols_displayed = 0

         items_displayed = 0

         previous_section = None

@@ -989,12 +1446,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))

 

-                component = create_setting_component(k)

-                component_dict[k] = component

-                components.append(component)

-                items_displayed += 1

+                if k in quicksettings_names:

+                    quicksettings_list.append((i, k, item))

+                    components.append(dummy_component)

+                else:

+                    component = create_setting_component(k)

+                    component_dict[k] = component

+                    components.append(component)

+                    items_displayed += 1

+

+        with gr.Row():

+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")

+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')

+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')

 

-        request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")

         request_notifications.click(

             fn=lambda: None,

             inputs=[],

@@ -1002,6 +1467,27 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'

         )

 

+        def reload_scripts():

+            modules.scripts.reload_script_body_only()

+

+        reload_script_bodies.click(

+            fn=reload_scripts,

+            inputs=[],

+            outputs=[],

+            _js='function(){}'

+        )

+

+        def request_restart():

+            shared.state.interrupt()

+            settings_interface.gradio_ref.do_restart = True

+

+        restart_gradio.click(

+            fn=request_restart,

+            inputs=[],

+            outputs=[],

+            _js='function(){restart_reload()}'

+        )

+

         if column is not None:

             column.__exit__()

 

@@ -1010,7 +1496,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         (img2img_interface, "img2img", "img2img"),

         (extras_interface, "Extras", "extras"),

         (pnginfo_interface, "PNG Info", "pnginfo"),

+        (images_history, "History", "images_history"),

         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),

+        (train_interface, "Train", "ti"),

         (settings_interface, "Settings", "settings"),

     ]

 

@@ -1026,12 +1514,18 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         css += css_hide_progressbar

 

     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:

+        with gr.Row(elem_id="quicksettings"):

+            for i, k, item in quicksettings_list:

+                component = create_setting_component(k, is_quicksettings=True)

+                component_dict[k] = component

+

+        settings_interface.gradio_ref = demo

 

-        with gr.Tabs() as tabs:

+        with gr.Tabs(elem_id="tabs") as tabs:

             for interface, label, ifid in interfaces:

-                with gr.TabItem(label, id=ifid):

+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):

                     interface.render()

-        

+

         if os.path.exists(os.path.join(script_path, "notification.mp3")):

             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)

 

@@ -1041,14 +1535,23 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             inputs=components,

             outputs=[result, text_settings],

         )

-        

+

+        for i, k, item in quicksettings_list:

+            component = component_dict[k]

+

+            component.change(

+                fn=lambda value, k=k: run_settings_single(value, key=k),

+                inputs=[component],

+                outputs=[component, text_settings],

+            )

+

         def modelmerger(*args):

             try:

-                results = run_modelmerger(*args)

+                results = modules.extras.run_modelmerger(*args)

             except Exception as e:

                 print("Error loading/saving model file:", file=sys.stderr)

                 print(traceback.format_exc(), file=sys.stderr)

-                modules.sd_models.list_models() #To remove the potentially missing models from the list

+                modules.sd_models.list_models()  # to remove the potentially missing models from the list

                 return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]

             return results

 

@@ -1057,6 +1560,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             inputs=[

                 primary_model_name,

                 secondary_model_name,

+                tertiary_model_name,

                 interp_method,

                 interp_amount,

                 save_as_half,

@@ -1066,6 +1570,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 submit_result,

                 primary_model_name,

                 secondary_model_name,

+                tertiary_model_name,

                 component_dict['sd_model_checkpoint'],

             ]

         )

@@ -1132,8 +1637,22 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             outputs=[extras_image],

         )

 

-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img')

-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img')

+        settings_map = {

+            'sd_hypernetwork': 'Hypernet',

+            'CLIP_stop_at_last_layers': 'Clip skip',

+            'sd_model_checkpoint': 'Model hash',

+        }

+

+        settings_paste_fields = [

+            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))

+            for k, v in settings_map.items()

+        ]

+

+        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)

+        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)

+

+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')

+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')

 

     ui_config_file = cmd_opts.ui_config_file

     ui_settings = {}

@@ -1155,10 +1674,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             if getattr(obj,'custom_script_source',None) is not None:

               key = 'customscript/' + obj.custom_script_source + '/' + key

-            

+

             if getattr(obj, 'do_not_save_to_config', False):

                 return

-            

+

             saved_value = ui_settings.get(key, None)

             if saved_value is None:

                 ui_settings[key] = getattr(obj, field)

@@ -1182,10 +1701,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

         if type(x) == gr.Textbox:

             apply_field(x, 'value')

-        

+

         if type(x) == gr.Number:

             apply_field(x, 'value')

-        

+

     visit(txt2img_interface, loadsave, "txt2img")

     visit(img2img_interface, loadsave, "img2img")

     visit(extras_interface, loadsave, "extras")

@@ -1206,12 +1725,13 @@ for filename in sorted(os.listdir(jsdir)):
         javascript += f"\n<script>{jsfile.read()}</script>"

 

 

-def template_response(*args, **kwargs):

-    res = gradio_routes_templates_response(*args, **kwargs)

-    res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))

-    res.init_headers()

-    return res

+if 'gradio_routes_templates_response' not in globals():

+    def template_response(*args, **kwargs):

+        res = gradio_routes_templates_response(*args, **kwargs)

+        res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))

+        res.init_headers()

+        return res

 

+    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse

+    gradio.routes.templates.TemplateResponse = template_response

 

-gradio_routes_templates_response = gradio.routes.templates.TemplateResponse

-gradio.routes.templates.TemplateResponse = template_response

diff --git a/modules/upscaler.py b/modules/upscaler.py
index d9d7c5e2..6ab2fb40 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -36,10 +36,11 @@ class Upscaler:
         self.half = not modules.shared.cmd_opts.no_half
         self.pre_pad = 0
         self.mod_scale = None
-        if self.name is not None and create_dirs:
+
+        if self.model_path is None and self.name:
             self.model_path = os.path.join(models_path, self.name)
-            if not os.path.exists(self.model_path):
-                os.makedirs(self.model_path)
+        if self.model_path and create_dirs:
+            os.makedirs(self.model_path, exist_ok=True)
 
         try:
             import cv2