Merge branch 'master' into stable

23 files changed, 1406 insertions, 361 deletions

diff --git a/modules/devices.py b/modules/devices.py
index df63dd88..ebf40082 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -33,10 +33,9 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-
 device = get_optimal_device()
 device_codeformer = cpu if has_mps else device
-
+dtype = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ea91abfe..4aed9283 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -73,8 +73,8 @@ 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)

diff --git a/modules/images.py b/modules/images.py
index f1aed5d6..d7563244 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -311,7 +311,12 @@ def apply_filename_pattern(x, p, seed, prompt):
         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))

+        

+        #currently disabled if using the save button, will work otherwise 

+        # if enabled it will cause a bug because styles is not included in the save_files data dictionary

+        if hasattr(p, "styles"):

+            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)

diff --git a/modules/img2img.py b/modules/img2img.py
index 03e934e9..f4455c90 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -103,7 +103,9 @@ 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

 

diff --git a/modules/interrogate.py b/modules/interrogate.py
index f62a4745..eed87144 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

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/paths.py b/modules/paths.py
index ceb80417..606f7d66 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -20,7 +20,6 @@ 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 a838ebb3..2f5a2967 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -56,7 +56,7 @@ 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

@@ -79,7 +79,7 @@ class StableDiffusionProcessing:
         self.paste_to = None

         self.color_corrections = None

         self.denoising_strength: float = 0

-

+        self.sampler_noise_scheduler_override = None

         self.ddim_discretize = opts.ddim_discretize

         self.s_churn = opts.s_churn

         self.s_tmin = opts.s_tmin

@@ -130,7 +130,7 @@ class Processed:
         self.s_tmin = p.s_tmin

         self.s_tmax = p.s_tmax

         self.s_noise = p.s_noise

-        

+        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])

@@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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),

     }

 

     generation_params.update(p.extra_generation_params)

@@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

     fix_seed(p)

 

-    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)

 

@@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         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 = []

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..7987ac14
--- /dev/null
+++ b/modules/scunet_model.py
@@ -0,0 +1,90 @@
+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 shared, modelloader
+from modules.paths import models_path
+from modules.scunet_model_arch import SCUNet as net
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_path = os.path.join(models_path, self.name)
+        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 = shared.device
+        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.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 = shared.device
+        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..972a2639
--- /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 sqaure.
+        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)
+        # sqaure 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 317e0c4c..a6fa890c 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -5,238 +5,44 @@ 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 ldm.util import default

-from einops import rearrange

 import ldm.modules.attention

 import ldm.modules.diffusionmodules.model

-from torch.nn.functional import silu

-

-

-# 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 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)

+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

 

-        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

+def apply_optimizations():

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

 

-        if mem_required > mem_free_total:

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

+    if cmd_opts.opt_split_attention_v1:

+        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 or torch.cuda.is_available()):

+        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

 

-        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

+def undo_optimizations():

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

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

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

 

-            # 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

 

 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

@@ -246,13 +52,7 @@ class StableDiffusionModelHijack:
 

         self.clip = m.cond_stage_model

 

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

-

-        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.AttnBlock.forward = cross_attention_attnblock_forward

+        apply_optimizations()

 

         def flatten(el):

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

@@ -290,7 +90,7 @@ 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 = {}

@@ -311,7 +111,6 @@ 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

@@ -333,28 +132,19 @@ 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)

 

-                if possible_matches is None:

+                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

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

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

+                    remade_tokens += [0] * emb_len

+                    multipliers += [weight] * 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()}

@@ -425,32 +215,23 @@ 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()}

@@ -458,6 +239,7 @@ 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]

@@ -478,7 +260,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         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

 

@@ -511,14 +292,19 @@ 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..ea4cfdfc
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,156 @@
+import math

+import torch

+from torch import einsum

+

+from ldm.util import default

+from einops import rearrange

+

+

+# 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 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

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2539f14c..5f992064 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,14 +8,11 @@ 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'])

 checkpoints_list = {}

@@ -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:

@@ -69,7 +64,7 @@ def list_models():
         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

+        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:

@@ -106,8 +101,11 @@ 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)

 

@@ -134,6 +132,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     if not shared.cmd_opts.no_half:

         model.half()

 

+    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16

+

     model.sd_model_hash = sd_model_hash

     model.sd_model_checkpint = checkpoint_file

 

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index dff89c09..9316875a 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -77,7 +77,9 @@ 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):

+    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:

             break

 

@@ -207,7 +209,9 @@ 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):

+    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:

             break

 

@@ -290,7 +294,10 @@ class KDiffusionSampler:
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

         steps, t_enc = setup_img2img_steps(p, steps)

 

-        sigmas = self.model_wrap.get_sigmas(steps)

+        if p.sampler_noise_scheduler_override:

+          sigmas = p.sampler_noise_scheduler_override(steps)

+        else:

+          sigmas = self.model_wrap.get_sigmas(steps)

 

         noise = noise * sigmas[steps - t_enc - 1]

         xi = x + noise

@@ -306,7 +313,10 @@ class KDiffusionSampler:
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

         steps = steps or p.steps

 

-        sigmas = self.model_wrap.get_sigmas(steps)

+        if p.sampler_noise_scheduler_override:

+          sigmas = p.sampler_noise_scheduler_override(steps)

+        else:

+          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..7246eadc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 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("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))

 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.")

@@ -57,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
 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)

+

 

 cmd_opts = parser.parse_args()

 device = get_optimal_device()

@@ -78,6 +82,7 @@ class State:
     current_latent = None

     current_image = None

     current_image_sampling_step = 0

+    textinfo = None

 

     def interrupt(self):

         self.interrupted = True

@@ -88,7 +93,7 @@ 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()

@@ -165,9 +170,10 @@ 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"), {

@@ -318,14 +324,14 @@ 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..9bd454c6 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -5,6 +5,7 @@ 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
@@ -122,18 +123,20 @@ 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/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..e8394ff6
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,78 @@
+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

+

+

+class PersonalizedBase(Dataset):

+    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):

+

+        self.placeholder_token = placeholder_token

+

+        self.size = 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'

+

+        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):

+            image = Image.open(path)

+            image = image.convert('RGB')

+            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)

+

+            filename = os.path.basename(path)

+            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')

+            filename_tokens = [token for token in filename_tokens if token.isalpha()]

+

+            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)

+

+            self.dataset.append((init_latent, filename_tokens))

+

+        self.length = len(self.dataset) * repeats

+

+        self.initial_indexes = np.arange(self.length) % len(self.dataset)

+        self.indexes = None

+        self.shuffle()

+

+    def shuffle(self):

+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]

+

+    def __len__(self):

+        return self.length

+

+    def __getitem__(self, i):

+        if i % len(self.dataset) == 0:

+            self.shuffle()

+

+        index = self.indexes[i % len(self.indexes)]

+        x, filename_tokens = self.dataset[index]

+

+        text = random.choice(self.lines)

+        text = text.replace("[name]", self.placeholder_token)

+        text = text.replace("[filewords]", ' '.join(filename_tokens))

+

+        return x, text

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
new file mode 100644
index 00000000..209e928f
--- /dev/null
+++ b/modules/textual_inversion/preprocess.py
@@ -0,0 +1,75 @@
+import os

+from PIL import Image, ImageOps

+import tqdm

+

+from modules import shared, images

+

+

+def preprocess(process_src, process_dst, process_flip, process_split, process_caption):

+    size = 512

+    src = os.path.abspath(process_src)

+    dst = os.path.abspath(process_dst)

+

+    assert src != dst, 'same directory specified as source and desitnation'

+

+    os.makedirs(dst, exist_ok=True)

+

+    files = os.listdir(src)

+

+    shared.state.textinfo = "Preprocessing..."

+    shared.state.job_count = len(files)

+

+    if process_caption:

+        shared.interrogator.load()

+

+    def save_pic_with_caption(image, index):

+        if process_caption:

+            caption = "-" + shared.interrogator.generate_caption(image)

+        else:

+            caption = ""

+

+        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))

+        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)

+        img = Image.open(filename).convert("RGB")

+

+        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((size, size * img.height // img.width))

+

+            top = img.crop((0, 0, size, size))

+            save_pic(top, index)

+

+            bot = img.crop((0, img.height - size, size, img.height))

+            save_pic(bot, index)

+        elif process_split and is_wide:

+            img = img.resize((size * img.width // img.height, size))

+

+            left = img.crop((0, 0, size, size))

+            save_pic(left, index)

+

+            right = img.crop((img.width - size, 0, img.width, size))

+            save_pic(right, index)

+        else:

+            img = images.resize_image(1, img, size, size)

+            save_pic(img, index)

+

+        shared.state.nextjob()

+

+    if process_caption:

+        shared.interrogator.send_blip_to_ram()

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 00000000..8686f534
--- /dev/null
+++ b/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,271 @@
+import os

+import sys

+import traceback

+

+import torch

+import tqdm

+import html

+import datetime

+

+

+from modules import shared, devices, sd_hijack, processing, sd_models

+import modules.textual_inversion.dataset

+

+

+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 = 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 train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):

+    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-%d-%m"), 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

+

+    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, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)

+

+    hijack = sd_hijack.model_hijack

+

+    embedding = hijack.embedding_db.word_embeddings[embedding_name]

+    embedding.vec.requires_grad = True

+

+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)

+

+    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

+

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

+    for i, (x, text) in pbar:

+        embedding.step = i + ititial_step

+

+        if embedding.step > steps:

+            break

+

+        if shared.state.interrupted:

+            break

+

+        with torch.autocast("cuda"):

+            c = cond_model([text])

+

+            x = x.to(devices.device)

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

+            del x

+

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

+

+            optimizer.zero_grad()

+            loss.backward()

+            optimizer.step()

+

+        pbar.set_description(f"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)

+

+        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,

+                prompt=text,

+                steps=20,

+                do_not_save_grid=True,

+                do_not_save_samples=True,

+            )

+

+            processed = processing.process_images(p)

+            image = processed.images[0]

+

+            shared.state.current_image = image

+            image.save(last_saved_image)

+

+            last_saved_image += f", prompt: {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(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..f19ac5e0
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,40 @@
+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):

+

+    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..d4406c3c 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         denoising_strength=denoising_strength 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:

diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..d9d02ece 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

@@ -21,6 +22,7 @@ import gradio as gr
 import gradio.utils

 import gradio.routes

 

+from modules import sd_hijack

 from modules.paths import script_path

 from modules.shared import opts, cmd_opts

 import modules.shared as shared

@@ -32,6 +34,9 @@ import modules.gfpgan_model
 import modules.codeformer_model

 import modules.styles

 import modules.generation_parameters_copypaste

+from modules.prompt_parser import get_learned_conditioning_prompt_schedules

+from modules.images import apply_filename_pattern, get_next_sequence_number

+import modules.textual_inversion.ui

 

 # 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

 mimetypes.init()

@@ -94,14 +99,31 @@ def send_gradio_gallery_to_image(x):
 

 

 def save_files(js_data, images, index):

-    import csv

-

-    os.makedirs(opts.outdir_save, exist_ok=True)

-

+    import csv    

     filenames = []

 

+    #quick dictionary to class object conversion. Its neccesary 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

+

+    if save_to_dirs:

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

+        path = os.path.join(opts.outdir_save, dirname)

+

+    os.makedirs(path, exist_ok=True)

+  

+

     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:

@@ -113,11 +135,20 @@ 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))

+        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"

+        if file_decoration != "":

+            file_decoration = "-" + file_decoration.lower()

+        file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)

+        truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration

+        filename_base = truncated

         extension = opts.samples_format.lower()

+

+        basecount = get_next_sequence_number(path, "")

         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)

+            file_number = f"{basecount+i:05}"

+            filename = file_number + filename_base + f".{extension}"

+            filepath = os.path.join(path, filename)

+

 

             if filedata.startswith("data:image/png;base64,"):

                 filedata = filedata[len("data:image/png;base64,"):]

@@ -142,8 +173,8 @@ def save_files(js_data, images, index):
     return '', '', plaintext_to_html(f"Saved: {filenames[0]}")

 

 

-def wrap_gradio_call(func):

-    def f(*args, **kwargs):

+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()

@@ -159,7 +190,10 @@ def wrap_gradio_call(func):
             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

 

@@ -179,6 +213,7 @@ def wrap_gradio_call(func):
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"

 

         shared.state.interrupted = False

+        shared.state.job_count = 0

 

         return tuple(res)

 

@@ -187,7 +222,7 @@ 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 +254,19 @@ 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)

 

@@ -345,8 +386,11 @@ 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):

+    prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)

+    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>"

 

@@ -364,8 +408,7 @@ def create_toprow(is_img2img):
                     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)

@@ -396,16 +439,19 @@ def create_toprow(is_img2img):
                 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, 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)

@@ -413,13 +459,16 @@ 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 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, paste, token_counter, token_button = create_toprow(is_img2img=False)

         dummy_component = gr.Label(visible=False)

 

         with gr.Row(elem_id='txt2img_progress_row'):

@@ -483,7 +532,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             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,

@@ -539,6 +588,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 

             roll.click(

                 fn=roll_artist,

+                _js="update_txt2img_tokens",

                 inputs=[

                     txt2img_prompt,

                 ],

@@ -567,9 +617,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

             ]

             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_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)

 

         with gr.Row(elem_id='img2img_progress_row'):

             with gr.Column(scale=1):

@@ -675,7 +726,7 @@ 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,

@@ -743,6 +794,7 @@ 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 +805,7 @@ 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 +817,10 @@ 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],

                 )

@@ -789,6 +843,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (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):

@@ -828,7 +883,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 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,

@@ -878,7 +933,7 @@ 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],

         )

@@ -887,7 +942,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         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")

@@ -896,10 +951,134 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 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")

                 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)

 

+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

+

+    with gr.Blocks() as textual_inversion_interface:

+        with gr.Row().style(equal_height=False):

+            with gr.Column():

+                with gr.Group():

+                    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>")

+

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")

+

+                    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", variant='primary')

+

+                with gr.Group():

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")

+

+                    process_src = gr.Textbox(label='Source directory')

+                    process_dst = gr.Textbox(label='Destination directory')

+

+                    with gr.Row():

+                        process_flip = gr.Checkbox(label='Flip')

+                        process_split = gr.Checkbox(label='Split into two')

+                        process_caption = gr.Checkbox(label='Add caption')

+

+                    with gr.Row():

+                        with gr.Column(scale=3):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')

+

+                with gr.Group():

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")

+                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))

+                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)

+                    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"))

+                    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)

+

+                    with gr.Row():

+                        with gr.Column(scale=2):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            with gr.Row():

+                                interrupt_training = gr.Button(value="Interrupt")

+                                train_embedding = gr.Button(value="Train", 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,

+            ]

+        )

+

+        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_flip,

+                process_split,

+                process_caption,

+            ],

+            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,

+                dataset_directory,

+                log_directory,

+                steps,

+                create_image_every,

+                save_embedding_every,

+                template_file,

+            ],

+            outputs=[

+                ti_output,

+                ti_outcome,

+            ]

+        )

+

+        interrupt_training.click(

+            fn=lambda: shared.state.interrupt(),

+            inputs=[],

+            outputs=[],

+        )

+

     def create_setting_component(key):

         def fun():

             return opts.data[key] if key in opts.data else opts.data_labels[key].default

@@ -1002,6 +1181,31 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'

         )

 

+        with gr.Row():

+            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')

+

+

+        def reload_scripts():

+            modules.scripts.reload_script_body_only()

+

+        reload_script_bodies.click(

+            fn=reload_scripts,

+            inputs=[],

+            outputs=[],

+            _js='function(){}'

+        )

+

+        def request_restart():

+            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__()

 

@@ -1011,6 +1215,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         (extras_interface, "Extras", "extras"),

         (pnginfo_interface, "PNG Info", "pnginfo"),

         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),

+        (textual_inversion_interface, "Textual inversion", "ti"),

         (settings_interface, "Settings", "settings"),

     ]

 

@@ -1026,7 +1231,9 @@ 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:

-

+        

+        settings_interface.gradio_ref = demo

+        

         with gr.Tabs() as tabs:

             for interface, label, ifid in interfaces:

                 with gr.TabItem(label, id=ifid):

@@ -1044,11 +1251,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         

         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

 

@@ -1206,12 +1413,12 @@ 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