Merge branch 'master' into focal-point-cropping

34 files changed, 2120 insertions, 884 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 5b0c934e..a860a964 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,5 +1,5 @@
-from modules.api.processing import StableDiffusionProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
 import modules.shared as shared
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
+from PIL import Image
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
@@ -18,6 +19,11 @@ class TextToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -25,8 +31,17 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
 
-    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+    def __base64_to_image(self, base64_string):
+        # if has a comma, deal with prefix
+        if "," in base64_string:
+            base64_string = base64_string.split(",")[1]
+        imgdata = base64.b64decode(base64_string)
+        # convert base64 to PIL image
+        return Image.open(io.BytesIO(imgdata))
+
+    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
         if sampler_index is None:
@@ -54,8 +69,49 @@ class Api:
         
         
 
-    def img2imgapi(self):
-        raise NotImplementedError
+    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
+        sampler_index = sampler_to_index(img2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+
+
+        init_images = img2imgreq.init_images
+        if init_images is None:
+            raise HTTPException(status_code=404, detail="Init image not found") 
+
+        mask = img2imgreq.mask
+        if mask:
+            mask = self.__base64_to_image(mask)
+
+        
+        populate = img2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": sampler_index[0],
+            "do_not_save_samples": True,
+            "do_not_save_grid": True, 
+            "mask": mask
+            }
+        )
+        p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+        imgs = []
+        for img in init_images:
+            img = self.__base64_to_image(img)
+            imgs = [img] * p.batch_size
+
+        p.init_images = imgs
+        # Override object param
+        with self.queue_lock:
+            processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
 
     def extrasapi(self):
         raise NotImplementedError
diff --git a/modules/api/processing.py b/modules/api/models.py
index 4c541241..f551fa35 100644
--- a/modules/api/processing.py
+++ b/modules/api/models.py
@@ -1,7 +1,8 @@
+from array import array
 from inflection import underscore
 from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 import inspect
 
 
@@ -92,8 +93,14 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = PydanticModelGenerator(
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img", 
     StableDiffusionProcessingTxt2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
 ).generate_model()
\ No newline at end of file
diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
deleted file mode 100644
index 737e1a76..00000000
--- a/modules/bsrgan_model.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os.path
-import sys
-import traceback
-
-import PIL.Image
-import numpy as np
-import torch
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.upscaler
-from modules import devices, modelloader
-from modules.bsrgan_model_arch import RRDBNet
-
-
-class UpscalerBSRGAN(modules.upscaler.Upscaler):
-    def __init__(self, dirname):
-        self.name = "BSRGAN"
-        self.model_name = "BSRGAN 4x"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
-        scalers = []
-        if len(model_paths) == 0:
-            scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
-            scalers.append(scaler_data)
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-            try:
-                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
-                scalers.append(scaler_data)
-            except Exception:
-                print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-        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
-        model.to(devices.device_bsrgan)
-        torch.cuda.empty_cache()
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(devices.device_bsrgan)
-        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):
-        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(filename) or filename is None:
-            print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
-            return None
-        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4)  # define network
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for k, v in model.named_parameters():
-            v.requires_grad = False
-        return model
-
diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
deleted file mode 100644
index cb4d1c13..00000000
--- a/modules/bsrgan_model_arch.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.init as init
-
-
-def initialize_weights(net_l, scale=1):
-    if not isinstance(net_l, list):
-        net_l = [net_l]
-    for net in net_l:
-        for m in net.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale  # for residual block
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias.data, 0.0)
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-        self.sf = sf
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        if self.sf==4:
-            self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        if self.sf==4:
-            fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
\ No newline at end of file
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8914662d..8bbc90a4 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -50,11 +50,12 @@ def create_deepbooru_process(threshold, deepbooru_opts):
     the tags.
     """
     from modules import shared  # prevents circular reference
-    shared.deepbooru_process_manager = multiprocessing.Manager()
+    context = multiprocessing.get_context("spawn")
+    shared.deepbooru_process_manager = context.Manager()
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
+    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
     shared.deepbooru_process.start()
 
 
diff --git a/modules/devices.py b/modules/devices.py
index eb422583..7511e1dc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
+import sys, os, shlex
 import contextlib
-
 import torch
-
 from modules import errors
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -9,10 +8,22 @@ has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
 
+def extract_device_id(args, name):
+    for x in range(len(args)):
+        if name in args[x]: return args[x+1]
+    return None
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        return torch.device("cuda")
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+            return torch.device(cuda_device)
+        else:
+            return torch.device("cuda")
 
     if has_mps:
         return torch.device("mps")
@@ -34,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
@@ -70,3 +81,7 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 46ad0da3..a13cf6ac 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -11,62 +11,109 @@ from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts

 

 

-def fix_model_layers(crt_model, pretrained_net):

-    # this code is adapted from https://github.com/xinntao/ESRGAN

-    if 'conv_first.weight' in pretrained_net:

-        return pretrained_net

-

-    if 'model.0.weight' not in pretrained_net:

-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]

-        if is_realesrgan:

-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")

-        else:

-            raise Exception("The file is not a ESRGAN model.")

 

-    crt_net = crt_model.state_dict()

-    load_net_clean = {}

-    for k, v in pretrained_net.items():

-        if k.startswith('module.'):

-            load_net_clean[k[7:]] = v

-        else:

-            load_net_clean[k] = v

-    pretrained_net = load_net_clean

-

-    tbd = []

-    for k, v in crt_net.items():

-        tbd.append(k)

-

-    # directly copy

-    for k, v in crt_net.items():

-        if k in pretrained_net and pretrained_net[k].size() == v.size():

-            crt_net[k] = pretrained_net[k]

-            tbd.remove(k)

-

-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']

-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']

-

-    for k in tbd.copy():

-        if 'RDB' in k:

-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')

-            if '.weight' in k:

-                ori_k = ori_k.replace('.weight', '.0.weight')

-            elif '.bias' in k:

-                ori_k = ori_k.replace('.bias', '.0.bias')

-            crt_net[k] = pretrained_net[ori_k]

-            tbd.remove(k)

-

-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']

-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']

-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']

-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']

-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']

-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']

-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']

-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']

-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']

-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']

-

-    return crt_net

+def mod2normal(state_dict):

+    # this code is copied from https://github.com/victorca25/iNNfer

+    if 'conv_first.weight' in state_dict:

+        crt_net = {}

+        items = []

+        for k, v in state_dict.items():

+            items.append(k)

+

+        crt_net['model.0.weight'] = state_dict['conv_first.weight']

+        crt_net['model.0.bias'] = state_dict['conv_first.bias']

+

+        for k in items.copy():

+            if 'RDB' in k:

+                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')

+                if '.weight' in k:

+                    ori_k = ori_k.replace('.weight', '.0.weight')

+                elif '.bias' in k:

+                    ori_k = ori_k.replace('.bias', '.0.bias')

+                crt_net[ori_k] = state_dict[k]

+                items.remove(k)

+

+        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']

+        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']

+        crt_net['model.3.weight'] = state_dict['upconv1.weight']

+        crt_net['model.3.bias'] = state_dict['upconv1.bias']

+        crt_net['model.6.weight'] = state_dict['upconv2.weight']

+        crt_net['model.6.bias'] = state_dict['upconv2.bias']

+        crt_net['model.8.weight'] = state_dict['HRconv.weight']

+        crt_net['model.8.bias'] = state_dict['HRconv.bias']

+        crt_net['model.10.weight'] = state_dict['conv_last.weight']

+        crt_net['model.10.bias'] = state_dict['conv_last.bias']

+        state_dict = crt_net

+    return state_dict

+

+

+def resrgan2normal(state_dict, nb=23):

+    # this code is copied from https://github.com/victorca25/iNNfer

+    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:

+        crt_net = {}

+        items = []

+        for k, v in state_dict.items():

+            items.append(k)

+

+        crt_net['model.0.weight'] = state_dict['conv_first.weight']

+        crt_net['model.0.bias'] = state_dict['conv_first.bias']

+

+        for k in items.copy():

+            if "rdb" in k:

+                ori_k = k.replace('body.', 'model.1.sub.')

+                ori_k = ori_k.replace('.rdb', '.RDB')

+                if '.weight' in k:

+                    ori_k = ori_k.replace('.weight', '.0.weight')

+                elif '.bias' in k:

+                    ori_k = ori_k.replace('.bias', '.0.bias')

+                crt_net[ori_k] = state_dict[k]

+                items.remove(k)

+

+        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']

+        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']

+        crt_net['model.3.weight'] = state_dict['conv_up1.weight']

+        crt_net['model.3.bias'] = state_dict['conv_up1.bias']

+        crt_net['model.6.weight'] = state_dict['conv_up2.weight']

+        crt_net['model.6.bias'] = state_dict['conv_up2.bias']

+        crt_net['model.8.weight'] = state_dict['conv_hr.weight']

+        crt_net['model.8.bias'] = state_dict['conv_hr.bias']

+        crt_net['model.10.weight'] = state_dict['conv_last.weight']

+        crt_net['model.10.bias'] = state_dict['conv_last.bias']

+        state_dict = crt_net

+    return state_dict

+

+

+def infer_params(state_dict):

+    # this code is copied from https://github.com/victorca25/iNNfer

+    scale2x = 0

+    scalemin = 6

+    n_uplayer = 0

+    plus = False

+

+    for block in list(state_dict):

+        parts = block.split(".")

+        n_parts = len(parts)

+        if n_parts == 5 and parts[2] == "sub":

+            nb = int(parts[3])

+        elif n_parts == 3:

+            part_num = int(parts[1])

+            if (part_num > scalemin

+                and parts[0] == "model"

+                and parts[2] == "weight"):

+                scale2x += 1

+            if part_num > n_uplayer:

+                n_uplayer = part_num

+                out_nc = state_dict[block].shape[0]

+        if not plus and "conv1x1" in block:

+            plus = True

+

+    nf = state_dict["model.0.weight"].shape[0]

+    in_nc = state_dict["model.0.weight"].shape[1]

+    out_nc = out_nc

+    scale = 2 ** scale2x

+

+    return in_nc, out_nc, nf, nb, plus, scale

+

 

 class UpscalerESRGAN(Upscaler):

     def __init__(self, dirname):

@@ -109,22 +156,41 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))

             return None

 

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

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

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

+

+        if "params_ema" in state_dict:

+            state_dict = state_dict["params_ema"]

+        elif "params" in state_dict:

+            state_dict = state_dict["params"]

+            num_conv = 16 if "realesr-animevideov3" in filename else 32

+            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')

+            model.load_state_dict(state_dict)

+            model.eval()

+            return model

+

+        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:

+            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23

+            state_dict = resrgan2normal(state_dict, nb)

+        elif "conv_first.weight" in state_dict:

+            state_dict = mod2normal(state_dict)

+        elif "model.0.weight" not in state_dict:

+            raise Exception("The file is not a recognized ESRGAN model.")

+

+        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)

 

-        pretrained_net = fix_model_layers(crt_model, pretrained_net)

-        crt_model.load_state_dict(pretrained_net)

-        crt_model.eval()

+        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)

+        model.load_state_dict(state_dict)

+        model.eval()

 

-        return crt_model

+        return model

 

 

 def upscale_without_tiling(model, img):

     img = np.array(img)

     img = img[:, :, ::-1]

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

+    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255

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

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

+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)

     with torch.no_grad():

         output = model(img)

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

diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
index e413d36e..bc9ceb2a 100644
--- a/modules/esrgan_model_arch.py
+++ b/modules/esrgan_model_arch.py
@@ -1,80 +1,463 @@
-# this file is taken from https://github.com/xinntao/ESRGAN

+# this file is adapted from https://github.com/victorca25/iNNfer

 

+import math

 import functools

 import torch

 import torch.nn as nn

 import torch.nn.functional as F

 

 

-def make_layer(block, n_layers):

-    layers = []

-    for _ in range(n_layers):

-        layers.append(block())

-    return nn.Sequential(*layers)

+####################

+# RRDBNet Generator

+####################

 

+class RRDBNet(nn.Module):

+    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,

+            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',

+            finalact=None, gaussian_noise=False, plus=False):

+        super(RRDBNet, self).__init__()

+        n_upscale = int(math.log(upscale, 2))

+        if upscale == 3:

+            n_upscale = 1

 

-class ResidualDenseBlock_5C(nn.Module):

-    def __init__(self, nf=64, gc=32, bias=True):

-        super(ResidualDenseBlock_5C, self).__init__()

-        # gc: growth channel, i.e. intermediate channels

-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)

-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)

-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)

-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)

-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)

-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

+        self.resrgan_scale = 0

+        if in_nc % 16 == 0:

+            self.resrgan_scale = 1

+        elif in_nc != 4 and in_nc % 4 == 0:

+            self.resrgan_scale = 2

 

-        # initialization

-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)

+        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)

+        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',

+            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,

+            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]

+        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)

 

-    def forward(self, x):

-        x1 = self.lrelu(self.conv1(x))

-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))

-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))

-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))

-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))

-        return x5 * 0.2 + x

+        if upsample_mode == 'upconv':

+            upsample_block = upconv_block

+        elif upsample_mode == 'pixelshuffle':

+            upsample_block = pixelshuffle_block

+        else:

+            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))

+        if upscale == 3:

+            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)

+        else:

+            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]

+        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)

+        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)

+

+        outact = act(finalact) if finalact else None

+

+        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),

+            *upsampler, HR_conv0, HR_conv1, outact)

+

+    def forward(self, x, outm=None):

+        if self.resrgan_scale == 1:

+            feat = pixel_unshuffle(x, scale=4)

+        elif self.resrgan_scale == 2:

+            feat = pixel_unshuffle(x, scale=2)

+        else:

+            feat = x

+

+        return self.model(feat)

 

 

 class RRDB(nn.Module):

-    '''Residual in Residual Dense Block'''

+    """

+    Residual in Residual Dense Block

+    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)

+    """

 

-    def __init__(self, nf, gc=32):

+    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',

+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',

+            spectral_norm=False, gaussian_noise=False, plus=False):

         super(RRDB, self).__init__()

-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)

-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)

-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)

+        # This is for backwards compatibility with existing models

+        if nr == 3:

+            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,

+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,

+                    gaussian_noise=gaussian_noise, plus=plus)

+            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,

+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,

+                    gaussian_noise=gaussian_noise, plus=plus)

+            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,

+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,

+                    gaussian_noise=gaussian_noise, plus=plus)

+        else:

+            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,

+                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,

+                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]

+            self.RDBs = nn.Sequential(*RDB_list)

 

     def forward(self, x):

-        out = self.RDB1(x)

-        out = self.RDB2(out)

-        out = self.RDB3(out)

+        if hasattr(self, 'RDB1'):

+            out = self.RDB1(x)

+            out = self.RDB2(out)

+            out = self.RDB3(out)

+        else:

+            out = self.RDBs(x)

         return out * 0.2 + x

 

 

-class RRDBNet(nn.Module):

-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):

-        super(RRDBNet, self).__init__()

-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)

+class ResidualDenseBlock_5C(nn.Module):

+    """

+    Residual Dense Block

+    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)

+    Modified options that can be used:

+        - "Partial Convolution based Padding" arXiv:1811.11718

+        - "Spectral normalization" arXiv:1802.05957

+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 

+            {Rakotonirina} and A. {Rasoanaivo}

+    """

+

+    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',

+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',

+            spectral_norm=False, gaussian_noise=False, plus=False):

+        super(ResidualDenseBlock_5C, self).__init__()

 

-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)

-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)

-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        #### upsampling

-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)

+        self.noise = GaussianNoise() if gaussian_noise else None

+        self.conv1x1 = conv1x1(nf, gc) if plus else None

 

-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

+        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,

+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,

+            spectral_norm=spectral_norm)

+        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,

+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,

+            spectral_norm=spectral_norm)

+        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,

+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,

+            spectral_norm=spectral_norm)

+        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,

+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,

+            spectral_norm=spectral_norm)

+        if mode == 'CNA':

+            last_act = None

+        else:

+            last_act = act_type

+        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,

+            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,

+            spectral_norm=spectral_norm)

 

     def forward(self, x):

-        fea = self.conv_first(x)

-        trunk = self.trunk_conv(self.RRDB_trunk(fea))

-        fea = fea + trunk

+        x1 = self.conv1(x)

+        x2 = self.conv2(torch.cat((x, x1), 1))

+        if self.conv1x1:

+            x2 = x2 + self.conv1x1(x)

+        x3 = self.conv3(torch.cat((x, x1, x2), 1))

+        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))

+        if self.conv1x1:

+            x4 = x4 + x2

+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))

+        if self.noise:

+            return self.noise(x5.mul(0.2) + x)

+        else:

+            return x5 * 0.2 + x

+

 

-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))

-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))

-        out = self.conv_last(self.lrelu(self.HRconv(fea)))

+####################

+# ESRGANplus

+####################

 

+class GaussianNoise(nn.Module):

+    def __init__(self, sigma=0.1, is_relative_detach=False):

+        super().__init__()

+        self.sigma = sigma

+        self.is_relative_detach = is_relative_detach

+        self.noise = torch.tensor(0, dtype=torch.float)

+

+    def forward(self, x):

+        if self.training and self.sigma != 0:

+            self.noise = self.noise.to(x.device)

+            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x

+            sampled_noise = self.noise.repeat(*x.size()).normal_() * scale

+            x = x + sampled_noise

+        return x 

+

+def conv1x1(in_planes, out_planes, stride=1):

+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

+

+

+####################

+# SRVGGNetCompact

+####################

+

+class SRVGGNetCompact(nn.Module):

+    """A compact VGG-style network structure for super-resolution.

+    This class is copied from https://github.com/xinntao/Real-ESRGAN

+    """

+

+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):

+        super(SRVGGNetCompact, self).__init__()

+        self.num_in_ch = num_in_ch

+        self.num_out_ch = num_out_ch

+        self.num_feat = num_feat

+        self.num_conv = num_conv

+        self.upscale = upscale

+        self.act_type = act_type

+

+        self.body = nn.ModuleList()

+        # the first conv

+        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))

+        # the first activation

+        if act_type == 'relu':

+            activation = nn.ReLU(inplace=True)

+        elif act_type == 'prelu':

+            activation = nn.PReLU(num_parameters=num_feat)

+        elif act_type == 'leakyrelu':

+            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)

+        self.body.append(activation)

+

+        # the body structure

+        for _ in range(num_conv):

+            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))

+            # activation

+            if act_type == 'relu':

+                activation = nn.ReLU(inplace=True)

+            elif act_type == 'prelu':

+                activation = nn.PReLU(num_parameters=num_feat)

+            elif act_type == 'leakyrelu':

+                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)

+            self.body.append(activation)

+

+        # the last conv

+        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))

+        # upsample

+        self.upsampler = nn.PixelShuffle(upscale)

+

+    def forward(self, x):

+        out = x

+        for i in range(0, len(self.body)):

+            out = self.body[i](out)

+

+        out = self.upsampler(out)

+        # add the nearest upsampled image, so that the network learns the residual

+        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')

+        out += base

         return out

+

+

+####################

+# Upsampler

+####################

+

+class Upsample(nn.Module):

+    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.

+    The input data is assumed to be of the form

+    `minibatch x channels x [optional depth] x [optional height] x width`.

+    """

+

+    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):

+        super(Upsample, self).__init__()

+        if isinstance(scale_factor, tuple):

+            self.scale_factor = tuple(float(factor) for factor in scale_factor)

+        else:

+            self.scale_factor = float(scale_factor) if scale_factor else None

+        self.mode = mode

+        self.size = size

+        self.align_corners = align_corners

+

+    def forward(self, x):

+        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)

+

+    def extra_repr(self):

+        if self.scale_factor is not None:

+            info = 'scale_factor=' + str(self.scale_factor)

+        else:

+            info = 'size=' + str(self.size)

+        info += ', mode=' + self.mode

+        return info

+

+

+def pixel_unshuffle(x, scale):

+    """ Pixel unshuffle.

+    Args:

+        x (Tensor): Input feature with shape (b, c, hh, hw).

+        scale (int): Downsample ratio.

+    Returns:

+        Tensor: the pixel unshuffled feature.

+    """

+    b, c, hh, hw = x.size()

+    out_channel = c * (scale**2)

+    assert hh % scale == 0 and hw % scale == 0

+    h = hh // scale

+    w = hw // scale

+    x_view = x.view(b, c, h, scale, w, scale)

+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)

+

+

+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,

+                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):

+    """

+    Pixel shuffle layer

+    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional

+    Neural Network, CVPR17)

+    """

+    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,

+                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)

+    pixel_shuffle = nn.PixelShuffle(upscale_factor)

+

+    n = norm(norm_type, out_nc) if norm_type else None

+    a = act(act_type) if act_type else None

+    return sequential(conv, pixel_shuffle, n, a)

+

+

+def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,

+                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):

+    """ Upconv layer """

+    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor

+    upsample = Upsample(scale_factor=upscale_factor, mode=mode)

+    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,

+                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)

+    return sequential(upsample, conv)

+

+

+

+

+

+

+

+

+####################

+# Basic blocks

+####################

+

+

+def make_layer(basic_block, num_basic_block, **kwarg):

+    """Make layers by stacking the same blocks.

+    Args:

+        basic_block (nn.module): nn.module class for basic block. (block)

+        num_basic_block (int): number of blocks. (n_layers)

+    Returns:

+        nn.Sequential: Stacked blocks in nn.Sequential.

+    """

+    layers = []

+    for _ in range(num_basic_block):

+        layers.append(basic_block(**kwarg))

+    return nn.Sequential(*layers)

+

+

+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):

+    """ activation helper """

+    act_type = act_type.lower()

+    if act_type == 'relu':

+        layer = nn.ReLU(inplace)

+    elif act_type in ('leakyrelu', 'lrelu'):

+        layer = nn.LeakyReLU(neg_slope, inplace)

+    elif act_type == 'prelu':

+        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)

+    elif act_type == 'tanh':  # [-1, 1] range output

+        layer = nn.Tanh()

+    elif act_type == 'sigmoid':  # [0, 1] range output

+        layer = nn.Sigmoid()

+    else:

+        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))

+    return layer

+

+

+class Identity(nn.Module):

+    def __init__(self, *kwargs):

+        super(Identity, self).__init__()

+

+    def forward(self, x, *kwargs):

+        return x

+

+

+def norm(norm_type, nc):

+    """ Return a normalization layer """

+    norm_type = norm_type.lower()

+    if norm_type == 'batch':

+        layer = nn.BatchNorm2d(nc, affine=True)

+    elif norm_type == 'instance':

+        layer = nn.InstanceNorm2d(nc, affine=False)

+    elif norm_type == 'none':

+        def norm_layer(x): return Identity()

+    else:

+        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))

+    return layer

+

+

+def pad(pad_type, padding):

+    """ padding layer helper """

+    pad_type = pad_type.lower()

+    if padding == 0:

+        return None

+    if pad_type == 'reflect':

+        layer = nn.ReflectionPad2d(padding)

+    elif pad_type == 'replicate':

+        layer = nn.ReplicationPad2d(padding)

+    elif pad_type == 'zero':

+        layer = nn.ZeroPad2d(padding)

+    else:

+        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))

+    return layer

+

+

+def get_valid_padding(kernel_size, dilation):

+    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)

+    padding = (kernel_size - 1) // 2

+    return padding

+

+

+class ShortcutBlock(nn.Module):

+    """ Elementwise sum the output of a submodule to its input """

+    def __init__(self, submodule):

+        super(ShortcutBlock, self).__init__()

+        self.sub = submodule

+

+    def forward(self, x):

+        output = x + self.sub(x)

+        return output

+

+    def __repr__(self):

+        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')

+

+

+def sequential(*args):

+    """ Flatten Sequential. It unwraps nn.Sequential. """

+    if len(args) == 1:

+        if isinstance(args[0], OrderedDict):

+            raise NotImplementedError('sequential does not support OrderedDict input.')

+        return args[0]  # No sequential is needed.

+    modules = []

+    for module in args:

+        if isinstance(module, nn.Sequential):

+            for submodule in module.children():

+                modules.append(submodule)

+        elif isinstance(module, nn.Module):

+            modules.append(module)

+    return nn.Sequential(*modules)

+

+

+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,

+               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',

+               spectral_norm=False):

+    """ Conv layer with padding, normalization, activation """

+    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)

+    padding = get_valid_padding(kernel_size, dilation)

+    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None

+    padding = padding if pad_type == 'zero' else 0

+

+    if convtype=='PartialConv2D':

+        c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

+               dilation=dilation, bias=bias, groups=groups)

+    elif convtype=='DeformConv2D':

+        c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

+               dilation=dilation, bias=bias, groups=groups)

+    elif convtype=='Conv3D':

+        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

+                dilation=dilation, bias=bias, groups=groups)

+    else:

+        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,

+                dilation=dilation, bias=bias, groups=groups)

+

+    if spectral_norm:

+        c = nn.utils.spectral_norm(c)

+

+    a = act(act_type) if act_type else None

+    if 'CNA' in mode:

+        n = norm(norm_type, out_nc) if norm_type else None

+        return sequential(p, c, n, a)

+    elif mode == 'NAC':

+        if norm_type is None and act_type is not None:

+            a = act(act_type, inplace=False)

+        n = norm(norm_type, in_nc) if norm_type else None

+        return sequential(n, a, p, c)

diff --git a/modules/extras.py b/modules/extras.py
index b853fa5b..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,9 +39,12 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 

         if input_dir == '':

             return outputs, "Please select an input directory.", ''

-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]

+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]

         for img in image_list:

-            image = Image.open(img)

+            try:

+                image = Image.open(img)

+            except Exception:

+                continue

             imageArr.append(image)

             imageNameArr.append(img)

     else:

@@ -118,10 +121,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 

         while len(cached_images) > 2:

             del cached_images[next(iter(cached_images.keys()))]

-

-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,

-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,

-                          forced_filename=image_name if opts.use_original_name_batch else None)

+        

+        if opts.use_original_name_batch and image_name != None:

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

+        else:

+            basename = ''

+

+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,

+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)

 

         if opts.enable_pnginfo:

             image.info = existing_pnginfo

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index c27826b6..f73647da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -4,13 +4,22 @@ import gradio as gr
 from modules.shared import script_path

 from modules import shared

 

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

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

 re_param = re.compile(re_param_code)

 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")

 re_imagesize = re.compile(r"^(\d+)x(\d+)$")

 type_of_gr_update = type(gr.update())

 

 

+def quote(text):

+    if ',' not in str(text):

+        return text

+

+    text = str(text)

+    text = text.replace('\\', '\\\\')

+    text = text.replace('"', '\\"')

+    return f'"{text}"'

+

 def parse_generation_parameters(x: str):

     """parses generation parameters string, the one you see in text field under the picture in UI:

 ```

@@ -45,11 +54,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:

             prompt += ("" if prompt == "" else "\n") + line

 

-    if len(prompt) > 0:

-        res["Prompt"] = prompt

-

-    if len(negative_prompt) > 0:

-        res["Negative prompt"] = negative_prompt

+    res["Prompt"] = prompt

+    res["Negative prompt"] = negative_prompt

 

     for k, v in re_param.findall(lastline):

         m = re_imagesize.match(v)

@@ -86,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None):
             else:

                 try:

                     valtype = type(output.value)

-                    val = valtype(v)

+

+                    if valtype == bool and v == "False":

+                        val = False

+                    else:

+                        val = valtype(v)

+

                     res.append(gr.update(value=val))

                 except Exception:

                     res.append(gr.update())

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b8695fc1..d647ea55 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,46 +1,100 @@
+import csv

 import datetime

 import glob

 import html

 import os

 import sys

 import traceback

-import tqdm

-import csv

 

+import modules.textual_inversion.dataset

 import torch

-

-from ldm.util import default

-from modules import devices, shared, processing, sd_models

-import torch

-from torch import einsum

+import tqdm

 from einops import rearrange, repeat

-import modules.textual_inversion.dataset

+from ldm.util import default

+from modules import devices, processing, sd_models, shared

 from modules.textual_inversion import textual_inversion

 from modules.textual_inversion.learn_schedule import LearnRateScheduler

+from torch import einsum

 

+from collections import defaultdict, deque

+from statistics import stdev, mean

 

 class HypernetworkModule(torch.nn.Module):

     multiplier = 1.0

-

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

+    activation_dict = {

+        "relu": torch.nn.ReLU,

+        "leakyrelu": torch.nn.LeakyReLU,

+        "elu": torch.nn.ELU,

+        "swish": torch.nn.Hardswish,

+    }

+

+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):

         super().__init__()

 

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

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

+        assert layer_structure is not None, "layer_structure must not be None"

+        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"

+        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"

+

+        linears = []

+        for i in range(len(layer_structure) - 1):

+

+            # Add a fully-connected layer

+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))

+

+            # Add an activation func

+            if activation_func == "linear" or activation_func is None:

+                pass

+            elif activation_func in self.activation_dict:

+                linears.append(self.activation_dict[activation_func]())

+            else:

+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')

+

+            # Add layer normalization

+            if add_layer_norm:

+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))

+

+            # Add dropout expect last layer

+            if use_dropout and i < len(layer_structure) - 3:

+                linears.append(torch.nn.Dropout(p=0.3))

+

+        self.linear = torch.nn.Sequential(*linears)

 

         if state_dict is not None:

-            self.load_state_dict(state_dict, strict=True)

+            self.fix_old_state_dict(state_dict)

+            self.load_state_dict(state_dict)

         else:

-

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

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

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

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

+            for layer in self.linear:

+                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:

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

+                    layer.bias.data.zero_()

 

         self.to(devices.device)

 

+    def fix_old_state_dict(self, state_dict):

+        changes = {

+            'linear1.bias': 'linear.0.bias',

+            'linear1.weight': 'linear.0.weight',

+            'linear2.bias': 'linear.1.bias',

+            'linear2.weight': 'linear.1.weight',

+        }

+

+        for fr, to in changes.items():

+            x = state_dict.get(fr, None)

+            if x is None:

+                continue

+

+            del state_dict[fr]

+            state_dict[to] = x

+

     def forward(self, x):

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

+        return x + self.linear(x) * self.multiplier

+

+    def trainables(self):

+        layer_structure = []

+        for layer in self.linear:

+            if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:

+                layer_structure += [layer.weight, layer.bias]

+        return layer_structure

 

 

 def apply_strength(value=None):

@@ -51,16 +105,23 @@ class Hypernetwork:
     filename = None

     name = None

 

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

+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):

         self.filename = None

         self.name = name

         self.layers = {}

         self.step = 0

         self.sd_checkpoint = None

         self.sd_checkpoint_name = None

+        self.layer_structure = layer_structure

+        self.activation_func = activation_func

+        self.add_layer_norm = add_layer_norm

+        self.use_dropout = use_dropout

 

         for size in enable_sizes or []:

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

+            self.layers[size] = (

+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),

+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),

+            )

 

     def weights(self):

         res = []

@@ -68,7 +129,7 @@ class Hypernetwork:
         for k, layers in self.layers.items():

             for layer in layers:

                 layer.train()

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

+                res += layer.trainables()

 

         return res

 

@@ -80,6 +141,10 @@ class Hypernetwork:
 

         state_dict['step'] = self.step

         state_dict['name'] = self.name

+        state_dict['layer_structure'] = self.layer_structure

+        state_dict['activation_func'] = self.activation_func

+        state_dict['is_layer_norm'] = self.add_layer_norm

+        state_dict['use_dropout'] = self.use_dropout

         state_dict['sd_checkpoint'] = self.sd_checkpoint

         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name

 

@@ -92,9 +157,17 @@ class Hypernetwork:
 

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

 

+        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])

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

+        self.add_layer_norm = state_dict.get('is_layer_norm', False)

+        self.use_dropout = state_dict.get('use_dropout', False)

+

         for size, sd in state_dict.items():

             if type(size) == int:

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

+                self.layers[size] = (

+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),

+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),

+                )

 

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

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

@@ -196,7 +269,39 @@ def stack_conds(conds):
 

     return torch.stack(conds)

 

+

+def statistics(data):

+    if len(data) < 2:

+        std = 0

+    else:

+        std = stdev(data)

+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"

+    recent_data = data[-32:]

+    if len(recent_data) < 2:

+        std = 0

+    else:

+        std = stdev(recent_data)

+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"

+    return total_information, recent_information

+

+

+def report_statistics(loss_info:dict):

+    keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))

+    for key in keys:

+        try:

+            print("Loss statistics for file " + key)

+            info, recent = statistics(list(loss_info[key]))

+            print(info)

+            print(recent)

+        except Exception as e:

+            print(e)

+

+

+

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

+    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

+    from modules import images

+

     assert hypernetwork_name, 'hypernetwork not selected'

 

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

@@ -226,7 +331,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

     with torch.autocast("cuda"):

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

-

     if unload:

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

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

@@ -236,22 +340,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for weight in weights:

         weight.requires_grad = True

 

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

+    size = len(ds.indexes)

+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))

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

+    previous_mean_losses = [0]

+    previous_mean_loss = 0

+    print("Mean loss of {} elements".format(size))

 

     last_saved_file = "<none>"

     last_saved_image = "<none>"

+    forced_filename = "<none>"

 

     ititial_step = hypernetwork.step or 0

     if ititial_step > steps:

         return hypernetwork, filename

 

     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...

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

 

+    steps_without_grad = 0

+

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

     for i, entries in pbar:

         hypernetwork.step = i + ititial_step

-

+        if len(loss_dict) > 0:

+            previous_mean_losses = [i[-1] for i in loss_dict.values()]

+            previous_mean_loss = mean(previous_mean_losses)

+            

         scheduler.apply(optimizer, hypernetwork.step)

         if scheduler.finished:

             break

@@ -261,33 +377,52 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

         with torch.autocast("cuda"):

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

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

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

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

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

             del x

             del c

 

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

-

+            for entry in entries:

+                loss_dict[entry.filename].append(loss.item())

+                

             optimizer.zero_grad()

+            weights[0].grad = None

             loss.backward()

+

+            if weights[0].grad is None:

+                steps_without_grad += 1

+            else:

+                steps_without_grad = 0

+            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'

+

             optimizer.step()

-        mean_loss = losses.mean()

-        if torch.isnan(mean_loss):

+

+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):

             raise RuntimeError("Loss diverged.")

-        pbar.set_description(f"loss: {mean_loss:.7f}")

+        

+        if len(previous_mean_losses) > 1:

+            std = stdev(previous_mean_losses)

+        else:

+            std = 0

+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"

+        pbar.set_description(dataset_loss_info)

 

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

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

+            # Before saving, change name to match current checkpoint.

+            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'

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

             hypernetwork.save(last_saved_file)

 

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

-            "loss": f"{mean_loss:.7f}",

+            "loss": f"{previous_mean_loss:.7f}",

             "learn_rate": scheduler.learn_rate

         })

 

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

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

+            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'

+            last_saved_image = os.path.join(images_dir, forced_filename)

 

             optimizer.zero_grad()

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

@@ -323,27 +458,29 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

             if image is not None:

                 shared.state.current_image = image

-                image.save(last_saved_image)

+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)

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

 

         shared.state.job_no = hypernetwork.step

 

         shared.state.textinfo = f"""

 <p>

-Loss: {mean_loss:.7f}<br/>

+Loss: {previous_mean_loss:.7f}<br/>

 Step: {hypernetwork.step}<br/>

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

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

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

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

 </p>

 """

-

+        

+    report_statistics(loss_dict)

     checkpoint = sd_models.select_checkpoint()

 

     hypernetwork.sd_checkpoint = checkpoint.hash

     hypernetwork.sd_checkpoint_name = checkpoint.model_name

+    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).

+    hypernetwork.name = hypernetwork_name

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

     hypernetwork.save(filename)

 

     return hypernetwork, filename

-

-

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index dfa599af..2b472d87 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -1,19 +1,33 @@
 import html

 import os

+import re

 

 import gradio as gr

-

-import modules.textual_inversion.textual_inversion

 import modules.textual_inversion.preprocess

-from modules import sd_hijack, shared, devices

+import modules.textual_inversion.textual_inversion

+from modules import devices, sd_hijack, shared

 from modules.hypernetworks import hypernetwork

 

 

-def create_hypernetwork(name, enable_sizes):

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

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

+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):

+    # Remove illegal characters from name.

+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))

 

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

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

+    if not overwrite_old:

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

+

+    if type(layer_structure) == str:

+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]

+

+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(

+        name=name,

+        enable_sizes=[int(x) for x in enable_sizes],

+        layer_structure=layer_structure,

+        activation_func=activation_func,

+        add_layer_norm=add_layer_norm,

+        use_dropout=use_dropout,

+    )

     hypernet.save(fn)

 

     shared.reload_hypernetworks()

diff --git a/modules/images.py b/modules/images.py
index b9589563..286de2ae 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,8 @@
 import datetime

+import sys

+import traceback

+

+import pytz

 import io

 import math

 import os

@@ -12,7 +16,7 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto

 import string

 

-from modules import sd_samplers, shared

+from modules import sd_samplers, shared, script_callbacks

 from modules.shared import opts, cmd_opts

 

 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)

@@ -273,10 +277,15 @@ invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '

 invalid_filename_postfix = ' .'

 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')

+re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")

+re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")

 max_filename_part_length = 128

 

 

 def sanitize_filename_part(text, replace_spaces=True):

+    if text is None:

+        return None

+

     if replace_spaces:

         text = text.replace(' ', '_')

 

@@ -286,49 +295,106 @@ def sanitize_filename_part(text, replace_spaces=True):
     return text

 

 

-def apply_filename_pattern(x, p, seed, prompt):

-    max_prompt_words = opts.directories_max_prompt_words

-

-    if seed is not None:

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

-

-    if p is not None:

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

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

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

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

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

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

-

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

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

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

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

+class FilenameGenerator:

+    replacements = {

+        'seed': lambda self: self.seed if self.seed is not None else '',

+        'steps': lambda self:  self.p and self.p.steps,

+        'cfg': lambda self: self.p and self.p.cfg_scale,

+        'width': lambda self: self.p and self.p.width,

+        'height': lambda self: self.p and self.p.height,

+        'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),

+        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),

+        'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),

+        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),

+        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]

+        'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),

+        'prompt': lambda self: sanitize_filename_part(self.prompt),

+        'prompt_no_styles': lambda self: self.prompt_no_style(),

+        'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),

+        'prompt_words': lambda self: self.prompt_words(),

+    }

+    default_time_format = '%Y%m%d%H%M%S'

+

+    def __init__(self, p, seed, prompt):

+        self.p = p

+        self.seed = seed

+        self.prompt = prompt

+

+    def prompt_no_style(self):

+        if self.p is None or self.prompt is None:

+            return None

+

+        prompt_no_style = self.prompt

+        for style in shared.prompt_styles.get_style_prompts(self.p.styles):

+            if len(style) > 0:

+                for part in style.split("{prompt}"):

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

+

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

+

+        return sanitize_filename_part(prompt_no_style, replace_spaces=False)

+

+    def prompt_words(self):

+        words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]

+        if len(words) == 0:

+            words = ["empty"]

+        return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)

+

+    def datetime(self, *args):

+        time_datetime = datetime.datetime.now()

+

+        time_format = args[0] if len(args) > 0 else self.default_time_format

+        try:

+            time_zone = pytz.timezone(args[1]) if len(args) > 1 else None

+        except pytz.exceptions.UnknownTimeZoneError as _:

+            time_zone = None

+

+        time_zone_time = time_datetime.astimezone(time_zone)

+        try:

+            formatted_time = time_zone_time.strftime(time_format)

+        except (ValueError, TypeError) as _:

+            formatted_time = time_zone_time.strftime(self.default_time_format)

+

+        return sanitize_filename_part(formatted_time, replace_spaces=False)

+

+    def apply(self, x):

+        res = ''

+

+        for m in re_pattern.finditer(x):

+            text, pattern = m.groups()

+

+            if pattern is None:

+                res += text

+                continue

 

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

-    if prompt is not None:

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

-        if "[prompt_no_styles]" in x:

-            prompt_no_style = prompt

-            for style in shared.prompt_styles.get_style_prompts(p.styles):

-                if len(style) > 0:

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

-                    for part in style_parts:

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

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

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

+            pattern_args = []

+            while True:

+                m = re_pattern_arg.match(pattern)

+                if m is None:

+                    break

+

+                pattern, arg = m.groups()

+                pattern_args.insert(0, arg)

+

+            fun = self.replacements.get(pattern.lower())

+            if fun is not None:

+                try:

+                    replacement = fun(self, *pattern_args)

+                except Exception:

+                    replacement = None

+                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)

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

+

+                if replacement is None:

+                    res += f'[{pattern}]'

+                else:

+                    res += str(replacement)

 

-        x = x.replace("[prompt_spaces]", sanitize_filename_part(prompt, replace_spaces=False))

-        if "[prompt_words]" in x:

-            words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]

-            if len(words) == 0:

-                words = ["empty"]

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

+                continue

 

-    if cmd_opts.hide_ui_dir_config:

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

+            res += f'[{pattern}]'

 

-    return x

+        return res

 

 

 def get_next_sequence_number(path, basename):

@@ -354,7 +420,7 @@ def get_next_sequence_number(path, basename):
 

 

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

-    '''Save an image.

+    """Save an image.

 

     Args:

         image (`PIL.Image`):

@@ -385,18 +451,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             The full path of the saved imaged.

         txt_fullfn (`str` or None):

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

-    '''

-    if short_filename or prompt is None or seed is None:

-        file_decoration = ""

-    elif opts.save_to_dirs:

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

-    else:

-        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, seed, prompt) + suffix

+    """

+    namegen = FilenameGenerator(p, seed, prompt)

 

     if extension == 'png' and opts.enable_pnginfo and info is not None:

         pnginfo = PngImagePlugin.PngInfo()

@@ -413,21 +469,39 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)

 

     if save_to_dirs:

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

+        dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')

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

 

     os.makedirs(path, exist_ok=True)

 

     if forced_filename is None:

-        basecount = get_next_sequence_number(path, basename)

-        fullfn = "a.png"

-        fullfn_without_extension = "a"

-        for i in range(500):

-            fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"

-            fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")

-            fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")

-            if not os.path.exists(fullfn):

-                break

+        if short_filename or seed is None:

+            file_decoration = ""

+        elif opts.save_to_dirs:

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

+        else:

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

+

+        add_number = opts.save_images_add_number or file_decoration == ''

+

+        if file_decoration != "" and add_number:

+            file_decoration = "-" + file_decoration

+

+        file_decoration = namegen.apply(file_decoration) + suffix

+

+        if add_number:

+            basecount = get_next_sequence_number(path, basename)

+            fullfn = None

+            fullfn_without_extension = None

+            for i in range(500):

+                fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"

+                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")

+                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")

+                if not os.path.exists(fullfn):

+                    break

+        else:

+            fullfn = os.path.join(path, f"{file_decoration}.{extension}")

+            fullfn_without_extension = os.path.join(path, file_decoration)

     else:

         fullfn = os.path.join(path, f"{forced_filename}.{extension}")

         fullfn_without_extension = os.path.join(path, forced_filename)

@@ -467,6 +541,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:

         txt_fullfn = None

 

+    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)

     return fullfn, txt_fullfn

 

 

diff --git a/modules/images_history.py b/modules/images_history.py
deleted file mode 100644
index 46b23e56..00000000
--- a/modules/images_history.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import shutil
-import sys
-
-def traverse_all_files(output_dir, image_list, curr_dir=None):
-    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
-    try:
-        f_list = os.listdir(curr_path)
-    except:
-        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
-            image_list.append(curr_dir)
-        return image_list
-    for file in f_list:
-        file = file if curr_dir is None else os.path.join(curr_dir, file)
-        file_path = os.path.join(curr_path, file)
-        if file[-4:] == ".txt":
-            pass
-        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
-            image_list.append(file)
-        else:
-            image_list = traverse_all_files(output_dir, image_list, file)
-    return image_list
-
-
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    page_index = int(page_index)
-    image_list = []
-    if not os.path.exists(dir_name):
-        pass
-    elif os.path.isdir(dir_name):
-        image_list = traverse_all_files(dir_name, image_list)
-        image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-    else:
-        print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
-    num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num
-    image_list = image_list[idx_frm:idx_frm + num]
-    image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list) - 1:
-        current_file = None
-        hidden = None
-    else:
-        current_file = image_list[int(image_index)]
-        hidden = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
-
-
-def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname)
-
-
-def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname)
-
-
-def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
-
-
-def next_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
-
-
-def page_index_change(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
-
-
-def show_image_info(num, image_path, filenames):
-    # print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, os.path.join(image_path, file)
-
-
-def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
-    if name == "":
-        return filenames, delete_num
-    else:
-        delete_num = int(delete_num)
-        index = list(filenames).index(name)
-        i = 0
-        new_file_list = []
-        for name in filenames:
-            if i >= index and i < index + delete_num:
-                path = os.path.join(dir_name, name)
-                if os.path.exists(path):
-                    print(f"Delete file {path}")
-                    os.remove(path)
-                    txt_file = os.path.splitext(path)[0] + ".txt"
-                    if os.path.exists(txt_file):
-                        os.remove(txt_file)
-                else:
-                    print(f"Not exists file {path}")
-            else:
-                new_file_list.append(name)
-            i += 1
-    return new_file_list, 1
-
-
-def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if opts.outdir_samples != "":
-        dir_name = opts.outdir_samples
-    elif tabname == "txt2img":
-        dir_name = opts.outdir_txt2img_samples
-    elif tabname == "img2img":
-        dir_name = opts.outdir_img2img_samples
-    elif tabname == "extras":
-        dir_name = opts.outdir_extras_samples
-    else:
-        return
-    with gr.Row():
-        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
-        first_page = gr.Button('First Page')
-        prev_page = gr.Button('Prev Page')
-        page_index = gr.Number(value=1, label="Page Index")
-        next_page = gr.Button('Next Page')
-        end_page = gr.Button('End Page')
-    with gr.Row(elem_id=tabname + "_images_history"):
-        with gr.Row():
-            with gr.Column(scale=2):
-                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                with gr.Row():
-                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-            with gr.Column():
-                with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                with gr.Row():
-                    with gr.Column():
-                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(label="File Name", interactive=False)
-                with gr.Row():
-                    # hiden items
-
-                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
-                    tabname_box = gr.Textbox(tabname, visible=False)
-                    image_index = gr.Textbox(value=-1, visible=False)
-                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
-                    filenames = gr.State()
-                    hidden = gr.Image(type="pil", visible=False)
-                    info1 = gr.Textbox(visible=False)
-                    info2 = gr.Textbox(visible=False)
-
-    # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
-
-    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
-
-    # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
-    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-
-    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-
-
-def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
-    with gr.Blocks(analytics_enabled=False) as images_history:
-        with gr.Tabs() as tabs:
-            with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
-            with gr.Tab("img2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-            with gr.Tab("extras history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
-    return images_history
diff --git a/modules/img2img.py b/modules/img2img.py
index 24126774..8d9f7cf9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -109,6 +109,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,

     )

 

+    p.scripts = modules.scripts.scripts_txt2img

+    p.script_args = args

+

     if shared.cmd_opts.enable_console_prompts:

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

 

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 64b91eb4..65b05d34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -28,9 +28,11 @@ class InterrogateModels:
     clip_preprocess = None

     categories = None

     dtype = None

+    running_on_cpu = None

 

     def __init__(self, content_dir):

         self.categories = []

+        self.running_on_cpu = devices.device_interrogate == torch.device("cpu")

 

         if os.path.exists(content_dir):

             for filename in os.listdir(content_dir):

@@ -53,7 +55,11 @@ class InterrogateModels:
     def load_clip_model(self):

         import clip

 

-        model, preprocess = clip.load(clip_model_name)

+        if self.running_on_cpu:

+            model, preprocess = clip.load(clip_model_name, device="cpu")

+        else:

+            model, preprocess = clip.load(clip_model_name)

+

         model.eval()

         model = model.to(devices.device_interrogate)

 

@@ -62,14 +68,14 @@ class InterrogateModels:
     def load(self):

         if self.blip_model is None:

             self.blip_model = self.load_blip_model()

-            if not shared.cmd_opts.no_half:

+            if not shared.cmd_opts.no_half and not self.running_on_cpu:

                 self.blip_model = self.blip_model.half()

 

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

 

         if self.clip_model is None:

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

-            if not shared.cmd_opts.no_half:

+            if not shared.cmd_opts.no_half and not self.running_on_cpu:

                 self.clip_model = self.clip_model.half()

 

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

diff --git a/modules/lowvram.py b/modules/lowvram.py
index 7eba1349..f327c3df 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -1,9 +1,8 @@
 import torch

-from modules.devices import get_optimal_device

+from modules import devices

 

 module_in_gpu = None

 cpu = torch.device("cpu")

-device = gpu = get_optimal_device()

 

 

 def send_everything_to_cpu():

@@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         if module_in_gpu is not None:

             module_in_gpu.to(cpu)

 

-        module.to(gpu)

+        module.to(devices.device)

         module_in_gpu = module

 

     # see below for register_forward_pre_hook;

@@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
     # send the model to GPU. Then put modules back. the modules will be in CPU.

     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model

     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None

-    sd_model.to(device)

+    sd_model.to(devices.device)

     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored

 

     # register hooks for those the first two models

@@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         # so that only one of them is in GPU at a time

         stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed

         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None

-        sd_model.model.to(device)

+        sd_model.model.to(devices.device)

         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored

 

         # install hooks for bits of third model

diff --git a/modules/processing.py b/modules/processing.py
index ea926fc3..c61bbfbd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional

 

 import modules.sd_hijack

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

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

 from modules.sd_hijack import model_hijack

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

@@ -47,6 +47,25 @@ def apply_color_correction(correction, image):
     return image

 

 

+def apply_overlay(image, paste_loc, index, overlays):

+    if overlays is None or index >= len(overlays):

+        return image

+

+    overlay = overlays[index]

+

+    if paste_loc is not None:

+        x, y, w, h = paste_loc

+        base_image = Image.new('RGBA', (overlay.width, overlay.height))

+        image = images.resize_image(1, image, w, h)

+        base_image.paste(image, (x, y))

+        image = base_image

+

+    image = image.convert('RGBA')

+    image.alpha_composite(overlay)

+    image = image.convert('RGB')

+

+    return image

+

 def get_correct_sampler(p):

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

         return sd_samplers.samplers

@@ -104,6 +123,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0

             self.seed_resize_from_w = 0

 

+        self.scripts = None

+        self.script_args = None

+        self.all_prompts = None

+        self.all_seeds = None

+        self.all_subseeds = None

+

 

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

         pass

@@ -304,7 +329,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",

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

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

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

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

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

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

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

@@ -318,7 +343,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 

     generation_params.update(p.extra_generation_params)

 

-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])

+    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])

 

     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""

 

@@ -350,32 +375,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)

 

     if type(p.prompt) == list:

-        all_prompts = p.prompt

+        p.all_prompts = p.prompt

     else:

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

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

 

     if type(seed) == list:

-        all_seeds = seed

+        p.all_seeds = seed

     else:

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

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

 

     if type(subseed) == list:

-        all_subseeds = subseed

+        p.all_subseeds = subseed

     else:

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

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

 

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

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

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

 

     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

         model_hijack.embedding_db.load_textual_inversion_embeddings()

 

+    if p.scripts is not None:

+        p.scripts.run_alwayson_scripts(p)

+

     infotexts = []

     output_images = []

 

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

         with devices.autocast():

-            p.init(all_prompts, all_seeds, all_subseeds)

+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

 

         if state.job_count == -1:

             state.job_count = p.n_iter

@@ -387,15 +415,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:

                 break

 

-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

 

             if (len(prompts) == 0):

                 break

 

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

-            #c = p.sd_model.get_learned_conditioning(prompts)

             with devices.autocast():

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

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

@@ -442,22 +468,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

                 if p.color_corrections is not None and i < len(p.color_corrections):

                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:

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

+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)

+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")

                     image = apply_color_correction(p.color_corrections[i], image)

 

-                if p.overlay_images is not None and i < len(p.overlay_images):

-                    overlay = p.overlay_images[i]

-

-                    if p.paste_to is not None:

-                        x, y, w, h = p.paste_to

-                        base_image = Image.new('RGBA', (overlay.width, overlay.height))

-                        image = images.resize_image(1, image, w, h)

-                        base_image.paste(image, (x, y))

-                        image = base_image

-

-                    image = image.convert('RGBA')

-                    image.alpha_composite(overlay)

-                    image = image.convert('RGB')

+                image = apply_overlay(image, p.paste_to, i, p.overlay_images)

 

                 if opts.samples_save and not p.do_not_save_samples:

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

@@ -490,10 +505,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1

 

             if opts.grid_save:

-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

 

     devices.torch_gc()

-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)

+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)

 

 

 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

@@ -515,6 +530,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:

                 state.job_count = state.job_count * 2

 

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

+

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

                 desired_pixel_count = 512 * 512

                 actual_pixel_count = self.width * self.height

@@ -536,21 +553,40 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height

                     firstphase_height_truncated = self.firstphase_height

 

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

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

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

 

+    def create_dummy_mask(self, x, width=None, height=None):

+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:

+            height = height or self.height

+            width = width or self.width

+

+            # The "masked-image" in this case will just be all zeros since the entire image is masked.

+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 

+

+            # Add the fake full 1s mask to the first dimension.

+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)

+            image_conditioning = image_conditioning.to(x.dtype)

+

+        else:

+            # Dummy zero conditioning if we're not using inpainting model.

+            # Still takes up a bit of memory, but no encoder call.

+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

+

+        return image_conditioning

 

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

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

 

         if not self.enable_hr:

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

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

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

             return samples

 

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

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

+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))

 

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

 

@@ -587,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None

         devices.torch_gc()

 

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

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

 

         return samples

 

@@ -595,7 +631,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

     sampler = None

 

-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):

+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):

         super().__init__(**kwargs)

 

         self.init_images = init_images

@@ -613,6 +649,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert

         self.mask = None

         self.nmask = None

+        self.image_conditioning = None

 

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

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

@@ -685,6 +722,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)

             if self.overlay_images is not None:

                 self.overlay_images = self.overlay_images * self.batch_size

+

+            if self.color_corrections is not None and len(self.color_corrections) == 1:

+                self.color_corrections = self.color_corrections * self.batch_size

+

         elif len(imgs) <= self.batch_size:

             self.batch_size = len(imgs)

             batch_images = np.array(imgs)

@@ -714,10 +755,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:

                 self.init_latent = self.init_latent * self.mask

 

+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:

+            if self.image_mask is not None:

+                conditioning_mask = np.array(self.image_mask.convert("L"))

+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0

+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])

+

+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0

+                conditioning_mask = torch.round(conditioning_mask)

+            else:

+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])

+

+            # Create another latent image, this time with a masked version of the original input.

+            conditioning_mask = conditioning_mask.to(image.device)

+            conditioning_image = image * (1.0 - conditioning_mask)

+            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))

+

+            # Create the concatenated conditioning tensor to be fed to `c_concat`

+            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])

+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)

+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)

+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)

+        else:

+            self.image_conditioning = torch.zeros(

+                self.init_latent.shape[0], 5, 1, 1,

+                dtype=self.init_latent.dtype,

+                device=self.init_latent.device

+            )

+

+

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

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

 

-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)

+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

 

         if self.mask is not None:

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

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
new file mode 100644
index 00000000..dc520abc
--- /dev/null
+++ b/modules/script_callbacks.py
@@ -0,0 +1,100 @@
+import sys

+import traceback

+from collections import namedtuple

+import inspect

+

+

+def report_exception(c, job):

+    print(f"Error executing callback {job} for {c.script}", file=sys.stderr)

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

+

+

+ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])

+callbacks_model_loaded = []

+callbacks_ui_tabs = []

+callbacks_ui_settings = []

+callbacks_image_saved = []

+

+def clear_callbacks():

+    callbacks_model_loaded.clear()

+    callbacks_ui_tabs.clear()

+    callbacks_image_saved.clear()

+

+

+def model_loaded_callback(sd_model):

+    for c in callbacks_model_loaded:

+        try:

+            c.callback(sd_model)

+        except Exception:

+            report_exception(c, 'model_loaded_callback')

+

+

+def ui_tabs_callback():

+    res = []

+    

+    for c in callbacks_ui_tabs:

+        try:

+            res += c.callback() or []

+        except Exception:

+            report_exception(c, 'ui_tabs_callback')

+

+    return res

+

+

+def ui_settings_callback():

+    for c in callbacks_ui_settings:

+        try:

+            c.callback()

+        except Exception:

+            report_exception(c, 'ui_settings_callback')

+

+

+def image_saved_callback(image, p, fullfn, txt_fullfn):

+    for c in callbacks_image_saved:

+        try:

+            c.callback(image, p, fullfn, txt_fullfn)

+        except Exception:

+            report_exception(c, 'image_saved_callback')

+

+

+def add_callback(callbacks, fun):

+    stack = [x for x in inspect.stack() if x.filename != __file__]

+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'

+

+    callbacks.append(ScriptCallback(filename, fun))

+

+

+

+def on_model_loaded(callback):

+    """register a function to be called when the stable diffusion model is created; the model is

+    passed as an argument"""

+    add_callback(callbacks_model_loaded, callback)

+

+

+def on_ui_tabs(callback):

+    """register a function to be called when the UI is creating new tabs.

+    The function must either return a None, which means no new tabs to be added, or a list, where

+    each element is a tuple:

+        (gradio_component, title, elem_id)

+

+    gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)

+    title is tab text displayed to user in the UI

+    elem_id is HTML id for the tab

+    """

+    add_callback(callbacks_ui_tabs, callback)

+

+

+def on_ui_settings(callback):

+    """register a function to be called before UI settings are populated; add your settings

+    by using shared.opts.add_option(shared.OptionInfo(...)) """

+    add_callback(callbacks_ui_settings, callback)

+

+

+def on_save_imaged(callback):

+    """register a function to be called after modules.images.save_image is called.

+    The callback is called with three arguments:

+        - p - procesing object (or a dummy object with same fields if the image is saved using save button)

+        - fullfn - image filename

+        - txt_fullfn - text file with parameters; may be None

+    """

+    add_callback(callbacks_image_saved, callback)

diff --git a/modules/scripts.py b/modules/scripts.py
index 1039fa9c..9323af3e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,86 +1,175 @@
 import os

 import sys

 import traceback

+from collections import namedtuple

 

 import modules.ui as ui

 import gradio as gr

 

 from modules.processing import StableDiffusionProcessing

-from modules import shared

+from modules import shared, paths, script_callbacks

+

+AlwaysVisible = object()

+

 

 class Script:

     filename = None

     args_from = None

     args_to = None

+    alwayson = False

+

+    infotext_fields = None

+    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when

+    parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example

+    """

 

-    # The title of the script. This is what will be displayed in the dropdown menu.

     def title(self):

+        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""

+

         raise NotImplementedError()

 

-    # How the script is displayed in the UI. See https://gradio.app/docs/#components

-    # for the different UI components you can use and how to create them.

-    # Most UI components can return a value, such as a boolean for a checkbox.

-    # The returned values are passed to the run method as parameters.

     def ui(self, is_img2img):

+        """this function should create gradio UI elements. See https://gradio.app/docs/#components

+        The return value should be an array of all components that are used in processing.

+        Values of those returned componenbts will be passed to run() and process() functions.

+        """

+

         pass

 

-    # Determines when the script should be shown in the dropdown menu via the 

-    # returned value. As an example:

-    # is_img2img is True if the current tab is img2img, and False if it is txt2img.

-    # Thus, return is_img2img to only show the script on the img2img tab.

     def show(self, is_img2img):

+        """

+        is_img2img is True if this function is called for the img2img interface, and Fasle otherwise

+

+        This function should return:

+         - False if the script should not be shown in UI at all

+         - True if the script should be shown in UI if it's scelected in the scripts drowpdown

+         - script.AlwaysVisible if the script should be shown in UI at all times

+         """

+

         return True

 

-    # This is where the additional processing is implemented. The parameters include

-    # self, the model object "p" (a StableDiffusionProcessing class, see

-    # processing.py), and the parameters returned by the ui method.

-    # Custom functions can be defined here, and additional libraries can be imported 

-    # to be used in processing. The return value should be a Processed object, which is

-    # what is returned by the process_images method.

-    def run(self, *args):

+    def run(self, p, *args):

+        """

+        This function is called if the script has been selected in the script dropdown.

+        It must do all processing and return the Processed object with results, same as

+        one returned by processing.process_images.

+

+        Usually the processing is done by calling the processing.process_images function.

+

+        args contains all values returned by components from ui()

+        """

+

         raise NotImplementedError()

 

-    # The description method is currently unused.

-    # To add a description that appears when hovering over the title, amend the "titles" 

-    # dict in script.js to include the script title (returned by title) as a key, and 

-    # your description as the value.

+    def process(self, p, *args):

+        """

+        This function is called before processing begins for AlwaysVisible scripts.

+        scripts. You can modify the processing object (p) here, inject hooks, etc.

+        """

+

+        pass

+

     def describe(self):

+        """unused"""

         return ""

 

 

+current_basedir = paths.script_path

+

+

+def basedir():

+    """returns the base directory for the current script. For scripts in the main scripts directory,

+    this is the main directory (where webui.py resides), and for scripts in extensions directory

+    (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)

+    """

+    return current_basedir

+

+

 scripts_data = []

+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])

+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])

 

 

-def load_scripts(basedir):

-    if not os.path.exists(basedir):

-        return

+def list_scripts(scriptdirname, extension):

+    scripts_list = []

 

-    for filename in sorted(os.listdir(basedir)):

-        path = os.path.join(basedir, filename)

+    basedir = os.path.join(paths.script_path, scriptdirname)

+    if os.path.exists(basedir):

+        for filename in sorted(os.listdir(basedir)):

+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))

 

-        if os.path.splitext(path)[1].lower() != '.py':

-            continue

+    extdir = os.path.join(paths.script_path, "extensions")

+    if os.path.exists(extdir):

+        for dirname in sorted(os.listdir(extdir)):

+            dirpath = os.path.join(extdir, dirname)

+            scriptdirpath = os.path.join(dirpath, scriptdirname)

+

+            if not os.path.isdir(scriptdirpath):

+                continue

+

+            for filename in sorted(os.listdir(scriptdirpath)):

+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))

+

+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]

+

+    return scripts_list

+

+

+def list_files_with_name(filename):

+    res = []

+

+    dirs = [paths.script_path]

+

+    extdir = os.path.join(paths.script_path, "extensions")

+    if os.path.exists(extdir):

+        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]

 

-        if not os.path.isfile(path):

+    for dirpath in dirs:

+        if not os.path.isdir(dirpath):

             continue

 

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

+        if os.path.isfile(filename):

+            res.append(path)

+

+    return res

+

+

+def load_scripts():

+    global current_basedir

+    scripts_data.clear()

+    script_callbacks.clear_callbacks()

+

+    scripts_list = list_scripts("scripts", ".py")

+

+    syspath = sys.path

+

+    for scriptfile in sorted(scripts_list):

         try:

-            with open(path, "r", encoding="utf8") as file:

+            if scriptfile.basedir != paths.script_path:

+                sys.path = [scriptfile.basedir] + sys.path

+            current_basedir = scriptfile.basedir

+

+            with open(scriptfile.path, "r", encoding="utf8") as file:

                 text = file.read()

 

             from types import ModuleType

-            compiled = compile(text, path, 'exec')

-            module = ModuleType(filename)

+            compiled = compile(text, scriptfile.path, 'exec')

+            module = ModuleType(scriptfile.filename)

             exec(compiled, module.__dict__)

 

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

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

-                    scripts_data.append((script_class, path))

+                    scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))

 

         except Exception:

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

+            print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)

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

 

+        finally:

+            sys.path = syspath

+            current_basedir = paths.script_path

+

 

 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):

     try:

@@ -96,56 +185,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:

     def __init__(self):

         self.scripts = []

+        self.selectable_scripts = []

+        self.alwayson_scripts = []

         self.titles = []

+        self.infotext_fields = []

 

     def setup_ui(self, is_img2img):

-        for script_class, path in scripts_data:

+        for script_class, path, basedir in scripts_data:

             script = script_class()

             script.filename = path

 

-            if not script.show(is_img2img):

-                continue

+            visibility = script.show(is_img2img)

 

-            self.scripts.append(script)

+            if visibility == AlwaysVisible:

+                self.scripts.append(script)

+                self.alwayson_scripts.append(script)

+                script.alwayson = True

 

-        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]

+            elif visibility:

+                self.scripts.append(script)

+                self.selectable_scripts.append(script)

 

-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")

-        dropdown.save_to_config = True

-        inputs = [dropdown]

+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]

+

+        inputs = [None]

+        inputs_alwayson = [True]

 

-        for script in self.scripts:

+        def create_script_ui(script, inputs, inputs_alwayson):

             script.args_from = len(inputs)

             script.args_to = len(inputs)

 

             controls = wrap_call(script.ui, script.filename, "ui", is_img2img)

 

             if controls is None:

-                continue

+                return

 

             for control in controls:

                 control.custom_script_source = os.path.basename(script.filename)

-                control.visible = False

+                if not script.alwayson:

+                    control.visible = False

+

+            if script.infotext_fields is not None:

+                self.infotext_fields += script.infotext_fields

 

             inputs += controls

+            inputs_alwayson += [script.alwayson for _ in controls]

             script.args_to = len(inputs)

 

+        for script in self.alwayson_scripts:

+            with gr.Group():

+                create_script_ui(script, inputs, inputs_alwayson)

+

+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")

+        dropdown.save_to_config = True

+        inputs[0] = dropdown

+

+        for script in self.selectable_scripts:

+            create_script_ui(script, inputs, inputs_alwayson)

+

         def select_script(script_index):

-            if 0 < script_index <= len(self.scripts):

-                script = self.scripts[script_index-1]

+            if 0 < script_index <= len(self.selectable_scripts):

+                script = self.selectable_scripts[script_index-1]

                 args_from = script.args_from

                 args_to = script.args_to

             else:

                 args_from = 0

                 args_to = 0

 

-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]

+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]

 

         def init_field(title):

             if title == 'None':

                 return

             script_index = self.titles.index(title)

-            script = self.scripts[script_index]

+            script = self.selectable_scripts[script_index]

             for i in range(script.args_from, script.args_to):

                 inputs[i].visible = True

 

@@ -164,7 +277,7 @@ class ScriptRunner:
         if script_index == 0:

             return None

 

-        script = self.scripts[script_index-1]

+        script = self.selectable_scripts[script_index-1]

 

         if script is None:

             return None

@@ -176,7 +289,16 @@ class ScriptRunner:
 

         return processed

 

-    def reload_sources(self):

+    def run_alwayson_scripts(self, p):

+        for script in self.alwayson_scripts:

+            try:

+                script_args = p.script_args[script.args_from:script.args_to]

+                script.process(p, *script_args)

+            except Exception:

+                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)

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

+

+    def reload_sources(self, cache):

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

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

                 args_from = script.args_from

@@ -186,9 +308,12 @@ class ScriptRunner:
 

                 from types import ModuleType

 

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

-                module = ModuleType(script.filename)

-                exec(compiled, module.__dict__)

+                module = cache.get(filename, None)

+                if module is None:

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

+                    module = ModuleType(script.filename)

+                    exec(compiled, module.__dict__)

+                    cache[filename] = module

 

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

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

@@ -197,19 +322,22 @@ class ScriptRunner:
                         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()

+    cache = {}

+    scripts_txt2img.reload_sources(cache)

+    scripts_img2img.reload_sources(cache)

 

 

-def reload_scripts(basedir):

+def reload_scripts():

     global scripts_txt2img, scripts_img2img

 

-    scripts_data.clear()

-    load_scripts(basedir)

+    load_scripts()

 

     scripts_txt2img = ScriptRunner()

     scripts_img2img = ScriptRunner()

+

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..59532274 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,9 +54,8 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
+        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 984b35c4..0f10828e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -19,6 +19,7 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity

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

 

+

 def apply_optimizations():

     undo_optimizations()

 

@@ -167,11 +168,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

                     remade_tokens = remade_tokens[:last_comma]

                     length = len(remade_tokens)

-                    

+

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

                     remade_tokens += [id_end] * rem + reloc_tokens

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

-                

+

                 if embedding is None:

                     remade_tokens.append(token)

                     multipliers.append(weight)

@@ -223,7 +224,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

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

 

-

     def process_text_old(self, text):

         id_start = self.wrapped.tokenizer.bos_token_id

         id_end = self.wrapped.tokenizer.eos_token_id

@@ -280,7 +280,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

                 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]

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

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

 

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

@@ -290,7 +290,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)

             batch_multipliers.append(multipliers)

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

-    

+

     def forward(self, text):

         use_old = opts.use_old_emphasis_implementation

         if use_old:

@@ -302,11 +302,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

         if len(used_custom_terms) > 0:

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

-        

+

         if use_old:

             self.hijack.fixes = hijack_fixes

             return self.process_tokens(remade_batch_tokens, batch_multipliers)

-        

+

         z = None

         i = 0

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

@@ -320,7 +320,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     if fix[0] == i:

                         fixes.append(fix[1])

                 self.hijack.fixes.append(fixes)

-            

+

             tokens = []

             multipliers = []

             for j in range(len(remade_batch_tokens)):

@@ -333,19 +333,18 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 

             z1 = self.process_tokens(tokens, multipliers)

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

-            

+

             remade_batch_tokens = rem_tokens

             batch_multipliers = rem_multipliers

             i += 1

-        

+

         return z

-        

-    

+

     def process_tokens(self, remade_batch_tokens, batch_multipliers):

         if not opts.use_old_emphasis_implementation:

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

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

-        

+

         tokens = torch.asarray(remade_batch_tokens).to(device)

         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)

 

@@ -385,8 +384,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         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:]])

+                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)

 

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
new file mode 100644
index 00000000..fd92a335
--- /dev/null
+++ b/modules/sd_hijack_inpainting.py
@@ -0,0 +1,331 @@
+import torch
+
+from einops import repeat
+from omegaconf import ListConfig
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+
+# =================================================================================================
+# Monkey patch DDIMSampler methods from RunwayML repo directly.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
+# =================================================================================================
+@torch.no_grad()
+def sample_ddim(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+    samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+@torch.no_grad()
+def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None):
+    b, *_, device = *x.shape, x.device
+
+    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        e_t = self.model.apply_model(x, t, c)
+    else:
+        x_in = torch.cat([x] * 2)
+        t_in = torch.cat([t] * 2)
+        if isinstance(c, dict):
+            assert isinstance(unconditional_conditioning, dict)
+            c_in = dict()
+            for k in c:
+                if isinstance(c[k], list):
+                    c_in[k] = [
+                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                        for i in range(len(c[k]))
+                    ]
+                else:
+                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+        else:
+            c_in = torch.cat([unconditional_conditioning, c])
+        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+    if score_corrector is not None:
+        assert self.model.parameterization == "eps"
+        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+    # select parameters corresponding to the currently considered timestep
+    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+    # current prediction for x_0
+    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+    if quantize_denoised:
+        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+    # direction pointing to x_t
+    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+    if noise_dropout > 0.:
+        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+    return x_prev, pred_x0
+
+
+# =================================================================================================
+# Monkey patch PLMSSampler methods.
+# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
+# Adapted from:
+# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
+# =================================================================================================
+@torch.no_grad()
+def sample_plms(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for PLMS sampling is {size}')
+
+    samples, intermediates = self.plms_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [
+                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                            for i in range(len(c[k]))
+                        ]
+                    else:
+                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+    
+# =================================================================================================
+# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
+# =================================================================================================
+
+@torch.no_grad()
+def get_unconditional_conditioning(self, batch_size, null_label=None):
+    if null_label is not None:
+        xc = null_label
+        if isinstance(xc, ListConfig):
+            xc = list(xc)
+        if isinstance(xc, dict) or isinstance(xc, list):
+            c = self.get_learned_conditioning(xc)
+        else:
+            if hasattr(xc, "to"):
+                xc = xc.to(self.device)
+            c = self.get_learned_conditioning(xc)
+    else:
+        # todo: get null label from cond_stage_model
+        raise NotImplementedError()
+    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
+    return c
+
+
+class LatentInpaintDiffusion(LatentDiffusion):
+    def __init__(
+        self,
+        concat_keys=("mask", "masked_image"),
+        masked_image_key="masked_image",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.concat_keys = concat_keys
+
+
+def should_hijack_inpainting(checkpoint_info):
+    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+
+    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index eae22e87..e697bb72 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,8 +7,9 @@ from omegaconf import OmegaConf
 

 from ldm.util import instantiate_from_config

 

-from modules import shared, modelloader, devices

+from modules import shared, modelloader, devices, script_callbacks

 from modules.paths import models_path

+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting

 

 model_dir = "Stable-diffusion"

 model_path = os.path.abspath(os.path.join(models_path, model_dir))

@@ -20,7 +21,7 @@ checkpoints_loaded = collections.OrderedDict()
 try:

     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

 

-    from transformers import logging

+    from transformers import logging, CLIPModel

 

     logging.set_verbosity_error()

 except Exception:

@@ -154,6 +155,9 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd

 

 

+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}

+

+

 def load_model_weights(model, checkpoint_info):

     checkpoint_file = checkpoint_info.filename

     sd_model_hash = checkpoint_info.hash

@@ -185,7 +189,7 @@ def load_model_weights(model, checkpoint_info):
         if os.path.exists(vae_file):

             print(f"Loading VAE weights from: {vae_file}")

             vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}

+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}

             model.first_stage_model.load_state_dict(vae_dict)

 

         model.first_stage_model.to(devices.dtype_vae)

@@ -203,14 +207,26 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info

 

 

-def load_model():

+def load_model(checkpoint_info=None):

     from modules import lowvram, sd_hijack

-    checkpoint_info = select_checkpoint()

+    checkpoint_info = checkpoint_info or select_checkpoint()

 

     if checkpoint_info.config != shared.cmd_opts.config:

         print(f"Loading config from: {checkpoint_info.config}")

 

     sd_config = OmegaConf.load(checkpoint_info.config)

+    

+    if should_hijack_inpainting(checkpoint_info):

+        # Hardcoded config for now...

+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"

+        sd_config.model.params.use_ema = False

+        sd_config.model.params.conditioning_key = "hybrid"

+        sd_config.model.params.unet_config.params.in_channels = 9

+

+        # Create a "fake" config with a different name so that we know to unload it when switching models.

+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))

+

+    do_inpainting_hijack()

     sd_model = instantiate_from_config(sd_config.model)

     load_model_weights(sd_model, checkpoint_info)

 

@@ -222,6 +238,9 @@ def load_model():
     sd_hijack.model_hijack.hijack(sd_model)

 

     sd_model.eval()

+    shared.sd_model = sd_model

+

+    script_callbacks.model_loaded_callback(sd_model)

 

     print(f"Model loaded.")

     return sd_model

@@ -234,9 +253,9 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:

         return

 

-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:

+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):

         checkpoints_loaded.clear()

-        shared.sd_model = load_model()

+        load_model(checkpoint_info)

         return shared.sd_model

 

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

@@ -249,6 +268,7 @@ def reload_model_weights(sd_model, info=None):
     load_model_weights(sd_model, checkpoint_info)

 

     sd_hijack.model_hijack.hijack(sd_model)

+    script_callbacks.model_loaded_callback(sd_model)

 

     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:

         sd_model.to(devices.device)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..3670b57d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling

 import ldm.models.diffusion.ddim

 import ldm.models.diffusion.plms

-from modules import prompt_parser, devices, processing

+from modules import prompt_parser, devices, processing, images

 

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

@@ -71,6 +71,7 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

 }

 

+

 def setup_img2img_steps(p, steps=None):

     if opts.img2img_fix_steps or steps is not None:

         steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

@@ -82,14 +83,22 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc

 

 

-def sample_to_image(samples):

-    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]

+def single_sample_to_image(sample):

+    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]

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

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

     x_sample = x_sample.astype(np.uint8)

     return Image.fromarray(x_sample)

 

 

+def sample_to_image(samples):

+    return single_sample_to_image(samples[0])

+

+

+def samples_to_image_grid(samples):

+    return images.image_grid([single_sample_to_image(sample) for sample in samples])

+

+

 def store_latent(decoded):

     state.current_latent = decoded

 

@@ -117,6 +126,8 @@ class VanillaStableDiffusionSampler:
         self.config = None

         self.last_latent = None

 

+        self.conditioning_key = sd_model.model.conditioning_key

+

     def number_of_needed_noises(self, p):

         return 0

 

@@ -136,6 +147,12 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:

             raise InterruptedException

 

+        # Have to unwrap the inpainting conditioning here to perform pre-processing

+        image_conditioning = None

+        if isinstance(cond, dict):

+            image_conditioning = cond["c_concat"][0]

+            cond = cond["c_crossattn"][0]

+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]

 

         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

@@ -157,6 +174,12 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)

             x_dec = img_orig * self.mask + self.nmask * x_dec

 

+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.

+        # Note that they need to be lists because it just concatenates them later.

+        if image_conditioning is not None:

+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+

         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)

 

         if self.mask is not None:

@@ -182,7 +205,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None

         self.nmask = p.nmask if hasattr(p, 'nmask') else None

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         steps, t_enc = setup_img2img_steps(p, steps)

 

         self.initialize(p)

@@ -196,20 +219,33 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

 

         self.init_latent = x

+        self.last_latent = x

         self.step = 0

 

-        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))

+        # Wrap the conditioning models with additional image conditioning for inpainting model

+        if image_conditioning is not None:

+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+            

+            

+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         self.initialize(p)

 

         self.init_latent = None

+        self.last_latent = x

         self.step = 0

 

         steps = steps or p.steps

 

+        # Wrap the conditioning models with additional image conditioning for inpainting model

+        if image_conditioning is not None:

+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

+

         # existing code fails with certain step counts, like 9

         try:

             samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])

@@ -228,7 +264,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None

         self.step = 0

 

-    def forward(self, x, sigma, uncond, cond, cond_scale):

+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):

         if state.interrupted or state.skipped:

             raise InterruptedException

 

@@ -239,28 +275,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]

 

         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])

+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

 

         if tensor.shape[1] == uncond.shape[1]:

             cond_in = torch.cat([tensor, uncond])

 

             if shared.batch_cond_uncond:

-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)

+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})

             else:

                 x_out = torch.zeros_like(x_in)

                 for batch_offset in range(0, x_out.shape[0], batch_size):

                     a = batch_offset

                     b = a + batch_size

-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])

+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})

         else:

             x_out = torch.zeros_like(x_in)

             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size

             for batch_offset in range(0, tensor.shape[0], batch_size):

                 a = batch_offset

                 b = min(a + batch_size, tensor.shape[0])

-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])

+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})

 

-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)

+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

 

         denoised_uncond = x_out[-uncond.shape[0]:]

         denoised = torch.clone(denoised_uncond)

@@ -306,6 +343,8 @@ class KDiffusionSampler:
         self.config = None

         self.last_latent = None

 

+        self.conditioning_key = sd_model.model.conditioning_key

+

     def callback_state(self, d):

         step = d['i']

         latent = d["denoised"]

@@ -361,7 +400,7 @@ class KDiffusionSampler:
 

         return extra_params_kwargs

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

         steps, t_enc = setup_img2img_steps(p, steps)

 

         if p.sampler_noise_scheduler_override:

@@ -388,12 +427,18 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched

 

         self.model_wrap_cfg.init_latent = x

+        self.last_latent = x

 

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={

+            'cond': conditioning, 

+            'image_cond': image_conditioning, 

+            'uncond': unconditional_conditioning, 

+            'cond_scale': p.cfg_scale

+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):

         steps = steps or p.steps

 

         if p.sampler_noise_scheduler_override:

@@ -414,7 +459,13 @@ class KDiffusionSampler:
         else:

             extra_params_kwargs['sigmas'] = sigmas

 

-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))

+        self.last_latent = x

+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

+            'cond': conditioning, 

+            'image_cond': image_conditioning, 

+            'uncond': unconditional_conditioning, 

+            'cond_scale': p.cfg_scale

+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

 

         return samples

 

diff --git a/modules/shared.py b/modules/shared.py
index f7d66870..308fccce 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,6 +3,7 @@ import datetime
 import json

 import os

 import sys

+from collections import OrderedDict

 

 import gradio as gr

 import tqdm

@@ -57,12 +58,13 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")

 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

-parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)

+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)

 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)

 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)

 parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))

 parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)

+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)

 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))

 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")

 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)

@@ -78,10 +80,13 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)

 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")

 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")

+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")

+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)

 

 cmd_opts = parser.parse_args()

 restricted_opts = [

     "samples_filename_pattern",

+    "directories_filename_pattern",

     "outdir_samples",

     "outdir_txt2img_samples",

     "outdir_img2img_samples",

@@ -91,8 +96,8 @@ restricted_opts = [
     "outdir_save",

 ]

 

-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \

-(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])

+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \

+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])

 

 device = devices.device

 weight_load_location = None if cmd_opts.lowram else "cpu"

@@ -137,7 +142,7 @@ class State:
         self.job_no += 1

         self.sampling_step = 0

         self.current_image_sampling_step = 0

-        

+

     def get_job_timestamp(self):

         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?

 

@@ -162,13 +167,13 @@ def realesrgan_models_names():
 

 

 class OptionInfo:

-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):

+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):

         self.default = default

         self.label = label

         self.component = component

         self.component_args = component_args

         self.onchange = onchange

-        self.section = None

+        self.section = section

         self.refresh = refresh

 

 

@@ -186,7 +191,8 @@ options_templates = {}
 options_templates.update(options_section(('saving-images', "Saving images/grids"), {

     "samples_save": OptionInfo(True, "Always save all generated images"),

     "samples_format": OptionInfo('png', 'File format for images'),

-    "samples_filename_pattern": OptionInfo("", "Images filename pattern"),

+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),

+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),

 

     "grid_save": OptionInfo(True, "Always save all generated image grids"),

     "grid_format": OptionInfo('png', 'File format for grids'),

@@ -221,8 +227,8 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "save_to_dirs": OptionInfo(False, "Save images to a 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 for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),

+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),

+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),

 }))

 

 options_templates.update(options_section(('upscaling', "Upscaling"), {

@@ -249,7 +255,7 @@ options_templates.update(options_section(('system', "System"), {
 }))

 

 options_templates.update(options_section(('training', "Training"), {

-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),

+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),

     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

@@ -291,6 +297,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {

     "show_progressbar": OptionInfo(True, "Show progressbar"),

     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),

+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),

     "return_grid": OptionInfo(True, "Show grid in results for web"),

     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),

     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),

@@ -343,7 +350,7 @@ class Options:
 

     def save(self, filename):

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

-            json.dump(self.data, file)

+            json.dump(self.data, file, indent=4)

 

     def same_type(self, x, y):

         if x is None or y is None:

@@ -378,6 +385,20 @@ class Options:
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}

         return json.dumps(d)

 

+    def add_option(self, key, info):

+        self.data_labels[key] = info

+

+    def reorder(self):

+        """reorder settings so that all items related to section always go together"""

+

+        section_ids = {}

+        settings_items = self.data_labels.items()

+        for k, item in settings_items:

+            if item.section not in section_ids:

+                section_ids[item.section] = len(section_ids)

+

+        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}

+

 

 opts = Options()

 if os.path.exists(config_filename):

@@ -387,6 +408,8 @@ sd_upscalers = []
 

 sd_model = None

 

+clip_model = None

+

 progress_print_out = sys.stdout

 

 

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index baa02e3d..4253b66d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -7,8 +7,8 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
-from modules import modelloader
-from modules.shared import cmd_opts, opts, device
+from modules import modelloader, devices
+from modules.shared import cmd_opts, opts
 from modules.swinir_model_arch import SwinIR as net
 from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -42,7 +42,7 @@ class UpscalerSwinIR(Upscaler):
         model = self.load_model(model_file)
         if model is None:
             return img
-        model = model.to(device)
+        model = model.to(devices.device_swinir)
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(device)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
@@ -139,8 +139,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     stride = tile - tile_overlap
     h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
     w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    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)
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
 
     with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
         for h_idx in h_idx_list:
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 23bb4b6a..5b1c5002 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -83,7 +83,7 @@ class PersonalizedBase(Dataset):
 

             self.dataset.append(entry)

 

-        assert len(self.dataset) > 1, "No images have been found in the dataset."

+        assert len(self.dataset) > 0, "No images have been found in the dataset."

         self.length = len(self.dataset) * repeats // batch_size

 

         self.initial_indexes = np.arange(len(self.dataset))

@@ -91,7 +91,7 @@ class PersonalizedBase(Dataset):
         self.shuffle()

 

     def shuffle(self):

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

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

 

     def create_text(self, filename_text):

         text = random.choice(self.lines)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 898ce3b3..ea653806 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -5,6 +5,7 @@ import zlib
 from PIL import Image, PngImagePlugin, ImageDraw, ImageFont

 from fonts.ttf import Roboto

 import torch

+from modules.shared import opts

 

 

 class EmbeddingEncoder(json.JSONEncoder):

@@ -133,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     from math import cos

 

     image = srcimage.copy()

-

+    fontsize = 32

     if textfont is None:

         try:

             textfont = ImageFont.truetype(opts.font or Roboto, fontsize)

@@ -150,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))

 

     draw = ImageDraw.Draw(image)

-    fontsize = 32

+

     font = ImageFont.truetype(textfont, fontsize)

     padding = 10

 

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 0c79f012..a8c17c6f 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,6 @@
 import os

 from PIL import Image, ImageOps

+import math

 import platform

 import sys

 import tqdm

@@ -12,7 +13,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru

 

 

-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):

+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):

     try:

         if process_caption:

             shared.interrogator.load()

@@ -22,7 +23,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

 

-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, process_entropy_focus)

+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_entropy_focus)

 

     finally:

 

@@ -34,11 +35,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 

 

 

-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):

+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):

     width = process_width

     height = process_height

     src = os.path.abspath(process_src)

     dst = os.path.abspath(process_dst)

+    split_threshold = max(0.0, min(1.0, split_threshold))

+    overlap_ratio = max(0.0, min(0.9, overlap_ratio))

 

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

 

@@ -49,7 +52,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."

     shared.state.job_count = len(files)

 

-    def save_pic_with_caption(image, index):

+    def save_pic_with_caption(image, index, existing_caption=None):

         caption = ""

 

         if process_caption:

@@ -67,17 +70,49 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         basename = f"{index:05}-{subindex[0]}-{filename_part}"

         image.save(os.path.join(dst, f"{basename}.png"))

 

+        if preprocess_txt_action == 'prepend' and existing_caption:

+            caption = existing_caption + ' ' + caption

+        elif preprocess_txt_action == 'append' and existing_caption:

+            caption = caption + ' ' + existing_caption

+        elif preprocess_txt_action == 'copy' and existing_caption:

+            caption = existing_caption

+

+        caption = caption.strip()

+        

         if len(caption) > 0:

             with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:

                 file.write(caption)

 

         subindex[0] += 1

 

-    def save_pic(image, index):

-        save_pic_with_caption(image, index)

+    def save_pic(image, index, existing_caption=None):

+        save_pic_with_caption(image, index, existing_caption=existing_caption)

 

         if process_flip:

-            save_pic_with_caption(ImageOps.mirror(image), index)

+            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)

+

+    def split_pic(image, inverse_xy):

+        if inverse_xy:

+            from_w, from_h = image.height, image.width

+            to_w, to_h = height, width

+        else:

+            from_w, from_h = image.width, image.height

+            to_w, to_h = width, height

+        h = from_h * to_w // from_w

+        if inverse_xy:

+            image = image.resize((h, to_w))

+        else:

+            image = image.resize((to_w, h))

+

+        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))

+        y_step = (h - to_h) / (split_count - 1)

+        for i in range(split_count):

+            y = int(y_step * i)

+            if inverse_xy:

+                splitted = image.crop((y, 0, y + to_h, to_w))

+            else:

+                splitted = image.crop((0, y, to_w, y + to_h))

+            yield splitted

 

 

     for index, imagefile in enumerate(tqdm.tqdm(files)):

@@ -88,34 +123,27 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         except Exception:

             continue

 

+        existing_caption = None

+        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'

+        if os.path.exists(existing_caption_filename):

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

+                existing_caption = file.read()

+

         if shared.state.interrupted:

             break

 

-        ratio = img.height / img.width

-        is_tall = ratio > 1.35

-        is_wide = ratio < 1 / 1.35

+        if img.height > img.width:

+            ratio = (img.width * height) / (img.height * width)

+            inverse_xy = False

+        else:

+            ratio = (img.height * width) / (img.width * height)

+            inverse_xy = True

 

         processing_option_ran = False

 

-        if process_split and is_tall:

-            img = img.resize((width, height * img.height // img.width))

-

-            top = img.crop((0, 0, width, height))

-            save_pic(top, index)

-

-            bot = img.crop((0, img.height - height, width, img.height))

-            save_pic(bot, index)

-

-            processing_option_ran = True

-        elif process_split and is_wide:

-            img = img.resize((width * img.width // img.height, height))

-

-            left = img.crop((0, 0, width, height))

-            save_pic(left, index)

-

-            right = img.crop((img.width - width, 0, img.width, height))

-            save_pic(right, index)

-            

+        if process_split and ratio < 1.0 and ratio <= split_threshold:

+            for splitted in split_pic(img, inverse_xy):

+                save_pic(splitted, index, existing_caption=existing_caption)

             processing_option_ran = True

 

         if process_entropy_focus and img.height != img.width:

@@ -128,12 +156,11 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
                 annotate_image = False

             )

             focal = autocrop.crop_image(img, autocrop_settings)

-            save_pic(focal, index)

-

+            save_pic(focal, index, existing_caption=existing_caption)

             processing_option_ran = True

 

         if not processing_option_ran:

             img = images.resize_image(1, img, width, height)

-            save_pic(img, index)

+            save_pic(img, index, existing_caption=existing_caption)

 

         shared.state.nextjob()
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3be69562..529ed3e2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
         return None, None

 

 

-def create_embedding(name, num_vectors_per_token, init_text='*'):

+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):

     cond_model = shared.sd_model.cond_stage_model

     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings

 

@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
         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"

+    if not overwrite_old:

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

 

     embedding = Embedding(vec, name)

     embedding.step = 0

@@ -275,6 +276,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()

             optimizer.step()

 

+

         epoch_num = embedding.step // len(ds)

         epoch_step = embedding.step - (epoch_num * len(ds)) + 1

 

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 36881e7a..e712284d 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -7,8 +7,8 @@ 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)

+def create_embedding(name, initialization_text, nvpt, overwrite_old):

+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)

 

     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

 

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 2381347f..c9d5a090 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,5 +1,6 @@
 import modules.scripts

-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images

+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \

+    StableDiffusionProcessingImg2Img, process_images

 from modules.shared import opts, cmd_opts

 import modules.shared as shared

 import modules.processing as processing

@@ -35,6 +36,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,

     )

 

+    p.scripts = modules.scripts.scripts_txt2img

+    p.script_args = args

+

     if cmd_opts.enable_console_prompts:

         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

 

@@ -53,4 +57,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         processed.images = []

 

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

-

diff --git a/modules/ui.py b/modules/ui.py
index b6be713b..028eb4e5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,47 +5,56 @@ import json
 import math

 import mimetypes

 import os

+import platform

 import random

+import subprocess as sp

 import sys

 import tempfile

 import time

 import traceback

-import platform

-import subprocess as sp

 from functools import partial, reduce

 

+import gradio as gr

+import gradio.routes

+import gradio.utils

 import numpy as np

+import piexif

 import torch

 from PIL import Image, PngImagePlugin

-import piexif

 

 import gradio as gr

 import gradio.utils

 import gradio.routes

 

-from modules import sd_hijack, sd_models, localization

+from modules import sd_hijack, sd_models, localization, script_callbacks

 from modules.paths import script_path

+

 from modules.shared import opts, cmd_opts, restricted_opts

+

 if cmd_opts.deepdanbooru:

     from modules.deepbooru import get_deepbooru_tags

-import modules.shared as shared

-from modules.sd_samplers import samplers, samplers_for_img2img

-from modules.sd_hijack import model_hijack

+

+import modules.codeformer_model

+import modules.generation_parameters_copypaste

+import modules.gfpgan_model

+import modules.hypernetworks.ui

 import modules.ldsr_model

 import modules.scripts

-import modules.gfpgan_model

-import modules.codeformer_model

+import modules.shared as shared

 import modules.styles

-import modules.generation_parameters_copypaste

+import modules.textual_inversion.ui

 from modules import prompt_parser

 from modules.images import save_image

+from modules.sd_hijack import model_hijack

+from modules.sd_samplers import samplers, samplers_for_img2img

 import modules.textual_inversion.ui

 import modules.hypernetworks.ui

-import modules.images_history as img_his

 

 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI

 mimetypes.init()

 mimetypes.add_type('application/javascript', '.js')

+txt2img_paste_fields = []

+img2img_paste_fields = []

 

 

 if not cmd_opts.share and not cmd_opts.listen:

@@ -268,8 +277,13 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start

         eta = (time_since_start/progress)

         eta_relative = eta-time_since_start

-        if (eta_relative > threshold and progress > 0.02) or force_display:           

-            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        

+        if (eta_relative > threshold and progress > 0.02) or force_display:

+            if eta_relative > 3600:

+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))

+            elif eta_relative > 60:

+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))

+            else:

+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))

         else:

             return ""

 

@@ -285,7 +299,7 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:

         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps

 

-    time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )

+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )

     if time_left != "":

         shared.state.time_left_force_display = True

 

@@ -293,7 +307,7 @@ def check_progress_call(id_part):
 

     progressbar = ""

     if opts.show_progressbar:

-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""

+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""

 

     image = gr_show(False)

     preview_visibility = gr_show(False)

@@ -302,7 +316,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:

 

             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:

-                shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)

+                if opts.show_progress_grid:

+                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)

+                else:

+                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)

                 shared.state.current_image_sampling_step = shared.state.sampling_step

 

         image = shared.state.current_image

@@ -477,14 +494,14 @@ def create_toprow(is_img2img):
             with gr.Row():

                 with gr.Column(scale=80):

                     with gr.Row():

-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 

+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,

                             placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"

                         )

 

             with gr.Row():

                 with gr.Column(scale=80):

                     with gr.Row():

-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 

+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,

                             placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"

                         )

 

@@ -561,6 +578,9 @@ def apply_setting(key, value):
     if value is None:

         return gr.update()

 

+    if shared.cmd_opts.freeze_settings:

+        return gr.update()

+

     # dont allow model to be swapped when model hash exists in prompt

     if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:

         return gr.update()

@@ -587,27 +607,29 @@ def apply_setting(key, value):
     return value

 

 

-def create_ui(wrap_gradio_gpu_call):

-    import modules.img2img

-    import modules.txt2img

+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

+    def refresh():

+        refresh_method()

+        args = refreshed_args() if callable(refreshed_args) else refreshed_args

 

-    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

-        def refresh():

-            refresh_method()

-            args = refreshed_args() if callable(refreshed_args) else refreshed_args

+        for k, v in args.items():

+            setattr(refresh_component, k, v)

 

-            for k, v in args.items():

-                setattr(refresh_component, k, v)

+        return gr.update(**(args or {}))

 

-            return gr.update(**(args or {}))

+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)

+    refresh_button.click(

+        fn=refresh,

+        inputs=[],

+        outputs=[refresh_component]

+    )

+    return refresh_button

+

+

+def create_ui(wrap_gradio_gpu_call):

+    import modules.img2img

+    import modules.txt2img

 

-        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)

-        refresh_button.click(

-            fn = refresh,

-            inputs = [],

-            outputs = [refresh_component]

-        )

-        return refresh_button

 

     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, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)

@@ -705,6 +727,7 @@ def create_ui(wrap_gradio_gpu_call):
                     firstphase_width,

                     firstphase_height,

                 ] + custom_inputs,

+

                 outputs=[

                     txt2img_gallery,

                     generation_info,

@@ -761,6 +784,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]

             )

 

+            global txt2img_paste_fields 

             txt2img_paste_fields = [

                 (txt2img_prompt, "Prompt"),

                 (txt2img_negative_prompt, "Negative prompt"),

@@ -781,6 +805,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

                 (firstphase_width, "First pass size-1"),

                 (firstphase_height, "First pass size-2"),

+                *modules.scripts.scripts_txt2img.infotext_fields

             ]

 

             txt2img_preview_params = [

@@ -848,8 +873,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")

 

                 with gr.Group():

-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")

+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")

 

                 with gr.Row():

                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)

@@ -1030,6 +1055,7 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],

                 )

 

+            global img2img_paste_fields

             img2img_paste_fields = [

                 (img2img_prompt, "Prompt"),

                 (img2img_negative_prompt, "Negative prompt"),

@@ -1046,6 +1072,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),

                 (seed_resize_from_h, "Seed resize from-2"),

                 (denoising_strength, "Denoising strength"),

+                *modules.scripts.scripts_img2img.infotext_fields

             ]

             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])

 

@@ -1077,9 +1104,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)

                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)

                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)

-

+                

                 with gr.Group():

-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 

 

                 with gr.Group():

                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

@@ -1166,15 +1193,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],

             outputs=[html, generation_info, html2],

         )

-    #images history

-    images_history_switch_dict = {

-        "fn":modules.generation_parameters_copypaste.connect_paste,

-        "t2i":txt2img_paste_fields,

-        "i2i":img2img_paste_fields

-    }

-

-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)

-

+   

     with gr.Blocks() as modelmerger_interface:

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

             with gr.Column(variant='panel'):

@@ -1206,6 +1225,7 @@ def create_ui(wrap_gradio_gpu_call):
                     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)

+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1217,6 +1237,11 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Create hypernetwork"):

                     new_hypernetwork_name = gr.Textbox(label="Name")

                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")

+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])

+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")

+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1230,14 +1255,19 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')

                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])

 

                     with gr.Row():

                         process_flip = gr.Checkbox(label='Create flipped copies')

-                        process_split = gr.Checkbox(label='Split oversized images into two')

+                        process_split = gr.Checkbox(label='Split oversized images')

                         process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')

                         process_caption = gr.Checkbox(label='Use BLIP for caption')

                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

 

+                    with gr.Row(visible=False) as process_split_extra_row:

+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)

+

                     with gr.Row():

                         with gr.Column(scale=3):

                             gr.HTML(value="")

@@ -1245,15 +1275,24 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():

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

 

+                    process_split.change(

+                        fn=lambda show: gr_show(show),

+                        inputs=[process_split],

+                        outputs=[process_split_extra_row],

+                    )

+

                 with gr.Tab(label="Train"):

-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")

                     with gr.Row():

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

                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")

                     with gr.Row():

                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])

                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")

-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")

+                    with gr.Row():

+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")

+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

+

                     batch_size = gr.Number(label='Batch size', value=1, precision=0)

                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

@@ -1287,6 +1326,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,

                 initialization_text,

                 nvpt,

+                overwrite_old_embedding,

             ],

             outputs=[

                 train_embedding_name,

@@ -1300,6 +1340,11 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[

                 new_hypernetwork_name,

                 new_hypernetwork_sizes,

+                overwrite_old_hypernetwork,

+                new_hypernetwork_layer_structure,

+                new_hypernetwork_activation_func,

+                new_hypernetwork_add_layer_norm,

+                new_hypernetwork_use_dropout

             ],

             outputs=[

                 train_hypernetwork_name,

@@ -1316,11 +1361,14 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,

                 process_width,

                 process_height,

+                preprocess_txt_action,

                 process_flip,

                 process_split,

                 process_caption,

                 process_caption_deepbooru,

-                process_entropy_focus

+                process_split_threshold,

+                process_overlap_ratio,

+                process_entropy_focus,

             ],

             outputs=[

                 ti_output,

@@ -1333,7 +1381,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_embedding_name,

-                learn_rate,

+                embedding_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1358,7 +1406,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_hypernetwork_name,

-                learn_rate,

+                hypernetwork_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1422,6 +1470,9 @@ def create_ui(wrap_gradio_gpu_call):
     components = []

     component_dict = {}

 

+    script_callbacks.ui_settings_callback()

+    opts.reorder()

+

     def open_folder(f):

         if not os.path.exists(f):

             print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')

@@ -1447,6 +1498,8 @@ Requested path was: {f}
     def run_settings(*args):

         changed = 0

 

+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"

+

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

             if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):

                 return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()

@@ -1476,13 +1529,15 @@ Requested path was: {f}
         return f'{changed} settings changed.', opts.dumpjson()

 

     def run_settings_single(value, key):

+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"

+

         if not opts.same_type(value, opts.data_labels[key].default):

             return gr.update(visible=True), opts.dumpjson()

 

+        oldval = opts.data.get(key, None)

         if cmd_opts.hide_ui_dir_config and key in restricted_opts:

             return gr.update(value=oldval), opts.dumpjson()

 

-        oldval = opts.data.get(key, None)

         opts.data[key] = value

 

         if oldval != value:

@@ -1525,9 +1580,10 @@ Requested path was: {f}
 

                     previous_section = item.section

 

-                    gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))

+                    elem_id, text = item.section

+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))

 

-                if k in quicksettings_names:

+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:

                     quicksettings_list.append((i, k, item))

                     components.append(dummy_component)

                 else:

@@ -1560,7 +1616,7 @@ Requested path was: {f}
 

         def reload_scripts():

             modules.scripts.reload_script_body_only()

-            reload_javascript() # need to refresh the html page

+            reload_javascript()  # need to refresh the html page

 

         reload_script_bodies.click(

             fn=reload_scripts,

@@ -1588,19 +1644,26 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),

         (extras_interface, "Extras", "extras"),

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

-        (images_history, "History", "images_history"),

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

         (train_interface, "Train", "ti"),

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

     ]

 

-    with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:

-        css = file.read()

+    interfaces += script_callbacks.ui_tabs_callback()

+

+    interfaces += [(settings_interface, "Settings", "settings")]

+

+    css = ""

+

+    for cssfile in modules.scripts.list_files_with_name("style.css"):

+        if not os.path.isfile(cssfile):

+            continue

+

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

+            css += file.read() + "\n"

 

     if os.path.exists(os.path.join(script_path, "user.css")):

         with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:

-            usercss = file.read()

-            css += usercss

+            css += file.read() + "\n"

 

     if not cmd_opts.no_progressbar_hiding:

         css += css_hide_progressbar

@@ -1823,9 +1886,10 @@ def load_javascript(raw_response):
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:

         javascript = f'<script>{jsfile.read()}</script>'

 

-    jsdir = os.path.join(script_path, "javascript")

-    for filename in sorted(os.listdir(jsdir)):

-        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:

+    scripts_list = modules.scripts.list_scripts("javascript", ".js")

+    

+    for basedir, filename, path in scripts_list:

+        with open(path, "r", encoding="utf8") as jsfile:

             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"

 

     if cmd_opts.theme is not None:

@@ -1843,6 +1907,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response

 

 

-reload_javascript = partial(load_javascript,

-                            gradio.routes.templates.TemplateResponse)

+reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)

 reload_javascript()