Merge branch 'dev' into master

2025-08-09 13:49:48 +00:00 · 2023-04-29 17:02:14 +03:00
parent 9a4e650800 17cce45613
commit 3894609b52
48 changed files with 618 additions and 273 deletions
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -6,7 +6,6 @@ import uvicorn
 import gradio as gr
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import decode_base64_to_file
 from fastapi import APIRouter, Depends, FastAPI, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from fastapi.exceptions import HTTPException
@@ -272,7 +271,9 @@ class Api:
                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
                # always on script with no arg should always run so you don't really need to add them to the requests
                if "args" in request.alwayson_scripts[alwayson_script_name]:
-                    script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
+                    # min between arg length in scriptrunner and arg length in the request
+                    for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
+                        script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
        return script_args

    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
@@ -395,16 +396,11 @@ class Api:
    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
        reqDict = setUpscalers(req)

-        def prepareFiles(file):
-            file = decode_base64_to_file(file.data, file_path=file.name)
-            file.orig_name = file.name
-            return file
-
-        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
-        reqDict.pop('imageList')
+        image_list = reqDict.pop('imageList', [])
+        image_folder = [decode_base64_to_image(x.data) for x in image_list]

        with self.queue_lock:
-            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)

        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])

--- a/modules/devices.py
+++ b/modules/devices.py
@@ -92,14 +92,18 @@ def cond_cast_float(input):


 def randn(seed, shape):
+    from modules.shared import opts
+
    torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.randn_source == "CPU" or device.type == 'mps':
        return torch.randn(shape, device=cpu).to(device)
    return torch.randn(shape, device=device)


 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
        return torch.randn(shape, device=cpu).to(device)
    return torch.randn(shape, device=device)

--- a/modules/extra_networks_hypernet.py
+++ b/modules/extra_networks_hypernet.py
@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
    def activate(self, p, params_list):
        additional = shared.opts.sd_hypernetwork

-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))

--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model

    restore_old_hires_fix_params(res)

+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
    return res


@@ -304,6 +308,8 @@ infotext_to_setting_name_mapping = [
    ('UniPC skip type', 'uni_pc_skip_type'),
    ('UniPC order', 'uni_pc_order'),
    ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('RNG', 'randn_source'),
+    ('NGMS', 's_min_uncond'),
 ]


--- a/modules/images.py
+++ b/modules/images.py
@@ -318,6 +318,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
 re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
+NOTHING_AND_SKIP_PREVIOUS_TEXT = object()


 def sanitize_filename_part(text, replace_spaces=True):
@@ -352,6 +353,10 @@ class FilenameGenerator:
        '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(),
+        'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
+        'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+        'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
+        'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
    }
    default_time_format = '%Y%m%d%H%M%S'

@@ -360,6 +365,22 @@ class FilenameGenerator:
        self.seed = seed
        self.prompt = prompt
        self.image = image
+        
+    def hasprompt(self, *args):
+        lower = self.prompt.lower()
+        if self.p is None or self.prompt is None:
+            return None
+        outres = ""
+        for arg in args:
+            if arg != "":
+                division = arg.split("|")
+                expected = division[0].lower()
+                default = division[1] if len(division) > 1 else ""
+                if lower.find(expected) >= 0:
+                    outres = f'{outres}{expected}'
+                else:
+                    outres = outres if default == "" else f'{outres}{default}'
+        return sanitize_filename_part(outres)

    def prompt_no_style(self):
        if self.p is None or self.prompt is None:
@@ -403,9 +424,9 @@ class FilenameGenerator:

        for m in re_pattern.finditer(x):
            text, pattern = m.groups()
-            res += text

            if pattern is None:
+                res += text
                continue

            pattern_args = []
@@ -426,11 +447,13 @@ class FilenameGenerator:
                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)
                    print(traceback.format_exc(), file=sys.stderr)

-                if replacement is not None:
-                    res += str(replacement)
+                if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
+                    continue
+                elif replacement is not None:
+                    res += text + str(replacement)
                    continue

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

        return res

--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
 import traceback

 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError

 from modules import devices, sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -46,7 +46,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
        if state.interrupted:
            break

-        img = Image.open(image)
+        try:
+            img = Image.open(image)
+        except UnidentifiedImageError:
+            continue
        # Use the EXIF orientation of photos taken by smartphones.
        img = ImageOps.exif_transpose(img)
        p.init_images = [img] * p.batch_size
@@ -151,7 +154,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
        override_settings=override_settings,
    )

-    p.scripts = modules.scripts.scripts_txt2img
+    p.scripts = modules.scripts.scripts_img2img
    p.script_args = args

    if shared.cmd_opts.enable_console_prompts:
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
    category_types = ["artists", "flavors", "mediums", "movements"]

    try:
-        os.makedirs(tmpdir)
+        os.makedirs(tmpdir, exist_ok=True)
        for category_type in category_types:
            torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
        os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
        errors.display(e, "downloading default CLIP interrogate categories")
    finally:
        if os.path.exists(tmpdir):
-            os.remove(tmpdir)
+            os.removedirs(tmpdir)


 class InterrogateModels:
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -13,6 +13,18 @@ def connect(token, port, region):
    config = conf.PyngrokConfig(
        auth_token=token, region=region
    )
+    
+    # Guard for existing tunnels
+    existing = ngrok.get_tunnels(pyngrok_config=config)
+    if existing:
+        for established in existing:
+            # Extra configuration in the case that the user is also using ngrok for other tunnels
+            if established.config['addr'][-4:] == str(port):
+                public_url = existing[0].public_url
+                print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
+                    'You can use this link after the launch is complete.')
+                return
+    
    try:
        if account is None:
            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -18,9 +18,14 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,

    if extras_mode == 1:
        for img in image_folder:
-            image = Image.open(img.name)
+            if isinstance(img, Image.Image):
+                image = img
+                fn = ''
+            else:
+                image = Image.open(os.path.abspath(img.name))
+                fn = os.path.splitext(img.orig_name)[0]
            image_data.append(image)
-            image_names.append(os.path.splitext(img.orig_name)[0])
+            image_names.append(fn)
    elif extras_mode == 2:
        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
        assert input_dir, 'input directory not selected'
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -3,6 +3,7 @@ import math
 import os
 import sys
 import warnings
+import hashlib

 import torch
 import numpy as np
@@ -105,7 +106,7 @@ class StableDiffusionProcessing:
    """
    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
    """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
        if sampler_index is not None:
            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)

@@ -140,6 +141,7 @@ class StableDiffusionProcessing:
        self.denoising_strength: float = denoising_strength
        self.sampler_noise_scheduler_override = None
        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
+        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
        self.s_churn = s_churn or opts.s_churn
        self.s_tmin = s_tmin or opts.s_tmin
        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
@@ -162,6 +164,8 @@ class StableDiffusionProcessing:
        self.all_seeds = None
        self.all_subseeds = None
        self.iteration = 0
+        self.is_hr_pass = False
+        

    @property
    def sd_model(self):
@@ -476,6 +480,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
        "Clip skip": None if clip_skip <= 1 else clip_skip,
        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
    }

    generation_params.update(p.extra_generation_params)
@@ -639,8 +646,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                    processed = Processed(p, [], p.seed, "")
                    file.write(processed.infotext(p, 0))

-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+            step_multiplier = 1
+            if not shared.opts.dont_fix_second_order_samplers_schedule:
+                try:
+                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+                except:
+                    pass
+            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)

            if len(model_hijack.comments) > 0:
                for comment in model_hijack.comments:
@@ -670,6 +683,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

            for i, x_sample in enumerate(x_samples_ddim):
+                p.batch_index = i
+
                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                x_sample = x_sample.astype(np.uint8)

@@ -706,9 +721,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                    image.info["parameters"] = text
                output_images.append(image)

-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
                    image_mask = p.mask_for_overlay.convert('RGB')
-                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')

                    if opts.save_mask:
                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
@@ -718,7 +733,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

                    if opts.return_mask:
                        output_images.append(image_mask)
-                    
+
                    if opts.return_mask_composite:
                        output_images.append(image_mask_composite)

@@ -871,6 +886,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        if not self.enable_hr:
            return samples

+        self.is_hr_pass = True
+
        target_width = self.hr_upscale_to_x
        target_height = self.hr_upscale_to_y

@@ -940,6 +957,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

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

+        self.is_hr_pass = False
+
        return samples


@@ -1007,6 +1026,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            self.color_corrections = []
        imgs = []
        for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
            image = images.flatten(img, opts.img2img_background_color)

            if crop_region is None and self.resize_mode != 3:
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -9,7 +9,7 @@ from realesrgan import RealESRGANer

 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
-
+from modules import modelloader

 class UpscalerRealESRGAN(Upscaler):
    def __init__(self, path):
@@ -23,7 +23,15 @@ class UpscalerRealESRGAN(Upscaler):
            self.enable = True
            self.scalers = []
            scalers = self.load_models(path)
+
+            local_model_paths = self.find_models(ext_filter=[".pth"])
            for scaler in scalers:
+                if scaler.local_data_path.startswith("http"):
+                    filename = modelloader.friendly_name(scaler.local_data_path)
+                    local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
+                    if local:
+                        scaler.local_data_path = local
+
                if scaler.name in opts.realesrgan_enabled_models:
                    self.scalers.append(scaler)

@@ -64,7 +72,9 @@ class UpscalerRealESRGAN(Upscaler):
                print(f"Unable to find model info: {path}")
                return None

-            info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+            if info.local_data_path.startswith("http"):
+                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+
            return info
        except Exception as e:
            print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -1,6 +1,5 @@
 # this code is adapted from the script contributed by anon from /h/

-import io
 import pickle
 import collections
 import sys
@@ -12,11 +11,9 @@ import _codecs
 import zipfile
 import re

-
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
 TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage

-
 def encode(*args):
    out = _codecs.encode(*args)
    return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):

    def persistent_load(self, saved_id):
        assert saved_id[0] == 'storage'
-        return TypedStorage()
+        return TypedStorage(_internal=True)

    def find_class(self, module, name):
        if self.extra_handler is not None:
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -60,3 +60,13 @@ def store_latent(decoded):

 class InterruptedException(BaseException):
    pass
+
+
+if opts.randn_source == "CPU":
+    import torchsde._brownian.brownian_interval
+
+    def torchsde_randn(size, dtype, device, seed):
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+    torchsde._brownian.brownian_interval._randn = torchsde_randn
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module):

        return denoised

-    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
        if state.interrupted or state.skipped:
            raise sd_samplers_common.InterruptedException

@@ -115,12 +115,21 @@ class CFGDenoiser(torch.nn.Module):
        sigma_in = denoiser_params.sigma
        tensor = denoiser_params.text_cond
        uncond = denoiser_params.text_uncond
+        skip_uncond = False

-        if tensor.shape[1] == uncond.shape[1]:
-            if not is_edit_model:
-                cond_in = torch.cat([tensor, uncond])
-            else:
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
                cond_in = torch.cat([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = torch.cat([tensor, uncond])

            if shared.batch_cond_uncond:
                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
@@ -144,7 +153,13 @@ class CFGDenoiser(torch.nn.Module):

                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))

-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be

        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
        cfg_denoised_callback(denoised_params)
@@ -152,20 +167,21 @@ class CFGDenoiser(torch.nn.Module):
        devices.test_for_nans(x_out, "unet")

        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
        elif opts.live_preview_content == "Negative prompt":
            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])

-        if not is_edit_model:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-        else:
+        if is_edit_model:
            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

        if self.mask is not None:
            denoised = self.init_latent * self.mask + self.nmask * denoised

        self.step += 1
-
        return denoised


@@ -190,7 +206,7 @@ class TorchHijack:
            if noise.shape == x.shape:
                return noise

-        if x.device.type == 'mps':
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
            return torch.randn_like(x, device=devices.cpu).to(x.device)
        else:
            return torch.randn_like(x)
@@ -210,6 +226,7 @@ class KDiffusionSampler:
        self.eta = None
        self.config = None
        self.last_latent = None
+        self.s_min_uncond = None

        self.conditioning_key = sd_model.model.conditioning_key

@@ -244,6 +261,7 @@ class KDiffusionSampler:
        self.model_wrap_cfg.step = 0
        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)

        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

@@ -326,6 +344,7 @@ class KDiffusionSampler:
            'image_cond': image_conditioning, 
            'uncond': unconditional_conditioning, 
            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
        }

        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
@@ -359,7 +378,8 @@ class KDiffusionSampler:
            'cond': conditioning, 
            'image_cond': image_conditioning, 
            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

        return samples
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -4,6 +4,7 @@ import json
 import os
 import sys
 import time
+import requests

 from PIL import Image
 import gradio as gr
@@ -39,6 +40,7 @@ restricted_opts = {
    "outdir_grids",
    "outdir_txt2img_grids",
    "outdir_save",
+    "outdir_init_images"
 }

 ui_reorder_categories = [
@@ -54,6 +56,21 @@ ui_reorder_categories = [
    "scripts",
 ]

+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+    "gradio/glass",
+    "gradio/monochrome",
+    "gradio/seafoam",
+    "gradio/soft",
+    "freddyaboulton/dracula_revamped",
+    "gradio/dracula_test",
+    "abidlabs/dracula_test",
+    "abidlabs/pakistan",
+    "dawood/microsoft_windows",
+    "ysharma/steampunk"
+]
+
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access

 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -252,7 +269,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+    "save_init_img": OptionInfo(False, "Save init images when using img2img"),

    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -268,6 +285,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))

 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
@@ -283,6 +301,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))

 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -331,6 +351,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
 }))

 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -338,6 +359,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
+    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
 }))

 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -361,7 +383,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
 }))

 options_templates.update(options_section(('ui', "User interface"), {
@@ -382,11 +404,13 @@ options_templates.update(options_section(('ui', "User interface"), {
    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
    "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
    "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
    "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
    "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+    "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
 }))

 options_templates.update(options_section(('ui', "Live previews"), {
@@ -405,6 +429,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
@@ -600,6 +625,24 @@ clip_model = None

 progress_print_out = sys.stdout

+gradio_theme = gr.themes.Base()
+
+
+def reload_gradio_theme(theme_name=None):
+    global gradio_theme
+    if not theme_name:
+        theme_name = opts.gradio_theme
+
+    if theme_name == "Default":
+        gradio_theme = gr.themes.Default()
+    else:
+        try:
+            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+        except requests.exceptions.ConnectionError:
+            print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+            gradio_theme = gr.themes.Default()
+
+

 class TotalTQDM:
    def __init__(self):
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -72,16 +72,14 @@ class StyleDatabase:
        return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])

    def save_styles(self, path: str) -> None:
-        # Write to temporary file first, so we don't nuke the file if something goes wrong
-        fd, temp_path = tempfile.mkstemp(".csv")
+        # Always keep a backup file around
+        if os.path.exists(path):
+            shutil.copy(path, path + ".bak")
+
+        fd = os.open(path, os.O_RDWR|os.O_CREAT)
        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
            # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
            # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
            writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
            writer.writeheader()
            writer.writerows(style._asdict() for k,     style in self.styles.items())
-
-        # Always keep a backup file around
-        if os.path.exists(path):
-            shutil.move(path, path + ".bak")
-        shutil.move(temp_path, path)
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -161,7 +161,9 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
        params.subindex = 0
        filename = os.path.join(src, imagefile)
        try:
-            img = Image.open(filename).convert("RGB")
+            img = Image.open(filename)
+            img = ImageOps.exif_transpose(img)
+            img = img.convert("RGB")
        except Exception:
            continue

--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -233,6 +233,12 @@ class EmbeddingDatabase:
            self.load_from_dir(embdir)
            embdir.update()

+        # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
+        # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
+        sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
+        self.word_embeddings.clear()
+        self.word_embeddings.update(sorted_word_embeddings)
+
        displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
        if self.previously_displayed_embeddings != displayed_embeddings:
            self.previously_displayed_embeddings = displayed_embeddings
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -171,8 +171,8 @@ def create_seed_inputs(target_interface):
    with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
        seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')

        seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)

@@ -468,7 +468,7 @@ def create_ui():
                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")

                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")

                            if opts.dimensions_and_batch_together:
                                with gr.Column(elem_id="txt2img_column_batch"):
@@ -1204,7 +1204,7 @@ def create_ui():

            with gr.Column(elem_id='ti_gallery_container'):
                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
                ti_progress = gr.HTML(elem_id="ti_progress", value="")
                ti_outcome = gr.HTML(elem_id="ti_error", value="")

@@ -1565,7 +1565,7 @@ def create_ui():
    for _interface, label, _ifid in interfaces:
        shared.tab_names.append(label)

-    with gr.Blocks(analytics_enabled=False, title="Stable Diffusion") as demo:
+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
        with gr.Row(elem_id="quicksettings", variant="compact"):
            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                component = create_setting_component(k, is_quicksettings=True)
@@ -1705,7 +1705,7 @@ def create_ui():
                if init_field is not None:
                    init_field(saved_value)

-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
            apply_field(x, 'visible')

        if type(x) == gr.Slider:
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -125,7 +125,7 @@ Requested path was: {f}

    with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
        with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)

        generation_info = None
        with gr.Column():
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown):

    def get_block_name(self):
        return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+    """Same as gr.Dropdown but allows editing value"""
+    def __init__(self, **kwargs):
+        super().__init__(allow_custom_value=True, **kwargs)
+
+    def get_block_name(self):
+        return "dropdown"
+
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -129,7 +129,7 @@ def normalize_git_url(url):
    return url


-def install_extension_from_url(dirname, url):
+def install_extension_from_url(dirname, url, branch_name=None):
    check_access()

    assert url, 'No URL specified'
@@ -150,10 +150,17 @@ def install_extension_from_url(dirname, url):

    try:
        shutil.rmtree(tmpdir, True)
-        with git.Repo.clone_from(url, tmpdir) as repo:
-            repo.remote().fetch()
-            for submodule in repo.submodules:
-                submodule.update()
+        if not branch_name:
+            # if no branch is specified, use the default branch
+            with git.Repo.clone_from(url, tmpdir) as repo:
+                repo.remote().fetch()
+                for submodule in repo.submodules:
+                    submodule.update()
+        else:
+            with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+                repo.remote().fetch()
+                for submodule in repo.submodules:
+                    submodule.update()
        try:
            os.rename(tmpdir, target_dir)
        except OSError as err:
@@ -376,13 +383,14 @@ def create_ui():

            with gr.TabItem("Install from URL"):
                install_url = gr.Text(label="URL for extension's git repository")
+                install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
                install_button = gr.Button(value="Install", variant="primary")
                install_result = gr.HTML(elem_id="extension_install_result")

                install_button.click(
                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
-                    inputs=[install_dirname, install_url],
+                    inputs=[install_dirname, install_url, install_branch],
                    outputs=[extensions_table, install_result],
                )

--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -13,7 +13,7 @@ def create_ui():
                    extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")

                with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
-                    image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+                    image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")

                with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
                    extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")