split codebase into multiple files; to anyone this affects negatively: sorry

modules/gfpgan_model.py

@@ -0,0 +1,58 @@
+import os
+import sys
+import traceback
+
+from modules.paths import script_path
+from modules.shared import cmd_opts
+
+
+def gfpgan_model_path():
+    places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
+    files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
+    found = [x for x in files if os.path.exists(x)]
+
+    if len(found) == 0:
+        raise Exception("GFPGAN model not found in paths: " + ", ".join(files))
+
+    return found[0]
+
+
+loaded_gfpgan_model = None
+
+
+def gfpgan():
+    global loaded_gfpgan_model
+
+    if loaded_gfpgan_model is None and gfpgan_constructor is not None:
+        loaded_gfpgan_model = gfpgan_constructor(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+
+    return loaded_gfpgan_model
+
+
+def gfpgan_fix_faces(np_image):
+    np_image_bgr = np_image[:, :, ::-1]
+    cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+    np_image = gfpgan_output_bgr[:, :, ::-1]
+
+    return np_image
+
+
+have_gfpgan = False
+gfpgan_constructor = None
+
+def setup_gfpgan():
+    try:
+        gfpgan_model_path()
+
+        if os.path.exists(cmd_opts.gfpgan_dir):
+            sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
+        from gfpgan import GFPGANer
+
+        global have_gfpgan
+        have_gfpgan = True
+
+        global gfpgan_constructor
+        gfpgan_constructor = GFPGANer
+    except Exception:
+        print("Error setting up GFPGAN:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)

modules/images.py

@@ -0,0 +1,290 @@
+import math
+import os
+from collections import namedtuple
+import re
+
+import numpy as np
+from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
+
+from modules.shared import opts
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+
+
+def image_grid(imgs, batch_size=1, rows=None):
+    if rows is None:
+        if opts.n_rows > 0:
+            rows = opts.n_rows
+        elif opts.n_rows == 0:
+            rows = batch_size
+        else:
+            rows = math.sqrt(len(imgs))
+            rows = round(rows)
+
+    cols = math.ceil(len(imgs) / rows)
+
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+
+    return grid
+
+
+Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
+
+
+def split_grid(image, tile_w=512, tile_h=512, overlap=64):
+    w = image.width
+    h = image.height
+
+    now = tile_w - overlap  # non-overlap width
+    noh = tile_h - overlap
+
+    cols = math.ceil((w - overlap) / now)
+    rows = math.ceil((h - overlap) / noh)
+
+    grid = Grid([], tile_w, tile_h, w, h, overlap)
+    for row in range(rows):
+        row_images = []
+
+        y = row * noh
+
+        if y + tile_h >= h:
+            y = h - tile_h
+
+        for col in range(cols):
+            x = col * now
+
+            if x+tile_w >= w:
+                x = w - tile_w
+
+            tile = image.crop((x, y, x + tile_w, y + tile_h))
+
+            row_images.append([x, tile_w, tile])
+
+        grid.tiles.append([y, tile_h, row_images])
+
+    return grid
+
+
+def combine_grid(grid):
+    def make_mask_image(r):
+        r = r * 255 / grid.overlap
+        r = r.astype(np.uint8)
+        return Image.fromarray(r, 'L')
+
+    mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+    mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+
+    combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
+    for y, h, row in grid.tiles:
+        combined_row = Image.new("RGB", (grid.image_w, h))
+        for x, w, tile in row:
+            if x == 0:
+                combined_row.paste(tile, (0, 0))
+                continue
+
+            combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
+            combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
+
+        if y == 0:
+            combined_image.paste(combined_row, (0, 0))
+            continue
+
+        combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
+        combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
+
+    return combined_image
+
+
+class GridAnnotation:
+    def __init__(self, text='', is_active=True):
+        self.text = text
+        self.is_active = is_active
+        self.size = None
+
+
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
+    def wrap(drawing, text, font, line_length):
+        lines = ['']
+        for word in text.split():
+            line = f'{lines[-1]} {word}'.strip()
+            if drawing.textlength(line, font=font) <= line_length:
+                lines[-1] = line
+            else:
+                lines.append(word)
+        return lines
+
+    def draw_texts(drawing, draw_x, draw_y, lines):
+        for i, line in enumerate(lines):
+            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
+
+            if not line.is_active:
+                drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)
+
+            draw_y += line.size[1] + line_spacing
+
+    fontsize = (width + height) // 25
+    line_spacing = fontsize // 2
+    fnt = ImageFont.truetype(opts.font, fontsize)
+    color_active = (0, 0, 0)
+    color_inactive = (153, 153, 153)
+
+    pad_left = width * 3 // 4 if len(ver_texts) > 0 else 0
+
+    cols = im.width // width
+    rows = im.height // height
+
+    assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
+    assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
+
+    calc_img = Image.new("RGB", (1, 1), "white")
+    calc_d = ImageDraw.Draw(calc_img)
+
+    for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
+        items = [] + texts
+        texts.clear()
+
+        for line in items:
+            wrapped = wrap(calc_d, line.text, fnt, allowed_width)
+            texts += [GridAnnotation(x, line.is_active) for x in wrapped]
+
+        for line in texts:
+            bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
+            line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
+
+    hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
+    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
+
+    pad_top = max(hor_text_heights) + line_spacing * 2
+
+    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
+    result.paste(im, (pad_left, pad_top))
+
+    d = ImageDraw.Draw(result)
+
+    for col in range(cols):
+        x = pad_left + width * col + width / 2
+        y = pad_top / 2 - hor_text_heights[col] / 2
+
+        draw_texts(d, x, y, hor_texts[col])
+
+    for row in range(rows):
+        x = pad_left / 2
+        y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
+
+        draw_texts(d, x, y, ver_texts[row])
+
+    return result
+
+
+def draw_prompt_matrix(im, width, height, all_prompts):
+    prompts = all_prompts[1:]
+    boundary = math.ceil(len(prompts) / 2)
+
+    prompts_horiz = prompts[:boundary]
+    prompts_vert = prompts[boundary:]
+
+    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
+    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
+
+    return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
+
+
+def resize_image(resize_mode, im, width, height):
+    if resize_mode == 0:
+        res = im.resize((width, height), resample=LANCZOS)
+    elif resize_mode == 1:
+        ratio = width / height
+        src_ratio = im.width / im.height
+
+        src_w = width if ratio > src_ratio else im.width * height // im.height
+        src_h = height if ratio <= src_ratio else im.height * width // im.width
+
+        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        res = Image.new("RGB", (width, height))
+        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+    else:
+        ratio = width / height
+        src_ratio = im.width / im.height
+
+        src_w = width if ratio < src_ratio else im.width * height // im.height
+        src_h = height if ratio >= src_ratio else im.height * width // im.width
+
+        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        res = Image.new("RGB", (width, height))
+        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
+        if ratio < src_ratio:
+            fill_height = height // 2 - src_h // 2
+            res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
+            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
+        elif ratio > src_ratio:
+            fill_width = width // 2 - src_w // 2
+            res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
+            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
+
+    return res
+
+
+invalid_filename_chars = '<>:"/\\|?*\n'
+
+
+def sanitize_filename_part(text):
+    return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
+
+
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False):
+    if short_filename or prompt is None or seed is None:
+        file_decoration = ""
+    elif opts.save_to_dirs:
+        file_decoration = f"-{seed}"
+    else:
+        file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
+
+    if extension == 'png' and opts.enable_pnginfo and info is not None:
+        pnginfo = PngImagePlugin.PngInfo()
+        pnginfo.add_text("parameters", info)
+    else:
+        pnginfo = None
+
+    if opts.save_to_dirs and not no_prompt:
+        words = re.findall(r'\w+', prompt or "")
+        if len(words) == 0:
+            words = ["empty"]
+
+        dirname = " ".join(words[0:opts.save_to_dirs_prompt_len])
+        path = os.path.join(path, dirname)
+
+    os.makedirs(path, exist_ok=True)
+
+    filecount = len([x for x in os.listdir(path) if os.path.splitext(x)[1] == '.' + extension])
+    fullfn = "a.png"
+    fullfn_without_extension = "a"
+    for i in range(100):
+        fn = f"{filecount:05}" if basename == '' else f"{basename}-{filecount: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
+
+    image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
+
+    target_side_length = 4000
+    oversize = image.width > target_side_length or image.height > target_side_length
+    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
+        ratio = image.width / image.height
+
+        if oversize and ratio > 1:
+            image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
+        elif oversize:
+            image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
+
+        image.save(f"{fullfn_without_extension}.jpg", quality=opts.jpeg_quality, pnginfo=pnginfo)
+
+    if opts.save_txt and info is not None:
+        with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
+            file.write(info + "\n")
+

modules/img2img.py

@@ -0,0 +1,133 @@
+import math
+from PIL import Image
+
+from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
+from modules.shared import opts, state
+import modules.shared as shared
+import modules.processing as processing
+from modules.ui import plaintext_to_html
+import modules.images as images
+
+def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
+    is_inpaint = mode == 1
+    is_loopback = mode == 2
+    is_upscale = mode == 3
+
+    if is_inpaint:
+        image = init_img_with_mask['image']
+        mask = init_img_with_mask['mask']
+    else:
+        image = init_img
+        mask = None
+
+    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
+
+    p = StableDiffusionProcessingImg2Img(
+        sd_model=shared.sd_model,
+        outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
+        outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
+        prompt=prompt,
+        seed=seed,
+        sampler_index=sampler_index,
+        batch_size=batch_size,
+        n_iter=n_iter,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        width=width,
+        height=height,
+        prompt_matrix=prompt_matrix,
+        use_GFPGAN=use_GFPGAN,
+        init_images=[image],
+        mask=mask,
+        mask_blur=mask_blur,
+        inpainting_fill=inpainting_fill,
+        resize_mode=resize_mode,
+        denoising_strength=denoising_strength,
+        inpaint_full_res=inpaint_full_res,
+        extra_generation_params={"Denoising Strength": denoising_strength}
+    )
+
+    if is_loopback:
+        output_images, info = None, None
+        history = []
+        initial_seed = None
+        initial_info = None
+
+        for i in range(n_iter):
+            p.n_iter = 1
+            p.batch_size = 1
+            p.do_not_save_grid = True
+
+            state.job = f"Batch {i + 1} out of {n_iter}"
+            processed = process_images(p)
+
+            if initial_seed is None:
+                initial_seed = processed.seed
+                initial_info = processed.info
+
+            p.init_images = [processed.images[0]]
+            p.seed = processed.seed + 1
+            p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
+            history.append(processed.images[0])
+
+        grid = images.image_grid(history, batch_size, rows=1)
+
+        images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
+
+        processed = Processed(p, history, initial_seed, initial_info)
+
+    elif is_upscale:
+        initial_seed = None
+        initial_info = None
+
+        upscaler = shared.sd_upscalers.get(upscaler_name, next(iter(shared.sd_upscalers.values())))
+        img = upscaler(init_img)
+
+        processing.torch_gc()
+
+        grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
+
+        p.n_iter = 1
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+
+        work = []
+        work_results = []
+
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                work.append(tiledata[2])
+
+        batch_count = math.ceil(len(work) / p.batch_size)
+        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
+
+        for i in range(batch_count):
+            p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
+
+            state.job = f"Batch {i + 1} out of {batch_count}"
+            processed = process_images(p)
+
+            if initial_seed is None:
+                initial_seed = processed.seed
+                initial_info = processed.info
+
+            p.seed = processed.seed + 1
+            work_results += processed.images
+
+        image_index = 0
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+                image_index += 1
+
+        combined_image = images.combine_grid(grid)
+
+        if opts.samples_save:
+            images.save_image(combined_image, p.outpath_samples, "", initial_seed, prompt, opts.grid_format, info=initial_info)
+
+        processed = Processed(p, [combined_image], initial_seed, initial_info)
+
+    else:
+        processed = process_images(p)
+
+    return processed.images, processed.js(), plaintext_to_html(processed.info)

modules/lowvram.py

@@ -0,0 +1,73 @@
+import torch
+
+module_in_gpu = None
+cpu = torch.device("cpu")
+gpu = torch.device("cuda")
+device = gpu if torch.cuda.is_available() else cpu
+
+
+def setup_for_low_vram(sd_model, use_medvram):
+    parents = {}
+
+    def send_me_to_gpu(module, _):
+        """send this module to GPU; send whatever tracked module was previous in GPU to CPU;
+        we add this as forward_pre_hook to a lot of modules and this way all but one of them will
+        be in CPU
+        """
+        global module_in_gpu
+
+        module = parents.get(module, module)
+
+        if module_in_gpu == module:
+            return
+
+        if module_in_gpu is not None:
+            module_in_gpu.to(cpu)
+
+        module.to(gpu)
+        module_in_gpu = module
+
+    # see below for register_forward_pre_hook;
+    # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
+    # useless here, and we just replace those methods
+    def first_stage_model_encode_wrap(self, encoder, x):
+        send_me_to_gpu(self, None)
+        return encoder(x)
+
+    def first_stage_model_decode_wrap(self, decoder, z):
+        send_me_to_gpu(self, None)
+        return decoder(z)
+
+    # remove three big modules, cond, first_stage, and unet from the model and then
+    # 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.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
+
+    # register hooks for those the first two models
+    sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+    sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
+    sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
+    sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+    parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
+
+    if use_medvram:
+        sd_model.model.register_forward_pre_hook(send_me_to_gpu)
+    else:
+        diff_model = sd_model.model.diffusion_model
+
+        # the third remaining model is still too big for 4 GB, so we also do the same for its submodules
+        # 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)
+        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_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
+        for block in diff_model.input_blocks:
+            block.register_forward_pre_hook(send_me_to_gpu)
+        diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
+        for block in diff_model.output_blocks:
+            block.register_forward_pre_hook(send_me_to_gpu)

modules/paths.py

@@ -0,0 +1,21 @@
+import argparse
+import os
+import sys
+
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+sys.path.insert(0, script_path)
+
+# use current directory as SD dir if it has related files, otherwise parent dir of script as stated in guide
+sd_path = os.path.abspath('.') if os.path.exists('./ldm/models/diffusion/ddpm.py') else os.path.dirname(script_path)
+
+# add parent directory to path; this is where Stable diffusion repo should be
+path_dirs = [
+    (sd_path, 'ldm', 'Stable Diffusion'),
+    (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers')
+]
+for d, must_exist, what in path_dirs:
+    must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
+    if not os.path.exists(must_exist_path):
+        print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
+    else:
+        sys.path.append(os.path.join(script_path, d))

modules/processing.py

@@ -0,0 +1,409 @@
+import contextlib
+import json
+import math
+import os
+import sys
+
+import torch
+import numpy as np
+from PIL import Image, ImageFilter, ImageOps
+import random
+
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
+import modules.gfpgan_model as gfpgan
+import modules.images as images
+
+# some of those options should not be changed at all because they would break the model, so I removed them from options.
+opt_C = 4
+opt_f = 8
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+class StableDiffusionProcessing:
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+        self.sd_model = sd_model
+        self.outpath_samples: str = outpath_samples
+        self.outpath_grids: str = outpath_grids
+        self.prompt: str = prompt
+        self.negative_prompt: str = (negative_prompt or "")
+        self.seed: int = seed
+        self.sampler_index: int = sampler_index
+        self.batch_size: int = batch_size
+        self.n_iter: int = n_iter
+        self.steps: int = steps
+        self.cfg_scale: float = cfg_scale
+        self.width: int = width
+        self.height: int = height
+        self.prompt_matrix: bool = prompt_matrix
+        self.use_GFPGAN: bool = use_GFPGAN
+        self.do_not_save_samples: bool = do_not_save_samples
+        self.do_not_save_grid: bool = do_not_save_grid
+        self.extra_generation_params: dict = extra_generation_params
+        self.overlay_images = overlay_images
+        self.paste_to = None
+
+    def init(self):
+        pass
+
+    def sample(self, x, conditioning, unconditional_conditioning):
+        raise NotImplementedError()
+
+
+class Processed:
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed, info):
+        self.images = images_list
+        self.prompt = p.prompt
+        self.seed = seed
+        self.info = info
+        self.width = p.width
+        self.height = p.height
+        self.sampler = samplers[p.sampler_index].name
+        self.cfg_scale = p.cfg_scale
+        self.steps = p.steps
+
+    def js(self):
+        obj = {
+            "prompt": self.prompt,
+            "seed": int(self.seed),
+            "width": self.width,
+            "height": self.height,
+            "sampler": self.sampler,
+            "cfg_scale": self.cfg_scale,
+            "steps": self.steps,
+        }
+
+        return json.dumps(obj)
+
+
+def create_random_tensors(shape, seeds):
+    xs = []
+    for seed in seeds:
+        torch.manual_seed(seed)
+
+        # randn results depend on device; gpu and cpu get different results for same seed;
+        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
+        # but the original script had it like this so I do not dare change it for now because
+        # it will break everyone's seeds.
+        xs.append(torch.randn(shape, device=shared.device))
+    x = torch.stack(xs)
+    return x
+
+
+def process_images(p: StableDiffusionProcessing) -> Processed:
+    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
+
+    prompt = p.prompt
+
+    assert p.prompt is not None
+    torch_gc()
+
+    seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
+
+    os.makedirs(p.outpath_samples, exist_ok=True)
+    os.makedirs(p.outpath_grids, exist_ok=True)
+
+    comments = []
+
+    prompt_matrix_parts = []
+    if p.prompt_matrix:
+        all_prompts = []
+        prompt_matrix_parts = prompt.split("|")
+        combination_count = 2 ** (len(prompt_matrix_parts) - 1)
+        for combination_num in range(combination_count):
+            selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
+
+            if opts.prompt_matrix_add_to_start:
+                selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
+            else:
+                selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
+
+            all_prompts.append(", ".join(selected_prompts))
+
+        p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
+        all_seeds = len(all_prompts) * [seed]
+
+        print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
+    else:
+        all_prompts = p.batch_size * p.n_iter * [prompt]
+        all_seeds = [seed + x for x in range(len(all_prompts))]
+
+    def infotext(iteration=0, position_in_batch=0):
+        generation_params = {
+            "Steps": p.steps,
+            "Sampler": samplers[p.sampler_index].name,
+            "CFG scale": p.cfg_scale,
+            "Seed": all_seeds[position_in_batch + iteration * p.batch_size],
+            "GFPGAN": ("GFPGAN" if p.use_GFPGAN else None)
+        }
+
+        if p.extra_generation_params is not None:
+            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])
+
+        return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
+
+    if os.path.exists(cmd_opts.embeddings_dir):
+        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
+
+    output_images = []
+    precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
+    ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
+    with torch.no_grad(), precision_scope("cuda"), ema_scope():
+        p.init()
+
+        for n in range(p.n_iter):
+            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]
+
+            uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
+            c = p.sd_model.get_learned_conditioning(prompts)
+
+            if len(model_hijack.comments) > 0:
+                comments += model_hijack.comments
+
+            # we manually generate all input noises because each one should have a specific seed
+            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
+
+            if p.n_iter > 1:
+                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+
+            samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
+
+            x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
+            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+            for i, x_sample in enumerate(x_samples_ddim):
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+
+                if p.use_GFPGAN:
+                    torch_gc()
+
+                    x_sample = gfpgan.gfpgan_fix_faces(x_sample)
+
+                image = Image.fromarray(x_sample)
+
+                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')
+
+                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))
+
+                output_images.append(image)
+
+        unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
+        if not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            return_grid = opts.return_grid
+
+            if p.prompt_matrix:
+                grid = images.image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
+
+                try:
+                    grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
+                except Exception:
+                    import traceback
+                    print("Error creating prompt_matrix text:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+
+                return_grid = True
+            else:
+                grid = images.image_grid(output_images, p.batch_size)
+
+            if return_grid:
+                output_images.insert(0, grid)
+
+            if opts.grid_save:
+                images.save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
+
+    torch_gc()
+    return Processed(p, output_images, seed, infotext())
+
+
+class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+    sampler = None
+
+    def init(self):
+        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
+
+    def sample(self, x, conditioning, unconditional_conditioning):
+        samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+        return samples_ddim
+
+
+def get_crop_region(mask, pad=0):
+    h, w = mask.shape
+
+    crop_left = 0
+    for i in range(w):
+        if not (mask[:, i] == 0).all():
+            break
+        crop_left += 1
+
+    crop_right = 0
+    for i in reversed(range(w)):
+        if not (mask[:, i] == 0).all():
+            break
+        crop_right += 1
+
+    crop_top = 0
+    for i in range(h):
+        if not (mask[i] == 0).all():
+            break
+        crop_top += 1
+
+    crop_bottom = 0
+    for i in reversed(range(h)):
+        if not (mask[i] == 0).all():
+            break
+        crop_bottom += 1
+
+    return (
+        int(max(crop_left-pad, 0)),
+        int(max(crop_top-pad, 0)),
+        int(min(w - crop_right + pad, w)),
+        int(min(h - crop_bottom + pad, h))
+    )
+
+
+def fill(image, mask):
+    image_mod = Image.new('RGBA', (image.width, image.height))
+
+    image_masked = Image.new('RGBa', (image.width, image.height))
+    image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
+
+    image_masked = image_masked.convert('RGBa')
+
+    for radius, repeats in [(64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
+        blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
+        for _ in range(repeats):
+            image_mod.alpha_composite(blurred)
+
+    return image_mod.convert("RGB")
+
+
+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, **kwargs):
+        super().__init__(**kwargs)
+
+        self.init_images = init_images
+        self.resize_mode: int = resize_mode
+        self.denoising_strength: float = denoising_strength
+        self.init_latent = None
+        self.image_mask = mask
+        self.mask_for_overlay = None
+        self.mask_blur = mask_blur
+        self.inpainting_fill = inpainting_fill
+        self.inpaint_full_res = inpaint_full_res
+        self.mask = None
+        self.nmask = None
+
+    def init(self):
+        self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        crop_region = None
+
+        if self.image_mask is not None:
+            if self.mask_blur > 0:
+                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
+
+            if self.inpaint_full_res:
+                self.mask_for_overlay = self.image_mask
+                mask = self.image_mask.convert('L')
+                crop_region = get_crop_region(np.array(mask), 64)
+                x1, y1, x2, y2 = crop_region
+
+                mask = mask.crop(crop_region)
+                self.image_mask = images.resize_image(2, mask, self.width, self.height)
+                self.paste_to = (x1, y1, x2-x1, y2-y1)
+            else:
+                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
+                self.mask_for_overlay = self.image_mask
+
+            self.overlay_images = []
+
+        imgs = []
+        for img in self.init_images:
+            image = img.convert("RGB")
+
+            if crop_region is None:
+                image = images.resize_image(self.resize_mode, image, self.width, self.height)
+
+            if self.image_mask is not None:
+                if self.inpainting_fill != 1:
+                    image = fill(image, self.mask_for_overlay)
+
+                image_masked = Image.new('RGBa', (image.width, image.height))
+                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+
+                self.overlay_images.append(image_masked.convert('RGBA'))
+
+            if crop_region is not None:
+                image = image.crop(crop_region)
+                image = images.resize_image(2, image, self.width, self.height)
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = np.moveaxis(image, 2, 0)
+
+            imgs.append(image)
+
+        if len(imgs) == 1:
+            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
+        elif len(imgs) <= self.batch_size:
+            self.batch_size = len(imgs)
+            batch_images = np.array(imgs)
+        else:
+            raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
+
+        image = torch.from_numpy(batch_images)
+        image = 2. * image - 1.
+        image = image.to(shared.device)
+
+        self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+
+        if self.image_mask is not None:
+            latmask = self.image_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
+            latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
+            latmask = latmask[0]
+            latmask = np.tile(latmask[None], (4, 1, 1))
+
+            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
+            self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
+
+            if self.inpainting_fill == 2:
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [self.seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask
+            elif self.inpainting_fill == 3:
+                self.init_latent = self.init_latent * self.mask
+
+    def sample(self, x, conditioning, unconditional_conditioning):
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
+
+        if self.mask is not None:
+            samples = samples * self.nmask + self.init_latent * self.mask
+
+        return samples

modules/realesrgan_model.py

@@ -0,0 +1,70 @@
+import sys
+import traceback
+from collections import namedtuple
+import numpy as np
+from PIL import Image
+
+from modules.shared import cmd_opts
+
+RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
+
+realesrgan_models = []
+have_realesrgan = False
+RealESRGANer_constructor = None
+
+def setup_realesrgan():
+    global realesrgan_models
+    global have_realesrgan
+    global RealESRGANer_constructor
+
+    try:
+        from basicsr.archs.rrdbnet_arch import RRDBNet
+        from realesrgan import RealESRGANer
+        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+        realesrgan_models = [
+            RealesrganModelInfo(
+                name="Real-ESRGAN 4x plus",
+                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+                netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+            ),
+            RealesrganModelInfo(
+                name="Real-ESRGAN 4x plus anime 6B",
+                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+                netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+            ),
+            RealesrganModelInfo(
+                name="Real-ESRGAN 2x plus",
+                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+                netscale=2, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+            ),
+        ]
+        have_realesrgan = True
+        RealESRGANer_constructor = RealESRGANer
+
+    except Exception:
+        print("Error importing Real-ESRGAN:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+        realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
+        have_realesrgan = False
+
+
+def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
+    if not have_realesrgan or RealESRGANer_constructor is None:
+        return image
+
+    info = realesrgan_models[RealESRGAN_model_index]
+
+    model = info.model()
+    upsampler = RealESRGANer_constructor(
+        scale=info.netscale,
+        model_path=info.location,
+        model=model,
+        half=not cmd_opts.no_half
+    )
+
+    upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
+
+    image = Image.fromarray(upsampled)
+    return image

modules/scripts.py

@@ -0,0 +1,53 @@
+import os
+import sys
+import traceback
+
+import gradio as gr
+
+class Script:
+    filename = None
+
+    def title(self):
+        raise NotImplementedError()
+
+
+scripts = []
+
+
+def load_scripts(basedir, globs):
+    for filename in os.listdir(basedir):
+        path = os.path.join(basedir, filename)
+
+        if not os.path.isfile(path):
+            continue
+
+        with open(path, "r", encoding="utf8") as file:
+            text = file.read()
+
+        from types import ModuleType
+        compiled = compile(text, path, 'exec')
+        module = ModuleType(filename)
+        module.__dict__.update(globs)
+        exec(compiled, module.__dict__)
+
+        for key, item in module.__dict__.items():
+            if type(item) == type and issubclass(item, Script):
+                item.filename = path
+
+                scripts.append(item)
+
+
+def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
+    try:
+        res = func()
+        return res
+    except Exception:
+        print(f"Error calling: {filename/funcname}", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    return default
+
+def setup_ui():
+    titles = [wrap_call(script.title, script.filename, "title") for script in scripts]
+
+    gr.Dropdown(options=[""] + titles, value="", type="index")

modules/sd_hijack.py

@@ -0,0 +1,208 @@
+import os
+import sys
+import traceback
+import torch
+import numpy as np
+
+from modules.shared import opts, device
+
+
+class StableDiffusionModelHijack:
+    ids_lookup = {}
+    word_embeddings = {}
+    word_embeddings_checksums = {}
+    fixes = None
+    comments = []
+    dir_mtime = None
+
+    def load_textual_inversion_embeddings(self, dirname, model):
+        mt = os.path.getmtime(dirname)
+        if self.dir_mtime is not None and mt <= self.dir_mtime:
+            return
+
+        self.dir_mtime = mt
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+
+        tokenizer = model.cond_stage_model.tokenizer
+
+        def const_hash(a):
+            r = 0
+            for v in a:
+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+            return r
+
+        def process_file(path, filename):
+            name = os.path.splitext(filename)[0]
+
+            data = torch.load(path)
+            param_dict = data['string_to_param']
+            if hasattr(param_dict, '_parameters'):
+                param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
+            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+            emb = next(iter(param_dict.items()))[1]
+            self.word_embeddings[name] = emb.detach()
+            self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1))&0xffff:04x}'
+
+            ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
+
+            first_id = ids[0]
+            if first_id not in self.ids_lookup:
+                self.ids_lookup[first_id] = []
+            self.ids_lookup[first_id].append((ids, name))
+
+        for fn in os.listdir(dirname):
+            try:
+                process_file(os.path.join(dirname, fn), fn)
+            except Exception:
+                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                continue
+
+        print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
+
+    def hijack(self, m):
+        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+
+        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+        m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+
+class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
+    def __init__(self, wrapped, hijack):
+        super().__init__()
+        self.wrapped = wrapped
+        self.hijack = hijack
+        self.tokenizer = wrapped.tokenizer
+        self.max_length = wrapped.max_length
+        self.token_mults = {}
+
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        for text, ident in tokens_with_parens:
+            mult = 1.0
+            for c in text:
+                if c == '[':
+                    mult /= 1.1
+                if c == ']':
+                    mult *= 1.1
+                if c == '(':
+                    mult *= 1.1
+                if c == ')':
+                    mult /= 1.1
+
+            if mult != 1.0:
+                self.token_mults[ident] = mult
+
+    def forward(self, text):
+        self.hijack.fixes = []
+        self.hijack.comments = []
+        remade_batch_tokens = []
+        id_start = self.wrapped.tokenizer.bos_token_id
+        id_end = self.wrapped.tokenizer.eos_token_id
+        maxlen = self.wrapped.max_length - 2
+        used_custom_terms = []
+
+        cache = {}
+        batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
+        batch_multipliers = []
+        for tokens in batch_tokens:
+            tuple_tokens = tuple(tokens)
+
+            if tuple_tokens in cache:
+                remade_tokens, fixes, multipliers = cache[tuple_tokens]
+            else:
+                fixes = []
+                remade_tokens = []
+                multipliers = []
+                mult = 1.0
+
+                i = 0
+                while i < len(tokens):
+                    token = tokens[i]
+
+                    possible_matches = self.hijack.ids_lookup.get(token, None)
+
+                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
+                    if mult_change is not None:
+                        mult *= mult_change
+                    elif possible_matches is None:
+                        remade_tokens.append(token)
+                        multipliers.append(mult)
+                    else:
+                        found = False
+                        for ids, word in possible_matches:
+                            if tokens[i:i+len(ids)] == ids:
+                                emb_len = int(self.hijack.word_embeddings[word].shape[0])
+                                fixes.append((len(remade_tokens), word))
+                                remade_tokens += [0] * emb_len
+                                multipliers += [mult] * emb_len
+                                i += len(ids) - 1
+                                found = True
+                                used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
+                                break
+
+                        if not found:
+                            remade_tokens.append(token)
+                            multipliers.append(mult)
+
+                    i += 1
+
+                if len(remade_tokens) > maxlen - 2:
+                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+                    ovf = remade_tokens[maxlen - 2:]
+                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
+                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+
+                    self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+                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))
+            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+            remade_batch_tokens.append(remade_tokens)
+            self.hijack.fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+
+        if len(used_custom_terms) > 0:
+            self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+
+        tokens = torch.asarray(remade_batch_tokens).to(device)
+        outputs = self.wrapped.transformer(input_ids=tokens)
+        z = outputs.last_hidden_state
+
+        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+        batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
+        original_mean = z.mean()
+        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        new_mean = z.mean()
+        z *= original_mean / new_mean
+
+        return z
+
+
+class EmbeddingsWithFixes(torch.nn.Module):
+    def __init__(self, wrapped, embeddings):
+        super().__init__()
+        self.wrapped = wrapped
+        self.embeddings = embeddings
+
+    def forward(self, input_ids):
+        batch_fixes = self.embeddings.fixes
+        self.embeddings.fixes = None
+
+        inputs_embeds = self.wrapped(input_ids)
+
+        if batch_fixes is not None:
+            for fixes, tensor in zip(batch_fixes, inputs_embeds):
+                for offset, word in fixes:
+                    emb = self.embeddings.word_embeddings[word]
+                    emb_len = min(tensor.shape[0]-offset, emb.shape[0])
+                    tensor[offset:offset+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
+
+        return inputs_embeds
+
+
+model_hijack = StableDiffusionModelHijack()

modules/sd_samplers.py

@@ -0,0 +1,137 @@
+from collections import namedtuple
+import torch
+import tqdm
+
+import k_diffusion.sampling
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+
+from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
+samplers = [
+    *[SamplerData(x[0], lambda model, funcname=x[1]: KDiffusionSampler(funcname, model)) for x in [
+        ('Euler a', 'sample_euler_ancestral'),
+        ('Euler', 'sample_euler'),
+        ('LMS', 'sample_lms'),
+        ('Heun', 'sample_heun'),
+        ('DPM2', 'sample_dpm_2'),
+        ('DPM2 a', 'sample_dpm_2_ancestral'),
+    ] if hasattr(k_diffusion.sampling, x[1])],
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(DDIMSample, model)),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(PLMSSampler, model)),
+]
+samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
+
+
+def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
+    if sampler_wrapper.mask is not None:
+        img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
+        x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
+
+    return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
+
+
+class VanillaStableDiffusionSampler:
+    def __init__(self, constructor, sd_model):
+        self.sampler = constructor(sd_model)
+        self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else None
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
+        t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
+
+        # existing code fails with cetin step counts, like 9
+        try:
+            self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
+        except Exception:
+            self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
+
+        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+        self.sampler.p_sample_ddim = lambda x_dec, cond, ts, *args, **kwargs: p_sample_ddim_hook(self, x_dec, cond, ts, *args, **kwargs)
+        self.mask = p.mask
+        self.nmask = p.nmask
+        self.init_latent = p.init_latent
+
+        samples = 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):
+        samples_ddim, _ = self.sampler.sample(S=p.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)
+        return samples_ddim
+
+
+class CFGDenoiser(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+
+    def forward(self, x, sigma, uncond, cond, cond_scale):
+        if shared.batch_cond_uncond:
+            x_in = torch.cat([x] * 2)
+            sigma_in = torch.cat([sigma] * 2)
+            cond_in = torch.cat([uncond, cond])
+            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+            denoised = uncond + (cond - uncond) * cond_scale
+        else:
+            uncond = self.inner_model(x, sigma, cond=uncond)
+            cond = self.inner_model(x, sigma, cond=cond)
+            denoised = uncond + (cond - uncond) * cond_scale
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        return denoised
+
+
+def extended_trange(*args, **kwargs):
+    for x in tqdm.trange(*args, desc=state.job, **kwargs):
+        if state.interrupted:
+            break
+
+        yield x
+
+
+class KDiffusionSampler:
+    def __init__(self, funcname, sd_model):
+        self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
+        self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
+        t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
+        sigmas = self.model_wrap.get_sigmas(p.steps)
+        noise = noise * sigmas[p.steps - t_enc - 1]
+
+        xi = x + noise
+
+        sigma_sched = sigmas[p.steps - t_enc - 1:]
+
+        self.model_wrap_cfg.mask = p.mask
+        self.model_wrap_cfg.nmask = p.nmask
+        self.model_wrap_cfg.init_latent = p.init_latent
+
+        if hasattr(k_diffusion.sampling, 'trange'):
+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+
+        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
+
+    def sample(self, p, x, conditioning, unconditional_conditioning):
+        sigmas = self.model_wrap.get_sigmas(p.steps)
+        x = x * sigmas[0]
+
+        if hasattr(k_diffusion.sampling, 'trange'):
+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
+
+        samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
+        return samples_ddim
+

modules/shared.py

@@ -0,0 +1,121 @@
+import argparse
+import json
+import os
+import gradio as gr
+import torch
+
+from modules.paths import script_path, sd_path
+
+config_filename = "config.json"
+
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+if not os.path.exists(sd_model_file):
+    sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a little speed for low VRM usage")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a lot of speed for very low VRM usage")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="a workaround test; may help with speed in you use --lowvram")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
+cmd_opts = parser.parse_args()
+
+cpu = torch.device("cpu")
+gpu = torch.device("cuda")
+device = gpu if torch.cuda.is_available() else cpu
+batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
+
+class State:
+    interrupted = False
+    job = ""
+
+    def interrupt(self):
+        self.interrupted = True
+
+state = State()
+
+
+class Options:
+    class OptionInfo:
+        def __init__(self, default=None, label="", component=None, component_args=None):
+            self.default = default
+            self.label = label
+            self.component = component
+            self.component_args = component_args
+
+    data = None
+    data_labels = {
+        "outdir_samples": OptionInfo("", "Output dictectory for images; if empty, defaults to two directories below"),
+        "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output dictectory for txt2img images'),
+        "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output dictectory for img2img images'),
+        "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output dictectory for images from extras tab'),
+        "outdir_grids": OptionInfo("", "Output dictectory for grids; if empty, defaults to two directories below"),
+        "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output dictectory for txt2img grids'),
+        "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'),
+        "save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"),
+        "save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}),
+        "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button"),
+        "samples_save": OptionInfo(True, "Save indiviual samples"),
+        "samples_format": OptionInfo('png', 'File format for indiviual samples'),
+        "grid_save": OptionInfo(True, "Save image grids"),
+        "return_grid": OptionInfo(True, "Show grid in results for web"),
+        "grid_format": OptionInfo('png', 'File format for grids'),
+        "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
+        "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+        "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+        "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+        "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
+        "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
+        "font": OptionInfo("arial.ttf", "Font for image grids  that have text"),
+        "prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
+        "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"),
+        "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
+
+    }
+
+    def __init__(self):
+        self.data = {k: v.default for k, v in self.data_labels.items()}
+
+    def __setattr__(self, key, value):
+        if self.data is not None:
+            if key in self.data:
+                self.data[key] = value
+
+        return super(Options, self).__setattr__(key, value)
+
+    def __getattr__(self, item):
+        if self.data is not None:
+            if item in self.data:
+                return self.data[item]
+
+        if item in self.data_labels:
+            return self.data_labels[item].default
+
+        return super(Options, self).__getattribute__(item)
+
+    def save(self, filename):
+        with open(filename, "w", encoding="utf8") as file:
+            json.dump(self.data, file)
+
+    def load(self, filename):
+        with open(filename, "r", encoding="utf8") as file:
+            self.data = json.load(file)
+
+
+opts = Options()
+if os.path.exists(config_filename):
+    opts.load(config_filename)
+
+
+sd_upscalers = {}
+
+sd_model = None

modules/txt2img.py

@@ -0,0 +1,52 @@
+
+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
+from modules.ui import plaintext_to_html
+
+
+def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
+    p = StableDiffusionProcessingTxt2Img(
+        sd_model=shared.sd_model,
+        outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
+        outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        seed=seed,
+        sampler_index=sampler_index,
+        batch_size=batch_size,
+        n_iter=n_iter,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        width=width,
+        height=height,
+        prompt_matrix=prompt_matrix,
+        use_GFPGAN=use_GFPGAN
+    )
+
+    if code != '' and cmd_opts.allow_code:
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+
+        display_result_data = [[], -1, ""]
+
+        def display(imgs, s=display_result_data[1], i=display_result_data[2]):
+            display_result_data[0] = imgs
+            display_result_data[1] = s
+            display_result_data[2] = i
+
+        from types import ModuleType
+        compiled = compile(code, '', 'exec')
+        module = ModuleType("testmodule")
+        module.__dict__.update(globals())
+        module.p = p
+        module.display = display
+        exec(compiled, module.__dict__)
+
+        processed = Processed(p, *display_result_data)
+    else:
+        processed = process_images(p)
+
+    return processed.images, processed.js(), plaintext_to_html(processed.info)
+

modules/ui.py

@@ -0,0 +1,539 @@
+import base64
+import html
+import io
+import json
+import mimetypes
+import os
+import sys
+import time
+import traceback
+
+from PIL import Image
+
+import gradio as gr
+import gradio.utils
+
+from modules.paths import script_path
+from modules.shared import opts, cmd_opts
+import modules.shared as shared
+from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.gfpgan_model as gfpgan
+import modules.realesrgan_model as realesrgan
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+
+if not cmd_opts.share:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+"""
+
+
+def plaintext_to_html(text):
+    text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
+    return text
+
+
+def image_from_url_text(filedata):
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+
+    return image_from_url_text(x[0])
+
+
+def save_files(js_data, images):
+    import csv
+
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
+    filenames = []
+
+    data = json.loads(js_data)
+
+    with open("log/log.csv", "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename"])
+
+        filename_base = str(int(time.time() * 1000))
+        for i, filedata in enumerate(images):
+            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
+            filepath = os.path.join(opts.outdir_save, filename)
+
+            if filedata.startswith("data:image/png;base64,"):
+                filedata = filedata[len("data:image/png;base64,"):]
+
+            with open(filepath, "wb") as imgfile:
+                imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
+
+            filenames.append(filename)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0]])
+
+    return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+
+def wrap_gradio_call(func):
+    def f(*args, **kwargs):
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            print("Error completing request", file=sys.stderr)
+            print("Arguments:", args, kwargs, file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+        elapsed = time.perf_counter() - t
+
+        # last item is always HTML
+        res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
+
+        shared.state.interrupted = False
+
+        return tuple(res)
+
+    return f
+
+
+def create_ui(opts, cmd_opts, txt2img, img2img, run_extras, run_pnginfo):
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        with gr.Row():
+            prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
+            negative_prompt = gr.Textbox(label="Negative prompt", elem_id="txt2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1, visible=False)
+            submit = gr.Button('Generate', elem_id="txt2img_generate", variant='primary')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+                with gr.Row():
+                    use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
+
+                with gr.Group():
+                    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)
+
+                seed = gr.Number(label='Seed', value=-1)
+
+                code = gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
+
+            with gr.Column(variant='panel'):
+                with gr.Group():
+                    txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery')
+
+                with gr.Group():
+                    with gr.Row():
+                        save = gr.Button('Save')
+                        send_to_img2img = gr.Button('Send to img2img')
+                        send_to_inpaint = gr.Button('Send to inpaint')
+                        send_to_extras = gr.Button('Send to extras')
+                        interrupt = gr.Button('Interrupt')
+
+                with gr.Group():
+                    html_info = gr.HTML()
+                    generation_info = gr.Textbox(visible=False)
+
+            txt2img_args = dict(
+                fn=txt2img,
+                inputs=[
+                    prompt,
+                    negative_prompt,
+                    steps,
+                    sampler_index,
+                    use_GFPGAN,
+                    prompt_matrix,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    height,
+                    width,
+                    code
+                ],
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info
+                ]
+            )
+
+            prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            interrupt.click(
+                fn=lambda: shared.state.interrupt(),
+                inputs=[],
+                outputs=[],
+            )
+
+            save.click(
+                fn=wrap_gradio_call(save_files),
+                inputs=[
+                    generation_info,
+                    txt2img_gallery,
+                ],
+                outputs=[
+                    html_info,
+                    html_info,
+                    html_info,
+                ]
+            )
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        with gr.Row():
+            prompt = gr.Textbox(label="Prompt", elem_id="img2img_prompt", show_label=False, placeholder="Prompt", lines=1)
+            submit = gr.Button('Generate', elem_id="img2img_generate", variant='primary')
+
+        with gr.Row().style(equal_height=False):
+
+            with gr.Column(variant='panel'):
+                with gr.Group():
+                    switch_mode = gr.Radio(label='Mode', elem_id="img2img_mode", choices=['Redraw whole image', 'Inpaint a part of image', 'Loopback', 'SD upscale'], value='Redraw whole image', type="index", show_label=False)
+                    init_img = gr.Image(label="Image for img2img", source="upload", interactive=True, type="pil")
+                    init_img_with_mask = gr.Image(label="Image for inpainting with mask", elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", visible=False)
+                    resize_mode = gr.Radio(label="Resize mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
+                inpainting_fill = gr.Radio(label='Msked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
+
+                with gr.Row():
+                    use_GFPGAN = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    prompt_matrix = gr.Checkbox(label='Prompt matrix', value=False)
+                    inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=True, visible=False)
+
+                with gr.Row():
+                    sd_upscale_upscaler_name = gr.Radio(label='Upscaler', choices=list(shared.sd_upscalers.keys()), value=list(shared.sd_upscalers.keys())[0], visible=False)
+                    sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                with gr.Group():
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75)
+
+                with gr.Group():
+                    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)
+
+                seed = gr.Number(label='Seed', value=-1)
+
+            with gr.Column(variant='panel'):
+                with gr.Group():
+                    img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
+
+                with gr.Group():
+                    with gr.Row():
+                        interrupt = gr.Button('Interrupt')
+                        save = gr.Button('Save')
+                        img2img_send_to_extras = gr.Button('Send to extras')
+
+                with gr.Group():
+                    html_info = gr.HTML()
+                    generation_info = gr.Textbox(visible=False)
+
+            def apply_mode(mode):
+                is_classic = mode == 0
+                is_inpaint = mode == 1
+                is_loopback = mode == 2
+                is_upscale = mode == 3
+
+                return {
+                    init_img: gr_show(not is_inpaint),
+                    init_img_with_mask: gr_show(is_inpaint),
+                    mask_blur: gr_show(is_inpaint),
+                    inpainting_fill: gr_show(is_inpaint),
+                    prompt_matrix: gr_show(is_classic),
+                    batch_count: gr_show(not is_upscale),
+                    batch_size: gr_show(not is_loopback),
+                    sd_upscale_upscaler_name: gr_show(is_upscale),
+                    sd_upscale_overlap:gr_show(is_upscale),
+                    inpaint_full_res: gr_show(is_inpaint),
+                }
+
+            switch_mode.change(
+                apply_mode,
+                inputs=[switch_mode],
+                outputs=[
+                    init_img,
+                    init_img_with_mask,
+                    mask_blur,
+                    inpainting_fill,
+                    prompt_matrix,
+                    batch_count,
+                    batch_size,
+                    sd_upscale_upscaler_name,
+                    sd_upscale_overlap,
+                    inpaint_full_res,
+                ]
+            )
+
+            img2img_args = dict(
+                fn=img2img,
+                inputs=[
+                    prompt,
+                    init_img,
+                    init_img_with_mask,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    inpainting_fill,
+                    use_GFPGAN,
+                    prompt_matrix,
+                    switch_mode,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    denoising_strength,
+                    seed,
+                    height,
+                    width,
+                    resize_mode,
+                    sd_upscale_upscaler_name,
+                    sd_upscale_overlap,
+                    inpaint_full_res,
+                ],
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info
+                ]
+            )
+
+            prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            interrupt.click(
+                fn=lambda: shared.state.interrupt(),
+                inputs=[],
+                outputs=[],
+            )
+
+            save.click(
+                fn=wrap_gradio_call(save_files),
+                inputs=[
+                    generation_info,
+                    img2img_gallery,
+                ],
+                outputs=[
+                    html_info,
+                    html_info,
+                    html_info,
+                ]
+            )
+
+            send_to_img2img.click(
+                fn=lambda x: image_from_url_text(x),
+                _js="extract_image_from_gallery",
+                inputs=[txt2img_gallery],
+                outputs=[init_img],
+            )
+
+            send_to_inpaint.click(
+                fn=lambda x: image_from_url_text(x),
+                _js="extract_image_from_gallery",
+                inputs=[txt2img_gallery],
+                outputs=[init_img_with_mask],
+            )
+
+
+
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                with gr.Group():
+                    image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+                    gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=gfpgan.have_gfpgan)
+                    realesrgan_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=realesrgan.have_realesrgan)
+                    realesrgan_model = gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan.realesrgan_models], value=realesrgan.realesrgan_models[0].name, type="index", interactive=realesrgan.have_realesrgan)
+
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+            with gr.Column(variant='panel'):
+                result_image = gr.Image(label="Result")
+                html_info_x = gr.HTML()
+                html_info = gr.HTML()
+
+        extras_args = dict(
+            fn=run_extras,
+            inputs=[
+                image,
+                gfpgan_strength,
+                realesrgan_resize,
+                realesrgan_model,
+            ],
+            outputs=[
+                result_image,
+                html_info_x,
+                html_info,
+            ]
+        )
+
+        submit.click(**extras_args)
+
+
+        send_to_extras.click(
+            fn=lambda x: image_from_url_text(x),
+            _js="extract_image_from_gallery",
+            inputs=[txt2img_gallery],
+            outputs=[image],
+        )
+
+        img2img_send_to_extras.click(
+            fn=lambda x: image_from_url_text(x),
+            _js="extract_image_from_gallery",
+            inputs=[img2img_gallery],
+            outputs=[image],
+        )
+
+
+    pnginfo_interface = gr.Interface(
+        wrap_gradio_call(run_pnginfo),
+        inputs=[
+            gr.Image(label="Source", source="upload", interactive=True, type="pil"),
+        ],
+        outputs=[
+            gr.HTML(),
+            gr.HTML(),
+            gr.HTML(),
+        ],
+        allow_flagging="never",
+        analytics_enabled=False,
+    )
+
+
+    def create_setting_component(key):
+        def fun():
+            return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+        info = opts.data_labels[key]
+        t = type(info.default)
+
+        if info.component is not None:
+            item = info.component(label=info.label, value=fun, **(info.component_args or {}))
+        elif t == str:
+            item = gr.Textbox(label=info.label, value=fun, lines=1)
+        elif t == int:
+            item = gr.Number(label=info.label, value=fun)
+        elif t == bool:
+            item = gr.Checkbox(label=info.label, value=fun)
+        else:
+            raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+        return item
+
+    def run_settings(*args):
+        up = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, settings_interface.input_components):
+            opts.data[key] = value
+            up.append(comp.update(value=value))
+
+        opts.save(shared.config_filename)
+
+        return 'Settings saved.', '', ''
+
+    settings_interface = gr.Interface(
+        run_settings,
+        inputs=[create_setting_component(key) for key in opts.data_labels.keys()],
+        outputs=[
+            gr.Textbox(label='Result'),
+            gr.HTML(),
+            gr.HTML(),
+        ],
+        title=None,
+        description=None,
+        allow_flagging="never",
+        analytics_enabled=False,
+    )
+
+    interfaces = [
+        (txt2img_interface, "txt2img"),
+        (img2img_interface, "img2img"),
+        (extras_interface, "Extras"),
+        (pnginfo_interface, "PNG Info"),
+        (settings_interface, "Settings"),
+    ]
+
+    with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
+        css = file.read()
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
+    demo = gr.TabbedInterface(
+        interface_list=[x[0] for x in interfaces],
+        tab_names=[x[1] for x in interfaces],
+        analytics_enabled=False,
+        css=css,
+    )
+
+    return demo
+
+
+with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as file:
+    javascript = file.read()
+
+def inject_gradio_html(javascript):
+    import gradio.routes
+
+    def template_response(*args, **kwargs):
+        res = gradio_routes_templates_response(*args, **kwargs)
+        res.body = res.body.replace(b'</head>', f'<script>{javascript}</script></head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+    gradio.routes.templates.TemplateResponse = template_response
+
+
+inject_gradio_html(javascript)

style.css

@@ -9,11 +9,6 @@ button{
     align-self: stretch !important;
 }
 
-#img2img_mode{
-    padding: 0 0 1em 0;
-    border: none !important;
-}
-
 #img2img_prompt, #txt2img_prompt{
     padding: 0;
     border: none !important;