diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/gfpgan_model.py | 58 | ||||
| -rw-r--r-- | modules/images.py | 290 | ||||
| -rw-r--r-- | modules/img2img.py | 133 | ||||
| -rw-r--r-- | modules/lowvram.py | 73 | ||||
| -rw-r--r-- | modules/paths.py | 21 | ||||
| -rw-r--r-- | modules/processing.py | 409 | ||||
| -rw-r--r-- | modules/realesrgan_model.py | 70 | ||||
| -rw-r--r-- | modules/scripts.py | 53 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 208 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 137 | ||||
| -rw-r--r-- | modules/shared.py | 121 | ||||
| -rw-r--r-- | modules/txt2img.py | 52 | ||||
| -rw-r--r-- | modules/ui.py | 539 |
13 files changed, 2164 insertions, 0 deletions
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py new file mode 100644 index 00000000..3f42c163 --- /dev/null +++ b/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)
diff --git a/modules/images.py b/modules/images.py new file mode 100644 index 00000000..b05276c3 --- /dev/null +++ b/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")
+
diff --git a/modules/img2img.py b/modules/img2img.py new file mode 100644 index 00000000..f2817ba8 --- /dev/null +++ b/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)
diff --git a/modules/lowvram.py b/modules/lowvram.py new file mode 100644 index 00000000..4b78deab --- /dev/null +++ b/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)
diff --git a/modules/paths.py b/modules/paths.py new file mode 100644 index 00000000..6d11b304 --- /dev/null +++ b/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))
diff --git a/modules/processing.py b/modules/processing.py new file mode 100644 index 00000000..faf56c9c --- /dev/null +++ b/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
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py new file mode 100644 index 00000000..5a6666a3 --- /dev/null +++ b/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
diff --git a/modules/scripts.py b/modules/scripts.py new file mode 100644 index 00000000..20f489ce --- /dev/null +++ b/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")
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py new file mode 100644 index 00000000..6ee92e77 --- /dev/null +++ b/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()
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py new file mode 100644 index 00000000..54c5fd7c --- /dev/null +++ b/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
+
diff --git a/modules/shared.py b/modules/shared.py new file mode 100644 index 00000000..33a08559 --- /dev/null +++ b/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
diff --git a/modules/txt2img.py b/modules/txt2img.py new file mode 100644 index 00000000..d03a29f2 --- /dev/null +++ b/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)
+
diff --git a/modules/ui.py b/modules/ui.py new file mode 100644 index 00000000..5223179f --- /dev/null +++ b/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)
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