402 lines
15 KiB
Python
402 lines
15 KiB
Python
import contextlib
|
|
import json
|
|
import math
|
|
import os
|
|
import sys
|
|
|
|
import torch
|
|
import numpy as np
|
|
from PIL import Image, ImageFilter, ImageOps
|
|
import random
|
|
|
|
import modules.sd_hijack
|
|
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, use_GFPGAN=False, tiling=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.prompt_for_display: str = None
|
|
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.use_GFPGAN: bool = use_GFPGAN
|
|
self.tiling: bool = tiling
|
|
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 if type(self.prompt) != list else self.prompt[0],
|
|
"seed": int(self.seed if type(self.seed) != list else self.seed[0]),
|
|
"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)
|
|
|
|
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
|
|
|
comments = []
|
|
|
|
if type(prompt) == list:
|
|
all_prompts = prompt
|
|
else:
|
|
all_prompts = p.batch_size * p.n_iter * [prompt]
|
|
|
|
if type(seed) == list:
|
|
all_seeds = seed
|
|
else:
|
|
all_seeds = [int(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),
|
|
"Batch size": (None if p.batch_size < 2 else p.batch_size),
|
|
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
|
|
}
|
|
|
|
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"{p.prompt_for_display or 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
|
|
|
|
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, all_prompts[0], 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 [(256, 1), (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, inpainting_mask_invert=0, **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.image_unblurred_mask = None
|
|
self.latent_mask = None
|
|
self.mask_for_overlay = None
|
|
self.mask_blur = mask_blur
|
|
self.inpainting_fill = inpainting_fill
|
|
self.inpaint_full_res = inpaint_full_res
|
|
self.inpainting_mask_invert = inpainting_mask_invert
|
|
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:
|
|
self.image_mask = self.image_mask.convert('L')
|
|
|
|
if self.inpainting_mask_invert:
|
|
self.image_mask = ImageOps.invert(self.image_mask)
|
|
|
|
#self.image_unblurred_mask = self.image_mask
|
|
|
|
if self.mask_blur > 0:
|
|
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
|
|
|
|
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), opts.upscale_at_full_resolution_padding)
|
|
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:
|
|
init_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
|
|
latmask = init_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
|