stable-diffusion-webui/modules/processing.py

1049 lines
48 KiB
Python

import json
import math
import os
import sys
import warnings
import torch
import numpy as np
from PIL import Image, ImageFilter, ImageOps
import random
import cv2
from skimage import exposure
from typing import Any, Dict, List, Optional
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.paths as paths
import modules.face_restoration
import modules.images as images
import modules.styles
import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
# 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 setup_color_correction(image):
logging.info("Calibrating color correction.")
correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
return correction_target
def apply_color_correction(correction, original_image):
logging.info("Applying color correction.")
image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
cv2.cvtColor(
np.asarray(original_image),
cv2.COLOR_RGB2LAB
),
correction,
channel_axis=2
), cv2.COLOR_LAB2RGB).astype("uint8"))
image = blendLayers(image, original_image, BlendType.LUMINOSITY)
return image
def apply_overlay(image, paste_loc, index, overlays):
if overlays is None or index >= len(overlays):
return image
overlay = overlays[index]
if paste_loc is not None:
x, y, w, h = paste_loc
base_image = Image.new('RGBA', (overlay.width, overlay.height))
image = images.resize_image(1, image, w, h)
base_image.paste(image, (x, y))
image = base_image
image = image.convert('RGBA')
image.alpha_composite(overlay)
image = image.convert('RGB')
return image
def txt2img_image_conditioning(sd_model, x, width, height):
if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
# Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
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.styles: list = styles or []
self.seed: int = seed
self.subseed: int = subseed
self.subseed_strength: float = subseed_strength
self.seed_resize_from_h: int = seed_resize_from_h
self.seed_resize_from_w: int = seed_resize_from_w
self.sampler_name: str = sampler_name
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.restore_faces: bool = restore_faces
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 or {}
self.overlay_images = overlay_images
self.eta = eta
self.do_not_reload_embeddings = do_not_reload_embeddings
self.paste_to = None
self.color_corrections = None
self.denoising_strength: float = denoising_strength
self.sampler_noise_scheduler_override = None
self.ddim_discretize = ddim_discretize or opts.ddim_discretize
self.s_churn = s_churn or opts.s_churn
self.s_tmin = s_tmin or opts.s_tmin
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
self.s_noise = s_noise or opts.s_noise
self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
self.override_settings_restore_afterwards = override_settings_restore_afterwards
self.is_using_inpainting_conditioning = False
self.disable_extra_networks = False
if not seed_enable_extras:
self.subseed = -1
self.subseed_strength = 0
self.seed_resize_from_h = 0
self.seed_resize_from_w = 0
self.scripts = None
self.script_args = script_args
self.all_prompts = None
self.all_negative_prompts = None
self.all_seeds = None
self.all_subseeds = None
self.iteration = 0
@property
def sd_model(self):
return shared.sd_model
def txt2img_image_conditioning(self, x, width=None, height=None):
self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
def depth2img_image_conditioning(self, source_image):
# Use the AddMiDaS helper to Format our source image to suit the MiDaS model
transformer = AddMiDaS(model_type="dpt_hybrid")
transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
conditioning = torch.nn.functional.interpolate(
self.sd_model.depth_model(midas_in),
size=conditioning_image.shape[2:],
mode="bicubic",
align_corners=False,
)
(depth_min, depth_max) = torch.aminmax(conditioning)
conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
return conditioning
def edit_image_conditioning(self, source_image):
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
return conditioning_image
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
self.is_using_inpainting_conditioning = True
# Handle the different mask inputs
if image_mask is not None:
if torch.is_tensor(image_mask):
conditioning_mask = image_mask
else:
conditioning_mask = np.array(image_mask.convert("L"))
conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask = torch.round(conditioning_mask)
else:
conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
# Create another latent image, this time with a masked version of the original input.
# Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
conditioning_image = torch.lerp(
source_image,
source_image * (1.0 - conditioning_mask),
getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
)
# Encode the new masked image using first stage of network.
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
# Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
return image_conditioning
def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
source_image = devices.cond_cast_float(source_image)
# HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
# identify itself with a field common to all models. The conditioning_key is also hybrid.
if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
return self.depth2img_image_conditioning(source_image)
if self.sd_model.cond_stage_key == "edit":
return self.edit_image_conditioning(source_image)
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
def init(self, all_prompts, all_seeds, all_subseeds):
pass
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
raise NotImplementedError()
def close(self):
self.sampler = None
class Processed:
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
self.images = images_list
self.prompt = p.prompt
self.negative_prompt = p.negative_prompt
self.seed = seed
self.subseed = subseed
self.subseed_strength = p.subseed_strength
self.info = info
self.comments = comments
self.width = p.width
self.height = p.height
self.sampler_name = p.sampler_name
self.cfg_scale = p.cfg_scale
self.steps = p.steps
self.batch_size = p.batch_size
self.restore_faces = p.restore_faces
self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
self.sd_model_hash = shared.sd_model.sd_model_hash
self.seed_resize_from_w = p.seed_resize_from_w
self.seed_resize_from_h = p.seed_resize_from_h
self.denoising_strength = getattr(p, 'denoising_strength', None)
self.extra_generation_params = p.extra_generation_params
self.index_of_first_image = index_of_first_image
self.styles = p.styles
self.job_timestamp = state.job_timestamp
self.clip_skip = opts.CLIP_stop_at_last_layers
self.eta = p.eta
self.ddim_discretize = p.ddim_discretize
self.s_churn = p.s_churn
self.s_tmin = p.s_tmin
self.s_tmax = p.s_tmax
self.s_noise = p.s_noise
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
def js(self):
obj = {
"prompt": self.all_prompts[0],
"all_prompts": self.all_prompts,
"negative_prompt": self.all_negative_prompts[0],
"all_negative_prompts": self.all_negative_prompts,
"seed": self.seed,
"all_seeds": self.all_seeds,
"subseed": self.subseed,
"all_subseeds": self.all_subseeds,
"subseed_strength": self.subseed_strength,
"width": self.width,
"height": self.height,
"sampler_name": self.sampler_name,
"cfg_scale": self.cfg_scale,
"steps": self.steps,
"batch_size": self.batch_size,
"restore_faces": self.restore_faces,
"face_restoration_model": self.face_restoration_model,
"sd_model_hash": self.sd_model_hash,
"seed_resize_from_w": self.seed_resize_from_w,
"seed_resize_from_h": self.seed_resize_from_h,
"denoising_strength": self.denoising_strength,
"extra_generation_params": self.extra_generation_params,
"index_of_first_image": self.index_of_first_image,
"infotexts": self.infotexts,
"styles": self.styles,
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
}
return json.dumps(obj)
def infotext(self, p: StableDiffusionProcessing, index):
return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
dot = (low_norm*high_norm).sum(1)
if dot.mean() > 0.9995:
return low * val + high * (1 - val)
omega = torch.acos(dot)
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res
def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
xs = []
# if we have multiple seeds, this means we are working with batch size>1; this then
# enables the generation of additional tensors with noise that the sampler will use during its processing.
# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
# produce the same images as with two batches [100], [101].
if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
else:
sampler_noises = None
for i, seed in enumerate(seeds):
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
subnoise = None
if subseeds is not None:
subseed = 0 if i >= len(subseeds) else subseeds[i]
subnoise = devices.randn(subseed, noise_shape)
# 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.
noise = devices.randn(seed, noise_shape)
if subnoise is not None:
noise = slerp(subseed_strength, noise, subnoise)
if noise_shape != shape:
x = devices.randn(seed, shape)
dx = (shape[2] - noise_shape[2]) // 2
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
tx = 0 if dx < 0 else dx
ty = 0 if dy < 0 else dy
dx = max(-dx, 0)
dy = max(-dy, 0)
x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
noise = x
if sampler_noises is not None:
cnt = p.sampler.number_of_needed_noises(p)
if eta_noise_seed_delta > 0:
torch.manual_seed(seed + eta_noise_seed_delta)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
xs.append(noise)
if sampler_noises is not None:
p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
x = torch.stack(xs).to(shared.device)
return x
def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae):
x = model.decode_first_stage(x)
return x
def get_fixed_seed(seed):
if seed is None or seed == '' or seed == -1:
return int(random.randrange(4294967294))
return seed
def fix_seed(p):
p.seed = get_fixed_seed(p.seed)
p.subseed = get_fixed_seed(p.subseed)
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
index = position_in_batch + iteration * p.batch_size
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
generation_params = {
"Steps": p.steps,
"Sampler": p.sampler_name,
"CFG scale": p.cfg_scale,
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
}
generation_params.update(p.extra_generation_params)
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
def process_images(p: StableDiffusionProcessing) -> Processed:
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
try:
for k, v in p.override_settings.items():
setattr(opts, k, v)
if k == 'sd_model_checkpoint':
sd_models.reload_model_weights()
if k == 'sd_vae':
sd_vae.reload_vae_weights()
res = process_images_inner(p)
finally:
# restore opts to original state
if p.override_settings_restore_afterwards:
for k, v in stored_opts.items():
setattr(opts, k, v)
if k == 'sd_model_checkpoint':
sd_models.reload_model_weights()
if k == 'sd_vae':
sd_vae.reload_vae_weights()
return res
def process_images_inner(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"""
if type(p.prompt) == list:
assert(len(p.prompt) > 0)
else:
assert p.prompt is not None
devices.torch_gc()
seed = get_fixed_seed(p.seed)
subseed = get_fixed_seed(p.subseed)
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
comments = {}
if type(p.prompt) == list:
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
else:
p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
if type(p.negative_prompt) == list:
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
else:
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
if type(seed) == list:
p.all_seeds = seed
else:
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
if type(subseed) == list:
p.all_subseeds = subseed
else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
_, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
if p.scripts is not None:
p.scripts.process(p)
infotexts = []
output_images = []
cached_uc = [None, None]
cached_c = [None, None]
def get_conds_with_caching(function, required_prompts, steps, cache):
"""
Returns the result of calling function(shared.sd_model, required_prompts, steps)
using a cache to store the result if the same arguments have been used before.
cache is an array containing two elements. The first element is a tuple
representing the previously used arguments, or None if no arguments
have been used before. The second element is where the previously
computed result is stored.
"""
if cache[0] is not None and (required_prompts, steps) == cache[0]:
return cache[1]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps)
return cache[1]
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
# for OSX, loading the model during sampling changes the generated picture, so it is loaded here
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
if not p.disable_extra_networks:
extra_networks.activate(p, extra_network_data)
with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
if state.job_count == -1:
state.job_count = p.n_iter
for n in range(p.n_iter):
p.iteration = n
if state.skipped:
state.skipped = False
if state.interrupted:
break
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
if len(prompts) == 0:
break
prompts, _ = extra_networks.parse_prompts(prompts)
if p.scripts is not None:
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
comments[comment] = 1
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
for x in x_samples_ddim:
devices.test_for_nans(x, "vae")
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
devices.torch_gc()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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.restore_faces:
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
devices.torch_gc()
image = Image.fromarray(x_sample)
if p.scripts is not None:
pp = scripts.PostprocessImageArgs(image)
p.scripts.postprocess_image(p, pp)
image = pp.image
if p.color_corrections is not None and i < len(p.color_corrections):
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if opts.samples_save and not p.do_not_save_samples:
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
text = infotext(n, i)
infotexts.append(text)
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
del x_samples_ddim
devices.torch_gc()
state.nextjob()
p.color_corrections = None
index_of_first_image = 0
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
grid = images.image_grid(output_images, p.batch_size)
if opts.return_grid:
text = infotext()
infotexts.insert(0, text)
if opts.enable_pnginfo:
grid.info["parameters"] = text
output_images.insert(0, grid)
index_of_first_image = 1
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
if not p.disable_extra_networks:
extra_networks.deactivate(p, extra_network_data)
devices.torch_gc()
res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
if p.scripts is not None:
p.scripts.postprocess(p, res)
return res
def old_hires_fix_first_pass_dimensions(width, height):
"""old algorithm for auto-calculating first pass size"""
desired_pixel_count = 512 * 512
actual_pixel_count = width * height
scale = math.sqrt(desired_pixel_count / actual_pixel_count)
width = math.ceil(scale * width / 64) * 64
height = math.ceil(scale * height / 64) * 64
return width, height
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
self.hr_scale = hr_scale
self.hr_upscaler = hr_upscaler
self.hr_second_pass_steps = hr_second_pass_steps
self.hr_resize_x = hr_resize_x
self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
self.hr_upscale_to_y = self.height
self.width = firstphase_width
self.height = firstphase_height
self.truncate_x = 0
self.truncate_y = 0
self.applied_old_hires_behavior_to = None
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
self.hr_resize_x = self.width
self.hr_resize_y = self.height
self.hr_upscale_to_x = self.width
self.hr_upscale_to_y = self.height
self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
self.applied_old_hires_behavior_to = (self.width, self.height)
if self.hr_resize_x == 0 and self.hr_resize_y == 0:
self.extra_generation_params["Hires upscale"] = self.hr_scale
self.hr_upscale_to_x = int(self.width * self.hr_scale)
self.hr_upscale_to_y = int(self.height * self.hr_scale)
else:
self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
if self.hr_resize_y == 0:
self.hr_upscale_to_x = self.hr_resize_x
self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
elif self.hr_resize_x == 0:
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
self.hr_upscale_to_y = self.hr_resize_y
else:
target_w = self.hr_resize_x
target_h = self.hr_resize_y
src_ratio = self.width / self.height
dst_ratio = self.hr_resize_x / self.hr_resize_y
if src_ratio < dst_ratio:
self.hr_upscale_to_x = self.hr_resize_x
self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
else:
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
self.hr_upscale_to_y = self.hr_resize_y
self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
# special case: the user has chosen to do nothing
if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
self.enable_hr = False
self.denoising_strength = None
self.extra_generation_params.pop("Hires upscale", None)
self.extra_generation_params.pop("Hires resize", None)
return
if not state.processing_has_refined_job_count:
if state.job_count == -1:
state.job_count = self.n_iter
shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
state.job_count = state.job_count * 2
state.processing_has_refined_job_count = True
if self.hr_second_pass_steps:
self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
if self.hr_upscaler is not None:
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
if self.enable_hr and latent_scale_mode is None:
assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
if not self.enable_hr:
return samples
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
def save_intermediate(image, index):
"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
return
if not isinstance(image, Image.Image):
image = sd_samplers.sample_to_image(image, index, approximation=0)
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
if latent_scale_mode is not None:
for i in range(samples.shape[0]):
save_intermediate(samples, i)
samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
# Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again.
if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
else:
image_conditioning = self.txt2img_image_conditioning(samples)
else:
decoded_samples = decode_first_stage(self.sd_model, samples)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
for i, x_sample in enumerate(lowres_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
save_intermediate(image, i)
image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device)
decoded_samples = 2. * decoded_samples - 1.
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
shared.state.nextjob()
img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM' # PLMS does not support img2img so we just silently switch ot DDIM
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
# GC now before running the next img2img to prevent running out of memory
x = None
devices.torch_gc()
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
return samples
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **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.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.inpaint_full_res_padding = inpaint_full_res_padding
self.inpainting_mask_invert = inpainting_mask_invert
self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
self.mask = None
self.nmask = None
self.image_conditioning = None
def init(self, all_prompts, all_seeds, all_subseeds):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
crop_region = None
image_mask = self.image_mask
if image_mask is not None:
image_mask = image_mask.convert('L')
if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
if self.mask_blur > 0:
image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
if self.inpaint_full_res:
self.mask_for_overlay = image_mask
mask = image_mask.convert('L')
crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
x1, y1, x2, y2 = crop_region
mask = mask.crop(crop_region)
image_mask = images.resize_image(2, mask, self.width, self.height)
self.paste_to = (x1, y1, x2-x1, y2-y1)
else:
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
np_mask = np.array(image_mask)
np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
self.mask_for_overlay = Image.fromarray(np_mask)
self.overlay_images = []
latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
if add_color_corrections:
self.color_corrections = []
imgs = []
for img in self.init_images:
image = images.flatten(img, opts.img2img_background_color)
if crop_region is None and self.resize_mode != 3:
image = images.resize_image(self.resize_mode, image, self.width, self.height)
if image_mask is not None:
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'))
# crop_region is not None if we are doing inpaint full res
if crop_region is not None:
image = image.crop(crop_region)
image = images.resize_image(2, image, self.width, self.height)
if image_mask is not None:
if self.inpainting_fill != 1:
image = masking.fill(image, latent_mask)
if add_color_corrections:
self.color_corrections.append(setup_color_correction(image))
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
if self.color_corrections is not None and len(self.color_corrections) == 1:
self.color_corrections = self.color_corrections * self.batch_size
elif len(imgs) <= self.batch_size:
self.batch_size = len(imgs)
batch_images = np.array(imgs)
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.resize_mode == 3:
self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
if image_mask is not None:
init_mask = latent_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.float32), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
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)
# this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
if self.initial_noise_multiplier != 1.0:
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
x *= self.initial_noise_multiplier
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
if self.mask is not None:
samples = samples * self.nmask + self.init_latent * self.mask
del x
devices.torch_gc()
return samples