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Merge branch 'dev' into 10141-gradio-user-exif

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AUTOMATIC1111
2023-06-27 09:05:53 +03:00
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parent f603275d84 bedcd2f377
commit 4147fd6b2f
104 changed files with 3450 additions and 1035 deletions
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115
modules/processing.py
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@@ -1,4 +1,5 @@
import json
import logging
import math
import os
import sys
@@ -6,14 +7,14 @@ import hashlib
import torch
import numpy as np
from PIL import Image, ImageFilter, ImageOps
from PIL import Image, ImageOps
import random
import cv2
from skimage import exposure
from typing import Any, Dict, List
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -23,7 +24,6 @@ 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
@@ -106,6 +106,9 @@ class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
cached_uc = [None, None]
cached_c = [None, None]
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -171,12 +174,13 @@ class StableDiffusionProcessing:
self.prompts = None
self.negative_prompts = None
self.extra_network_data = None
self.seeds = None
self.subseeds = None
self.step_multiplier = 1
self.cached_uc = [None, None]
self.cached_c = [None, None]
self.cached_uc = StableDiffusionProcessing.cached_uc
self.cached_c = StableDiffusionProcessing.cached_c
self.uc = None
self.c = None
@@ -290,8 +294,9 @@ class StableDiffusionProcessing:
self.sampler = None
self.c = None
self.uc = None
self.cached_c = [None, None]
self.cached_uc = [None, None]
if not opts.experimental_persistent_cond_cache:
StableDiffusionProcessing.cached_c = [None, None]
StableDiffusionProcessing.cached_uc = [None, None]
def get_token_merging_ratio(self, for_hr=False):
if for_hr:
@@ -313,7 +318,7 @@ class StableDiffusionProcessing:
self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
def get_conds_with_caching(self, function, required_prompts, steps, cache):
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
"""
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.
@@ -322,27 +327,29 @@ class StableDiffusionProcessing:
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.
caches is a list with items described above.
"""
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
return cache[1]
for cache in caches:
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
return cache[1]
cache = caches[0]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
return cache[1]
def setup_conds(self):
sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
def parse_extra_network_prompts(self):
self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
return extra_network_data
self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
class Processed:
@@ -591,6 +598,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
try:
@@ -676,10 +686,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
sd_unet.apply_unet()
if state.job_count == -1:
state.job_count = p.n_iter
extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
@@ -700,11 +711,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if len(p.prompts) == 0:
break
extra_network_data = p.parse_extra_network_prompts()
p.parse_extra_network_prompts()
if not p.disable_extra_networks:
with devices.autocast():
extra_networks.activate(p, extra_network_data)
extra_networks.activate(p, p.extra_network_data)
if p.scripts is not None:
p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@@ -739,7 +750,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
del samples_ddim
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
if lowvram.is_enabled(shared.sd_model):
lowvram.send_everything_to_cpu()
devices.torch_gc()
@@ -826,8 +837,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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 and extra_network_data:
extra_networks.deactivate(p, extra_network_data)
if not p.disable_extra_networks and p.extra_network_data:
extra_networks.deactivate(p, p.extra_network_data)
devices.torch_gc()
@@ -862,6 +873,8 @@ def old_hires_fix_first_pass_dimensions(width, height):
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
cached_hr_uc = [None, None]
cached_hr_c = [None, 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, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
@@ -894,6 +907,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_negative_prompts = None
self.hr_extra_network_data = None
self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
self.hr_c = None
self.hr_uc = None
@@ -973,7 +988,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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}"
if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
raise Exception(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))
@@ -1056,6 +1072,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with devices.autocast():
extra_networks.activate(self, self.hr_extra_network_data)
with devices.autocast():
self.calculate_hr_conds()
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
@@ -1067,8 +1086,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
def close(self):
super().close()
self.hr_c = None
self.hr_uc = None
if not opts.experimental_persistent_cond_cache:
StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
def setup_prompts(self):
super().setup_prompts()
@@ -1095,12 +1118,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
def calculate_hr_conds(self):
if self.hr_c is not None:
return
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self):
super().setup_conds()
self.hr_uc = None
self.hr_c = None
if self.enable_hr:
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
if shared.opts.hires_fix_use_firstpass_conds:
self.calculate_hr_conds()
elif lowvram.is_enabled(shared.sd_model): # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
with devices.autocast():
extra_networks.activate(self, self.hr_extra_network_data)
self.calculate_hr_conds()
with devices.autocast():
extra_networks.activate(self, self.extra_network_data)
def parse_extra_network_prompts(self):
res = super().parse_extra_network_prompts()
@@ -1117,7 +1159,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, 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):
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: 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
@@ -1128,7 +1170,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_mask = mask
self.latent_mask = None
self.mask_for_overlay = None
self.mask_blur = mask_blur
if mask_blur is not None:
mask_blur_x = mask_blur
mask_blur_y = mask_blur
self.mask_blur_x = mask_blur_x
self.mask_blur_y = mask_blur_y
self.inpainting_fill = inpainting_fill
self.inpaint_full_res = inpaint_full_res
self.inpaint_full_res_padding = inpaint_full_res_padding
@@ -1150,8 +1196,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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.mask_blur_x > 0:
np_mask = np.array(image_mask)
kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
image_mask = Image.fromarray(np_mask)
if self.mask_blur_y > 0:
np_mask = np.array(image_mask)
kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
if self.inpaint_full_res:
self.mask_for_overlay = image_mask