mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
add light theme, enhance profilng and logging
This commit is contained in:
Vladimir Mandic
+63
-58
@@ -1,29 +1,39 @@
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import os
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import json
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import math
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import os
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import time
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import hashlib
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import random
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from contextlib import nullcontext
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from typing import Any, Dict, List
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import torch
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import numpy as np
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from PIL import Image, ImageFilter, ImageOps
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import cv2
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from PIL import Image, ImageFilter, ImageOps
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from skimage import exposure
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from ldm.data.util import AddMiDaS
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from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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from installer import git_commit
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts, sd_samplers_common # pylint: disable=unused-import
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import modules.shared as shared
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import modules.paths as paths
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from modules import shared, devices
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import modules.memstats
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import modules.lowvram
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import modules.masking
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import modules.paths
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import modules.scripts
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import modules.prompt_parser
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import modules.extra_networks
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import modules.face_restoration
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import modules.images as images
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import modules.styles
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import modules.sd_models as sd_models
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import modules.sd_vae as sd_vae
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import modules.sd_hijack
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import modules.sd_samplers
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import modules.sd_samplers_common
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import modules.sd_models
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import modules.sd_vae
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import modules.sd_vae_approx
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import modules.generation_parameters_copypaste
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opt_C = 4
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@@ -451,7 +461,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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index = position_in_batch + iteration * p.batch_size
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if all_negative_prompts is None:
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all_negative_prompts = p.all_negative_prompts
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vae = (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD'
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vae = (None if not shared.opts.add_model_name_to_info or modules.sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(modules.sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD'
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comment = ', '.join(comments) if comments is not None and type(comments) is list else None
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args = {
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@@ -473,7 +483,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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"Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
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"Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None,
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# other
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"ENSD": shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None,
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"ENSD": shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None,
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"Tiling": p.tiling if p.tiling else None,
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# sdnext
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"Backend": 'Diffusers' if shared.backend == shared.Backend.DIFFUSERS else 'Original',
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@@ -524,7 +534,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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args['Token merging ratio hr'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
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args.update(p.extra_generation_params)
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params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None])
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params_text = ", ".join([k if k == v else f'{k}: {modules.generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None])
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negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
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infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip()
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return infotext
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@@ -568,18 +578,18 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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stored_opts[k] = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
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try:
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# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
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if p.override_settings.get('sd_model_checkpoint', None) is not None and sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
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if p.override_settings.get('sd_model_checkpoint', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
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p.override_settings.pop('sd_model_checkpoint', None)
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sd_models.reload_model_weights()
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modules.sd_models.reload_model_weights()
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for k, v in p.override_settings.items():
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setattr(shared.opts, k, v)
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if k == 'sd_model_checkpoint':
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sd_models.reload_model_weights()
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modules.sd_models.reload_model_weights()
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if k == 'sd_vae':
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sd_vae.reload_vae_weights()
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modules.sd_vae.reload_vae_weights()
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if not shared.opts.cuda_compile:
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sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
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modules.sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
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if shared.cmd_opts.profile:
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"""
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@@ -598,16 +608,16 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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res = process_images_inner(p)
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finally:
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if not shared.opts.cuda_compile:
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sd_models.apply_token_merging(p.sd_model, 0)
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modules.sd_models.apply_token_merging(p.sd_model, 0)
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if p.override_settings_restore_afterwards: # restore opts to original state
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for k, v in stored_opts.items():
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setattr(shared.opts, k, v)
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if k == 'sd_model_checkpoint':
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sd_models.reload_model_weights()
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modules.sd_models.reload_model_weights()
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if k == 'sd_model_refiner':
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sd_models.reload_model_weights()
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modules.sd_models.reload_model_weights()
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if k == 'sd_vae':
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sd_vae.reload_vae_weights()
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modules.sd_vae.reload_vae_weights()
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return res
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@@ -672,15 +682,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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cached_c = [None, None]
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def get_conds_with_caching(function, required_prompts, steps, cache):
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"""
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Returns the result of calling function(shared.sd_model, required_prompts, steps)
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using a cache to store the result if the same arguments have been used before.
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cache is an array containing two elements. The first element is a tuple
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representing the previously used arguments, or None if no arguments
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have been used before. The second element is where the previously
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computed result is stored.
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"""
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if cache[0] is not None and (required_prompts, steps) == cache[0]:
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return cache[1]
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with devices.autocast():
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@@ -692,11 +693,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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return ''
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ema_scope_context = p.sd_model.ema_scope if shared.backend == shared.Backend.ORIGINAL else nullcontext
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with torch.no_grad(), ema_scope_context():
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with torch.inference_mode(), ema_scope_context():
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t0 = time.time()
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with devices.autocast():
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p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
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if shared.opts.live_previews_enable and shared.opts.show_progress_type == "Approximate NN" and shared.backend == shared.Backend.ORIGINAL:
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sd_vae_approx.model()
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modules.sd_vae_approx.model()
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if shared.state.job_count == -1:
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shared.state.job_count = p.n_iter
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extra_network_data = None
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@@ -717,25 +719,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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if len(p.prompts) == 0:
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break
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p.prompts, extra_network_data = extra_networks.parse_prompts(p.prompts)
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p.prompts, extra_network_data = modules.extra_networks.parse_prompts(p.prompts)
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if not p.disable_extra_networks:
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with devices.autocast():
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extra_networks.activate(p, extra_network_data)
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modules.extra_networks.activate(p, extra_network_data)
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if p.scripts is not None:
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p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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if n == 0:
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with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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with open(os.path.join(modules.paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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step_multiplier = 1
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sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
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sampler_config = modules.sd_samplers.find_sampler_config(p.sampler_name)
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step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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if shared.backend == shared.Backend.ORIGINAL:
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uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
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c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c)
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uc = get_conds_with_caching(modules.prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
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c = get_conds_with_caching(modules.prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c)
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if len(modules.sd_hijack.model_hijack.comments) > 0:
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for comment in modules.sd_hijack.model_hijack.comments:
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comments[comment] = 1
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@@ -749,8 +751,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if not shared.opts.no_half and not shared.opts.no_half_vae and shared.cmd_opts.rollback_vae:
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shared.log.warning('Tensor with all NaNs was produced in VAE')
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devices.dtype_vae = torch.bfloat16
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vae_file, vae_source = sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint)
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sd_vae.load_vae(p.sd_model, vae_file, vae_source)
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vae_file, vae_source = modules.sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint)
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modules.sd_vae.load_vae(p.sd_model, vae_file, vae_source)
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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))]
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for x in x_samples_ddim:
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devices.test_for_nans(x, "vae")
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@@ -768,14 +770,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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raise ValueError(f"Unknown backend {shared.backend}")
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram and shared.backend == shared.Backend.ORIGINAL:
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lowvram.send_everything_to_cpu()
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modules.lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if p.scripts is not None:
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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if p.scripts is not None:
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p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
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batch_params = modules.scripts.PostprocessBatchListArgs(list(x_samples_ddim))
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p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
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x_samples_ddim = batch_params.images
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@@ -797,7 +799,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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x_sample = modules.face_restoration.restore_faces(x_sample)
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image = Image.fromarray(x_sample)
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if p.scripts is not None:
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pp = scripts.PostprocessImageArgs(image)
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pp = modules.scripts.PostprocessImageArgs(image)
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p.scripts.postprocess_image(p, pp)
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image = pp.image
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if p.color_corrections is not None and i < len(p.color_corrections):
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@@ -832,6 +834,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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devices.torch_gc()
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shared.state.nextjob()
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t1 = time.time()
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shared.log.info(f'Processed: images={len(output_images)} time={t1 - t0:.2f}s its={(p.steps * len(output_images)) / (t1 - t0):.2f} memory={modules.memstats.memory_stats()}')
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p.color_corrections = None
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index_of_first_image = 0
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unwanted_grid_because_of_img_count = len(output_images) < 2 and shared.opts.grid_only_if_multiple
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@@ -848,7 +853,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=infotext(), short_filename=not shared.opts.grid_extended_filename, p=p, grid=True)
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if not p.disable_extra_networks and extra_network_data:
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extra_networks.deactivate(p, extra_network_data)
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modules.extra_networks.deactivate(p, extra_network_data)
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res = Processed(
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p,
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@@ -905,7 +910,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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def init(self, all_prompts, all_seeds, all_subseeds):
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if shared.backend == shared.Backend.DIFFUSERS:
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sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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self.width = self.width or 512
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self.height = self.height or 512
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@@ -953,7 +958,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if not shared.opts.save or self.do_not_save_samples or not shared.opts.save_images_before_highres_fix:
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return
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if not isinstance(image, Image.Image):
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image = sd_samplers.sample_to_image(image, index, approximation=0)
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image = modules.sd_samplers.sample_to_image(image, index, approximation=0)
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orig1 = self.extra_generation_params
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orig2 = self.restore_faces
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self.extra_generation_params = {}
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@@ -964,10 +969,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], shared.opts.samples_format, info=info, suffix="-before-highres-fix")
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if shared.backend == shared.Backend.DIFFUSERS:
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sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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self.ops.append('txt2img')
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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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, "None")
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if self.enable_hr and latent_scale_mode is None:
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if len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) == 0:
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@@ -1020,14 +1025,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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if self.latent_sampler == "PLMS":
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self.latent_sampler = 'UniPC'
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self.sampler = sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
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self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
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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]
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
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x = None
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devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
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sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
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modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
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modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
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self.is_hr_pass = False
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return samples
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@@ -1065,14 +1070,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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def init(self, all_prompts, all_seeds, all_subseeds):
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if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None:
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sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
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modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
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elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None:
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sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
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modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
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self.sd_model.dtype = self.sd_model.unet.dtype
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if self.sampler_name == "PLMS":
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self.sampler_name = 'UniPC'
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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if self.image_mask is not None:
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self.ops.append('inpaint')
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@@ -1089,8 +1094,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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if self.inpaint_full_res:
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self.mask_for_overlay = image_mask
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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)
|
||||
crop_region = modules.masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
|
||||
crop_region = modules.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(3, mask, self.width, self.height)
|
||||
@@ -1137,7 +1142,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
self.init_images = [image] # assign early for diffusers
|
||||
if image_mask is not None:
|
||||
if self.inpainting_fill != 1:
|
||||
image = masking.fill(image, latent_mask)
|
||||
image = modules.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
|
||||
@@ -1183,9 +1188,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
||||
if shared.backend == shared.Backend.DIFFUSERS:
|
||||
if self.init_mask is None: # pylint: disable=no-member
|
||||
sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
|
||||
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
|
||||
else:
|
||||
sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
|
||||
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
|
||||
self.sd_model.dtype = self.sd_model.unet.dtype
|
||||
|
||||
x = create_random_tensors([4, self.height // 8, self.width // 8], 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)
|
||||
|
||||
Reference in New Issue
Block a user