mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
Merge branch 'feature/add-metadata-to-files'
# Conflicts: # language/en.json # modules/async_worker.py # modules/config.py # modules/flags.py # modules/meta_parser.py # modules/private_logger.py # modules/util.py # webui.py
This commit is contained in:
Manuel Schmid
+52
-161
@@ -23,7 +23,6 @@ def worker():
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import os
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import traceback
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import math
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import json
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import numpy as np
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import torch
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import time
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@@ -50,8 +49,10 @@ def worker():
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from modules.private_logger import log
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from extras.expansion import safe_str
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from modules.util import remove_empty_str, HWC3, resize_image, \
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, calculate_sha256, quote
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
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from modules.upscaler import perform_upscale
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from modules.flags import Performance, lora_count
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from modules.meta_parser import get_metadata_parser, MetadataScheme
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pid = os.getpid()
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print(f'Started worker with PID {pid}')
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@@ -134,7 +135,7 @@ def worker():
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negative_prompt = args.pop()
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translate_prompts = args.pop()
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style_selections = args.pop()
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performance_selection = args.pop()
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performance_selection = Performance(args.pop())
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aspect_ratios_selection = args.pop()
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image_number = args.pop()
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output_format = args.pop()
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@@ -144,7 +145,7 @@ def worker():
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base_model_name = args.pop()
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refiner_model_name = args.pop()
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refiner_switch = args.pop()
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loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
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loras = [[str(args.pop()), float(args.pop())] for _ in range(lora_count)]
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input_image_checkbox = args.pop()
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current_tab = args.pop()
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uov_method = args.pop()
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@@ -192,10 +193,10 @@ def worker():
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inpaint_erode_or_dilate = args.pop()
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save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
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metadata_scheme = args.pop() if not args_manager.args.disable_metadata else 'fooocus'
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metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
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cn_tasks = {x: [] for x in flags.ip_list}
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for _ in range(4):
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for _ in range(flags.controlnet_image_count):
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cn_img = args.pop()
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cn_stop = args.pop()
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cn_weight = args.pop()
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@@ -220,17 +221,9 @@ def worker():
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print(f'Refiner disabled because base model and refiner are same.')
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refiner_model_name = 'None'
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assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
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steps = performance_selection.steps()
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steps = 30
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if performance_selection == 'Speed':
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steps = 30
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if performance_selection == 'Quality':
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steps = 60
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if performance_selection == 'Extreme Speed':
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if performance_selection == Performance.EXTREME_SPEED:
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print('Enter LCM mode.')
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progressbar(async_task, 1, 'Downloading LCM components ...')
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loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
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@@ -248,24 +241,12 @@ def worker():
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adm_scaler_positive = 1.0
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adm_scaler_negative = 1.0
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adm_scaler_end = 0.0
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steps = 8
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if translate_prompts:
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from modules.translator import translate2en
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prompt = translate2en(prompt, 'prompt')
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negative_prompt = translate2en(negative_prompt, 'negative prompt')
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if not args_manager.args.disable_metadata:
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base_model_path = os.path.join(modules.config.path_checkpoints, base_model_name)
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base_model_hash = calculate_sha256(base_model_path)[0:10]
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lora_hashes = []
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for (n, w) in loras:
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if n != 'None':
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lora_path = os.path.join(modules.config.path_loras, n)
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lora_hashes.append(f'{n.split(".")[0]}: {calculate_sha256(lora_path)[0:10]}')
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lora_hashes_string = ", ".join(lora_hashes)
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print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
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print(f'[Parameters] Sharpness = {sharpness}')
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print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
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@@ -325,16 +306,7 @@ def worker():
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if 'fast' in uov_method:
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skip_prompt_processing = True
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else:
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steps = 18
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if performance_selection == 'Speed':
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steps = 18
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if performance_selection == 'Quality':
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steps = 36
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if performance_selection == 'Extreme Speed':
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steps = 8
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steps = performance_selection.steps_uov()
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progressbar(async_task, 1, 'Downloading upscale models ...')
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modules.config.downloading_upscale_model()
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@@ -422,9 +394,6 @@ def worker():
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progressbar(async_task, 1, 'Initializing ...')
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raw_prompt = prompt
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raw_negative_prompt = negative_prompt
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if not skip_prompt_processing:
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prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')
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@@ -850,130 +819,52 @@ def worker():
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imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
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img_paths = []
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metadata_string = ''
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if save_metadata_to_images and metadata_scheme == 'fooocus':
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metadata = {
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'prompt': raw_prompt, 'negative_prompt': raw_negative_prompt, 'styles': str(raw_style_selections),
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'real_prompt': task['log_positive_prompt'], 'real_negative_prompt': task['log_negative_prompt'],
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'seed': task['task_seed'], 'width': width, 'height': height,
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'sampler': sampler_name, 'scheduler': scheduler_name, 'performance': performance_selection,
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'steps': steps, 'refiner_switch': refiner_switch, 'sharpness': sharpness, 'cfg': cfg_scale,
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'base_model': base_model_name, 'refiner_model': refiner_model_name,
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'denoising_strength': denoising_strength,
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'freeu': freeu_enabled,
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'img2img': input_image_checkbox,
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'prompt_expansion': task['expansion']
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}
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if freeu_enabled:
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metadata |= {
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'freeu_b1': freeu_b1, 'freeu_b2': freeu_b2, 'freeu_s1': freeu_s1, 'freeu_s2': freeu_s2
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}
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if 'vary' in goals:
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metadata |= {
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'uov_method': uov_method
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}
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if 'upscale' in goals:
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metadata |= {
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'uov_method': uov_method, 'scale': f
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}
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if 'inpaint' in goals:
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if len(outpaint_selections) > 0:
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metadata |= {
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'outpaint_selections': outpaint_selections
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}
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else:
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metadata |= {
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'inpaint_additional_prompt': inpaint_additional_prompt, 'inpaint_mask_upload': inpaint_mask_upload_checkbox, 'invert_mask': invert_mask_checkbox,
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'inpaint_disable_initial_latent': inpaint_disable_initial_latent, 'inpaint_engine': inpaint_engine,
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'inpaint_strength': inpaint_strength, 'inpaint_respective_field': inpaint_respective_field,
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}
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if 'cn' in goals:
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metadata |= {
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'canny_low_threshold': canny_low_threshold, 'canny_high_threshold': canny_high_threshold,
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}
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ip_list = {x: [] for x in flags.ip_list}
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cn_task_index = 1
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for cn_type in ip_list:
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for cn_task in cn_tasks[cn_type]:
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cn_img, cn_stop, cn_weight = cn_task
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metadata |= {
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f'image_prompt_{cn_task_index}': {
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'cn_type': cn_type, 'cn_stop': cn_stop, 'cn_weight': cn_weight,
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}
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}
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cn_task_index += 1
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metadata |= {
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'software': f'Fooocus v{fooocus_version.version}',
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}
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if modules.config.metadata_created_by != 'None':
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metadata |= {
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'created_by': modules.config.metadata_created_by
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}
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metadata_string = json.dumps(metadata, ensure_ascii=False)
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elif save_metadata_to_images and metadata_scheme == 'a1111':
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generation_params = {
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"Steps": steps,
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"Sampler": sampler_name,
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"CFG scale": cfg_scale,
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"Seed": task['task_seed'],
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"Size": f"{width}x{height}",
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"Model hash": base_model_hash,
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"Model": base_model_name.split('.')[0],
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"Lora hashes": lora_hashes_string,
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"Denoising strength": denoising_strength,
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"Version": f'Fooocus v{fooocus_version.version}'
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}
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if modules.config.metadata_created_by != 'None':
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generation_params |= {
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'Created By': f'{modules.config.metadata_created_by}'
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}
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generation_params_text = ", ".join([k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if v is not None])
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positive_prompt_resolved = ', '.join(task['positive'])
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negative_prompt_resolved = ', '.join(task['negative'])
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negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
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metadata_string = f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
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if modules.config.default_black_out_nsfw or black_out_nsfw:
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progressbar_index = int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps))
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progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
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imgs = censor_batch(imgs)
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for x in imgs:
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d = [
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('Prompt', task['log_positive_prompt']),
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('Negative Prompt', task['log_negative_prompt']),
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('Fooocus V2 Expansion', task['expansion']),
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('Styles', str(raw_style_selections)),
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('Performance', performance_selection),
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('Resolution', str((width, height))),
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('Sharpness', sharpness),
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('Guidance Scale', guidance_scale),
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('ADM Guidance', str((
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', base_model_name),
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('Refiner Model', refiner_model_name),
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('Refiner Switch', refiner_switch),
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('Sampler', sampler_name),
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('Scheduler', scheduler_name),
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('Sampling Steps Override', overwrite_step),
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('Seed', task['task_seed']),
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]
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d = [('Prompt', 'prompt', task['log_positive_prompt']),
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('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
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('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
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('Styles', 'styles', str(raw_style_selections)),
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('Performance', 'performance', performance_selection.value),
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('Resolution', 'resolution', str((width, height))),
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('Guidance Scale', 'guidance_scale', guidance_scale),
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('Sharpness', 'sharpness', modules.patch.patch_settings[pid].sharpness),
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('ADM Guidance', 'adm_guidance', str((
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', 'base_model', base_model_name),
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('Refiner Model', 'refiner_model', refiner_model_name),
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('Refiner Switch', 'refiner_switch', refiner_switch)]
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if refiner_model_name != 'None':
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if overwrite_switch > 0:
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d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
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if refiner_swap_method != flags.refiner_swap_method:
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d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
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if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
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d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
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d.append(('Sampler', 'sampler', sampler_name))
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d.append(('Scheduler', 'scheduler', scheduler_name))
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d.append(('Seed', 'seed', task['task_seed']))
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if freeu_enabled:
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d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
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metadata_parser = None
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if save_metadata_to_images:
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metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
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metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
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task['log_negative_prompt'], task['negative'],
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steps, base_model_name, refiner_model_name, loras)
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for li, (n, w) in enumerate(loras):
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if n != 'None':
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d.append((f'LoRA {li + 1}', f'{n} : {w}'))
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d.append(('Version', 'v' + fooocus_version.version))
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img_paths.append(log(x, d, metadata_string, save_metadata_to_images, output_format))
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d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
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d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
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img_paths.append(log(x, d, metadata_parser, output_format))
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yield_result(async_task, img_paths, black_out_nsfw, do_not_show_finished_images=len(tasks) == 1
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or disable_intermediate_results or sampler_name == 'lcm')
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