mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
wip: add metadata mapping, reading and writing
applying data after reading currently not functional for A1111
This commit is contained in:
Manuel Schmid
+28
-119
@@ -17,7 +17,6 @@ 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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@@ -43,8 +42,9 @@ 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, calculate_sha256
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from modules.upscaler import perform_upscale
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from modules.metadata import MetadataScheme
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try:
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async_gradio_app = shared.gradio_root
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@@ -144,7 +144,8 @@ def worker():
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inpaint_additional_prompt = args.pop()
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inpaint_mask_image_upload = 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 = args.pop() if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS.value
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assert metadata_scheme in [item.value for item in MetadataScheme]
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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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@@ -793,129 +794,37 @@ def worker():
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if inpaint_worker.current_task is not None:
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imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
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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 with wildcards
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'prompt': raw_prompt, 'negative_prompt': raw_negative_prompt,
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# prompt with resolved wildcards
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'real_prompt': task['log_positive_prompt'], 'real_negative_prompt': task['log_negative_prompt'],
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# prompt with resolved wildcards, styles and prompt expansion
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'complete_prompt_positive': task['positive'], 'complete_prompt_negative': task['negative'],
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'styles': str(raw_style_selections),
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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, 'base_model_hash': base_model_hash, 'refiner_model': refiner_model_name,
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'denoising_strength': denoising_strength,
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'freeu': advanced_parameters.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 advanced_parameters.freeu_enabled:
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metadata |= {
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'freeu_b1': advanced_parameters.freeu_b1, 'freeu_b2': advanced_parameters.freeu_b2, 'freeu_s1': advanced_parameters.freeu_s1, 'freeu_s2': advanced_parameters.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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metadata |= {
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'inpaint_additional_prompt': inpaint_additional_prompt, 'inpaint_mask_upload': advanced_parameters.inpaint_mask_upload_checkbox, 'invert_mask': advanced_parameters.invert_mask_checkbox,
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'inpaint_disable_initial_latent': advanced_parameters.inpaint_disable_initial_latent, 'inpaint_engine': advanced_parameters.inpaint_engine,
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'inpaint_strength': advanced_parameters.inpaint_strength, 'inpaint_respective_field': advanced_parameters.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': advanced_parameters.canny_low_threshold, 'canny_high_threshold': advanced_parameters.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 != '':
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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 != '':
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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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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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('Prompt', 'prompt', task['log_positive_prompt'], True, True),
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('Full Positive Prompt', 'full_prompt', task['positive'], False, False),
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('Negative Prompt', 'negative_prompt', task['log_negative_prompt'], True, True),
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('Full Negative Prompt', 'full_negative_prompt', task['negative'], False, False),
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('Fooocus V2 Expansion', 'prompt_expansion', task['expansion'], True, True),
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('Styles', 'styles', str(raw_style_selections), True, True),
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('Performance', 'performance', performance_selection, True, True),
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('Steps', 'steps', steps, False, False),
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('Resolution', 'resolution', str((width, height)), True, True),
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('Sharpness', 'sharpness', sharpness, True, True),
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('Guidance Scale', 'guidance_scale', guidance_scale, True, True),
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('ADM Guidance', 'adm_guidance', str((
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modules.patch.positive_adm_scale,
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modules.patch.negative_adm_scale,
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modules.patch.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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('Seed', task['task_seed']),
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modules.patch.adm_scaler_end)), True, True),
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('Base Model', 'base_model', base_model_name, True, True),
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('Refiner Model', 'refiner_model', refiner_model_name, True, True),
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('Refiner Switch', 'refiner_switch', refiner_switch, True, True),
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('Sampler', 'sampler', sampler_name, True, True),
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('Scheduler', 'scheduler', scheduler_name, True, True),
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('Seed', 'seed', task['task_seed'], True, True)
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]
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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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log(x, d, metadata_string, save_metadata_to_images)
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d.append((f'LoRA {li + 1}', f'lora{li + 1}_combined', f'{n} : {w}', True, True))
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# d.append((f'LoRA {li + 1} Name', f'lora{li + 1}_name', n, False, False))
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# d.append((f'LoRA {li + 1} Weight', f'lora{li + 1}_weight', n, False, False))
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d.append(('Version', 'version', 'v' + fooocus_version.version, True, True))
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log(x, d, save_metadata_to_images, metadata_scheme)
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yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
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except ldm_patched.modules.model_management.InterruptProcessingException as e:
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