wip: add metadata mapping, reading and writing
applying data after reading currently not functional for A1111
This commit is contained in:
Manuel Schmid
parent
051faf78b8
commit
f3010313fc
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@ -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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@ -6,6 +6,7 @@ import args_manager
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import modules.flags
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import modules.sdxl_styles
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from modules.metadata import MetadataScheme
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from modules.model_loader import load_file_from_url
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from modules.util import get_files_from_folder
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@ -322,7 +323,7 @@ default_save_metadata_to_images = get_config_item_or_set_default(
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)
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default_metadata_scheme = get_config_item_or_set_default(
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key='default_metadata_scheme',
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default_value='fooocus',
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default_value=MetadataScheme.FOOOCUS.value,
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validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x]
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)
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metadata_created_by = get_config_item_or_set_default(
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@ -1,3 +1,5 @@
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from modules.metadata import MetadataScheme
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disabled = 'Disabled'
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enabled = 'Enabled'
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subtle_variation = 'Vary (Subtle)'
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@ -32,9 +34,10 @@ default_parameters = {
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cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
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} # stop, weight
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metadata_scheme =[
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('Fooocus (json)', 'fooocus'),
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('A1111 (plain text)', 'a1111'),
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# TODO use translation here
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metadata_scheme = [
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('Fooocus (json)', MetadataScheme.FOOOCUS.value),
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('A1111 (plain text)', MetadataScheme.A1111.value),
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]
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inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
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@ -1,138 +1,38 @@
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import json
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import gradio as gr
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import modules.config
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def load_parameter_button_click(raw_prompt_txt, is_generating):
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loaded_parameter_dict = json.loads(raw_prompt_txt)
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def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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loaded_parameter_dict = raw_metadata
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if isinstance(raw_metadata, str):
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loaded_parameter_dict = json.loads(raw_metadata)
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assert isinstance(loaded_parameter_dict, dict)
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results = [True, 1]
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try:
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h = loaded_parameter_dict.get('Prompt', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Negative Prompt', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Styles', None)
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h = eval(h)
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assert isinstance(h, list)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Performance', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Resolution', None)
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width, height = eval(h)
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formatted = modules.config.add_ratio(f'{width}*{height}')
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if formatted in modules.config.available_aspect_ratios:
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results.append(formatted)
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results.append(-1)
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results.append(-1)
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else:
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results.append(gr.update())
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results.append(width)
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results.append(height)
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except:
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Sharpness', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Guidance Scale', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('ADM Guidance', None)
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p, n, e = eval(h)
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results.append(float(p))
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results.append(float(n))
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results.append(float(e))
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except:
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Base Model', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Refiner Model', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Refiner Switch', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Sampler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Scheduler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Seed', None)
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assert h is not None
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h = int(h)
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results.append(False)
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results.append(h)
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except:
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results.append(gr.update())
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results.append(gr.update())
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get_str('prompt', 'Prompt', loaded_parameter_dict, results)
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get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
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get_list('styles', 'Styles', loaded_parameter_dict, results)
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get_str('performance', 'Performance', loaded_parameter_dict, results)
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get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
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get_float('sharpness', 'Sharpness', loaded_parameter_dict, results)
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get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)
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get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)
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get_str('base_model', 'Base Model', loaded_parameter_dict, results)
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get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)
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get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
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get_str('sampler', 'Sampler', loaded_parameter_dict, results)
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get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
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get_seed('seed', 'Seed', loaded_parameter_dict, results)
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if is_generating:
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results.append(gr.update())
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else:
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results.append(gr.update(visible=True))
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results.append(gr.update(visible=False))
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for i in range(1, 6):
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@ -146,3 +46,94 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
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results.append(gr.update())
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return results
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def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = source_dict.get(key, default)
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assert isinstance(h, str)
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results.append(h)
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except:
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if fallback is not None:
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get_str(fallback, None, source_dict, results, default)
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return
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results.append(gr.update())
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def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = source_dict.get(key, default)
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h = eval(h)
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assert isinstance(h, list)
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results.append(h)
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except:
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if fallback is not None:
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get_list(fallback, None, source_dict, results, default)
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return
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results.append(gr.update())
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def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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try:
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h = source_dict.get(key, default)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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if fallback is not None:
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get_float(fallback, None, source_dict, results, default)
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return
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results.append(gr.update())
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||||
def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, default)
|
||||
width, height = eval(h)
|
||||
formatted = modules.config.add_ratio(f'{width}*{height}')
|
||||
if formatted in modules.config.available_aspect_ratios:
|
||||
results.append(formatted)
|
||||
results.append(-1)
|
||||
results.append(-1)
|
||||
else:
|
||||
results.append(gr.update())
|
||||
results.append(width)
|
||||
results.append(height)
|
||||
except:
|
||||
if fallback is not None:
|
||||
get_resolution(fallback, None, source_dict, results, default)
|
||||
return
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, default)
|
||||
assert h is not None
|
||||
h = int(h)
|
||||
results.append(False)
|
||||
results.append(h)
|
||||
except:
|
||||
if fallback is not None:
|
||||
get_seed(fallback, None, source_dict, results, default)
|
||||
return
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
||||
|
||||
def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
|
||||
try:
|
||||
h = source_dict.get(key, default)
|
||||
p, n, e = eval(h)
|
||||
results.append(float(p))
|
||||
results.append(float(n))
|
||||
results.append(float(e))
|
||||
except:
|
||||
if fallback is not None:
|
||||
get_adm_guidance(fallback, None, source_dict, results, default)
|
||||
return
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
results.append(gr.update())
|
||||
|
|
|
|||
|
|
@ -0,0 +1,209 @@
|
|||
import json
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from PIL import Image
|
||||
|
||||
import modules.config
|
||||
import fooocus_version
|
||||
# import advanced_parameters
|
||||
from modules.util import quote, is_json
|
||||
|
||||
|
||||
class MetadataScheme(Enum):
|
||||
FOOOCUS = 'fooocus'
|
||||
A1111 = 'a1111'
|
||||
|
||||
|
||||
class MetadataParser(ABC):
|
||||
@abstractmethod
|
||||
def parse_json(self, metadata: dict):
|
||||
raise NotImplementedError
|
||||
|
||||
# TODO add data to parse
|
||||
@abstractmethod
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class A1111MetadataParser(MetadataParser):
|
||||
|
||||
def parse_json(self, metadata: dict):
|
||||
# TODO add correct mapping
|
||||
pass
|
||||
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
# TODO add correct mapping
|
||||
|
||||
data = {k: v for _, k, v, _, _ in metadata}
|
||||
|
||||
# TODO check if correct
|
||||
width, heigth = data['resolution'].split(', ')
|
||||
|
||||
generation_params = {
|
||||
"Steps": data['steps'],
|
||||
"Sampler": data['sampler'],
|
||||
"CFG scale": data['guidance_scale'],
|
||||
"Seed": data['seed'],
|
||||
"Size": f"{width}x{heigth}",
|
||||
# "Model hash": base_model_hash,
|
||||
"Model": data['base_model'].split('.')[0],
|
||||
# "Lora hashes": lora_hashes_string,
|
||||
# "Denoising strength": data['denoising_strength'],
|
||||
"Version": f"Fooocus {data['version']}"
|
||||
}
|
||||
|
||||
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])
|
||||
positive_prompt_resolved = ', '.join(data['full_prompt'])
|
||||
negative_prompt_resolved = ', '.join(data['full_negative_prompt'])
|
||||
negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
|
||||
return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
||||
class FooocusMetadataParser(MetadataParser):
|
||||
|
||||
def parse_json(self, metadata: dict):
|
||||
# TODO add mapping if necessary
|
||||
return metadata
|
||||
|
||||
def parse_string(self, metadata: dict) -> str:
|
||||
|
||||
return json.dumps({k: v for _, k, v, _, _ in metadata})
|
||||
# metadata = {
|
||||
# # prompt with wildcards
|
||||
# 'prompt': raw_prompt, 'negative_prompt': raw_negative_prompt,
|
||||
# # prompt with resolved wildcards
|
||||
# 'real_prompt': task['log_positive_prompt'], 'real_negative_prompt': task['log_negative_prompt'],
|
||||
# # prompt with resolved wildcards, styles and prompt expansion
|
||||
# 'complete_prompt_positive': task['positive'], 'complete_prompt_negative': task['negative'],
|
||||
# 'styles': str(raw_style_selections),
|
||||
# 'seed': task['task_seed'], 'width': width, 'height': height,
|
||||
# 'sampler': sampler_name, 'scheduler': scheduler_name, 'performance': performance_selection,
|
||||
# 'steps': steps, 'refiner_switch': refiner_switch, 'sharpness': sharpness, 'cfg': cfg_scale,
|
||||
# 'base_model': base_model_name, 'base_model_hash': base_model_hash, 'refiner_model': refiner_model_name,
|
||||
# 'denoising_strength': denoising_strength,
|
||||
# 'freeu': advanced_parameters.freeu_enabled,
|
||||
# 'img2img': input_image_checkbox,
|
||||
# 'prompt_expansion': task['expansion']
|
||||
# }
|
||||
#
|
||||
# if advanced_parameters.freeu_enabled:
|
||||
# metadata |= {
|
||||
# 'freeu_b1': advanced_parameters.freeu_b1, 'freeu_b2': advanced_parameters.freeu_b2,
|
||||
# 'freeu_s1': advanced_parameters.freeu_s1, 'freeu_s2': advanced_parameters.freeu_s2
|
||||
# }
|
||||
#
|
||||
# if 'vary' in goals:
|
||||
# metadata |= {
|
||||
# 'uov_method': uov_method
|
||||
# }
|
||||
#
|
||||
# if 'upscale' in goals:
|
||||
# metadata |= {
|
||||
# 'uov_method': uov_method, 'scale': f
|
||||
# }
|
||||
#
|
||||
# if 'inpaint' in goals:
|
||||
# if len(outpaint_selections) > 0:
|
||||
# metadata |= {
|
||||
# 'outpaint_selections': outpaint_selections
|
||||
# }
|
||||
#
|
||||
# metadata |= {
|
||||
# 'inpaint_additional_prompt': inpaint_additional_prompt,
|
||||
# 'inpaint_mask_upload': advanced_parameters.inpaint_mask_upload_checkbox,
|
||||
# 'invert_mask': advanced_parameters.invert_mask_checkbox,
|
||||
# 'inpaint_disable_initial_latent': advanced_parameters.inpaint_disable_initial_latent,
|
||||
# 'inpaint_engine': advanced_parameters.inpaint_engine,
|
||||
# 'inpaint_strength': advanced_parameters.inpaint_strength,
|
||||
# 'inpaint_respective_field': advanced_parameters.inpaint_respective_field,
|
||||
# }
|
||||
#
|
||||
# if 'cn' in goals:
|
||||
# metadata |= {
|
||||
# 'canny_low_threshold': advanced_parameters.canny_low_threshold,
|
||||
# 'canny_high_threshold': advanced_parameters.canny_high_threshold,
|
||||
# }
|
||||
#
|
||||
# ip_list = {x: [] for x in flags.ip_list}
|
||||
# cn_task_index = 1
|
||||
# for cn_type in ip_list:
|
||||
# for cn_task in cn_tasks[cn_type]:
|
||||
# cn_img, cn_stop, cn_weight = cn_task
|
||||
# metadata |= {
|
||||
# f'image_prompt_{cn_task_index}': {
|
||||
# 'cn_type': cn_type, 'cn_stop': cn_stop, 'cn_weight': cn_weight,
|
||||
# }
|
||||
# }
|
||||
# cn_task_index += 1
|
||||
#
|
||||
# metadata |= {
|
||||
# 'software': f'Fooocus v{fooocus_version.version}',
|
||||
# }
|
||||
# if modules.config.metadata_created_by != '':
|
||||
# metadata |= {
|
||||
# 'created_by': modules.config.metadata_created_by
|
||||
# }
|
||||
# # return json.dumps(metadata, ensure_ascii=True) TODO check if possible
|
||||
# return json.dumps(metadata, ensure_ascii=False)
|
||||
|
||||
|
||||
def get_metadata_parser(metadata_scheme: str) -> MetadataParser:
|
||||
match metadata_scheme:
|
||||
case MetadataScheme.FOOOCUS.value:
|
||||
return FooocusMetadataParser()
|
||||
case MetadataScheme.A1111.value:
|
||||
return A1111MetadataParser()
|
||||
case _:
|
||||
raise NotImplementedError
|
||||
|
||||
# IGNORED_INFO_KEYS = {
|
||||
# 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
|
||||
# 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
|
||||
# 'icc_profile', 'chromaticity', 'photoshop',
|
||||
# }
|
||||
|
||||
|
||||
def read_info_from_image(filepath) -> tuple[str | None, dict, str | None]:
|
||||
with Image.open(filepath) as image:
|
||||
items = (image.info or {}).copy()
|
||||
|
||||
parameters = items.pop('parameters', None)
|
||||
if parameters is not None and is_json(parameters):
|
||||
parameters = json.loads(parameters)
|
||||
|
||||
metadata_scheme = items.pop('fooocus_scheme', None)
|
||||
|
||||
# if "exif" in items:
|
||||
# exif_data = items["exif"]
|
||||
# try:
|
||||
# exif = piexif.load(exif_data)
|
||||
# except OSError:
|
||||
# # memory / exif was not valid so piexif tried to read from a file
|
||||
# exif = None
|
||||
# exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
|
||||
# try:
|
||||
# exif_comment = piexif.helper.UserComment.load(exif_comment)
|
||||
# except ValueError:
|
||||
# exif_comment = exif_comment.decode('utf8', errors="ignore")
|
||||
#
|
||||
# if exif_comment:
|
||||
# items['exif comment'] = exif_comment
|
||||
# parameters = exif_comment
|
||||
|
||||
# for field in IGNORED_INFO_KEYS:
|
||||
# items.pop(field, None)
|
||||
|
||||
# if items.get("Software", None) == "NovelAI":
|
||||
# try:
|
||||
# json_info = json.loads(items["Comment"])
|
||||
# sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
|
||||
#
|
||||
# geninfo = f"""{items["Description"]}
|
||||
# Negative prompt: {json_info["uc"]}
|
||||
# Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
|
||||
# except Exception:
|
||||
# errors.report("Error parsing NovelAI image generation parameters",
|
||||
# exc_info=True)
|
||||
|
||||
return parameters, items, metadata_scheme
|
||||
|
|
@ -7,6 +7,7 @@ import urllib.parse
|
|||
from PIL import Image
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
from modules.util import generate_temp_filename
|
||||
from modules.metadata import MetadataScheme
|
||||
|
||||
|
||||
log_cache = {}
|
||||
|
|
@ -19,7 +20,9 @@ def get_current_html_path():
|
|||
return html_name
|
||||
|
||||
|
||||
def log(img, dic, metadata=None, save_metadata_to_image=False):
|
||||
def log(img, metadata, save_metadata_to_image=False, metadata_scheme: str = MetadataScheme.FOOOCUS.value):
|
||||
assert metadata_scheme in [item.value for item in MetadataScheme]
|
||||
|
||||
if args_manager.args.disable_image_log:
|
||||
return
|
||||
|
||||
|
|
@ -27,8 +30,12 @@ def log(img, dic, metadata=None, save_metadata_to_image=False):
|
|||
os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
|
||||
|
||||
if save_metadata_to_image:
|
||||
metadata_parser = modules.metadata.get_metadata_parser(metadata_scheme)
|
||||
parsed_parameters = metadata_parser.parse_string(metadata)
|
||||
|
||||
pnginfo = PngInfo()
|
||||
pnginfo.add_text('parameters', metadata)
|
||||
pnginfo.add_text('parameters', parsed_parameters)
|
||||
pnginfo.add_text('fooocus_scheme', metadata_scheme)
|
||||
else:
|
||||
pnginfo = None
|
||||
Image.fromarray(img).save(local_temp_filename, pnginfo=pnginfo)
|
||||
|
|
@ -40,7 +47,7 @@ def log(img, dic, metadata=None, save_metadata_to_image=False):
|
|||
"body { background-color: #121212; color: #E0E0E0; } "
|
||||
"a { color: #BB86FC; } "
|
||||
".metadata { border-collapse: collapse; width: 100%; } "
|
||||
".metadata .key { width: 15%; } "
|
||||
".metadata .label { width: 15%; } "
|
||||
".metadata .value { width: 85%; font-weight: bold; } "
|
||||
".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } "
|
||||
".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } "
|
||||
|
|
@ -93,12 +100,13 @@ def log(img, dic, metadata=None, save_metadata_to_image=False):
|
|||
item = f"<div id=\"{div_name}\" class=\"image-container\"><hr><table><tr>\n"
|
||||
item += f"<td><a href=\"{only_name}\" target=\"_blank\"><img src='{only_name}' onerror=\"this.closest('.image-container').style.display='none';\" loading='lazy'></img></a><div>{only_name}</div></td>"
|
||||
item += "<td><table class='metadata'>"
|
||||
for key, value in dic:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='key'>{key}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
for label, key, value, showable, copyable in metadata:
|
||||
if showable:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
item += "</table>"
|
||||
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for k, v in dic}, indent=0), safe='')
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, _, copyable in metadata if copyable}, indent=0), safe='')
|
||||
item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
|
||||
|
||||
item += "</td>"
|
||||
|
|
|
|||
|
|
@ -178,6 +178,7 @@ def get_files_from_folder(folder_path, exensions=None, name_filter=None):
|
|||
|
||||
return sorted(filenames, key=lambda x: -1 if os.sep in x else 1)
|
||||
|
||||
|
||||
def calculate_sha256(filename):
|
||||
hash_sha256 = sha256()
|
||||
blksize = 1024 * 1024
|
||||
|
|
@ -188,8 +189,18 @@ def calculate_sha256(filename):
|
|||
|
||||
return hash_sha256.hexdigest()
|
||||
|
||||
|
||||
def quote(text):
|
||||
if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
|
||||
return text
|
||||
|
||||
return json.dumps(text, ensure_ascii=False)
|
||||
return json.dumps(text, ensure_ascii=False)
|
||||
|
||||
|
||||
def is_json(data: str) -> bool:
|
||||
try:
|
||||
loaded_json = json.loads(data)
|
||||
assert isinstance(loaded_json, dict)
|
||||
except ValueError:
|
||||
return False
|
||||
return True
|
||||
|
|
|
|||
83
webui.py
83
webui.py
|
|
@ -14,6 +14,7 @@ import modules.gradio_hijack as grh
|
|||
import modules.advanced_parameters as advanced_parameters
|
||||
import modules.style_sorter as style_sorter
|
||||
import modules.meta_parser
|
||||
import modules.metadata
|
||||
import args_manager
|
||||
import copy
|
||||
|
||||
|
|
@ -21,6 +22,7 @@ from modules.sdxl_styles import legal_style_names
|
|||
from modules.private_logger import get_current_html_path
|
||||
from modules.ui_gradio_extensions import reload_javascript
|
||||
from modules.auth import auth_enabled, check_auth
|
||||
from modules.util import is_json
|
||||
|
||||
|
||||
def generate_clicked(*args):
|
||||
|
|
@ -208,6 +210,28 @@ with shared.gradio_root:
|
|||
value=flags.desc_type_photo)
|
||||
desc_btn = gr.Button(value='Describe this Image into Prompt')
|
||||
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
|
||||
with gr.TabItem(label='Load Metadata') as load_tab:
|
||||
with gr.Column():
|
||||
metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
|
||||
metadata_json = gr.JSON(label='Metadata')
|
||||
metadata_import_button = gr.Button(value='Overwrite Input Values')
|
||||
|
||||
def trigger_metadata_preview(filepath):
|
||||
parameters, items, metadata_scheme = modules.metadata.read_info_from_image(filepath)
|
||||
|
||||
results = {}
|
||||
if parameters is not None:
|
||||
results['parameters'] = parameters
|
||||
if items:
|
||||
results['items'] = items
|
||||
if metadata_scheme is not None:
|
||||
results['metadata_scheme'] = metadata_scheme
|
||||
|
||||
return results
|
||||
|
||||
metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image,
|
||||
outputs=metadata_json)
|
||||
|
||||
switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}"
|
||||
down_js = "() => {viewer_to_bottom();}"
|
||||
|
||||
|
|
@ -548,14 +572,8 @@ with shared.gradio_root:
|
|||
|
||||
def parse_meta(raw_prompt_txt, is_generating):
|
||||
loaded_json = None
|
||||
try:
|
||||
if '{' in raw_prompt_txt:
|
||||
if '}' in raw_prompt_txt:
|
||||
if ':' in raw_prompt_txt:
|
||||
loaded_json = json.loads(raw_prompt_txt)
|
||||
assert isinstance(loaded_json, dict)
|
||||
except:
|
||||
loaded_json = None
|
||||
if is_json(raw_prompt_txt):
|
||||
loaded_json = json.loads(raw_prompt_txt)
|
||||
|
||||
if loaded_json is None:
|
||||
if is_generating:
|
||||
|
|
@ -567,31 +585,30 @@ with shared.gradio_root:
|
|||
|
||||
prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
|
||||
|
||||
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=[
|
||||
advanced_checkbox,
|
||||
image_number,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
style_selections,
|
||||
performance_selection,
|
||||
aspect_ratios_selection,
|
||||
overwrite_width,
|
||||
overwrite_height,
|
||||
sharpness,
|
||||
guidance_scale,
|
||||
adm_scaler_positive,
|
||||
adm_scaler_negative,
|
||||
adm_scaler_end,
|
||||
base_model,
|
||||
refiner_model,
|
||||
refiner_switch,
|
||||
sampler_name,
|
||||
scheduler_name,
|
||||
seed_random,
|
||||
image_seed,
|
||||
generate_button,
|
||||
load_parameter_button
|
||||
] + lora_ctrls, queue=False, show_progress=False)
|
||||
load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
|
||||
performance_selection, aspect_ratios_selection, overwrite_width, overwrite_height,
|
||||
sharpness, guidance_scale, adm_scaler_positive, adm_scaler_negative, adm_scaler_end,
|
||||
base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random,
|
||||
image_seed, generate_button, load_parameter_button] + lora_ctrls
|
||||
|
||||
load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
|
||||
|
||||
def trigger_metadata_import(filepath, state_is_generating):
|
||||
parameters, items, metadata_scheme = modules.metadata.read_info_from_image(filepath)
|
||||
|
||||
if parameters is None:
|
||||
pass
|
||||
|
||||
if metadata_scheme is None and isinstance(parameters, dict):
|
||||
metadata_scheme = modules.metadata.MetadataScheme.FOOOCUS.value
|
||||
|
||||
|
||||
metadata_parser = modules.metadata.get_metadata_parser(metadata_scheme)
|
||||
parsed_parameters = metadata_parser.parse_json(parameters)
|
||||
return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
|
||||
|
||||
|
||||
metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
|
||||
|
||||
generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
|
||||
outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
|
||||
|
|
|
|||
Loading…
Reference in New Issue