mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
Merge branch 'develop' into feature/add-nsfw-filter
This commit is contained in:
Manuel Schmid
+13
-6
@@ -44,7 +44,7 @@ def worker():
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import args_manager
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from extras.censor import censor_batch, censor_single
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from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
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from modules.sdxl_styles import apply_style, get_random_style, apply_wildcards, fooocus_expansion, apply_arrays, random_style_name
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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, get_image_shape_ceil, set_image_shape_ceil, \
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@@ -172,6 +172,7 @@ def worker():
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adaptive_cfg = args.pop()
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sampler_name = args.pop()
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scheduler_name = args.pop()
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vae_name = args.pop()
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overwrite_step = args.pop()
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overwrite_switch = args.pop()
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overwrite_width = args.pop()
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@@ -434,7 +435,7 @@ def worker():
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progressbar(async_task, 3, 'Loading models ...')
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pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
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loras=loras, base_model_additional_loras=base_model_additional_loras,
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use_synthetic_refiner=use_synthetic_refiner)
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use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
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progressbar(async_task, 3, 'Processing prompts ...')
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tasks = []
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@@ -455,8 +456,12 @@ def worker():
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positive_basic_workloads = []
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negative_basic_workloads = []
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task_styles = style_selections.copy()
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if use_style:
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for s in style_selections:
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for i, s in enumerate(task_styles):
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if s == random_style_name:
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s = get_random_style(task_rng)
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task_styles[i] = s
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p, n = apply_style(s, positive=task_prompt)
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positive_basic_workloads = positive_basic_workloads + p
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negative_basic_workloads = negative_basic_workloads + n
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@@ -484,6 +489,7 @@ def worker():
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negative_top_k=len(negative_basic_workloads),
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log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
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log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
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styles=task_styles
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))
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if use_expansion:
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@@ -856,7 +862,7 @@ def worker():
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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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('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
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('Performance', 'performance', performance_selection.value)]
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if performance_selection.steps() != steps:
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@@ -883,6 +889,7 @@ def worker():
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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(('VAE', 'vae', vae_name))
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d.append(('Seed', 'seed', str(task['task_seed'])))
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if freeu_enabled:
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@@ -897,10 +904,10 @@ def worker():
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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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steps, base_model_name, refiner_model_name, loras, vae_name)
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d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
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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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img_paths.append(log(x, d, metadata_parser, output_format, task))
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yield_result(async_task, img_paths, black_out_nsfw, False, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
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except ldm_patched.modules.model_management.InterruptProcessingException as e:
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+13
-1
@@ -189,6 +189,7 @@ paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/check
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paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
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path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
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path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
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path_vae = get_dir_or_set_default('path_vae', '../models/vae/')
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path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
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path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
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path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
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@@ -347,6 +348,11 @@ default_scheduler = get_config_item_or_set_default(
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default_value='karras',
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validator=lambda x: x in modules.flags.scheduler_list
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)
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default_vae = get_config_item_or_set_default(
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key='default_vae',
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default_value=modules.flags.default_vae,
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validator=lambda x: isinstance(x, str)
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)
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default_styles = get_config_item_or_set_default(
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key='default_styles',
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default_value=[
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@@ -541,6 +547,7 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
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model_filenames = []
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lora_filenames = []
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vae_filenames = []
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wildcard_filenames = []
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sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
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@@ -552,15 +559,20 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
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if extensions is None:
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extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
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files = []
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if not isinstance(folder_paths, list):
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folder_paths = [folder_paths]
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for folder in folder_paths:
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files += get_files_from_folder(folder, extensions, name_filter)
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return files
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def update_files():
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global model_filenames, lora_filenames, wildcard_filenames, available_presets
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global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
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model_filenames = get_model_filenames(paths_checkpoints)
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lora_filenames = get_model_filenames(paths_loras)
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vae_filenames = get_model_filenames(path_vae)
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wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
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available_presets = get_presets()
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return
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+6
-4
@@ -35,12 +35,13 @@ opModelSamplingDiscrete = ModelSamplingDiscrete()
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class StableDiffusionModel:
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def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
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def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
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self.unet = unet
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self.vae = vae
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self.clip = clip
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self.clip_vision = clip_vision
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self.filename = filename
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self.vae_filename = vae_filename
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self.unet_with_lora = unet
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self.clip_with_lora = clip
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self.visited_loras = ''
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@@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
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@torch.no_grad()
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@torch.inference_mode()
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def load_model(ckpt_filename):
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unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings)
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return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename)
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def load_model(ckpt_filename, vae_filename=None):
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unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
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vae_filename_param=vae_filename)
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return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)
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@torch.no_grad()
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@@ -3,6 +3,7 @@ import os
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import torch
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import modules.patch
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import modules.config
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import modules.flags
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import ldm_patched.modules.model_management
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import ldm_patched.modules.latent_formats
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import modules.inpaint_worker
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@@ -58,17 +59,21 @@ def assert_model_integrity():
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_base_model(name):
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def refresh_base_model(name, vae_name=None):
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global model_base
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filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
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if model_base.filename == filename:
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vae_filename = None
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if vae_name is not None and vae_name != modules.flags.default_vae:
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vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
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if model_base.filename == filename and model_base.vae_filename == vae_filename:
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return
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model_base = core.StableDiffusionModel()
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model_base = core.load_model(filename)
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model_base = core.load_model(filename, vae_filename)
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print(f'Base model loaded: {model_base.filename}')
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print(f'VAE loaded: {model_base.vae_filename}')
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return
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@@ -216,7 +221,7 @@ def prepare_text_encoder(async_call=True):
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@torch.no_grad()
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@torch.inference_mode()
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def refresh_everything(refiner_model_name, base_model_name, loras,
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base_model_additional_loras=None, use_synthetic_refiner=False):
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base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
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global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
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final_unet = None
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@@ -227,11 +232,11 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
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if use_synthetic_refiner and refiner_model_name == 'None':
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print('Synthetic Refiner Activated')
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refresh_base_model(base_model_name)
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refresh_base_model(base_model_name, vae_name)
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synthesize_refiner_model()
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else:
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refresh_refiner_model(refiner_model_name)
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refresh_base_model(base_model_name)
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refresh_base_model(base_model_name, vae_name)
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refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
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assert_model_integrity()
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@@ -254,7 +259,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
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refresh_everything(
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refiner_model_name=modules.config.default_refiner_model_name,
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base_model_name=modules.config.default_base_model_name,
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loras=get_enabled_loras(modules.config.default_loras)
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loras=get_enabled_loras(modules.config.default_loras),
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vae_name=modules.config.default_vae,
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)
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@@ -53,6 +53,8 @@ SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
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sampler_list = SAMPLER_NAMES
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scheduler_list = SCHEDULER_NAMES
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default_vae = 'Default (model)'
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refiner_swap_method = 'joint'
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cn_ip = "ImagePrompt"
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+24
-7
@@ -46,6 +46,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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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_str('vae', 'VAE', 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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@@ -253,6 +254,7 @@ class MetadataParser(ABC):
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self.refiner_model_name: str = ''
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self.refiner_model_hash: str = ''
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self.loras: list = []
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self.vae_name: str = ''
|
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|
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@abstractmethod
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def get_scheme(self) -> MetadataScheme:
|
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@@ -267,7 +269,7 @@ class MetadataParser(ABC):
|
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raise NotImplementedError
|
||||
|
||||
def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
|
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refiner_model_name, loras):
|
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refiner_model_name, loras, vae_name):
|
||||
self.raw_prompt = raw_prompt
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self.full_prompt = full_prompt
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self.raw_negative_prompt = raw_negative_prompt
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@@ -289,6 +291,7 @@ class MetadataParser(ABC):
|
||||
lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
|
||||
lora_hash = get_sha256(lora_path)
|
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self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
|
||||
self.vae_name = Path(vae_name).stem
|
||||
|
||||
@staticmethod
|
||||
def remove_special_loras(lora_filenames):
|
||||
@@ -310,6 +313,7 @@ class A1111MetadataParser(MetadataParser):
|
||||
'steps': 'Steps',
|
||||
'sampler': 'Sampler',
|
||||
'scheduler': 'Scheduler',
|
||||
'vae': 'VAE',
|
||||
'guidance_scale': 'CFG scale',
|
||||
'seed': 'Seed',
|
||||
'resolution': 'Size',
|
||||
@@ -397,13 +401,12 @@ class A1111MetadataParser(MetadataParser):
|
||||
data['sampler'] = k
|
||||
break
|
||||
|
||||
for key in ['base_model', 'refiner_model']:
|
||||
for key in ['base_model', 'refiner_model', 'vae']:
|
||||
if key in data:
|
||||
for filename in modules.config.model_filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = filename
|
||||
break
|
||||
if key == 'vae':
|
||||
self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
|
||||
else:
|
||||
self.add_extension_to_filename(data, modules.config.model_filenames, key)
|
||||
|
||||
lora_data = ''
|
||||
if 'lora_weights' in data and data['lora_weights'] != '':
|
||||
@@ -433,6 +436,7 @@ class A1111MetadataParser(MetadataParser):
|
||||
|
||||
sampler = data['sampler']
|
||||
scheduler = data['scheduler']
|
||||
|
||||
if sampler in SAMPLERS and SAMPLERS[sampler] != '':
|
||||
sampler = SAMPLERS[sampler]
|
||||
if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
|
||||
@@ -451,6 +455,7 @@ class A1111MetadataParser(MetadataParser):
|
||||
|
||||
self.fooocus_to_a1111['performance']: data['performance'],
|
||||
self.fooocus_to_a1111['scheduler']: scheduler,
|
||||
self.fooocus_to_a1111['vae']: Path(data['vae']).stem,
|
||||
# workaround for multiline prompts
|
||||
self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
|
||||
self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
|
||||
@@ -491,6 +496,14 @@ class A1111MetadataParser(MetadataParser):
|
||||
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()
|
||||
|
||||
@staticmethod
|
||||
def add_extension_to_filename(data, filenames, key):
|
||||
for filename in filenames:
|
||||
path = Path(filename)
|
||||
if data[key] == path.stem:
|
||||
data[key] = filename
|
||||
break
|
||||
|
||||
|
||||
class FooocusMetadataParser(MetadataParser):
|
||||
def get_scheme(self) -> MetadataScheme:
|
||||
@@ -499,6 +512,7 @@ class FooocusMetadataParser(MetadataParser):
|
||||
def parse_json(self, metadata: dict) -> dict:
|
||||
model_filenames = modules.config.model_filenames.copy()
|
||||
lora_filenames = modules.config.lora_filenames.copy()
|
||||
vae_filenames = modules.config.vae_filenames.copy()
|
||||
self.remove_special_loras(lora_filenames)
|
||||
for key, value in metadata.items():
|
||||
if value in ['', 'None']:
|
||||
@@ -507,6 +521,8 @@ class FooocusMetadataParser(MetadataParser):
|
||||
metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
|
||||
elif key.startswith('lora_combined_'):
|
||||
metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
|
||||
elif key == 'vae':
|
||||
metadata[key] = self.replace_value_with_filename(key, value, vae_filenames)
|
||||
else:
|
||||
continue
|
||||
|
||||
@@ -533,6 +549,7 @@ class FooocusMetadataParser(MetadataParser):
|
||||
res['refiner_model'] = self.refiner_model_name
|
||||
res['refiner_model_hash'] = self.refiner_model_hash
|
||||
|
||||
res['vae'] = self.vae_name
|
||||
res['loras'] = self.loras
|
||||
|
||||
if modules.config.metadata_created_by != '':
|
||||
|
||||
@@ -14,6 +14,8 @@ def load_file_from_url(
|
||||
|
||||
Returns the path to the downloaded file.
|
||||
"""
|
||||
domain = os.environ.get("HF_MIRROR", "https://huggingface.co").rstrip('/')
|
||||
url = str.replace(url, "https://huggingface.co", domain, 1)
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
if not file_name:
|
||||
parts = urlparse(url)
|
||||
|
||||
@@ -21,7 +21,7 @@ def get_current_html_path(output_format=None):
|
||||
return html_name
|
||||
|
||||
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
|
||||
def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None) -> str:
|
||||
path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
|
||||
output_format = output_format if output_format else modules.config.default_output_format
|
||||
date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
|
||||
@@ -111,9 +111,15 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
|
||||
for label, key, value in metadata:
|
||||
value_txt = str(value).replace('\n', ' </br> ')
|
||||
item += f"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\n"
|
||||
|
||||
if task is not None and 'positive' in task and 'negative' in task:
|
||||
full_prompt_details = f"""<details><summary>Positive</summary>{', '.join(task['positive'])}</details>
|
||||
<details><summary>Negative</summary>{', '.join(task['negative'])}</details>"""
|
||||
item += f"<tr><td class='label'>Full raw prompt</td><td class='value'>{full_prompt_details}</td></tr>\n"
|
||||
|
||||
item += "</table>"
|
||||
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='')
|
||||
js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='')
|
||||
item += f"</br><button onclick=\"to_clipboard('{js_txt}')\">Copy to Clipboard</button>"
|
||||
|
||||
item += "</td>"
|
||||
|
||||
@@ -5,6 +5,7 @@ import math
|
||||
import modules.config
|
||||
|
||||
from modules.util import get_files_from_folder
|
||||
from random import Random
|
||||
|
||||
# cannot use modules.config - validators causing circular imports
|
||||
styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
|
||||
@@ -50,8 +51,13 @@ for styles_file in styles_files:
|
||||
print(f'Failed to load style file {styles_file}')
|
||||
|
||||
style_keys = list(styles.keys())
|
||||
fooocus_expansion = "Fooocus V2"
|
||||
legal_style_names = [fooocus_expansion] + style_keys
|
||||
fooocus_expansion = 'Fooocus V2'
|
||||
random_style_name = 'Random Style'
|
||||
legal_style_names = [fooocus_expansion, random_style_name] + style_keys
|
||||
|
||||
|
||||
def get_random_style(rng: Random) -> str:
|
||||
return rng.choice(list(styles.items()))[0]
|
||||
|
||||
|
||||
def apply_style(style, positive):
|
||||
|
||||
@@ -39,7 +39,7 @@ def javascript_html():
|
||||
head += f'<script type="text/javascript" src="{edit_attention_js_path}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="{viewer_js_path}"></script>\n'
|
||||
head += f'<script type="text/javascript" src="{image_viewer_js_path}"></script>\n'
|
||||
head += f'<meta name="samples-path" content="{samples_path}"></meta>\n'
|
||||
head += f'<meta name="samples-path" content="{samples_path}">\n'
|
||||
|
||||
if args_manager.args.theme:
|
||||
head += f'<script type="text/javascript">set_theme(\"{args_manager.args.theme}\");</script>\n'
|
||||
|
||||
@@ -371,6 +371,9 @@ def is_json(data: str) -> bool:
|
||||
|
||||
|
||||
def get_file_from_folder_list(name, folders):
|
||||
if not isinstance(folders, list):
|
||||
folders = [folders]
|
||||
|
||||
for folder in folders:
|
||||
filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
|
||||
if os.path.isfile(filename):
|
||||
|
||||
Reference in New Issue
Block a user