mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
Merge branch 'main' of https://github.com/lllyasviel/Fooocus into lora-reference-parsing
This commit is contained in:
cantor-set
+58
-32
@@ -1,4 +1,5 @@
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import threading
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import re
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from modules.patch import PatchSettings, patch_settings, patch_all
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patch_all()
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@@ -45,6 +46,7 @@ def worker():
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from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
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from modules.private_logger import log
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from extras.expansion import safe_str
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<<<<<<< HEAD
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from modules.util import (
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remove_empty_str,
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HWC3, resize_image,
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@@ -56,6 +58,10 @@ def worker():
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ordinal_suffix,
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parse_lora_references_from_prompt
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)
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=======
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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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get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
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>>>>>>> 978267f461e204c6c4359a79ed818ee2e3e1af39
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from modules.upscaler import perform_upscale
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from modules.flags import Performance
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from modules.meta_parser import get_metadata_parser, MetadataScheme
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@@ -132,14 +138,6 @@ def worker():
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async_task.results = async_task.results + [wall]
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return
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def apply_enabled_loras(loras):
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enabled_loras = []
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for lora_enabled, lora_model, lora_weight in loras:
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if lora_enabled:
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enabled_loras.append([lora_model, lora_weight])
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return enabled_loras
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@torch.no_grad()
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@torch.inference_mode()
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def handler(async_task):
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@@ -157,14 +155,19 @@ def worker():
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image_number = args.pop()
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output_format = args.pop()
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image_seed = args.pop()
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read_wildcards_in_order = args.pop()
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sharpness = args.pop()
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guidance_scale = args.pop()
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base_model_name = args.pop()
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refiner_model_name = args.pop()
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refiner_switch = args.pop()
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<<<<<<< HEAD
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loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
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=======
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loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
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>>>>>>> 978267f461e204c6c4359a79ed818ee2e3e1af39
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input_image_checkbox = args.pop()
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current_tab = args.pop()
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uov_method = args.pop()
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@@ -261,6 +264,25 @@ def worker():
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adm_scaler_negative = 1.0
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adm_scaler_end = 0.0
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elif performance_selection == Performance.LIGHTNING:
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print('Enter Lightning mode.')
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progressbar(async_task, 1, 'Downloading Lightning components ...')
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loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
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if refiner_model_name != 'None':
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print(f'Refiner disabled in Lightning mode.')
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refiner_model_name = 'None'
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sampler_name = 'euler'
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scheduler_name = 'sgm_uniform'
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sharpness = 0.0
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guidance_scale = 1.0
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adaptive_cfg = 1.0
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refiner_switch = 1.0
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adm_scaler_positive = 1.0
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adm_scaler_negative = 1.0
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adm_scaler_end = 0.0
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print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
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print(f'[Parameters] Sharpness = {sharpness}')
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print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
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@@ -358,7 +380,7 @@ def worker():
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print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
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if refiner_model_name == 'None':
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use_synthetic_refiner = True
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refiner_switch = 0.5
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refiner_switch = 0.8
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else:
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inpaint_head_model_path, inpaint_patch_model_path = None, None
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print(f'[Inpaint] Parameterized inpaint is disabled.')
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@@ -437,16 +459,16 @@ def worker():
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for i in range(image_number):
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if disable_seed_increment:
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task_seed = seed
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task_seed = seed % (constants.MAX_SEED + 1)
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else:
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task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
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task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
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task_prompt = apply_wildcards(prompt, task_rng)
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task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
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task_prompt = apply_arrays(task_prompt, i)
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task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
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task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
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task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
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task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
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task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
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task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
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positive_basic_workloads = []
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negative_basic_workloads = []
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@@ -802,7 +824,7 @@ def worker():
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try:
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if async_task.last_stop is not False:
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ldm_patched.model_management.interrupt_current_processing()
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ldm_patched.modules.model_management.interrupt_current_processing()
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positive_cond, negative_cond = task['c'], task['uc']
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if 'cn' in goals:
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@@ -845,17 +867,21 @@ def worker():
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('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
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('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
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('Styles', 'styles', str(raw_style_selections)),
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('Performance', 'performance', performance_selection.value),
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('Resolution', 'resolution', str((width, height))),
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('Guidance Scale', 'guidance_scale', guidance_scale),
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('Sharpness', 'sharpness', sharpness),
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('ADM Guidance', 'adm_guidance', str((
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', 'base_model', base_model_name),
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('Refiner Model', 'refiner_model', refiner_model_name),
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('Refiner Switch', 'refiner_switch', refiner_switch)]
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('Performance', 'performance', performance_selection.value)]
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if performance_selection.steps() != steps:
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d.append(('Steps', 'steps', steps))
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d += [('Resolution', 'resolution', str((width, height))),
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('Guidance Scale', 'guidance_scale', guidance_scale),
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('Sharpness', 'sharpness', sharpness),
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('ADM Guidance', 'adm_guidance', str((
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', 'base_model', base_model_name),
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('Refiner Model', 'refiner_model', refiner_model_name),
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('Refiner Switch', 'refiner_switch', refiner_switch)]
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if refiner_model_name != 'None':
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if overwrite_switch > 0:
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@@ -867,22 +893,22 @@ 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(('Seed', 'seed', task['task_seed']))
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d.append(('Seed', 'seed', str(task['task_seed'])))
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if freeu_enabled:
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d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
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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'lora_combined_{li + 1}', f'{n} : {w}'))
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metadata_parser = None
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if save_metadata_to_images:
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metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
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metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
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task['log_negative_prompt'], task['negative'],
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steps, base_model_name, refiner_model_name, loras)
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for li, (n, w) in enumerate(loras):
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if n != 'None':
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d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
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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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