mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
Merge branch 'main_upstream' into hotfix/prevent-skipping-and-stopping-by-other-users
# Conflicts: # webui.py
This commit is contained in:
Manuel Schmid
+95
-34
@@ -133,13 +133,14 @@ def worker():
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base_model_name = args.pop()
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refiner_model_name = args.pop()
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refiner_switch = args.pop()
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loras = [(args.pop(), args.pop()) for _ in range(5)]
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loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
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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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uov_input_image = args.pop()
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outpaint_selections = args.pop()
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inpaint_input_image = args.pop()
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inpaint_additional_prompt = args.pop()
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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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@@ -180,7 +181,7 @@ def worker():
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if performance_selection == 'Extreme Speed':
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print('Enter LCM mode.')
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progressbar(async_task, 1, 'Downloading LCM components ...')
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base_model_additional_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
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loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
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if refiner_model_name != 'None':
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print(f'Refiner disabled in LCM mode.')
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@@ -206,8 +207,10 @@ def worker():
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modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
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modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
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modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
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print(
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f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}')
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print(f'[Parameters] ADM Scale = '
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f'{modules.patch.positive_adm_scale} : '
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f'{modules.patch.negative_adm_scale} : '
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f'{modules.patch.adm_scaler_end}')
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cfg_scale = float(guidance_scale)
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print(f'[Parameters] CFG = {cfg_scale}')
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@@ -215,7 +218,6 @@ def worker():
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initial_latent = None
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denoising_strength = 1.0
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tiled = False
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inpaint_worker.current_task = None
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width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
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width, height = int(width), int(height)
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@@ -223,9 +225,14 @@ def worker():
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skip_prompt_processing = False
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refiner_swap_method = advanced_parameters.refiner_swap_method
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inpaint_worker.current_task = None
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inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
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inpaint_image = None
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inpaint_mask = None
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inpaint_head_model_path = None
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use_synthetic_refiner = False
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controlnet_canny_path = None
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controlnet_cpds_path = None
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clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
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@@ -272,11 +279,24 @@ def worker():
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inpaint_image = HWC3(inpaint_image)
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if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
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and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
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progressbar(async_task, 1, 'Downloading inpainter ...')
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inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
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advanced_parameters.inpaint_engine)
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base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
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print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
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if inpaint_parameterized:
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progressbar(async_task, 1, 'Downloading inpainter ...')
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modules.config.downloading_upscale_model()
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inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
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advanced_parameters.inpaint_engine)
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base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
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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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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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if inpaint_additional_prompt != '':
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if prompt == '':
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prompt = inpaint_additional_prompt
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else:
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prompt = inpaint_additional_prompt + '\n' + prompt
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goals.append('inpaint')
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if current_tab == 'ip' or \
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advanced_parameters.mixing_image_prompt_and_inpaint or \
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@@ -335,7 +355,8 @@ 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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loras=loras, base_model_additional_loras=base_model_additional_loras,
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use_synthetic_refiner=use_synthetic_refiner)
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progressbar(async_task, 3, 'Processing prompts ...')
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tasks = []
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@@ -378,8 +399,8 @@ def worker():
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uc=None,
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positive_top_k=len(positive_basic_workloads),
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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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log_positive_prompt='; '.join([task_prompt] + task_extra_positive_prompts),
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log_negative_prompt='; '.join([task_negative_prompt] + task_extra_negative_prompts),
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))
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if use_expansion:
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@@ -424,7 +445,15 @@ def worker():
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initial_pixels = core.numpy_to_pytorch(uov_input_image)
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progressbar(async_task, 13, 'VAE encoding ...')
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initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels)
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candidate_vae, _ = pipeline.get_candidate_vae(
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steps=steps,
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switch=switch,
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denoise=denoising_strength,
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refiner_swap_method=refiner_swap_method
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)
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initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels)
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B, C, H, W = initial_latent['samples'].shape
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width = W * 8
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height = H * 8
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@@ -433,10 +462,7 @@ def worker():
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if 'upscale' in goals:
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H, W, C = uov_input_image.shape
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progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
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uov_input_image = core.numpy_to_pytorch(uov_input_image)
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uov_input_image = perform_upscale(uov_input_image)
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uov_input_image = core.pytorch_to_numpy(uov_input_image)[0]
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print(f'Image upscaled.')
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if '1.5x' in uov_method:
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@@ -482,14 +508,20 @@ def worker():
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initial_pixels = core.numpy_to_pytorch(uov_input_image)
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progressbar(async_task, 13, 'VAE encoding ...')
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candidate_vae, _ = pipeline.get_candidate_vae(
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steps=steps,
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switch=switch,
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denoise=denoising_strength,
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refiner_swap_method=refiner_swap_method
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)
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initial_latent = core.encode_vae(
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vae=pipeline.final_vae if pipeline.final_refiner_vae is None else pipeline.final_refiner_vae,
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vae=candidate_vae,
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pixels=initial_pixels, tiled=True)
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B, C, H, W = initial_latent['samples'].shape
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width = W * 8
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height = H * 8
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print(f'Final resolution is {str((height, width))}.')
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refiner_swap_method = 'upscale'
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if 'inpaint' in goals:
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if len(outpaint_selections) > 0:
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@@ -515,13 +547,19 @@ def worker():
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inpaint_image = np.ascontiguousarray(inpaint_image.copy())
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inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
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advanced_parameters.inpaint_strength = 1.0
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advanced_parameters.inpaint_respective_field = 1.0
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inpaint_worker.current_task = inpaint_worker.InpaintWorker(image=inpaint_image, mask=inpaint_mask,
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is_outpaint=len(outpaint_selections) > 0)
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denoising_strength = advanced_parameters.inpaint_strength
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pipeline.final_unet.model.diffusion_model.in_inpaint = True
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inpaint_worker.current_task = inpaint_worker.InpaintWorker(
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image=inpaint_image,
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mask=inpaint_mask,
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use_fill=denoising_strength > 0.99,
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k=advanced_parameters.inpaint_respective_field
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)
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if advanced_parameters.debugging_cn_preprocessor:
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if advanced_parameters.debugging_inpaint_preprocessor:
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yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
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do_not_show_finished_images=True)
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return
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@@ -532,33 +570,47 @@ def worker():
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inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
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inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
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candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
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steps=steps,
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switch=switch,
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denoise=denoising_strength,
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refiner_swap_method=refiner_swap_method
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)
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latent_inpaint, latent_mask = core.encode_vae_inpaint(
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mask=inpaint_pixel_mask,
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vae=pipeline.final_vae,
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vae=candidate_vae,
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pixels=inpaint_pixel_image)
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latent_swap = None
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if pipeline.final_refiner_vae is not None:
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progressbar(async_task, 13, 'VAE Inpaint SD15 encoding ...')
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if candidate_vae_swap is not None:
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progressbar(async_task, 13, 'VAE SD15 encoding ...')
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latent_swap = core.encode_vae(
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vae=pipeline.final_refiner_vae,
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vae=candidate_vae_swap,
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pixels=inpaint_pixel_fill)['samples']
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progressbar(async_task, 13, 'VAE encoding ...')
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latent_fill = core.encode_vae(
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vae=pipeline.final_vae,
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vae=candidate_vae,
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pixels=inpaint_pixel_fill)['samples']
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inpaint_worker.current_task.load_latent(latent_fill=latent_fill,
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latent_inpaint=latent_inpaint,
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latent_mask=latent_mask,
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latent_swap=latent_swap,
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inpaint_head_model_path=inpaint_head_model_path)
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inpaint_worker.current_task.load_latent(
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latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
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if inpaint_parameterized:
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pipeline.final_unet = inpaint_worker.current_task.patch(
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inpaint_head_model_path=inpaint_head_model_path,
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inpaint_latent=latent_inpaint,
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inpaint_latent_mask=latent_mask,
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model=pipeline.final_unet
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)
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if not advanced_parameters.inpaint_disable_initial_latent:
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initial_latent = {'samples': latent_fill}
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B, C, H, W = latent_fill.shape
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height, width = H * 8, W * 8
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final_height, final_width = inpaint_worker.current_task.image.shape[:2]
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initial_latent = {'samples': latent_fill}
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print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
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if 'cn' in goals:
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@@ -629,6 +681,15 @@ def worker():
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all_steps = steps * image_number
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print(f'[Parameters] Denoising Strength = {denoising_strength}')
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if isinstance(initial_latent, dict) and 'samples' in initial_latent:
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log_shape = initial_latent['samples'].shape
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else:
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log_shape = f'Image Space {(height, width)}'
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print(f'[Parameters] Initial Latent shape: {log_shape}')
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preparation_time = time.perf_counter() - execution_start_time
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print(f'Preparation time: {preparation_time:.2f} seconds')
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