mirror of
https://github.com/lllyasviel/Fooocus.git
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i (#559)
This commit is contained in:
lllyasviel
+370
-266
@@ -21,7 +21,10 @@ def worker():
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import modules.path
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import modules.patch
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import comfy.model_management
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import fooocus_extras.preprocessors as preprocessors
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import modules.inpaint_worker as inpaint_worker
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import modules.advanced_parameters as advanced_parameters
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import fooocus_extras.ip_adapter as ip_adapter
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from modules.sdxl_styles import apply_style, aspect_ratios, fooocus_expansion
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from modules.private_logger import log
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@@ -44,24 +47,42 @@ def worker():
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@torch.no_grad()
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@torch.inference_mode()
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def handler(task):
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def handler(args):
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execution_start_time = time.perf_counter()
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prompt, negative_prompt, style_selections, performance_selection, \
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aspect_ratios_selection, image_number, image_seed, sharpness, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, guidance_scale, adaptive_cfg, sampler_name, scheduler_name, \
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overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength, overwrite_upscale_strength, \
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base_model_name, refiner_model_name, \
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l1, w1, l2, w2, l3, w3, l4, w4, l5, w5, \
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input_image_checkbox, current_tab, \
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uov_method, uov_input_image, outpaint_selections, inpaint_input_image = task
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args.reverse()
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prompt = args.pop()
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negative_prompt = args.pop()
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style_selections = args.pop()
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performance_selection = args.pop()
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aspect_ratios_selection = args.pop()
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image_number = args.pop()
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image_seed = 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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loras = [(args.pop(), 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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cn_tasks = {flags.cn_ip: [], flags.cn_canny: [], flags.cn_cpds: []}
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for _ in range(4):
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cn_img = args.pop()
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cn_stop = args.pop()
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cn_weight = args.pop()
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cn_type = args.pop()
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if cn_img is not None:
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cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
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outpaint_selections = [o.lower() for o in outpaint_selections]
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loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
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loras_user_raw_input = copy.deepcopy(loras)
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loras_raw = copy.deepcopy(loras)
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raw_style_selections = copy.deepcopy(style_selections)
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uov_method = uov_method.lower()
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if fooocus_expansion in style_selections:
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@@ -72,15 +93,15 @@ def worker():
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use_style = len(style_selections) > 0
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modules.patch.adaptive_cfg = adaptive_cfg
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modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
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print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
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modules.patch.sharpness = sharpness
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print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
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modules.patch.positive_adm_scale = adm_scaler_positive
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modules.patch.negative_adm_scale = adm_scaler_negative
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modules.patch.adm_scaler_end = adm_scaler_end
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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(f'[Parameters] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}')
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cfg_scale = float(guidance_scale)
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@@ -90,197 +111,18 @@ def worker():
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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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if performance_selection == 'Speed':
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steps = 30
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switch = 20
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else:
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steps = 60
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switch = 40
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if overwrite_step > 0:
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steps = overwrite_step
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if overwrite_switch > 0:
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switch = overwrite_switch
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pipeline.clear_all_caches() # save memory
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width, height = aspect_ratios[aspect_ratios_selection]
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if overwrite_width > 0:
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width = overwrite_width
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if overwrite_height > 0:
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height = overwrite_height
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if input_image_checkbox:
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progressbar(0, 'Image processing ...')
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if current_tab == 'uov' and uov_method != flags.disabled and uov_input_image is not None:
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uov_input_image = HWC3(uov_input_image)
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if 'vary' in uov_method:
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if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height):
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uov_input_image = resize_image(uov_input_image, width=width, height=height)
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print(f'Resolution corrected - users are uploading their own images.')
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else:
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print(f'Processing images generated by Fooocus.')
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if 'subtle' in uov_method:
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denoising_strength = 0.5
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if 'strong' in uov_method:
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denoising_strength = 0.85
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if overwrite_vary_strength > 0:
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denoising_strength = overwrite_vary_strength
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initial_pixels = core.numpy_to_pytorch(uov_input_image)
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progressbar(0, 'VAE encoding ...')
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initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.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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print(f'Final resolution is {str((height, width))}.')
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elif 'upscale' in uov_method:
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H, W, C = uov_input_image.shape
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progressbar(0, 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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f = 1.5
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elif '2x' in uov_method:
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f = 2.0
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else:
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f = 1.0
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width_f = int(width * f)
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height_f = int(height * f)
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if image_is_generated_in_current_ui(uov_input_image, ui_width=width_f, ui_height=height_f):
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uov_input_image = resize_image(uov_input_image, width=int(W * f), height=int(H * f))
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print(f'Processing images generated by Fooocus.')
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else:
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uov_input_image = resize_image(uov_input_image, width=width_f, height=height_f)
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print(f'Resolution corrected - users are uploading their own images.')
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H, W, C = uov_input_image.shape
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image_is_super_large = H * W > 2800 * 2800
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if 'fast' in uov_method:
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direct_return = True
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elif image_is_super_large:
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print('Image is too large. Directly returned the SR image. '
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'Usually directly return SR image at 4K resolution '
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'yields better results than SDXL diffusion.')
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direct_return = True
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else:
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direct_return = False
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if direct_return:
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d = [('Upscale (Fast)', '2x')]
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log(uov_input_image, d, single_line_number=1)
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outputs.append(['results', [uov_input_image]])
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return
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tiled = True
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denoising_strength = 1.0 - 0.618
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steps = int(steps * 0.618)
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switch = int(steps * 0.67)
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if overwrite_upscale_strength > 0:
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denoising_strength = overwrite_upscale_strength
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if overwrite_step > 0:
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steps = overwrite_step
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if overwrite_switch > 0:
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switch = overwrite_switch
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initial_pixels = core.numpy_to_pytorch(uov_input_image)
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progressbar(0, 'VAE encoding ...')
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initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, 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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if current_tab == 'inpaint' and isinstance(inpaint_input_image, dict):
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inpaint_image = inpaint_input_image['image']
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inpaint_mask = inpaint_input_image['mask'][:, :, 0]
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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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if len(outpaint_selections) > 0:
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H, W, C = inpaint_image.shape
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if 'top' in outpaint_selections:
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inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')
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inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', constant_values=255)
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if 'bottom' in outpaint_selections:
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inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')
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inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', constant_values=255)
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H, W, C = inpaint_image.shape
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if 'left' in outpaint_selections:
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inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
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inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant', constant_values=255)
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if 'right' in outpaint_selections:
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inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
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inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant', constant_values=255)
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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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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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# print(f'Inpaint task: {str((height, width))}')
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# outputs.append(['results', inpaint_worker.current_task.visualize_mask_processing()])
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# return
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progressbar(0, 'Downloading inpainter ...')
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inpaint_head_model_path, inpaint_patch_model_path = modules.path.downloading_inpaint_models()
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loras += [(inpaint_patch_model_path, 1.0)]
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inpaint_pixels = core.numpy_to_pytorch(inpaint_worker.current_task.image_ready)
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progressbar(0, 'VAE encoding ...')
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initial_latent = core.encode_vae(vae=pipeline.xl_base_patched.vae, pixels=inpaint_pixels)
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inpaint_latent = initial_latent['samples']
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B, C, H, W = inpaint_latent.shape
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inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
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inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
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inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
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inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask)
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progressbar(0, 'VAE inpaint encoding ...')
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inpaint_mask = (inpaint_worker.current_task.mask_ready > 0).astype(np.float32)
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inpaint_mask = torch.tensor(inpaint_mask).float()
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vae_dict = core.encode_vae_inpaint(
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mask=inpaint_mask, vae=pipeline.xl_base_patched.vae, pixels=inpaint_pixels)
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inpaint_latent = vae_dict['samples']
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inpaint_mask = vae_dict['noise_mask']
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inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask, model_path=inpaint_head_model_path)
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B, C, H, W = inpaint_latent.shape
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height, width = inpaint_worker.current_task.image_raw.shape[:2]
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print(f'Final resolution is {str((height, width))}, latent is {str((H * 8, W * 8))}.')
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sampler_name = 'dpmpp_fooocus_2m_sde_inpaint_seamless'
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print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
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progressbar(1, 'Initializing ...')
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skip_prompt_processing = False
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raw_prompt = prompt
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raw_negative_prompt = negative_prompt
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prompts = remove_empty_str([safe_str(p) for p in prompt.split('\n')], default='')
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negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.split('\n')], default='')
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prompt = prompts[0]
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negative_prompt = negative_prompts[0]
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extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
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extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
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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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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 = None, None, None
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seed = image_seed
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max_seed = int(1024 * 1024 * 1024)
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@@ -290,77 +132,329 @@ def worker():
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seed = - seed
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seed = seed % max_seed
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progressbar(3, 'Loading models ...')
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pipeline.refresh_everything(
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refiner_model_name=refiner_model_name,
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base_model_name=base_model_name,
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loras=loras)
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pipeline.prepare_text_encoder(async_call=False)
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progressbar(3, 'Processing prompts ...')
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positive_basic_workloads = []
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negative_basic_workloads = []
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if use_style:
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for s in style_selections:
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p, n = apply_style(s, positive=prompt)
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positive_basic_workloads.append(p)
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negative_basic_workloads.append(n)
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if performance_selection == 'Speed':
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steps = 30
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switch = 20
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else:
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positive_basic_workloads.append(prompt)
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steps = 60
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switch = 40
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negative_basic_workloads.append(negative_prompt) # Always use independent workload for negative.
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sampler_name = advanced_parameters.sampler_name
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scheduler_name = advanced_parameters.scheduler_name
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positive_basic_workloads = positive_basic_workloads + extra_positive_prompts
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negative_basic_workloads = negative_basic_workloads + extra_negative_prompts
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goals = []
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tasks = []
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positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=prompt)
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negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=negative_prompt)
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if input_image_checkbox:
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progressbar(13, 'Image processing ...')
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if (current_tab == 'uov' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
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and uov_method != flags.disabled and uov_input_image is not None:
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uov_input_image = HWC3(uov_input_image)
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if 'vary' in uov_method:
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goals.append('vary')
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elif 'upscale' in uov_method:
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goals.append('upscale')
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if 'fast' in uov_method:
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skip_prompt_processing = True
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else:
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if performance_selection == 'Speed':
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steps = 18
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switch = 12
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else:
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steps = 36
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switch = 24
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if (current_tab == 'inpaint' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint))\
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and isinstance(inpaint_input_image, dict):
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inpaint_image = inpaint_input_image['image']
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inpaint_mask = inpaint_input_image['mask'][:, :, 0]
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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(1, 'Downloading inpainter ...')
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inpaint_head_model_path, inpaint_patch_model_path = modules.path.downloading_inpaint_models()
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loras += [(inpaint_patch_model_path, 1.0)]
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goals.append('inpaint')
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sampler_name = 'dpmpp_fooocus_2m_sde_inpaint_seamless'
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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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advanced_parameters.mixing_image_prompt_and_vary_upscale:
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goals.append('cn')
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progressbar(1, 'Downloading control models ...')
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if len(cn_tasks[flags.cn_canny]) > 0:
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controlnet_canny_path = modules.path.downloading_controlnet_canny()
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if len(cn_tasks[flags.cn_cpds]) > 0:
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controlnet_cpds_path = modules.path.downloading_controlnet_cpds()
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if len(cn_tasks[flags.cn_ip]) > 0:
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clip_vision_path, ip_negative_path, ip_adapter_path = modules.path.downloading_ip_adapters()
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progressbar(1, 'Loading control models ...')
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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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# Load or unload CNs
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pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
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ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
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tasks = [dict(
|
||||
task_seed=seed + i,
|
||||
positive=positive_basic_workloads,
|
||||
negative=negative_basic_workloads,
|
||||
expansion='',
|
||||
c=[None, None],
|
||||
uc=[None, None],
|
||||
) for i in range(image_number)]
|
||||
if advanced_parameters.overwrite_step > 0:
|
||||
steps = advanced_parameters.overwrite_step
|
||||
|
||||
if use_expansion:
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(5, f'Preparing Fooocus text #{i + 1} ...')
|
||||
expansion = pipeline.expansion(prompt, t['task_seed'])
|
||||
print(f'[Prompt Expansion] New suffix: {expansion}')
|
||||
t['expansion'] = expansion
|
||||
t['positive'] = copy.deepcopy(t['positive']) + [join_prompts(prompt, expansion)] # Deep copy.
|
||||
if advanced_parameters.overwrite_switch > 0:
|
||||
switch = advanced_parameters.overwrite_switch
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(7, f'Encoding base positive #{i + 1} ...')
|
||||
t['c'][0] = pipeline.clip_encode(sd=pipeline.xl_base_patched, texts=t['positive'],
|
||||
pool_top_k=positive_top_k)
|
||||
if advanced_parameters.overwrite_width > 0:
|
||||
width = advanced_parameters.overwrite_width
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(9, f'Encoding base negative #{i + 1} ...')
|
||||
t['uc'][0] = pipeline.clip_encode(sd=pipeline.xl_base_patched, texts=t['negative'],
|
||||
pool_top_k=negative_top_k)
|
||||
if advanced_parameters.overwrite_height > 0:
|
||||
height = advanced_parameters.overwrite_height
|
||||
|
||||
if pipeline.xl_refiner is not None:
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(11, f'Encoding refiner positive #{i + 1} ...')
|
||||
t['c'][1] = pipeline.clip_separate(t['c'][0])
|
||||
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
|
||||
print(f'[Parameters] Steps = {steps} - {switch}')
|
||||
|
||||
progressbar(1, 'Initializing ...')
|
||||
|
||||
if not skip_prompt_processing:
|
||||
|
||||
prompts = remove_empty_str([safe_str(p) for p in prompt.split('\n')], default='')
|
||||
negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.split('\n')], default='')
|
||||
|
||||
prompt = prompts[0]
|
||||
negative_prompt = negative_prompts[0]
|
||||
|
||||
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
|
||||
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
|
||||
|
||||
progressbar(3, 'Loading models ...')
|
||||
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, loras=loras)
|
||||
|
||||
progressbar(3, 'Processing prompts ...')
|
||||
positive_basic_workloads = []
|
||||
negative_basic_workloads = []
|
||||
|
||||
if use_style:
|
||||
for s in style_selections:
|
||||
p, n = apply_style(s, positive=prompt)
|
||||
positive_basic_workloads.append(p)
|
||||
negative_basic_workloads.append(n)
|
||||
else:
|
||||
positive_basic_workloads.append(prompt)
|
||||
|
||||
negative_basic_workloads.append(negative_prompt) # Always use independent workload for negative.
|
||||
|
||||
positive_basic_workloads = positive_basic_workloads + extra_positive_prompts
|
||||
negative_basic_workloads = negative_basic_workloads + extra_negative_prompts
|
||||
|
||||
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=prompt)
|
||||
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=negative_prompt)
|
||||
|
||||
positive_top_k = len(positive_basic_workloads)
|
||||
negative_top_k = len(negative_basic_workloads)
|
||||
|
||||
tasks = [dict(
|
||||
task_seed=seed + i,
|
||||
positive=positive_basic_workloads,
|
||||
negative=negative_basic_workloads,
|
||||
expansion='',
|
||||
c=None,
|
||||
uc=None,
|
||||
) for i in range(image_number)]
|
||||
|
||||
if use_expansion:
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(5, f'Preparing Fooocus text #{i + 1} ...')
|
||||
expansion = pipeline.final_expansion(prompt, t['task_seed'])
|
||||
print(f'[Prompt Expansion] New suffix: {expansion}')
|
||||
t['expansion'] = expansion
|
||||
t['positive'] = copy.deepcopy(t['positive']) + [join_prompts(prompt, expansion)] # Deep copy.
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(13, f'Encoding refiner negative #{i + 1} ...')
|
||||
t['uc'][1] = pipeline.clip_separate(t['uc'][0])
|
||||
progressbar(7, f'Encoding positive #{i + 1} ...')
|
||||
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=positive_top_k)
|
||||
|
||||
for i, t in enumerate(tasks):
|
||||
progressbar(10, f'Encoding negative #{i + 1} ...')
|
||||
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=negative_top_k)
|
||||
|
||||
if len(goals) > 0:
|
||||
progressbar(13, 'Image processing ...')
|
||||
|
||||
if 'vary' in goals:
|
||||
if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height):
|
||||
uov_input_image = resize_image(uov_input_image, width=width, height=height)
|
||||
print(f'Resolution corrected - users are uploading their own images.')
|
||||
else:
|
||||
print(f'Processing images generated by Fooocus.')
|
||||
if 'subtle' in uov_method:
|
||||
denoising_strength = 0.5
|
||||
if 'strong' in uov_method:
|
||||
denoising_strength = 0.85
|
||||
if advanced_parameters.overwrite_vary_strength > 0:
|
||||
denoising_strength = advanced_parameters.overwrite_vary_strength
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(13, 'VAE encoding ...')
|
||||
initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels)
|
||||
B, C, H, W = initial_latent['samples'].shape
|
||||
width = W * 8
|
||||
height = H * 8
|
||||
print(f'Final resolution is {str((height, width))}.')
|
||||
|
||||
if 'upscale' in goals:
|
||||
H, W, C = uov_input_image.shape
|
||||
progressbar(13, f'Upscaling image from {str((H, W))} ...')
|
||||
|
||||
uov_input_image = core.numpy_to_pytorch(uov_input_image)
|
||||
uov_input_image = perform_upscale(uov_input_image)
|
||||
uov_input_image = core.pytorch_to_numpy(uov_input_image)[0]
|
||||
print(f'Image upscaled.')
|
||||
|
||||
if '1.5x' in uov_method:
|
||||
f = 1.5
|
||||
elif '2x' in uov_method:
|
||||
f = 2.0
|
||||
else:
|
||||
f = 1.0
|
||||
|
||||
width_f = int(width * f)
|
||||
height_f = int(height * f)
|
||||
|
||||
if image_is_generated_in_current_ui(uov_input_image, ui_width=width_f, ui_height=height_f):
|
||||
uov_input_image = resize_image(uov_input_image, width=int(W * f), height=int(H * f))
|
||||
print(f'Processing images generated by Fooocus.')
|
||||
else:
|
||||
uov_input_image = resize_image(uov_input_image, width=width_f, height=height_f)
|
||||
print(f'Resolution corrected - users are uploading their own images.')
|
||||
|
||||
H, W, C = uov_input_image.shape
|
||||
image_is_super_large = H * W > 2800 * 2800
|
||||
|
||||
if 'fast' in uov_method:
|
||||
direct_return = True
|
||||
elif image_is_super_large:
|
||||
print('Image is too large. Directly returned the SR image. '
|
||||
'Usually directly return SR image at 4K resolution '
|
||||
'yields better results than SDXL diffusion.')
|
||||
direct_return = True
|
||||
else:
|
||||
direct_return = False
|
||||
|
||||
if direct_return:
|
||||
d = [('Upscale (Fast)', '2x')]
|
||||
log(uov_input_image, d, single_line_number=1)
|
||||
outputs.append(['results', [uov_input_image]])
|
||||
return
|
||||
|
||||
tiled = True
|
||||
denoising_strength = 0.382
|
||||
|
||||
if advanced_parameters.overwrite_upscale_strength > 0:
|
||||
denoising_strength = advanced_parameters.overwrite_upscale_strength
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(13, 'VAE encoding ...')
|
||||
|
||||
initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels, tiled=True)
|
||||
B, C, H, W = initial_latent['samples'].shape
|
||||
width = W * 8
|
||||
height = H * 8
|
||||
print(f'Final resolution is {str((height, width))}.')
|
||||
|
||||
if 'inpaint' in goals:
|
||||
if len(outpaint_selections) > 0:
|
||||
H, W, C = inpaint_image.shape
|
||||
if 'top' in outpaint_selections:
|
||||
inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')
|
||||
inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant',
|
||||
constant_values=255)
|
||||
if 'bottom' in outpaint_selections:
|
||||
inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')
|
||||
inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant',
|
||||
constant_values=255)
|
||||
|
||||
H, W, C = inpaint_image.shape
|
||||
if 'left' in outpaint_selections:
|
||||
inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
|
||||
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant',
|
||||
constant_values=255)
|
||||
if 'right' in outpaint_selections:
|
||||
inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
|
||||
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant',
|
||||
constant_values=255)
|
||||
|
||||
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
|
||||
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
|
||||
|
||||
inpaint_worker.current_task = inpaint_worker.InpaintWorker(image=inpaint_image, mask=inpaint_mask,
|
||||
is_outpaint=len(outpaint_selections) > 0)
|
||||
|
||||
# print(f'Inpaint task: {str((height, width))}')
|
||||
# outputs.append(['results', inpaint_worker.current_task.visualize_mask_processing()])
|
||||
# return
|
||||
|
||||
progressbar(13, 'VAE encoding ...')
|
||||
inpaint_pixels = core.numpy_to_pytorch(inpaint_worker.current_task.image_ready)
|
||||
initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=inpaint_pixels)
|
||||
inpaint_latent = initial_latent['samples']
|
||||
B, C, H, W = inpaint_latent.shape
|
||||
inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
|
||||
inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
|
||||
inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
|
||||
inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask)
|
||||
|
||||
progressbar(13, 'VAE inpaint encoding ...')
|
||||
|
||||
inpaint_mask = (inpaint_worker.current_task.mask_ready > 0).astype(np.float32)
|
||||
inpaint_mask = torch.tensor(inpaint_mask).float()
|
||||
|
||||
vae_dict = core.encode_vae_inpaint(
|
||||
mask=inpaint_mask, vae=pipeline.final_vae, pixels=inpaint_pixels)
|
||||
|
||||
inpaint_latent = vae_dict['samples']
|
||||
inpaint_mask = vae_dict['noise_mask']
|
||||
inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask,
|
||||
model_path=inpaint_head_model_path)
|
||||
|
||||
B, C, H, W = inpaint_latent.shape
|
||||
final_height, final_width = inpaint_worker.current_task.image_raw.shape[:2]
|
||||
height, width = H * 8, W * 8
|
||||
print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
|
||||
|
||||
if 'cn' in goals:
|
||||
for task in cn_tasks[flags.cn_canny]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||||
cn_img = preprocessors.canny_pyramid(cn_img)
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
outputs.append(['results', [cn_img]])
|
||||
return
|
||||
for task in cn_tasks[flags.cn_cpds]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||||
cn_img = preprocessors.cpds(cn_img)
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
outputs.append(['results', [cn_img]])
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = ip_adapter.preprocess(cn_img)
|
||||
|
||||
if len(cn_tasks[flags.cn_ip]) > 0:
|
||||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, cn_tasks[flags.cn_ip])
|
||||
|
||||
results = []
|
||||
all_steps = steps * image_number
|
||||
|
||||
preparation_time = time.perf_counter() - execution_start_time
|
||||
print(f'Preparation time: {preparation_time:.2f} seconds')
|
||||
|
||||
outputs.append(['preview', (13, 'Moving model to GPU ...', None)])
|
||||
execution_start_time = time.perf_counter()
|
||||
comfy.model_management.load_models_gpu([pipeline.final_unet])
|
||||
moving_time = time.perf_counter() - execution_start_time
|
||||
print(f'Moving model to GPU: {moving_time:.2f} seconds')
|
||||
|
||||
outputs.append(['preview', (13, 'Starting tasks ...', None)])
|
||||
|
||||
def callback(step, x0, x, total_steps, y):
|
||||
done_steps = current_task_id * steps + step
|
||||
outputs.append(['preview', (
|
||||
@@ -368,17 +462,25 @@ def worker():
|
||||
f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
|
||||
y)])
|
||||
|
||||
preparation_time = time.perf_counter() - execution_start_time
|
||||
print(f'Preparation time: {preparation_time:.2f} seconds')
|
||||
|
||||
outputs.append(['preview', (13, 'Starting tasks ...', None)])
|
||||
for current_task_id, task in enumerate(tasks):
|
||||
execution_start_time = time.perf_counter()
|
||||
|
||||
try:
|
||||
positive_cond, negative_cond = task['c'], task['uc']
|
||||
|
||||
if 'cn' in goals:
|
||||
for cn_flag, cn_path in [
|
||||
(flags.cn_canny, controlnet_canny_path),
|
||||
(flags.cn_cpds, controlnet_cpds_path)
|
||||
]:
|
||||
for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]:
|
||||
positive_cond, negative_cond = core.apply_controlnet(
|
||||
positive_cond, negative_cond,
|
||||
pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)
|
||||
|
||||
imgs = pipeline.process_diffusion(
|
||||
positive_cond=task['c'],
|
||||
negative_cond=task['uc'],
|
||||
positive_cond=positive_cond,
|
||||
negative_cond=negative_cond,
|
||||
steps=steps,
|
||||
switch=switch,
|
||||
width=width,
|
||||
@@ -393,6 +495,8 @@ def worker():
|
||||
cfg_scale=cfg_scale
|
||||
)
|
||||
|
||||
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
|
||||
|
||||
if inpaint_worker.current_task is not None:
|
||||
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
|
||||
|
||||
@@ -406,14 +510,14 @@ def worker():
|
||||
('Resolution', str((width, height))),
|
||||
('Sharpness', sharpness),
|
||||
('Guidance Scale', guidance_scale),
|
||||
('ADM Guidance', str((adm_scaler_positive, adm_scaler_negative))),
|
||||
('ADM Guidance', str((modules.patch.positive_adm_scale, modules.patch.negative_adm_scale))),
|
||||
('Base Model', base_model_name),
|
||||
('Refiner Model', refiner_model_name),
|
||||
('Sampler', sampler_name),
|
||||
('Scheduler', scheduler_name),
|
||||
('Seed', task['task_seed'])
|
||||
]
|
||||
for n, w in loras_user_raw_input:
|
||||
for n, w in loras_raw:
|
||||
if n != 'None':
|
||||
d.append((f'LoRA [{n}] weight', w))
|
||||
log(x, d, single_line_number=3)
|
||||
|
||||
Reference in New Issue
Block a user