Merge branch 'hotfix/prevent-skipping-and-stopping-by-other-users'
# Conflicts: # modules/advanced_parameters.py # modules/async_worker.py # webui.py
This commit is contained in:
Manuel Schmid
commit
d7c1f4a6aa
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@ -1,27 +1,26 @@
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import cv2
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import numpy as np
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import modules.advanced_parameters as advanced_parameters
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def centered_canny(x: np.ndarray):
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def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 2 and x.dtype == np.uint8
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y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
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y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
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y = y.astype(np.float32) / 255.0
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return y
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def centered_canny_color(x: np.ndarray):
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def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 3 and x.shape[2] == 3
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result = [centered_canny(x[..., i]) for i in range(3)]
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result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
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result = np.stack(result, axis=2)
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return result
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def pyramid_canny_color(x: np.ndarray):
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def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
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assert isinstance(x, np.ndarray)
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assert x.ndim == 3 and x.shape[2] == 3
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@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray):
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for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
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Hs, Ws = int(H * k), int(W * k)
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small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
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edge = centered_canny_color(small)
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edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
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if acc_edge is None:
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acc_edge = edge
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else:
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@ -54,11 +53,11 @@ def norm255(x, low=4, high=96):
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return x * 255.0
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def canny_pyramid(x):
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def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
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# For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
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# Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
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color_canny = pyramid_canny_color(x)
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color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
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result = np.sum(color_canny, axis=2)
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return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
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@ -1,32 +0,0 @@
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disable_preview, disable_intermediate_results, black_out_nsfw, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
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scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
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overwrite_vary_strength, overwrite_upscale_strength, \
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mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
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debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
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refiner_swap_method, \
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freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
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debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
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inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 37
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def set_all_advanced_parameters(*args):
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global disable_preview, disable_intermediate_results, black_out_nsfw, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
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scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
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overwrite_vary_strength, overwrite_upscale_strength, \
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mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
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debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
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refiner_swap_method, \
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freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
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debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
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inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
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disable_preview, disable_intermediate_results, black_out_nsfw, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
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scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
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overwrite_vary_strength, overwrite_upscale_strength, \
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mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
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debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
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refiner_swap_method, \
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freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
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debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
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inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = args
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return
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@ -1,4 +1,8 @@
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import threading
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import os
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from modules.patch import PatchSettings, patch_settings, patch_all
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patch_all()
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class AsyncTask:
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@ -35,7 +39,6 @@ def worker():
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import extras.preprocessors as preprocessors
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import modules.inpaint_worker as inpaint_worker
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import modules.constants as constants
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import modules.advanced_parameters as advanced_parameters
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import extras.ip_adapter as ip_adapter
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import extras.face_crop
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import fooocus_version
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@ -50,6 +53,9 @@ def worker():
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, calculate_sha256, quote
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from modules.upscaler import perform_upscale
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pid = os.getpid()
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print(f'Started worker with PID {pid}')
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try:
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async_gradio_app = shared.gradio_root
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flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''
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@ -63,14 +69,10 @@ def worker():
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print(f'[Fooocus] {text}')
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async_task.yields.append(['preview', (number, text, None)])
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def yield_result(async_task, imgs, do_not_show_finished_images=False, progressbar_index=13):
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def yield_result(async_task, imgs, black_out_nsfw, do_not_show_finished_images=False):
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if not isinstance(imgs, list):
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imgs = [imgs]
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if modules.config.default_black_out_nsfw or advanced_parameters.black_out_nsfw:
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progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
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imgs = censor_batch(imgs)
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async_task.results = async_task.results + imgs
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if do_not_show_finished_images:
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@ -80,9 +82,6 @@ def worker():
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return
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def build_image_wall(async_task):
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if not advanced_parameters.generate_image_grid:
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return
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results = async_task.results
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if len(results) < 2:
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@ -154,6 +153,44 @@ def worker():
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inpaint_input_image = args.pop()
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inpaint_additional_prompt = args.pop()
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inpaint_mask_image_upload = args.pop()
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disable_preview = args.pop()
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disable_intermediate_results = args.pop()
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black_out_nsfw = args.pop()
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adm_scaler_positive = args.pop()
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adm_scaler_negative = args.pop()
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adm_scaler_end = args.pop()
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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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overwrite_step = args.pop()
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overwrite_switch = args.pop()
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overwrite_width = args.pop()
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overwrite_height = args.pop()
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overwrite_vary_strength = args.pop()
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overwrite_upscale_strength = args.pop()
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mixing_image_prompt_and_vary_upscale = args.pop()
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mixing_image_prompt_and_inpaint = args.pop()
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debugging_cn_preprocessor = args.pop()
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skipping_cn_preprocessor = args.pop()
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canny_low_threshold = args.pop()
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canny_high_threshold = args.pop()
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refiner_swap_method = args.pop()
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controlnet_softness = args.pop()
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freeu_enabled = args.pop()
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freeu_b1 = args.pop()
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freeu_b2 = args.pop()
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freeu_s1 = args.pop()
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freeu_s2 = args.pop()
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debugging_inpaint_preprocessor = args.pop()
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inpaint_disable_initial_latent = args.pop()
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inpaint_engine = args.pop()
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inpaint_strength = args.pop()
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inpaint_respective_field = args.pop()
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inpaint_mask_upload_checkbox = args.pop()
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invert_mask_checkbox = args.pop()
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inpaint_erode_or_dilate = args.pop()
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save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
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metadata_scheme = args.pop() if not args_manager.args.disable_metadata else 'fooocus'
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@ -202,15 +239,15 @@ def worker():
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print(f'Refiner disabled in LCM mode.')
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refiner_model_name = 'None'
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sampler_name = advanced_parameters.sampler_name = 'lcm'
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scheduler_name = advanced_parameters.scheduler_name = 'lcm'
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modules.patch.sharpness = sharpness = 0.0
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cfg_scale = guidance_scale = 1.0
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modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
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sampler_name = 'lcm'
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scheduler_name = 'lcm'
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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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modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
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modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
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modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
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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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steps = 8
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if translate_prompts:
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@ -229,19 +266,22 @@ def worker():
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lora_hashes.append(f'{n.split(".")[0]}: {calculate_sha256(lora_path)[0:10]}')
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lora_hashes_string = ", ".join(lora_hashes)
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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 = 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] 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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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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f'{adm_scaler_positive} : '
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f'{adm_scaler_negative} : '
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f'{adm_scaler_end}')
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patch_settings[pid] = PatchSettings(
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sharpness,
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adm_scaler_end,
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adm_scaler_positive,
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adm_scaler_negative,
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controlnet_softness,
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adaptive_cfg
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)
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cfg_scale = float(guidance_scale)
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print(f'[Parameters] CFG = {cfg_scale}')
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@ -254,10 +294,9 @@ def worker():
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width, height = int(width), int(height)
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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_parameterized = 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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@ -271,15 +310,12 @@ def worker():
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seed = int(image_seed)
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print(f'[Parameters] Seed = {seed}')
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sampler_name = advanced_parameters.sampler_name
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scheduler_name = advanced_parameters.scheduler_name
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goals = []
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tasks = []
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if input_image_checkbox:
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if (current_tab == 'uov' or (
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current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
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current_tab == 'ip' and 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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@ -303,12 +339,12 @@ def worker():
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progressbar(async_task, 1, 'Downloading upscale models ...')
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modules.config.downloading_upscale_model()
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if (current_tab == 'inpaint' or (
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current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
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current_tab == 'ip' and 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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if advanced_parameters.inpaint_mask_upload_checkbox:
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|
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if inpaint_mask_upload_checkbox:
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if isinstance(inpaint_mask_image_upload, np.ndarray):
|
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if inpaint_mask_image_upload.ndim == 3:
|
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H, W, C = inpaint_image.shape
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|
|
@ -317,10 +353,10 @@ def worker():
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inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
|
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inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
|
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|
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if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
|
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inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
|
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if int(inpaint_erode_or_dilate) != 0:
|
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inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
|
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|
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if advanced_parameters.invert_mask_checkbox:
|
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if invert_mask_checkbox:
|
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inpaint_mask = 255 - inpaint_mask
|
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|
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inpaint_image = HWC3(inpaint_image)
|
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|
|
@ -331,7 +367,7 @@ def worker():
|
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if inpaint_parameterized:
|
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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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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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|
|
@ -347,8 +383,8 @@ def worker():
|
|||
prompt = inpaint_additional_prompt + '\n' + prompt
|
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goals.append('inpaint')
|
||||
if current_tab == 'ip' or \
|
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advanced_parameters.mixing_image_prompt_and_inpaint or \
|
||||
advanced_parameters.mixing_image_prompt_and_vary_upscale:
|
||||
mixing_image_prompt_and_vary_upscale or \
|
||||
mixing_image_prompt_and_inpaint:
|
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goals.append('cn')
|
||||
progressbar(async_task, 1, 'Downloading control models ...')
|
||||
if len(cn_tasks[flags.cn_canny]) > 0:
|
||||
|
|
@ -369,17 +405,17 @@ def worker():
|
|||
|
||||
switch = int(round(steps * refiner_switch))
|
||||
|
||||
if advanced_parameters.overwrite_step > 0:
|
||||
steps = advanced_parameters.overwrite_step
|
||||
if overwrite_step > 0:
|
||||
steps = overwrite_step
|
||||
|
||||
if advanced_parameters.overwrite_switch > 0:
|
||||
switch = advanced_parameters.overwrite_switch
|
||||
if overwrite_switch > 0:
|
||||
switch = overwrite_switch
|
||||
|
||||
if advanced_parameters.overwrite_width > 0:
|
||||
width = advanced_parameters.overwrite_width
|
||||
if overwrite_width > 0:
|
||||
width = overwrite_width
|
||||
|
||||
if advanced_parameters.overwrite_height > 0:
|
||||
height = advanced_parameters.overwrite_height
|
||||
if overwrite_height > 0:
|
||||
height = overwrite_height
|
||||
|
||||
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
|
||||
print(f'[Parameters] Steps = {steps} - {switch}')
|
||||
|
|
@ -482,8 +518,8 @@ def worker():
|
|||
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
|
||||
if overwrite_vary_strength > 0:
|
||||
denoising_strength = overwrite_vary_strength
|
||||
|
||||
shape_ceil = get_image_shape_ceil(uov_input_image)
|
||||
if shape_ceil < 1024:
|
||||
|
|
@ -548,14 +584,14 @@ def worker():
|
|||
if direct_return:
|
||||
d = [('Upscale (Fast)', '2x')]
|
||||
uov_input_image_path = log(uov_input_image, d, output_format=output_format)
|
||||
yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
|
||||
yield_result(async_task, uov_input_image_path, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
tiled = True
|
||||
denoising_strength = 0.382
|
||||
|
||||
if advanced_parameters.overwrite_upscale_strength > 0:
|
||||
denoising_strength = advanced_parameters.overwrite_upscale_strength
|
||||
if overwrite_upscale_strength > 0:
|
||||
denoising_strength = overwrite_upscale_strength
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(async_task, 13, 'VAE encoding ...')
|
||||
|
|
@ -599,20 +635,20 @@ def worker():
|
|||
|
||||
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
|
||||
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
|
||||
advanced_parameters.inpaint_strength = 1.0
|
||||
advanced_parameters.inpaint_respective_field = 1.0
|
||||
inpaint_strength = 1.0
|
||||
inpaint_respective_field = 1.0
|
||||
|
||||
denoising_strength = advanced_parameters.inpaint_strength
|
||||
denoising_strength = inpaint_strength
|
||||
|
||||
inpaint_worker.current_task = inpaint_worker.InpaintWorker(
|
||||
image=inpaint_image,
|
||||
mask=inpaint_mask,
|
||||
use_fill=denoising_strength > 0.99,
|
||||
k=advanced_parameters.inpaint_respective_field
|
||||
k=inpaint_respective_field
|
||||
)
|
||||
|
||||
if advanced_parameters.debugging_inpaint_preprocessor:
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
|
||||
if debugging_inpaint_preprocessor:
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw,
|
||||
do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
|
|
@ -657,7 +693,7 @@ def worker():
|
|||
model=pipeline.final_unet
|
||||
)
|
||||
|
||||
if not advanced_parameters.inpaint_disable_initial_latent:
|
||||
if not inpaint_disable_initial_latent:
|
||||
initial_latent = {'samples': latent_fill}
|
||||
|
||||
B, C, H, W = latent_fill.shape
|
||||
|
|
@ -670,25 +706,25 @@ def worker():
|
|||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.canny_pyramid(cn_img)
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
|
||||
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
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)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
if not skipping_cn_preprocessor:
|
||||
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:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
|
|
@ -698,22 +734,22 @@ def worker():
|
|||
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
|
||||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip_face]:
|
||||
cn_img, cn_stop, cn_weight = task
|
||||
cn_img = HWC3(cn_img)
|
||||
|
||||
if not advanced_parameters.skipping_cn_preprocessor:
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = extras.face_crop.crop_image(cn_img)
|
||||
|
||||
# https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
|
||||
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
|
||||
|
||||
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, black_out_nsfw, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
|
||||
|
|
@ -721,14 +757,14 @@ def worker():
|
|||
if len(all_ip_tasks) > 0:
|
||||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
|
||||
|
||||
if advanced_parameters.freeu_enabled:
|
||||
if freeu_enabled:
|
||||
print(f'FreeU is enabled!')
|
||||
pipeline.final_unet = core.apply_freeu(
|
||||
pipeline.final_unet,
|
||||
advanced_parameters.freeu_b1,
|
||||
advanced_parameters.freeu_b2,
|
||||
advanced_parameters.freeu_s1,
|
||||
advanced_parameters.freeu_s2
|
||||
freeu_b1,
|
||||
freeu_b2,
|
||||
freeu_s1,
|
||||
freeu_s2
|
||||
)
|
||||
|
||||
all_steps = steps * image_number
|
||||
|
|
@ -804,7 +840,8 @@ def worker():
|
|||
denoise=denoising_strength,
|
||||
tiled=tiled,
|
||||
cfg_scale=cfg_scale,
|
||||
refiner_swap_method=refiner_swap_method
|
||||
refiner_swap_method=refiner_swap_method,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
|
||||
|
|
@ -823,15 +860,14 @@ def worker():
|
|||
'steps': steps, 'refiner_switch': refiner_switch, 'sharpness': sharpness, 'cfg': cfg_scale,
|
||||
'base_model': base_model_name, 'refiner_model': refiner_model_name,
|
||||
'denoising_strength': denoising_strength,
|
||||
'freeu': advanced_parameters.freeu_enabled,
|
||||
'freeu': freeu_enabled,
|
||||
'img2img': input_image_checkbox,
|
||||
'prompt_expansion': task['expansion']
|
||||
}
|
||||
|
||||
|
||||
if advanced_parameters.freeu_enabled:
|
||||
if freeu_enabled:
|
||||
metadata |= {
|
||||
'freeu_b1': advanced_parameters.freeu_b1, 'freeu_b2': advanced_parameters.freeu_b2, 'freeu_s1': advanced_parameters.freeu_s1, 'freeu_s2': advanced_parameters.freeu_s2
|
||||
'freeu_b1': freeu_b1, 'freeu_b2': freeu_b2, 'freeu_s1': freeu_s1, 'freeu_s2': freeu_s2
|
||||
}
|
||||
|
||||
if 'vary' in goals:
|
||||
|
|
@ -851,14 +887,14 @@ def worker():
|
|||
}
|
||||
else:
|
||||
metadata |= {
|
||||
'inpaint_additional_prompt': inpaint_additional_prompt, 'inpaint_mask_upload': advanced_parameters.inpaint_mask_upload_checkbox, 'invert_mask': advanced_parameters.invert_mask_checkbox,
|
||||
'inpaint_disable_initial_latent': advanced_parameters.inpaint_disable_initial_latent, 'inpaint_engine': advanced_parameters.inpaint_engine,
|
||||
'inpaint_strength': advanced_parameters.inpaint_strength, 'inpaint_respective_field': advanced_parameters.inpaint_respective_field,
|
||||
'inpaint_additional_prompt': inpaint_additional_prompt, 'inpaint_mask_upload': inpaint_mask_upload_checkbox, 'invert_mask': invert_mask_checkbox,
|
||||
'inpaint_disable_initial_latent': inpaint_disable_initial_latent, 'inpaint_engine': inpaint_engine,
|
||||
'inpaint_strength': inpaint_strength, 'inpaint_respective_field': inpaint_respective_field,
|
||||
}
|
||||
|
||||
if 'cn' in goals:
|
||||
metadata |= {
|
||||
'canny_low_threshold': advanced_parameters.canny_low_threshold, 'canny_high_threshold': advanced_parameters.canny_high_threshold,
|
||||
'canny_low_threshold': canny_low_threshold, 'canny_high_threshold': canny_high_threshold,
|
||||
}
|
||||
|
||||
ip_list = {x: [] for x in flags.ip_list}
|
||||
|
|
@ -906,6 +942,11 @@ def worker():
|
|||
negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
|
||||
metadata_string = f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
if modules.config.default_black_out_nsfw or black_out_nsfw:
|
||||
progressbar_index = int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps))
|
||||
progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
|
||||
imgs = censor_batch(imgs)
|
||||
|
||||
for x in imgs:
|
||||
d = [
|
||||
('Prompt', task['log_positive_prompt']),
|
||||
|
|
@ -917,15 +958,15 @@ def worker():
|
|||
('Sharpness', sharpness),
|
||||
('Guidance Scale', guidance_scale),
|
||||
('ADM Guidance', str((
|
||||
modules.patch.positive_adm_scale,
|
||||
modules.patch.negative_adm_scale,
|
||||
modules.patch.adm_scaler_end))),
|
||||
modules.patch.patch_settings[pid].positive_adm_scale,
|
||||
modules.patch.patch_settings[pid].negative_adm_scale,
|
||||
modules.patch.patch_settings[pid].adm_scaler_end))),
|
||||
('Base Model', base_model_name),
|
||||
('Refiner Model', refiner_model_name),
|
||||
('Refiner Switch', refiner_switch),
|
||||
('Sampler', sampler_name),
|
||||
('Scheduler', scheduler_name),
|
||||
('Sampling Steps Override', advanced_parameters.overwrite_step),
|
||||
('Sampling Steps Override', overwrite_step),
|
||||
('Seed', task['task_seed']),
|
||||
]
|
||||
for li, (n, w) in enumerate(loras):
|
||||
|
|
@ -934,7 +975,8 @@ def worker():
|
|||
d.append(('Version', 'v' + fooocus_version.version))
|
||||
img_paths.append(log(x, d, metadata_string, save_metadata_to_images, output_format))
|
||||
|
||||
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1, progressbar_index=int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps)))
|
||||
yield_result(async_task, img_paths, black_out_nsfw, do_not_show_finished_images=len(tasks) == 1
|
||||
or disable_intermediate_results or sampler_name == 'lcm')
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException as e:
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
|
|
@ -953,14 +995,20 @@ def worker():
|
|||
time.sleep(0.01)
|
||||
if len(async_tasks) > 0:
|
||||
task = async_tasks.pop(0)
|
||||
generate_image_grid = task.args.pop(0)
|
||||
|
||||
try:
|
||||
handler(task)
|
||||
build_image_wall(task)
|
||||
if generate_image_grid:
|
||||
build_image_wall(task)
|
||||
task.yields.append(['finish', task.results])
|
||||
pipeline.prepare_text_encoder(async_call=True)
|
||||
except:
|
||||
traceback.print_exc()
|
||||
task.yields.append(['finish', task.results])
|
||||
finally:
|
||||
if pid in modules.patch.patch_settings:
|
||||
del modules.patch.patch_settings[pid]
|
||||
pass
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,5 @@
|
|||
# modified version of https://github.com/AUTOMATIC1111/stable-diffusion-webui-nsfw-censor/blob/master/scripts/censor.py
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import modules.core as core
|
||||
|
||||
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
||||
from transformers import AutoFeatureExtractor
|
||||
|
|
@ -16,8 +13,6 @@ safety_checker = None
|
|||
|
||||
def numpy_to_pil(image):
|
||||
image = (image * 255).round().astype("uint8")
|
||||
|
||||
#pil_image = Image.fromarray(image, 'RGB')
|
||||
pil_image = Image.fromarray(image)
|
||||
|
||||
return pil_image
|
||||
|
|
|
|||
|
|
@ -1,8 +1,3 @@
|
|||
from modules.patch import patch_all
|
||||
|
||||
patch_all()
|
||||
|
||||
|
||||
import os
|
||||
import einops
|
||||
import torch
|
||||
|
|
@ -16,7 +11,6 @@ import ldm_patched.modules.controlnet
|
|||
import modules.sample_hijack
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.latent_formats
|
||||
import modules.advanced_parameters
|
||||
|
||||
from ldm_patched.modules.sd import load_checkpoint_guess_config
|
||||
from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
|
||||
|
|
@ -268,7 +262,7 @@ def get_previewer(model):
|
|||
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
|
||||
scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
|
||||
force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
|
||||
previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):
|
||||
|
||||
if sigmas is not None:
|
||||
sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())
|
||||
|
|
@ -299,7 +293,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
|
|||
def callback(step, x0, x, total_steps):
|
||||
ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()
|
||||
y = None
|
||||
if previewer is not None and not modules.advanced_parameters.disable_preview:
|
||||
if previewer is not None and not disable_preview:
|
||||
y = previewer(x0, previewer_start + step, previewer_end)
|
||||
if callback_function is not None:
|
||||
callback_function(previewer_start + step, x0, x, previewer_end, y)
|
||||
|
|
|
|||
|
|
@ -315,7 +315,7 @@ def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):
|
|||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint'):
|
||||
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):
|
||||
target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \
|
||||
= final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip
|
||||
|
||||
|
|
@ -374,6 +374,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
refiner_switch=switch,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
|
|
@ -392,6 +393,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
print('Refiner swapped by changing ksampler. Noise preserved.')
|
||||
|
||||
|
|
@ -414,6 +416,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
scheduler=scheduler_name,
|
||||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -422,7 +425,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
if refiner_swap_method == 'vae':
|
||||
modules.patch.eps_record = 'vae'
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.unswap()
|
||||
|
|
@ -440,7 +443,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
sampler_name=sampler_name,
|
||||
scheduler=scheduler_name,
|
||||
previewer_start=0,
|
||||
previewer_end=steps
|
||||
previewer_end=steps,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
print('Fooocus VAE-based swap.')
|
||||
|
||||
|
|
@ -459,7 +463,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
denoise=denoise)[switch:] * k_sigmas
|
||||
len_sigmas = len(sigmas) - 1
|
||||
|
||||
noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
|
||||
noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
|
||||
|
||||
if modules.inpaint_worker.current_task is not None:
|
||||
modules.inpaint_worker.current_task.swap()
|
||||
|
|
@ -479,7 +483,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
previewer_start=switch,
|
||||
previewer_end=steps,
|
||||
sigmas=sigmas,
|
||||
noise_mean=noise_mean
|
||||
noise_mean=noise_mean,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
target_model = target_refiner_vae
|
||||
|
|
@ -488,5 +493,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
|
|||
decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
|
||||
|
||||
images = core.pytorch_to_numpy(decoded_latent)
|
||||
modules.patch.eps_record = None
|
||||
modules.patch.patch_settings[os.getpid()].eps_record = None
|
||||
return images
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ import ldm_patched.controlnet.cldm
|
|||
import ldm_patched.modules.model_patcher
|
||||
import ldm_patched.modules.samplers
|
||||
import ldm_patched.modules.args_parser
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import warnings
|
||||
import safetensors.torch
|
||||
import modules.constants as constants
|
||||
|
|
@ -29,15 +28,25 @@ from modules.patch_precision import patch_all_precision
|
|||
from modules.patch_clip import patch_all_clip
|
||||
|
||||
|
||||
sharpness = 2.0
|
||||
class PatchSettings:
|
||||
def __init__(self,
|
||||
sharpness=2.0,
|
||||
adm_scaler_end=0.3,
|
||||
positive_adm_scale=1.5,
|
||||
negative_adm_scale=0.8,
|
||||
controlnet_softness=0.25,
|
||||
adaptive_cfg=7.0):
|
||||
self.sharpness = sharpness
|
||||
self.adm_scaler_end = adm_scaler_end
|
||||
self.positive_adm_scale = positive_adm_scale
|
||||
self.negative_adm_scale = negative_adm_scale
|
||||
self.controlnet_softness = controlnet_softness
|
||||
self.adaptive_cfg = adaptive_cfg
|
||||
self.global_diffusion_progress = 0
|
||||
self.eps_record = None
|
||||
|
||||
adm_scaler_end = 0.3
|
||||
positive_adm_scale = 1.5
|
||||
negative_adm_scale = 0.8
|
||||
|
||||
adaptive_cfg = 7.0
|
||||
global_diffusion_progress = 0
|
||||
eps_record = None
|
||||
patch_settings = {}
|
||||
|
||||
|
||||
def calculate_weight_patched(self, patches, weight, key):
|
||||
|
|
@ -201,14 +210,13 @@ class BrownianTreeNoiseSamplerPatched:
|
|||
|
||||
|
||||
def compute_cfg(uncond, cond, cfg_scale, t):
|
||||
global adaptive_cfg
|
||||
|
||||
mimic_cfg = float(adaptive_cfg)
|
||||
pid = os.getpid()
|
||||
mimic_cfg = float(patch_settings[pid].adaptive_cfg)
|
||||
real_cfg = float(cfg_scale)
|
||||
|
||||
real_eps = uncond + real_cfg * (cond - uncond)
|
||||
|
||||
if cfg_scale > adaptive_cfg:
|
||||
if cfg_scale > patch_settings[pid].adaptive_cfg:
|
||||
mimicked_eps = uncond + mimic_cfg * (cond - uncond)
|
||||
return real_eps * t + mimicked_eps * (1 - t)
|
||||
else:
|
||||
|
|
@ -216,13 +224,13 @@ def compute_cfg(uncond, cond, cfg_scale, t):
|
|||
|
||||
|
||||
def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
|
||||
global eps_record
|
||||
pid = os.getpid()
|
||||
|
||||
if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
|
||||
final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
|
||||
|
||||
if eps_record is not None:
|
||||
eps_record = ((x - final_x0) / timestep).cpu()
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
|
||||
|
||||
return final_x0
|
||||
|
||||
|
|
@ -231,16 +239,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
|
|||
positive_eps = x - positive_x0
|
||||
negative_eps = x - negative_x0
|
||||
|
||||
alpha = 0.001 * sharpness * global_diffusion_progress
|
||||
alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
|
||||
|
||||
positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
|
||||
positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
|
||||
|
||||
final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
|
||||
cfg_scale=cond_scale, t=global_diffusion_progress)
|
||||
cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
|
||||
|
||||
if eps_record is not None:
|
||||
eps_record = (final_eps / timestep).cpu()
|
||||
if patch_settings[pid].eps_record is not None:
|
||||
patch_settings[pid].eps_record = (final_eps / timestep).cpu()
|
||||
|
||||
return x - final_eps
|
||||
|
||||
|
|
@ -255,20 +263,19 @@ def round_to_64(x):
|
|||
|
||||
|
||||
def sdxl_encode_adm_patched(self, **kwargs):
|
||||
global positive_adm_scale, negative_adm_scale
|
||||
|
||||
clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
|
||||
width = kwargs.get("width", 1024)
|
||||
height = kwargs.get("height", 1024)
|
||||
target_width = width
|
||||
target_height = height
|
||||
pid = os.getpid()
|
||||
|
||||
if kwargs.get("prompt_type", "") == "negative":
|
||||
width = float(width) * negative_adm_scale
|
||||
height = float(height) * negative_adm_scale
|
||||
width = float(width) * patch_settings[pid].negative_adm_scale
|
||||
height = float(height) * patch_settings[pid].negative_adm_scale
|
||||
elif kwargs.get("prompt_type", "") == "positive":
|
||||
width = float(width) * positive_adm_scale
|
||||
height = float(height) * positive_adm_scale
|
||||
width = float(width) * patch_settings[pid].positive_adm_scale
|
||||
height = float(height) * patch_settings[pid].positive_adm_scale
|
||||
|
||||
def embedder(number_list):
|
||||
h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
|
||||
|
|
@ -322,7 +329,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
|
|||
|
||||
def timed_adm(y, timesteps):
|
||||
if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
|
||||
y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
|
||||
y_with_adm = y[..., :2816].clone()
|
||||
y_without_adm = y[..., 2816:].clone()
|
||||
return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
|
||||
|
|
@ -332,6 +339,7 @@ def timed_adm(y, timesteps):
|
|||
def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
||||
t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
pid = os.getpid()
|
||||
|
||||
guided_hint = self.input_hint_block(hint, emb, context)
|
||||
|
||||
|
|
@ -357,19 +365,17 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
|||
h = self.middle_block(h, emb, context)
|
||||
outs.append(self.middle_block_out(h, emb, context))
|
||||
|
||||
if advanced_parameters.controlnet_softness > 0:
|
||||
if patch_settings[pid].controlnet_softness > 0:
|
||||
for i in range(10):
|
||||
k = 1.0 - float(i) / 9.0
|
||||
outs[i] = outs[i] * (1.0 - advanced_parameters.controlnet_softness * k)
|
||||
outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
|
||||
|
||||
return outs
|
||||
|
||||
|
||||
def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
||||
global global_diffusion_progress
|
||||
|
||||
self.current_step = 1.0 - timesteps.to(x) / 999.0
|
||||
global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
||||
|
||||
y = timed_adm(y, timesteps)
|
||||
|
||||
|
|
@ -483,7 +489,7 @@ def patch_all():
|
|||
if ldm_patched.modules.model_management.directml_enabled:
|
||||
ldm_patched.modules.model_management.lowvram_available = True
|
||||
ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
|
||||
|
||||
|
||||
patch_all_precision()
|
||||
patch_all_clip()
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ The purpose of this fork is to add new features / fix bugs and contribute back t
|
|||
Included adjustments:
|
||||
|
||||
* ✨ https://github.com/lllyasviel/Fooocus/pull/958 - NSFW image censoring (config and UI)
|
||||
* 🐛 https://github.com/lllyasviel/Fooocus/pull/981 - prevent users from skipping/stopping other users tasks in queue (multi-user capabilities)
|
||||
* 🐛 https://github.com/lllyasviel/Fooocus/pull/981 - prevent users from skipping/stopping other users tasks in queue (multi-user capabilities) + rework advanced_parameters (removal + PID handling)
|
||||
* ✨ https://github.com/lllyasviel/Fooocus/pull/985 - add list of 100 animals to wildcards
|
||||
* ✨ https://github.com/lllyasviel/Fooocus/pull/1013 - add advanced parameter for disable_intermediate_results (progress_gallery, prevents UI lag when generation is too fast)
|
||||
* ✨ https://github.com/lllyasviel/Fooocus/pull/1039 - add prompt translation
|
||||
|
|
|
|||
37
webui.py
37
webui.py
|
|
@ -11,7 +11,6 @@ import modules.async_worker as worker
|
|||
import modules.constants as constants
|
||||
import modules.flags as flags
|
||||
import modules.gradio_hijack as grh
|
||||
import modules.advanced_parameters as advanced_parameters
|
||||
import modules.style_sorter as style_sorter
|
||||
import modules.meta_parser
|
||||
import args_manager
|
||||
|
|
@ -25,9 +24,9 @@ from modules.auth import auth_enabled, check_auth
|
|||
|
||||
def get_task(*args):
|
||||
args = list(args)
|
||||
currentTask = args.pop(0)
|
||||
currentTask = worker.AsyncTask(args=args)
|
||||
return currentTask
|
||||
args.pop(0)
|
||||
|
||||
return worker.AsyncTask(args=args)
|
||||
|
||||
def generate_clicked(task):
|
||||
import ldm_patched.modules.model_management as model_management
|
||||
|
|
@ -65,7 +64,7 @@ def generate_clicked(task):
|
|||
if flag == 'results':
|
||||
yield gr.update(visible=True), \
|
||||
gr.update(visible=True), \
|
||||
gr.update(visible=True, value=product) if not advanced_parameters.disable_intermediate_results and advanced_parameters.sampler_name != 'lcm' else gr.update(), \
|
||||
gr.update(visible=True, value=product), \
|
||||
gr.update(visible=False)
|
||||
if flag == 'finish':
|
||||
yield gr.update(visible=False), \
|
||||
|
|
@ -285,7 +284,7 @@ with shared.gradio_root:
|
|||
if args_manager.args.disable_image_log:
|
||||
return gr.update(value='')
|
||||
|
||||
return gr.update(value=f'<a href="/file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
|
||||
return gr.update(value=f'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
|
||||
|
||||
history_link = gr.HTML()
|
||||
shared.gradio_root.load(update_history_link,outputs=history_link)
|
||||
|
|
@ -484,7 +483,7 @@ with shared.gradio_root:
|
|||
'(default is 0, always process before any mask invert)')
|
||||
inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
|
||||
invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
|
||||
|
||||
|
||||
inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,
|
||||
inpaint_strength, inpaint_respective_field,
|
||||
inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]
|
||||
|
|
@ -501,15 +500,6 @@ with shared.gradio_root:
|
|||
freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
|
||||
freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
|
||||
|
||||
adps = [disable_preview, disable_intermediate_results, black_out_nsfw, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name,
|
||||
scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height,
|
||||
overwrite_vary_strength, overwrite_upscale_strength,
|
||||
mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint,
|
||||
debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness,
|
||||
canny_low_threshold, canny_high_threshold, refiner_swap_method]
|
||||
adps += freeu_ctrls
|
||||
adps += inpaint_ctrls
|
||||
|
||||
def dev_mode_checked(r):
|
||||
return gr.update(visible=r)
|
||||
|
||||
|
|
@ -648,8 +638,9 @@ with shared.gradio_root:
|
|||
inpaint_strength, inpaint_respective_field
|
||||
], show_progress=False, queue=False)
|
||||
|
||||
ctrls = [
|
||||
currentTask, prompt, negative_prompt, translate_prompts, style_selections,
|
||||
ctrls = [currentTask, generate_image_grid]
|
||||
ctrls += [
|
||||
prompt, negative_prompt, translate_prompts, style_selections,
|
||||
performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
|
||||
]
|
||||
|
||||
|
|
@ -657,6 +648,15 @@ with shared.gradio_root:
|
|||
ctrls += [input_image_checkbox, current_tab]
|
||||
ctrls += [uov_method, uov_input_image]
|
||||
ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
|
||||
ctrls += [disable_preview, disable_intermediate_results, black_out_nsfw]
|
||||
ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
|
||||
ctrls += [sampler_name, scheduler_name]
|
||||
ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
|
||||
ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
|
||||
ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
|
||||
ctrls += [refiner_swap_method, controlnet_softness]
|
||||
ctrls += freeu_ctrls
|
||||
ctrls += inpaint_ctrls
|
||||
|
||||
if not args_manager.args.disable_metadata:
|
||||
ctrls += [save_metadata_to_images, metadata_scheme]
|
||||
|
|
@ -689,7 +689,6 @@ with shared.gradio_root:
|
|||
generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
|
||||
outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
|
||||
.then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
|
||||
.then(advanced_parameters.set_all_advanced_parameters, inputs=adps) \
|
||||
.then(fn=get_task, inputs=ctrls, outputs=currentTask) \
|
||||
.then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
|
||||
.then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),
|
||||
|
|
|
|||
Loading…
Reference in New Issue