diff --git a/extras/preprocessors.py b/extras/preprocessors.py index 798fe15d2..0aa83109a 100644 --- a/extras/preprocessors.py +++ b/extras/preprocessors.py @@ -1,27 +1,26 @@ import cv2 import numpy as np -import modules.advanced_parameters as advanced_parameters -def centered_canny(x: np.ndarray): +def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 2 and x.dtype == np.uint8 - y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold)) + y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold)) y = y.astype(np.float32) / 255.0 return y -def centered_canny_color(x: np.ndarray): +def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 3 and x.shape[2] == 3 - result = [centered_canny(x[..., i]) for i in range(3)] + result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)] result = np.stack(result, axis=2) return result -def pyramid_canny_color(x: np.ndarray): +def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 3 and x.shape[2] == 3 @@ -31,7 +30,7 @@ def pyramid_canny_color(x: np.ndarray): for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: Hs, Ws = int(H * k), int(W * k) small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA) - edge = centered_canny_color(small) + edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold) if acc_edge is None: acc_edge = edge else: @@ -54,11 +53,11 @@ def norm255(x, low=4, high=96): return x * 255.0 -def canny_pyramid(x): +def canny_pyramid(x, canny_low_threshold, canny_high_threshold): # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions. # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions. - color_canny = pyramid_canny_color(x) + color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold) result = np.sum(color_canny, axis=2) return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8) diff --git a/modules/advanced_parameters.py b/modules/advanced_parameters.py deleted file mode 100644 index 0caa3eec8..000000000 --- a/modules/advanced_parameters.py +++ /dev/null @@ -1,33 +0,0 @@ -disable_preview, 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, \ - freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - 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 = [None] * 35 - - -def set_all_advanced_parameters(*args): - global disable_preview, 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, \ - freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - 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 - - disable_preview, 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, \ - freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - 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 = args - - return diff --git a/modules/async_worker.py b/modules/async_worker.py index 40abb7fa4..d0ce4ba91 100644 --- a/modules/async_worker.py +++ b/modules/async_worker.py @@ -1,4 +1,8 @@ import threading +import os +from modules.patch import PatchSettings, patch_settings, patch_all + +patch_all() class AsyncTask: @@ -6,6 +10,8 @@ def __init__(self, args): self.args = args self.yields = [] self.results = [] + self.last_stop = False + self.processing = False async_tasks = [] @@ -31,7 +37,6 @@ def worker(): import extras.preprocessors as preprocessors import modules.inpaint_worker as inpaint_worker import modules.constants as constants - import modules.advanced_parameters as advanced_parameters import extras.ip_adapter as ip_adapter import extras.face_crop import fooocus_version @@ -43,6 +48,9 @@ def worker(): get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix from modules.upscaler import perform_upscale + pid = os.getpid() + print(f'Started worker with PID {pid}') + try: async_gradio_app = shared.gradio_root 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)}''' @@ -69,9 +77,6 @@ def yield_result(async_task, imgs, do_not_show_finished_images=False): return def build_image_wall(async_task): - if not advanced_parameters.generate_image_grid: - return - results = async_task.results if len(results) < 2: @@ -115,6 +120,7 @@ def build_image_wall(async_task): @torch.inference_mode() def handler(async_task): execution_start_time = time.perf_counter() + async_task.processing = True args = async_task.args args.reverse() @@ -140,6 +146,40 @@ def handler(async_task): inpaint_input_image = args.pop() inpaint_additional_prompt = args.pop() inpaint_mask_image_upload = args.pop() + disable_preview = args.pop() + adm_scaler_positive = args.pop() + adm_scaler_negative = args.pop() + adm_scaler_end = args.pop() + adaptive_cfg = args.pop() + sampler_name = args.pop() + scheduler_name = args.pop() + overwrite_step = args.pop() + overwrite_switch = args.pop() + overwrite_width = args.pop() + overwrite_height = args.pop() + overwrite_vary_strength = args.pop() + overwrite_upscale_strength = args.pop() + mixing_image_prompt_and_vary_upscale = args.pop() + mixing_image_prompt_and_inpaint = args.pop() + debugging_cn_preprocessor = args.pop() + skipping_cn_preprocessor = args.pop() + canny_low_threshold = args.pop() + canny_high_threshold = args.pop() + refiner_swap_method = args.pop() + controlnet_softness = args.pop() + freeu_enabled = args.pop() + freeu_b1 = args.pop() + freeu_b2 = args.pop() + freeu_s1 = args.pop() + freeu_s2 = args.pop() + debugging_inpaint_preprocessor = args.pop() + inpaint_disable_initial_latent = args.pop() + inpaint_engine = args.pop() + inpaint_strength = args.pop() + inpaint_respective_field = args.pop() + inpaint_mask_upload_checkbox = args.pop() + invert_mask_checkbox = args.pop() + inpaint_erode_or_dilate = args.pop() cn_tasks = {x: [] for x in flags.ip_list} for _ in range(4): @@ -186,30 +226,33 @@ def handler(async_task): print(f'Refiner disabled in LCM mode.') refiner_model_name = 'None' - sampler_name = advanced_parameters.sampler_name = 'lcm' - scheduler_name = advanced_parameters.scheduler_name = 'lcm' - modules.patch.sharpness = sharpness = 0.0 - cfg_scale = guidance_scale = 1.0 - modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0 + sampler_name = 'lcm' + scheduler_name = 'lcm' + sharpness = 0.0 + guidance_scale = 1.0 + adaptive_cfg = 1.0 refiner_switch = 1.0 - modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0 - modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0 - modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0 + adm_scaler_positive = 1.0 + adm_scaler_negative = 1.0 + adm_scaler_end = 0.0 steps = 8 - modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg - print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}') - - modules.patch.sharpness = sharpness - print(f'[Parameters] Sharpness = {modules.patch.sharpness}') - - modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive - modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative - modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end + print(f'[Parameters] Adaptive CFG = {adaptive_cfg}') + print(f'[Parameters] Sharpness = {sharpness}') + print(f'[Parameters] ControlNet Softness = {controlnet_softness}') print(f'[Parameters] ADM Scale = ' - f'{modules.patch.positive_adm_scale} : ' - f'{modules.patch.negative_adm_scale} : ' - f'{modules.patch.adm_scaler_end}') + f'{adm_scaler_positive} : ' + f'{adm_scaler_negative} : ' + f'{adm_scaler_end}') + + patch_settings[pid] = PatchSettings( + sharpness, + adm_scaler_end, + adm_scaler_positive, + adm_scaler_negative, + controlnet_softness, + adaptive_cfg + ) cfg_scale = float(guidance_scale) print(f'[Parameters] CFG = {cfg_scale}') @@ -222,10 +265,9 @@ def handler(async_task): width, height = int(width), int(height) skip_prompt_processing = False - refiner_swap_method = advanced_parameters.refiner_swap_method inpaint_worker.current_task = None - inpaint_parameterized = advanced_parameters.inpaint_engine != 'None' + inpaint_parameterized = inpaint_engine != 'None' inpaint_image = None inpaint_mask = None inpaint_head_model_path = None @@ -239,15 +281,12 @@ def handler(async_task): seed = int(image_seed) print(f'[Parameters] Seed = {seed}') - sampler_name = advanced_parameters.sampler_name - scheduler_name = advanced_parameters.scheduler_name - goals = [] tasks = [] if input_image_checkbox: if (current_tab == 'uov' or ( - current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \ + current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \ and uov_method != flags.disabled and uov_input_image is not None: uov_input_image = HWC3(uov_input_image) if 'vary' in uov_method: @@ -271,12 +310,12 @@ def handler(async_task): progressbar(async_task, 1, 'Downloading upscale models ...') modules.config.downloading_upscale_model() if (current_tab == 'inpaint' or ( - current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \ + current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \ and isinstance(inpaint_input_image, dict): inpaint_image = inpaint_input_image['image'] inpaint_mask = inpaint_input_image['mask'][:, :, 0] - - if advanced_parameters.inpaint_mask_upload_checkbox: + + if inpaint_mask_upload_checkbox: if isinstance(inpaint_mask_image_upload, np.ndarray): if inpaint_mask_image_upload.ndim == 3: H, W, C = inpaint_image.shape @@ -285,10 +324,10 @@ def handler(async_task): inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255 inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload) - if int(advanced_parameters.inpaint_erode_or_dilate) != 0: - inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate) + if int(inpaint_erode_or_dilate) != 0: + inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate) - if advanced_parameters.invert_mask_checkbox: + if invert_mask_checkbox: inpaint_mask = 255 - inpaint_mask inpaint_image = HWC3(inpaint_image) @@ -299,7 +338,7 @@ def handler(async_task): if inpaint_parameterized: progressbar(async_task, 1, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( - advanced_parameters.inpaint_engine) + inpaint_engine) base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') if refiner_model_name == 'None': @@ -315,8 +354,8 @@ def handler(async_task): prompt = inpaint_additional_prompt + '\n' + prompt goals.append('inpaint') if current_tab == 'ip' or \ - 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: goals.append('cn') progressbar(async_task, 1, 'Downloading control models ...') if len(cn_tasks[flags.cn_canny]) > 0: @@ -335,19 +374,19 @@ def handler(async_task): ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path) - if advanced_parameters.overwrite_step > 0: - steps = advanced_parameters.overwrite_step + if overwrite_step > 0: + steps = overwrite_step switch = int(round(steps * refiner_switch)) - 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}') @@ -446,8 +485,8 @@ def handler(async_task): 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: @@ -518,8 +557,8 @@ def handler(async_task): 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 ...') @@ -563,19 +602,19 @@ def handler(async_task): 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: + if debugging_inpaint_preprocessor: yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), do_not_show_finished_images=True) return @@ -621,7 +660,7 @@ def handler(async_task): 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 @@ -634,24 +673,24 @@ def handler(async_task): 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: + if debugging_cn_preprocessor: yield_result(async_task, cn_img, 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: + if debugging_cn_preprocessor: yield_result(async_task, cn_img, do_not_show_finished_images=True) return for task in cn_tasks[flags.cn_ip]: @@ -662,21 +701,21 @@ def handler(async_task): 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: + if debugging_cn_preprocessor: yield_result(async_task, cn_img, 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: + if debugging_cn_preprocessor: yield_result(async_task, cn_img, do_not_show_finished_images=True) return @@ -685,14 +724,14 @@ def handler(async_task): 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 @@ -738,6 +777,8 @@ def callback(step, x0, x, total_steps, y): execution_start_time = time.perf_counter() try: + if async_task.last_stop is not False: + ldm_patched.model_management.interrupt_current_processing() positive_cond, negative_cond = task['c'], task['uc'] if 'cn' in goals: @@ -765,7 +806,8 @@ def callback(step, x0, x, total_steps, y): 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 @@ -784,9 +826,9 @@ def callback(step, x0, x, total_steps, y): ('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), @@ -802,8 +844,9 @@ def callback(step, x0, x, total_steps, y): yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1) except ldm_patched.modules.model_management.InterruptProcessingException as e: - if shared.last_stop == 'skip': + if async_task.last_stop == 'skip': print('User skipped') + async_task.last_stop = False continue else: print('User stopped') @@ -811,21 +854,27 @@ def callback(step, x0, x, total_steps, y): execution_time = time.perf_counter() - execution_start_time print(f'Generating and saving time: {execution_time:.2f} seconds') - + async_task.processing = False return while True: 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 diff --git a/modules/core.py b/modules/core.py index 989b8e321..7a29d9883 100644 --- a/modules/core.py +++ b/modules/core.py @@ -1,8 +1,3 @@ -from modules.patch import patch_all - -patch_all() - - import os import einops import torch @@ -16,7 +11,6 @@ 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 preview_function(x0, step, total_steps): 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) diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py index 6001d97f0..2f45667cf 100644 --- a/modules/default_pipeline.py +++ b/modules/default_pipeline.py @@ -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 diff --git a/modules/patch.py b/modules/patch.py index 2e2409c54..3c2dd8f47 100644 --- a/modules/patch.py +++ b/modules/patch.py @@ -17,7 +17,6 @@ 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_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 @@ def __call__(sigma, sigma_next): 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() diff --git a/shared.py b/shared.py index 269809e3f..21a2a864b 100644 --- a/shared.py +++ b/shared.py @@ -1,2 +1 @@ -gradio_root = None -last_stop = None +gradio_root = None \ No newline at end of file diff --git a/webui.py b/webui.py index b9b620d24..05b7d20ef 100644 --- a/webui.py +++ b/webui.py @@ -11,7 +11,6 @@ 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 @@ -22,17 +21,19 @@ from modules.ui_gradio_extensions import reload_javascript from modules.auth import auth_enabled, check_auth +def get_task(*args): + args = list(args) + args.pop(0) -def generate_clicked(*args): + return worker.AsyncTask(args=args) + +def generate_clicked(task): import ldm_patched.modules.model_management as model_management with model_management.interrupt_processing_mutex: model_management.interrupt_processing = False - # outputs=[progress_html, progress_window, progress_gallery, gallery] - execution_start_time = time.perf_counter() - task = worker.AsyncTask(args=list(args)) finished = False yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \ @@ -88,6 +89,7 @@ def generate_clicked(*args): css=modules.html.css).queue() with shared.gradio_root: + currentTask = gr.State(worker.AsyncTask(args=[])) with gr.Row(): with gr.Column(scale=2): with gr.Row(): @@ -115,21 +117,22 @@ def generate_clicked(*args): skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False) stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) - def stop_clicked(): + def stop_clicked(currentTask): import ldm_patched.modules.model_management as model_management - shared.last_stop = 'stop' - model_management.interrupt_current_processing() - return [gr.update(interactive=False)] * 2 + currentTask.last_stop = 'stop' + if (currentTask.processing): + model_management.interrupt_current_processing() + return currentTask - def skip_clicked(): + def skip_clicked(currentTask): import ldm_patched.modules.model_management as model_management - shared.last_stop = 'skip' - model_management.interrupt_current_processing() - return + currentTask.last_stop = 'skip' + if (currentTask.processing): + model_management.interrupt_current_processing() + return currentTask - stop_button.click(stop_clicked, outputs=[skip_button, stop_button], - queue=False, show_progress=False, _js='cancelGenerateForever') - skip_button.click(skip_clicked, queue=False, show_progress=False) + stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever') + skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False) with gr.Row(elem_classes='advanced_check_row'): input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check') advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check') @@ -435,7 +438,7 @@ def update_history_link(): '(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] @@ -452,15 +455,6 @@ def update_history_link(): 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, 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) @@ -525,7 +519,8 @@ def inpaint_mode_change(mode): inpaint_strength, inpaint_respective_field ], show_progress=False, queue=False) - ctrls = [ + ctrls = [currentTask, generate_image_grid] + ctrls += [ prompt, negative_prompt, style_selections, performance_selection, aspect_ratios_selection, image_number, image_seed, sharpness, guidance_scale ] @@ -534,6 +529,14 @@ def inpaint_mode_change(mode): 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, 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 ctrls += ip_ctrls state_is_generating = gr.State(False) @@ -588,8 +591,8 @@ def parse_meta(raw_prompt_txt, is_generating): 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=generate_clicked, inputs=ctrls, outputs=[progress_html, progress_window, progress_gallery, gallery]) \ + .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), outputs=[generate_button, stop_button, skip_button, state_is_generating]) \ .then(fn=update_history_link, outputs=history_link) \