import threading buffer = [] outputs = [] def worker(): global buffer, outputs import traceback import numpy as np import torch import time import shared import random import copy import modules.default_pipeline as pipeline import modules.core as core import modules.flags as flags import modules.path import modules.patch import fcbh.model_management import fooocus_extras.preprocessors as preprocessors import modules.inpaint_worker as inpaint_worker import modules.advanced_parameters as advanced_parameters import fooocus_extras.ip_adapter as ip_adapter from modules.sdxl_styles import apply_style, apply_wildcards, aspect_ratios, fooocus_expansion from modules.private_logger import log from modules.expansion import safe_str from modules.util import join_prompts, remove_empty_str, HWC3, resize_image, image_is_generated_in_current_ui, make_sure_that_image_is_not_too_large from modules.upscaler import perform_upscale 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)}''' if async_gradio_app.share: flag += f''' or {async_gradio_app.share_url}''' print(flag) except Exception as e: print(e) def progressbar(number, text): print(f'[Fooocus] {text}') outputs.append(['preview', (number, text, None)]) @torch.no_grad() @torch.inference_mode() def handler(args): execution_start_time = time.perf_counter() args.reverse() prompt = args.pop() negative_prompt = args.pop() style_selections = args.pop() performance_selection = args.pop() aspect_ratios_selection = args.pop() image_number = args.pop() image_seed = args.pop() sharpness = args.pop() guidance_scale = args.pop() base_model_name = args.pop() refiner_model_name = args.pop() loras = [(args.pop(), args.pop()) for _ in range(5)] input_image_checkbox = args.pop() current_tab = args.pop() uov_method = args.pop() uov_input_image = args.pop() outpaint_selections = args.pop() inpaint_input_image = args.pop() cn_tasks = {flags.cn_ip: [], flags.cn_canny: [], flags.cn_cpds: []} for _ in range(4): cn_img = args.pop() cn_stop = args.pop() cn_weight = args.pop() cn_type = args.pop() if cn_img is not None: cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) outpaint_selections = [o.lower() for o in outpaint_selections] loras_raw = copy.deepcopy(loras) raw_style_selections = copy.deepcopy(style_selections) uov_method = uov_method.lower() if fooocus_expansion in style_selections: use_expansion = True style_selections.remove(fooocus_expansion) else: use_expansion = False use_style = len(style_selections) > 0 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] ADM Scale = {modules.patch.positive_adm_scale} : {modules.patch.negative_adm_scale} : {modules.patch.adm_scaler_end}') cfg_scale = float(guidance_scale) print(f'[Parameters] CFG = {cfg_scale}') initial_latent = None denoising_strength = 1.0 tiled = False inpaint_worker.current_task = None width, height = aspect_ratios[aspect_ratios_selection] skip_prompt_processing = False refiner_swap_method = advanced_parameters.refiner_swap_method raw_prompt = prompt raw_negative_prompt = negative_prompt inpaint_image = None inpaint_mask = None inpaint_head_model_path = None controlnet_canny_path = None controlnet_cpds_path = None clip_vision_path, ip_negative_path, ip_adapter_path = None, None, None seed = image_seed max_seed = int(1024 * 1024 * 1024) if not isinstance(seed, int): seed = random.randint(1, max_seed) if seed < 0: seed = - seed seed = seed % max_seed if performance_selection == 'Speed': steps = 30 switch = 20 else: steps = 60 switch = 40 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)) \ and uov_method != flags.disabled and uov_input_image is not None: uov_input_image = HWC3(uov_input_image) if 'vary' in uov_method: goals.append('vary') elif 'upscale' in uov_method: goals.append('upscale') if 'fast' in uov_method: skip_prompt_processing = True else: if performance_selection == 'Speed': steps = 18 switch = 12 else: steps = 36 switch = 24 progressbar(1, 'Downloading upscale models ...') modules.path.downloading_upscale_model() if (current_tab == 'inpaint' or (current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint))\ and isinstance(inpaint_input_image, dict): inpaint_image = inpaint_input_image['image'] inpaint_mask = inpaint_input_image['mask'][:, :, 0] inpaint_image = HWC3(inpaint_image) if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0): progressbar(1, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.path.downloading_inpaint_models(advanced_parameters.inpaint_engine) loras += [(inpaint_patch_model_path, 1.0)] print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') goals.append('inpaint') sampler_name = 'dpmpp_2m_sde_gpu' # only support the patched dpmpp_2m_sde_gpu if current_tab == 'ip' or \ advanced_parameters.mixing_image_prompt_and_inpaint or \ advanced_parameters.mixing_image_prompt_and_vary_upscale: goals.append('cn') progressbar(1, 'Downloading control models ...') if len(cn_tasks[flags.cn_canny]) > 0: controlnet_canny_path = modules.path.downloading_controlnet_canny() if len(cn_tasks[flags.cn_cpds]) > 0: controlnet_cpds_path = modules.path.downloading_controlnet_cpds() if len(cn_tasks[flags.cn_ip]) > 0: clip_vision_path, ip_negative_path, ip_adapter_path = modules.path.downloading_ip_adapters() progressbar(1, 'Loading control models ...') # Load or unload CNs pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path]) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) if advanced_parameters.overwrite_step > 0: steps = advanced_parameters.overwrite_step if advanced_parameters.overwrite_switch > 0: switch = advanced_parameters.overwrite_switch if advanced_parameters.overwrite_width > 0: width = advanced_parameters.overwrite_width if advanced_parameters.overwrite_height > 0: height = advanced_parameters.overwrite_height print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}') print(f'[Parameters] Steps = {steps} - {switch}') progressbar(1, 'Initializing ...') if not skip_prompt_processing: prompts = remove_empty_str([safe_str(p) for p in prompt.split('\n')], default='') negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.split('\n')], default='') prompt = prompts[0] negative_prompt = negative_prompts[0] extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else [] progressbar(3, 'Loading models ...') pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, loras=loras) progressbar(3, 'Processing prompts ...') tasks = [] for i in range(image_number): task_seed = seed + i task_prompt = apply_wildcards(prompt, task_seed) positive_basic_workloads = [] negative_basic_workloads = [] if use_style: for s in style_selections: p, n = apply_style(s, positive=task_prompt) positive_basic_workloads.append(p) negative_basic_workloads.append(n) else: positive_basic_workloads.append(task_prompt) negative_basic_workloads.append(negative_prompt) # Always use independent workload for negative. positive_basic_workloads = positive_basic_workloads + extra_positive_prompts negative_basic_workloads = negative_basic_workloads + extra_negative_prompts positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt) negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=negative_prompt) positive_top_k = len(positive_basic_workloads) negative_top_k = len(negative_basic_workloads) tasks.append(dict( task_seed=task_seed, task_prompt=task_prompt, positive=positive_basic_workloads, negative=negative_basic_workloads, expansion='', c=None, uc=None )) if use_expansion: for i, t in enumerate(tasks): progressbar(5, f'Preparing Fooocus text #{i + 1} ...') expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed']) print(f'[Prompt Expansion] New suffix: {expansion}') t['expansion'] = expansion t['positive'] = copy.deepcopy(t['positive']) + [join_prompts(t['task_prompt'], expansion)] # Deep copy. for i, t in enumerate(tasks): progressbar(7, f'Encoding positive #{i + 1} ...') t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=positive_top_k) for i, t in enumerate(tasks): progressbar(10, f'Encoding negative #{i + 1} ...') t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=negative_top_k) if len(goals) > 0: progressbar(13, 'Image processing ...') if 'vary' in goals: if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height): uov_input_image = resize_image(uov_input_image, width=width, height=height) print(f'Resolution corrected - users are uploading their own images.') else: print(f'Processing images generated by Fooocus.') if 'subtle' in uov_method: denoising_strength = 0.5 if 'strong' in uov_method: denoising_strength = 0.85 if advanced_parameters.overwrite_vary_strength > 0: denoising_strength = advanced_parameters.overwrite_vary_strength uov_input_image = make_sure_that_image_is_not_too_large(uov_input_image) initial_pixels = core.numpy_to_pytorch(uov_input_image) progressbar(13, 'VAE encoding ...') initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=initial_pixels) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((height, width))}.') if 'upscale' in goals: H, W, C = uov_input_image.shape progressbar(13, f'Upscaling image from {str((H, W))} ...') uov_input_image = core.numpy_to_pytorch(uov_input_image) uov_input_image = perform_upscale(uov_input_image) uov_input_image = core.pytorch_to_numpy(uov_input_image)[0] print(f'Image upscaled.') if '1.5x' in uov_method: f = 1.5 elif '2x' in uov_method: f = 2.0 else: f = 1.0 width_f = int(width * f) height_f = int(height * f) if image_is_generated_in_current_ui(uov_input_image, ui_width=width_f, ui_height=height_f): uov_input_image = resize_image(uov_input_image, width=int(W * f), height=int(H * f)) print(f'Processing images generated by Fooocus.') else: uov_input_image = resize_image(uov_input_image, width=width_f, height=height_f) print(f'Resolution corrected - users are uploading their own images.') H, W, C = uov_input_image.shape image_is_super_large = H * W > 2800 * 2800 if 'fast' in uov_method: direct_return = True elif image_is_super_large: print('Image is too large. Directly returned the SR image. ' 'Usually directly return SR image at 4K resolution ' 'yields better results than SDXL diffusion.') direct_return = True else: direct_return = False if direct_return: d = [('Upscale (Fast)', '2x')] log(uov_input_image, d, single_line_number=1) outputs.append(['results', [uov_input_image]]) return tiled = True denoising_strength = 0.382 if advanced_parameters.overwrite_upscale_strength > 0: denoising_strength = advanced_parameters.overwrite_upscale_strength initial_pixels = core.numpy_to_pytorch(uov_input_image) progressbar(13, 'VAE encoding ...') initial_latent = core.encode_vae( vae=pipeline.final_vae if pipeline.final_refiner_vae is None else pipeline.final_refiner_vae, pixels=initial_pixels, tiled=True) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((height, width))}.') refiner_swap_method = 'upscale' if 'inpaint' in goals: if len(outpaint_selections) > 0: H, W, C = inpaint_image.shape if 'top' in outpaint_selections: inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', constant_values=255) if 'bottom' in outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', constant_values=255) H, W, C = inpaint_image.shape if 'left' in outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant', constant_values=255) if 'right' in outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant', constant_values=255) inpaint_image = np.ascontiguousarray(inpaint_image.copy()) inpaint_mask = np.ascontiguousarray(inpaint_mask.copy()) inpaint_worker.current_task = inpaint_worker.InpaintWorker(image=inpaint_image, mask=inpaint_mask, is_outpaint=len(outpaint_selections) > 0) pipeline.final_unet.model.diffusion_model.in_inpaint = True # print(f'Inpaint task: {str((height, width))}') # outputs.append(['results', inpaint_worker.current_task.visualize_mask_processing()]) # return progressbar(13, 'VAE encoding ...') inpaint_pixels = core.numpy_to_pytorch(inpaint_worker.current_task.image_ready) initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=inpaint_pixels) inpaint_latent = initial_latent['samples'] B, C, H, W = inpaint_latent.shape inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None]) inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8)) inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear') latent_after_swap = None if pipeline.final_refiner_vae is not None: progressbar(13, 'VAE SD15 encoding ...') latent_after_swap = core.encode_vae(vae=pipeline.final_refiner_vae, pixels=inpaint_pixels)['samples'] inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask, latent_after_swap=latent_after_swap) progressbar(13, 'VAE inpaint encoding ...') inpaint_mask = (inpaint_worker.current_task.mask_ready > 0).astype(np.float32) inpaint_mask = torch.tensor(inpaint_mask).float() vae_dict = core.encode_vae_inpaint( mask=inpaint_mask, vae=pipeline.final_vae, pixels=inpaint_pixels) inpaint_latent = vae_dict['samples'] inpaint_mask = vae_dict['noise_mask'] inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask, model_path=inpaint_head_model_path) B, C, H, W = inpaint_latent.shape final_height, final_width = inpaint_worker.current_task.image_raw.shape[:2] height, width = H * 8, W * 8 print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.') if 'cn' in goals: for task in cn_tasks[flags.cn_canny]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) cn_img = preprocessors.canny_pyramid(cn_img) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if advanced_parameters.debugging_cn_preprocessor: outputs.append(['results', [cn_img]]) return for task in cn_tasks[flags.cn_cpds]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) cn_img = preprocessors.cpds(cn_img) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if advanced_parameters.debugging_cn_preprocessor: outputs.append(['results', [cn_img]]) return for task in cn_tasks[flags.cn_ip]: cn_img, cn_stop, cn_weight = task cn_img = HWC3(cn_img) # 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) if advanced_parameters.debugging_cn_preprocessor: outputs.append(['results', [cn_img]]) return if len(cn_tasks[flags.cn_ip]) > 0: pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, cn_tasks[flags.cn_ip]) if advanced_parameters.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 ) results = [] all_steps = steps * image_number preparation_time = time.perf_counter() - execution_start_time print(f'Preparation time: {preparation_time:.2f} seconds') outputs.append(['preview', (13, 'Moving model to GPU ...', None)]) def callback(step, x0, x, total_steps, y): done_steps = current_task_id * steps + step outputs.append(['preview', ( int(15.0 + 85.0 * float(done_steps) / float(all_steps)), f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling', y)]) for current_task_id, task in enumerate(tasks): execution_start_time = time.perf_counter() try: positive_cond, negative_cond = task['c'], task['uc'] if 'cn' in goals: for cn_flag, cn_path in [ (flags.cn_canny, controlnet_canny_path), (flags.cn_cpds, controlnet_cpds_path) ]: for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]: positive_cond, negative_cond = core.apply_controlnet( positive_cond, negative_cond, pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop) imgs = pipeline.process_diffusion( positive_cond=positive_cond, negative_cond=negative_cond, steps=steps, switch=switch, width=width, height=height, image_seed=task['task_seed'], callback=callback, sampler_name=sampler_name, scheduler_name=scheduler_name, latent=initial_latent, denoise=denoising_strength, tiled=tiled, cfg_scale=cfg_scale, refiner_swap_method=refiner_swap_method ) del task['c'], task['uc'], positive_cond, negative_cond # Save memory if inpaint_worker.current_task is not None: imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] for x in imgs: d = [ ('Prompt', raw_prompt), ('Negative Prompt', raw_negative_prompt), ('Fooocus V2 Expansion', task['expansion']), ('Styles', str(raw_style_selections)), ('Performance', performance_selection), ('Resolution', str((width, height))), ('Sharpness', sharpness), ('Guidance Scale', guidance_scale), ('ADM Guidance', str((modules.patch.positive_adm_scale, modules.patch.negative_adm_scale))), ('Base Model', base_model_name), ('Refiner Model', refiner_model_name), ('Sampler', sampler_name), ('Scheduler', scheduler_name), ('Seed', task['task_seed']) ] for n, w in loras_raw: if n != 'None': d.append((f'LoRA [{n}] weight', w)) log(x, d, single_line_number=3) results += imgs except fcbh.model_management.InterruptProcessingException as e: if shared.last_stop == 'skip': print('User skipped') continue else: print('User stopped') break execution_time = time.perf_counter() - execution_start_time print(f'Generating and saving time: {execution_time:.2f} seconds') outputs.append(['results', results]) pipeline.prepare_text_encoder(async_call=True) return while True: time.sleep(0.01) if len(buffer) > 0: task = buffer.pop(0) try: handler(task) except: traceback.print_exc() outputs.append(['results', []]) pass threading.Thread(target=worker, daemon=True).start()