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Running on Zero
Running on Zero
| import os | |
| import uuid | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image, ImageChops | |
| from typing import Dict, Any, List | |
| from core.settings import INPUT_DIR, MULTIPLIERS_MAP, LORA_DIR, EMBEDDING_DIR, VAE_DIR | |
| from core.runtime_config import CONFIG | |
| from imagegen_utils.app_utils import ( | |
| sanitize_filename, | |
| get_lora_path, | |
| get_embedding_path, | |
| ensure_controlnet_model_downloaded, | |
| ensure_ipadapter_models_downloaded, | |
| _ensure_model_downloaded, | |
| ensure_sd3_ipadapter_models_downloaded, | |
| get_vae_path, | |
| ) | |
| def _temp_png(stem: str) -> str: | |
| return os.path.join(INPUT_DIR, f"{stem}_{uuid.uuid4().hex}.png") | |
| REFERENCE_IMAGE_LIMITS = { | |
| "controlnet_data": 5, | |
| "anima_controlnet_lllite_data": 5, | |
| "diffsynth_controlnet_data": 5, | |
| "krea2_controlnet_data": 5, | |
| "ipadapter_data": 5, | |
| "flux1_ipadapter_data": 5, | |
| "sd3_ipadapter_chain": 5, | |
| "style_data": 5, | |
| "reference_latent_data": 10, | |
| "hidream_o1_reference_data": 10, | |
| "joyai_reference_data": 2, | |
| "krea2_identity_edit_data": 2, | |
| "krea2_reference_edit_data": 3, | |
| "qwen_image_edit_data": 3, | |
| "boogu_edit_data": 10, | |
| "reference_image_data": 10, | |
| } | |
| def _pil_images(value: Any): | |
| if isinstance(value, Image.Image): | |
| yield value | |
| elif isinstance(value, dict): | |
| for child in value.values(): | |
| yield from _pil_images(child) | |
| elif isinstance(value, (list, tuple)): | |
| for child in value: | |
| yield from _pil_images(child) | |
| def _validate_reference_image_budget(ui_inputs: Dict[str, Any]) -> None: | |
| """Bound decoded reference images before saving or entering a workflow.""" | |
| all_images = [] | |
| for input_key, chain_limit in REFERENCE_IMAGE_LIMITS.items(): | |
| images = list(_pil_images(ui_inputs.get(input_key))) | |
| if len(images) > chain_limit: | |
| raise gr.Error( | |
| f"扩展“{input_key}”最多支持 {chain_limit} 张图片；当前为 {len(images)} 张。" | |
| ) | |
| all_images.extend(images) | |
| if len(all_images) > CONFIG.max_reference_images: | |
| raise gr.Error( | |
| f"一次任务最多使用 {CONFIG.max_reference_images} 张参考/控制图；" | |
| f"当前合计 {len(all_images)} 张。" | |
| ) | |
| total_megapixels = 0.0 | |
| for index, image in enumerate(all_images, start=1): | |
| megapixels = (image.width * image.height) / 1_000_000 | |
| if megapixels > CONFIG.max_input_megapixels: | |
| raise gr.Error( | |
| f"参考图 {index} 为 {megapixels:.1f}MP，超过单图上限 " | |
| f"{CONFIG.max_input_megapixels:g}MP。" | |
| ) | |
| total_megapixels += megapixels | |
| if total_megapixels > CONFIG.max_reference_megapixels: | |
| raise gr.Error( | |
| f"参考/控制图累计为 {total_megapixels:.1f}MP，超过上限 " | |
| f"{CONFIG.max_reference_megapixels:g}MP；请缩小图片或减少数量。" | |
| ) | |
| def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]: | |
| task_type = ui_inputs['task_type'] | |
| temp_files_to_clean = [] | |
| _validate_reference_image_budget(ui_inputs) | |
| multiplier = MULTIPLIERS_MAP.get(workflow_model_type, 8) | |
| img_w, img_h = 0, 0 | |
| if task_type == 'txt2img': | |
| img_w = int(ui_inputs.get('width', 0)) | |
| img_h = int(ui_inputs.get('height', 0)) | |
| elif task_type == 'img2img': | |
| input_image_pil = ui_inputs.get('img2img_image') | |
| if input_image_pil: | |
| img_w, img_h = input_image_pil.width, input_image_pil.height | |
| elif task_type == 'inpaint': | |
| inpaint_img = ui_inputs.get('inpaint_image') | |
| inpaint_dict = ui_inputs.get('inpaint_image_dict') | |
| if inpaint_img: | |
| img_w, img_h = inpaint_img.width, inpaint_img.height | |
| elif inpaint_dict and inpaint_dict.get('background'): | |
| img_w, img_h = inpaint_dict['background'].width, inpaint_dict['background'].height | |
| elif task_type == 'outpaint': | |
| input_image_pil = ui_inputs.get('outpaint_image') | |
| if input_image_pil: | |
| img_w, img_h = input_image_pil.width, input_image_pil.height | |
| elif task_type == 'hires_fix': | |
| input_image_pil = ui_inputs.get('hires_image') | |
| if input_image_pil: | |
| img_w, img_h = input_image_pil.width, input_image_pil.height | |
| if task_type == "txt2img" and (img_w <= 0 or img_h <= 0): | |
| raise gr.Error("文生图的宽度和高度必须为正整数。") | |
| if img_w > 0 and img_h > 0: | |
| input_megapixels = (img_w * img_h) / 1_000_000 | |
| if input_megapixels > CONFIG.max_input_megapixels: | |
| scale = (CONFIG.max_input_megapixels / input_megapixels) ** 0.5 | |
| suggested_w = max(multiplier, int(img_w * scale) // multiplier * multiplier) | |
| suggested_h = max(multiplier, int(img_h * scale) // multiplier * multiplier) | |
| raise gr.Error( | |
| f"输入图片为 {input_megapixels:.1f}MP，超过当前上限 " | |
| f"{CONFIG.max_input_megapixels:g}MP；建议缩小到约 " | |
| f"{suggested_w}×{suggested_h}。" | |
| ) | |
| projected_w, projected_h = img_w, img_h | |
| if task_type == "hires_fix": | |
| upscale = float(ui_inputs.get("hires_scale_by") or 1.5) | |
| projected_w, projected_h = int(img_w * upscale), int(img_h * upscale) | |
| elif task_type == "outpaint": | |
| projected_w = img_w + int(ui_inputs.get("left") or 0) + int(ui_inputs.get("right") or 0) | |
| projected_h = img_h + int(ui_inputs.get("top") or 0) + int(ui_inputs.get("bottom") or 0) | |
| projected_megapixels = (projected_w * projected_h) / 1_000_000 | |
| if projected_megapixels > CONFIG.max_output_megapixels: | |
| raise gr.Error( | |
| f"预计输出为 {projected_w}×{projected_h}（{projected_megapixels:.1f}MP），" | |
| f"超过当前上限 {CONFIG.max_output_megapixels:g}MP；请降低放大倍数或扩边尺寸。" | |
| ) | |
| if (img_w % multiplier != 0) or (img_h % multiplier != 0): | |
| suggested_w = max(multiplier, round(img_w / multiplier) * multiplier) | |
| suggested_h = max(multiplier, round(img_h / multiplier) * multiplier) | |
| warning_msg = ( | |
| f"当前模型要求宽高均为 {multiplier} 的倍数；" | |
| f"收到 {img_w}×{img_h}，可调整为约 {suggested_w}×{suggested_h}。" | |
| ) | |
| raise gr.Error(warning_msg) | |
| lora_data = ui_inputs.get('lora_data', []) | |
| active_loras_for_gpu, active_loras_for_meta = [], [] | |
| if lora_data: | |
| sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4] | |
| for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)): | |
| if scale > 0 and lora_id and lora_id.strip(): | |
| lora_filename = None | |
| if source == "File": | |
| lora_filename = sanitize_filename(lora_id) | |
| local_path = os.path.join(LORA_DIR, lora_filename) | |
| if not os.path.exists(local_path): | |
| raise gr.Error(f"已上传的 LoRA“{lora_id}”已不存在，请重新上传。") | |
| elif source in ("Civitai", "Hugging Face"): | |
| local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress) | |
| if local_path: lora_filename = os.path.basename(local_path) | |
| else: raise gr.Error(f"LoRA“{lora_id}”准备失败：{status}") | |
| if lora_filename: | |
| active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale}) | |
| active_loras_for_meta.append(f"{source} {lora_id}:{scale}") | |
| ui_inputs['denoise'] = 1.0 | |
| if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7) | |
| elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55) | |
| elif task_type == 'inpaint': ui_inputs['denoise'] = ui_inputs.get('inpaint_denoise', 1.0) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| if task_type == 'img2img': | |
| input_image_pil = ui_inputs.get('img2img_image') | |
| if not input_image_pil: | |
| raise gr.Error("图生图需要先上传源图片。") | |
| temp_file_path = _temp_png("temp_input") | |
| input_image_pil.save(temp_file_path, "PNG") | |
| ui_inputs['input_image'] = os.path.basename(temp_file_path) | |
| temp_files_to_clean.append(temp_file_path) | |
| ui_inputs['width'] = input_image_pil.width | |
| ui_inputs['height'] = input_image_pil.height | |
| elif task_type == 'inpaint': | |
| inpaint_img = ui_inputs.get('inpaint_image') | |
| inpaint_dict = ui_inputs.get('inpaint_image_dict') | |
| if inpaint_img: | |
| temp_file_path = _temp_png("temp_inpaint") | |
| inpaint_img.save(temp_file_path, "PNG") | |
| ui_inputs['input_image'] = os.path.basename(temp_file_path) | |
| temp_files_to_clean.append(temp_file_path) | |
| ui_inputs['width'] = inpaint_img.width | |
| ui_inputs['height'] = inpaint_img.height | |
| elif inpaint_dict and inpaint_dict.get('background') and inpaint_dict.get('layers'): | |
| background_img = inpaint_dict['background'].convert("RGBA") | |
| composite_mask_pil = Image.new('L', background_img.size, 0) | |
| for layer in inpaint_dict['layers']: | |
| if layer: | |
| layer_alpha = layer.split()[-1] | |
| composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha) | |
| inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L') | |
| r, g, b, _ = background_img.split() | |
| composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha]) | |
| temp_file_path = _temp_png("temp_inpaint_composite") | |
| composite_image_with_mask.save(temp_file_path, "PNG") | |
| ui_inputs['input_image'] = os.path.basename(temp_file_path) | |
| temp_files_to_clean.append(temp_file_path) | |
| ui_inputs.pop('inpaint_mask', None) | |
| ui_inputs['width'] = background_img.width | |
| ui_inputs['height'] = background_img.height | |
| else: | |
| raise gr.Error("局部重绘需要输入图片和有效蒙版。") | |
| elif task_type == 'outpaint': | |
| input_image_pil = ui_inputs.get('outpaint_image') | |
| if not input_image_pil: | |
| raise gr.Error("扩图需要先上传源图片。") | |
| temp_file_path = _temp_png("temp_input") | |
| input_image_pil.save(temp_file_path, "PNG") | |
| ui_inputs['input_image'] = os.path.basename(temp_file_path) | |
| temp_files_to_clean.append(temp_file_path) | |
| ui_inputs['megapixels'] = 0.25 | |
| ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10) | |
| ui_inputs['width'] = input_image_pil.width + int(ui_inputs.get('left') or 0) + int(ui_inputs.get('right') or 0) | |
| ui_inputs['height'] = input_image_pil.height + int(ui_inputs.get('top') or 0) + int(ui_inputs.get('bottom') or 0) | |
| elif task_type == 'hires_fix': | |
| input_image_pil = ui_inputs.get('hires_image') | |
| if not input_image_pil: | |
| raise gr.Error("高清修复需要先上传源图片。") | |
| temp_file_path = _temp_png("temp_input") | |
| input_image_pil.save(temp_file_path, "PNG") | |
| ui_inputs['input_image'] = os.path.basename(temp_file_path) | |
| temp_files_to_clean.append(temp_file_path) | |
| hires_scale = float(ui_inputs.get('hires_scale_by') or 1.5) | |
| ui_inputs['width'] = int(input_image_pil.width * hires_scale) | |
| ui_inputs['height'] = int(input_image_pil.height * hires_scale) | |
| embedding_data = ui_inputs.get('embedding_data', []) | |
| embedding_filenames = [] | |
| if embedding_data: | |
| emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3] | |
| for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)): | |
| if emb_id and emb_id.strip(): | |
| emb_filename = None | |
| if source == "File": | |
| emb_filename = sanitize_filename(emb_id) | |
| local_path = os.path.join(EMBEDDING_DIR, emb_filename) | |
| if not os.path.exists(local_path): | |
| raise gr.Error(f"已上传的 Embedding“{emb_id}”已不存在，请重新上传。") | |
| elif source in ("Civitai", "Hugging Face"): | |
| local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress) | |
| if local_path: emb_filename = os.path.basename(local_path) | |
| else: raise gr.Error(f"Embedding“{emb_id}”准备失败：{status}") | |
| if emb_filename: | |
| embedding_filenames.append(emb_filename) | |
| controlnet_data = ui_inputs.get('controlnet_data', []) | |
| active_controlnets = [] | |
| if controlnet_data: | |
| (cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)] | |
| for i in range(len(cn_images)): | |
| if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None": | |
| ensure_controlnet_model_downloaded(cn_filepaths[i], progress) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| cn_temp_path = _temp_png(f"temp_cn_{i}") | |
| cn_images[i].save(cn_temp_path, "PNG") | |
| temp_files_to_clean.append(cn_temp_path) | |
| active_controlnets.append({ | |
| "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i], | |
| "start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i] | |
| }) | |
| anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', []) | |
| active_anima_controlnets = [] | |
| if anima_controlnet_lllite_data: | |
| (cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)] | |
| for i in range(len(cn_images)): | |
| if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None": | |
| _ensure_model_downloaded(cn_filepaths[i], progress) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| cn_temp_path = _temp_png(f"temp_anima_cn_{i}") | |
| cn_images[i].save(cn_temp_path, "PNG") | |
| temp_files_to_clean.append(cn_temp_path) | |
| active_anima_controlnets.append({ | |
| "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i], | |
| "start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i] | |
| }) | |
| diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', []) | |
| active_diffsynth_controlnets = [] | |
| if diffsynth_controlnet_data: | |
| (cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)] | |
| for i in range(len(cn_images)): | |
| if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None": | |
| ensure_controlnet_model_downloaded(cn_filepaths[i], progress) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| cn_temp_path = _temp_png(f"temp_diffsynth_cn_{i}") | |
| cn_images[i].save(cn_temp_path, "PNG") | |
| temp_files_to_clean.append(cn_temp_path) | |
| active_diffsynth_controlnets.append({ | |
| "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i], | |
| "control_net_name": cn_filepaths[i] | |
| }) | |
| krea2_controlnet_data = ui_inputs.get('krea2_controlnet_data', []) | |
| active_krea2_controlnets = [] | |
| if krea2_controlnet_data: | |
| (cn_images, _, _, cn_strengths, cn_filepaths) = [krea2_controlnet_data[i::5] for i in range(5)] | |
| for i in range(len(cn_images)): | |
| if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None": | |
| ensure_controlnet_model_downloaded(cn_filepaths[i], progress) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| cn_temp_path = _temp_png(f"temp_krea2_cn_{i}") | |
| cn_images[i].save(cn_temp_path, "PNG") | |
| temp_files_to_clean.append(cn_temp_path) | |
| active_krea2_controlnets.append({ | |
| "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i], | |
| "control_net_name": cn_filepaths[i] | |
| }) | |
| ipadapter_data = ui_inputs.get('ipadapter_data', []) | |
| active_ipadapters = [] | |
| if ipadapter_data: | |
| num_ipa_units = (len(ipadapter_data) - 5) // 3 | |
| final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:] | |
| ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)] | |
| all_presets_to_download = set() | |
| for i in range(num_ipa_units): | |
| if ipa_images[i] and ipa_weights[i] > 0 and final_preset: | |
| all_presets_to_download.add(final_preset) | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| ipa_temp_path = _temp_png(f"temp_ipa_{i}") | |
| ipa_images[i].save(ipa_temp_path, "PNG") | |
| temp_files_to_clean.append(ipa_temp_path) | |
| active_ipadapters.append({ | |
| "image": os.path.basename(ipa_temp_path), "preset": final_preset, | |
| "weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i] | |
| }) | |
| if active_ipadapters and final_preset: | |
| all_presets_to_download.add(final_preset) | |
| for preset in all_presets_to_download: | |
| ensure_ipadapter_models_downloaded(preset, progress) | |
| model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl' | |
| if active_ipadapters: | |
| active_ipadapters.append({ | |
| 'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset, | |
| 'final_weight': final_weight, 'final_lora_strength': final_lora_strength, | |
| 'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method | |
| }) | |
| flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', []) | |
| active_flux1_ipadapters = [] | |
| if flux1_ipadapter_data: | |
| num_units = len(flux1_ipadapter_data) // 4 | |
| f_images = flux1_ipadapter_data[0*num_units : 1*num_units] | |
| f_weights = flux1_ipadapter_data[1*num_units : 2*num_units] | |
| f_starts = flux1_ipadapter_data[2*num_units : 3*num_units] | |
| f_ends = flux1_ipadapter_data[3*num_units : 4*num_units] | |
| for i in range(len(f_images)): | |
| if f_images[i] and f_weights[i] > 0: | |
| for filename in ["ip-adapter.bin"]: | |
| _ensure_model_downloaded(filename, progress) | |
| from huggingface_hub import snapshot_download | |
| progress(0.5, desc="Caching HF SigLIP model...") | |
| snapshot_download( | |
| repo_id="google/siglip-so400m-patch14-384", | |
| allow_patterns=["*.json", "*.safetensors", "*.txt"], | |
| ignore_patterns=["*.msgpack", "*.h5", "*.bin"] | |
| ) | |
| temp_path = _temp_png(f"temp_fipa_{i}") | |
| f_images[i].save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_flux1_ipadapters.append({ | |
| "image": os.path.basename(temp_path), | |
| "weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i] | |
| }) | |
| sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', []) | |
| active_sd3_ipadapters = [] | |
| if sd3_ipadapter_data: | |
| num_units = len(sd3_ipadapter_data) // 4 | |
| s_images = sd3_ipadapter_data[0*num_units : 1*num_units] | |
| s_weights = sd3_ipadapter_data[1*num_units : 2*num_units] | |
| s_starts = sd3_ipadapter_data[2*num_units : 3*num_units] | |
| s_ends = sd3_ipadapter_data[3*num_units : 4*num_units] | |
| sd3_ipa_downloaded = False | |
| for i in range(len(s_images)): | |
| if s_images[i] and s_weights[i] > 0: | |
| if not sd3_ipa_downloaded: | |
| ensure_sd3_ipadapter_models_downloaded(progress) | |
| sd3_ipa_downloaded = True | |
| temp_path = _temp_png(f"temp_s3ipa_{i}") | |
| s_images[i].save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_sd3_ipadapters.append({ | |
| "image": os.path.basename(temp_path), | |
| "weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i] | |
| }) | |
| style_data = ui_inputs.get('style_data', []) | |
| active_styles = [] | |
| if style_data: | |
| num_units = len(style_data) // 2 | |
| st_images = style_data[0*num_units : 1*num_units] | |
| st_strengths = style_data[1*num_units : 2*num_units] | |
| style_models_downloaded = False | |
| for i in range(len(st_images)): | |
| if st_images[i] and st_strengths[i] > 0: | |
| if not style_models_downloaded: | |
| _ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress) | |
| _ensure_model_downloaded("flux1-redux-dev.safetensors", progress) | |
| style_models_downloaded = True | |
| temp_path = _temp_png(f"temp_style_{i}") | |
| st_images[i].save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_styles.append({ | |
| "image": os.path.basename(temp_path), "strength": st_strengths[i] | |
| }) | |
| reference_latent_data = ui_inputs.get('reference_latent_data', []) | |
| active_reference_latents = [] | |
| if reference_latent_data: | |
| for img in reference_latent_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_reference_latents.append(os.path.basename(temp_path)) | |
| hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', []) | |
| active_hidream_o1_reference = [] | |
| if hidream_o1_reference_data: | |
| for img in hidream_o1_reference_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_ho1_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_hidream_o1_reference.append(os.path.basename(temp_path)) | |
| joyai_reference_data = ui_inputs.get('joyai_reference_data', []) | |
| active_joyai_reference = [] | |
| if joyai_reference_data: | |
| for img in joyai_reference_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_joyai_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_joyai_reference.append(os.path.basename(temp_path)) | |
| krea2_identity_edit_data = ui_inputs.get('krea2_identity_edit_data', []) | |
| active_krea2_identity_edit = [] | |
| if krea2_identity_edit_data: | |
| for img in krea2_identity_edit_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_krea2_id_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_krea2_identity_edit.append(os.path.basename(temp_path)) | |
| krea2_reference_edit_data = ui_inputs.get('krea2_reference_edit_data', []) | |
| active_krea2_reference_edit = [] | |
| if krea2_reference_edit_data: | |
| for img in krea2_reference_edit_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_krea2_reference_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_krea2_reference_edit.append(os.path.basename(temp_path)) | |
| qwen_image_edit_data = ui_inputs.get('qwen_image_edit_data', []) | |
| active_qwen_image_edit = [] | |
| if qwen_image_edit_data: | |
| for img in qwen_image_edit_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_qwen_edit_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_qwen_image_edit.append(os.path.basename(temp_path)) | |
| boogu_edit_data = ui_inputs.get('boogu_edit_data', []) | |
| active_boogu_edit = [] | |
| if boogu_edit_data: | |
| for img in boogu_edit_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_boogu_edit_ref") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_boogu_edit.append(os.path.basename(temp_path)) | |
| reference_image_data = ui_inputs.get('reference_image_data', []) | |
| active_reference_images = [] | |
| if reference_image_data: | |
| for img in reference_image_data: | |
| if img: | |
| if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR) | |
| temp_path = _temp_png("temp_ref_img") | |
| img.save(temp_path, "PNG") | |
| temp_files_to_clean.append(temp_path) | |
| active_reference_images.append(os.path.basename(temp_path)) | |
| vae_source = ui_inputs.get('vae_source') | |
| vae_id = ui_inputs.get('vae_id') | |
| vae_name_override = None | |
| if vae_source and vae_source != "None": | |
| if vae_source == "File": | |
| vae_name_override = sanitize_filename(vae_id) | |
| local_path = os.path.join(VAE_DIR, vae_name_override) | |
| if not os.path.exists(local_path): | |
| raise gr.Error(f"已上传的 VAE“{vae_id}”已不存在，请重新上传。") | |
| elif vae_source in ("Civitai", "Hugging Face") and vae_id and vae_id.strip(): | |
| local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress) | |
| if local_path: vae_name_override = os.path.basename(local_path) | |
| else: raise gr.Error(f"VAE“{vae_id}”准备失败：{status}") | |
| if vae_name_override: | |
| ui_inputs['vae_name'] = vae_name_override | |
| conditioning_data = ui_inputs.get('conditioning_data', []) | |
| active_conditioning = [] | |
| if conditioning_data: | |
| num_units = len(conditioning_data) // 6 | |
| prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)] | |
| for i in range(num_units): | |
| if prompts[i] and prompts[i].strip(): | |
| active_conditioning.append({ | |
| "prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]), | |
| "x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i]) | |
| }) | |
| return { | |
| "active_loras_for_gpu": active_loras_for_gpu, | |
| "active_loras_for_meta": active_loras_for_meta, | |
| "active_controlnets": active_controlnets, | |
| "active_anima_controlnets": active_anima_controlnets, | |
| "active_diffsynth_controlnets": active_diffsynth_controlnets, | |
| "active_krea2_controlnets": active_krea2_controlnets, | |
| "active_ipadapters": active_ipadapters, | |
| "active_flux1_ipadapters": active_flux1_ipadapters, | |
| "active_sd3_ipadapters": active_sd3_ipadapters, | |
| "active_styles": active_styles, | |
| "active_reference_latents": active_reference_latents, | |
| "active_hidream_o1_reference": active_hidream_o1_reference, | |
| "active_joyai_reference": active_joyai_reference, | |
| "active_krea2_identity_edit": active_krea2_identity_edit, | |
| "active_krea2_reference_edit": active_krea2_reference_edit, | |
| "active_qwen_image_edit": active_qwen_image_edit, | |
| "active_boogu_edit": active_boogu_edit, | |
| "active_reference_images": active_reference_images, | |
| "active_conditioning": active_conditioning, | |
| "temp_files_to_clean": temp_files_to_clean | |
| } | |