Create app-backup.py

app-backup.py

@@ -0,0 +1,1657 @@
+from diffusers_helper.hf_login import login
+
+import os
+import threading
+import time
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+import json
+
+os.environ['HF_HOME'] = os.path.abspath(os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download')))
+
+# 添加中英双语翻译字典
+translations = {
+    "en": {
+        "title": "FramePack - Image to Video Generation",
+        "upload_image": "Upload Image",
+        "prompt": "Prompt",
+        "quick_prompts": "Quick Prompts",
+        "start_generation": "Generate",
+        "stop_generation": "Stop",
+        "use_teacache": "Use TeaCache",
+        "teacache_info": "Faster speed, but may result in slightly worse finger and hand generation.",
+        "negative_prompt": "Negative Prompt",
+        "seed": "Seed",
+        "video_length": "Video Length (max 5 seconds)",
+        "latent_window": "Latent Window Size",
+        "steps": "Inference Steps",
+        "steps_info": "Changing this value is not recommended.",
+        "cfg_scale": "CFG Scale",
+        "distilled_cfg": "Distilled CFG Scale",
+        "distilled_cfg_info": "Changing this value is not recommended.",
+        "cfg_rescale": "CFG Rescale",
+        "gpu_memory": "GPU Memory Preservation (GB) (larger means slower)",
+        "gpu_memory_info": "Set this to a larger value if you encounter OOM errors. Larger values cause slower speed.",
+        "next_latents": "Next Latents",
+        "generated_video": "Generated Video",
+        "sampling_note": "Note: Due to reversed sampling, ending actions will be generated before starting actions. If the starting action is not in the video, please wait, it will be generated later.",
+        "error_message": "Error",
+        "processing_error": "Processing error",
+        "network_error": "Network connection is unstable, model download timed out. Please try again later.",
+        "memory_error": "GPU memory insufficient, please try increasing GPU memory preservation value or reduce video length.",
+        "model_error": "Failed to load model, possibly due to network issues or high server load. Please try again later.",
+        "partial_video": "Processing error, but partial video has been generated",
+        "processing_interrupt": "Processing was interrupted, but partial video has been generated"
+    },
+    "zh": {
+        "title": "FramePack - 图像到视频生成",
+        "upload_image": "上传图像",
+        "prompt": "提示词",
+        "quick_prompts": "快速提示词列表",
+        "start_generation": "开始生成",
+        "stop_generation": "结束生成",
+        "use_teacache": "使用TeaCache",
+        "teacache_info": "速度更快，但可能会使手指和手的生成效果稍差。",
+        "negative_prompt": "负面提示词",
+        "seed": "随机种子",
+        "video_length": "视频长度(最大5秒)",
+        "latent_window": "潜在窗口大小",
+        "steps": "推理步数",
+        "steps_info": "不建议修改此值。",
+        "cfg_scale": "CFG Scale",
+        "distilled_cfg": "蒸馏CFG比例",
+        "distilled_cfg_info": "不建议修改此值。",
+        "cfg_rescale": "CFG重缩放",
+        "gpu_memory": "GPU推理保留内存(GB)(值越大速度越慢)",
+        "gpu_memory_info": "如果出现OOM错误，请将此值设置得更大。值越大，速度越慢。",
+        "next_latents": "下一批潜变量",
+        "generated_video": "生成的视频",
+        "sampling_note": "注意：由于采样是倒序的，结束动作将在开始动作之前生成。如果视频中没有出现起始动作，请继续等待，它将在稍后生成。",
+        "error_message": "错误信息",
+        "processing_error": "处理过程出错",
+        "network_error": "网络连接不稳定，模型下载超时。请稍后再试。",
+        "memory_error": "GPU内存不足，请尝试增加GPU推理保留内存值或降低视频长度。",
+        "model_error": "模型加载失败，可能是网络问题或服务器负载过高。请稍后再试。",
+        "partial_video": "处理过程中出现错误，但已生成部分视频",
+        "processing_interrupt": "处理过程中断，但已生成部分视频"
+    }
+}
+
+# 语言切换功能
+def get_translation(key, lang="en"):
+    if lang in translations and key in translations[lang]:
+        return translations[lang][key]
+    # 默认返回英文
+    return translations["en"].get(key, key)
+
+# 默认语言设置
+current_language = "en"
+
+# 切换语言函数
+def switch_language():
+    global current_language
+    current_language = "zh" if current_language == "en" else "en"
+    return current_language
+
+import gradio as gr
+import torch
+import traceback
+import einops
+import safetensors.torch as sf
+import numpy as np
+import math
+
+# 检查是否在Hugging Face Space环境中
+IN_HF_SPACE = os.environ.get('SPACE_ID') is not None
+
+# 添加变量跟踪GPU可用性
+GPU_AVAILABLE = False
+GPU_INITIALIZED = False
+last_update_time = time.time()
+
+# 如果在Hugging Face Space中，导入spaces模块
+if IN_HF_SPACE:
+    try:
+        import spaces
+        print("在Hugging Face Space环境中运行，已导入spaces模块")
+        
+        # 检查GPU可用性
+        try:
+            GPU_AVAILABLE = torch.cuda.is_available()
+            print(f"GPU available: {GPU_AVAILABLE}")
+            if GPU_AVAILABLE:
+                print(f"GPU device name: {torch.cuda.get_device_name(0)}")
+                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9} GB")
+                
+                # 尝试进行小型GPU操作，确认GPU实际可用
+                test_tensor = torch.zeros(1, device='cuda')
+                test_tensor = test_tensor + 1
+                del test_tensor
+                print("成功进行GPU测试操作")
+            else:
+                print("警告: CUDA报告可用，但未检测到GPU设备")
+        except Exception as e:
+            GPU_AVAILABLE = False
+            print(f"检查GPU时出错: {e}")
+            print("将使用CPU模式运行")
+    except ImportError:
+        print("未能导入spaces模块，可能不在Hugging Face Space环境中")
+        GPU_AVAILABLE = torch.cuda.is_available()
+
+from PIL import Image
+from diffusers import AutoencoderKLHunyuanVideo
+from transformers import LlamaModel, CLIPTextModel, LlamaTokenizerFast, CLIPTokenizer
+from diffusers_helper.hunyuan import encode_prompt_conds, vae_decode, vae_encode, vae_decode_fake
+from diffusers_helper.utils import save_bcthw_as_mp4, crop_or_pad_yield_mask, soft_append_bcthw, resize_and_center_crop, state_dict_weighted_merge, state_dict_offset_merge, generate_timestamp
+from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
+from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
+from diffusers_helper.memory import cpu, gpu, get_cuda_free_memory_gb, move_model_to_device_with_memory_preservation, offload_model_from_device_for_memory_preservation, fake_diffusers_current_device, DynamicSwapInstaller, unload_complete_models, load_model_as_complete, IN_HF_SPACE as MEMORY_IN_HF_SPACE
+from diffusers_helper.thread_utils import AsyncStream, async_run
+from diffusers_helper.gradio.progress_bar import make_progress_bar_css, make_progress_bar_html
+from transformers import SiglipImageProcessor, SiglipVisionModel
+from diffusers_helper.clip_vision import hf_clip_vision_encode
+from diffusers_helper.bucket_tools import find_nearest_bucket
+
+outputs_folder = './outputs/'
+os.makedirs(outputs_folder, exist_ok=True)
+
+# 在Spaces环境中，我们延迟所有CUDA操作
+if not IN_HF_SPACE:
+    # 仅在非Spaces环境中获取CUDA内存
+    try:
+        if torch.cuda.is_available():
+            free_mem_gb = get_cuda_free_memory_gb(gpu)
+            print(f'Free VRAM {free_mem_gb} GB')
+        else:
+            free_mem_gb = 6.0  # 默认值
+            print("CUDA不可用，使用默认的内存设置")
+    except Exception as e:
+        free_mem_gb = 6.0  # 默认值
+        print(f"获取CUDA内存时出错: {e}，使用默认的内存设置")
+        
+    high_vram = free_mem_gb > 60
+    print(f'High-VRAM Mode: {high_vram}')
+else:
+    # 在Spaces环境中使用默认值
+    print("在Spaces环境中使用默认内存设置")
+    try:
+        if GPU_AVAILABLE:
+            free_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9 * 0.9  # 使用90%的GPU内存
+            high_vram = free_mem_gb > 10  # 更保守的条件
+        else:
+            free_mem_gb = 6.0  # 默认值
+            high_vram = False
+    except Exception as e:
+        print(f"获取GPU内存时出错: {e}")
+        free_mem_gb = 6.0  # 默认值
+        high_vram = False
+    
+    print(f'GPU内存: {free_mem_gb:.2f} GB, High-VRAM Mode: {high_vram}')
+
+# 使用models变量存储全局模型引用
+models = {}
+cpu_fallback_mode = not GPU_AVAILABLE  # 如果GPU不可用，使用CPU回退模式
+
+# 使用加载模型的函数
+def load_models():
+    global models, cpu_fallback_mode, GPU_INITIALIZED
+    
+    if GPU_INITIALIZED:
+        print("模型已加载，跳过重复加载")
+        return models
+    
+    print("开始加载模型...")
+    
+    try:
+        # 设置设备，根据GPU可用性确定
+        device = 'cuda' if GPU_AVAILABLE and not cpu_fallback_mode else 'cpu'
+        model_device = 'cpu'  # 初始加载到CPU
+        
+        # 降低精度以节省内存
+        dtype = torch.float16 if GPU_AVAILABLE else torch.float32
+        transformer_dtype = torch.bfloat16 if GPU_AVAILABLE else torch.float32
+        
+        print(f"使用设备: {device}, 模型精度: {dtype}, Transformer精度: {transformer_dtype}")
+        
+        # 加载模型
+        try:
+            text_encoder = LlamaModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder', torch_dtype=dtype).to(model_device)
+            text_encoder_2 = CLIPTextModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=dtype).to(model_device)
+            tokenizer = LlamaTokenizerFast.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer')
+            tokenizer_2 = CLIPTokenizer.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer_2')
+            vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='vae', torch_dtype=dtype).to(model_device)
+
+            feature_extractor = SiglipImageProcessor.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='feature_extractor')
+            image_encoder = SiglipVisionModel.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='image_encoder', torch_dtype=dtype).to(model_device)
+
+            transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePackI2V_HY', torch_dtype=transformer_dtype).to(model_device)
+            
+            print("成功加载所有模型")
+        except Exception as e:
+            print(f"加载模型时出错: {e}")
+            print("尝试降低精度重新加载...")
+            
+            # 降低精度重试
+            dtype = torch.float32
+            transformer_dtype = torch.float32
+            cpu_fallback_mode = True
+            
+            text_encoder = LlamaModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder', torch_dtype=dtype).to('cpu')
+            text_encoder_2 = CLIPTextModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=dtype).to('cpu')
+            tokenizer = LlamaTokenizerFast.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer')
+            tokenizer_2 = CLIPTokenizer.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer_2')
+            vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='vae', torch_dtype=dtype).to('cpu')
+
+            feature_extractor = SiglipImageProcessor.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='feature_extractor')
+            image_encoder = SiglipVisionModel.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='image_encoder', torch_dtype=dtype).to('cpu')
+
+            transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePackI2V_HY', torch_dtype=transformer_dtype).to('cpu')
+            
+            print("使用CPU模式成功加载所有模型")
+
+        vae.eval()
+        text_encoder.eval()
+        text_encoder_2.eval()
+        image_encoder.eval()
+        transformer.eval()
+
+        if not high_vram or cpu_fallback_mode:
+            vae.enable_slicing()
+            vae.enable_tiling()
+
+        transformer.high_quality_fp32_output_for_inference = True
+        print('transformer.high_quality_fp32_output_for_inference = True')
+
+        # 设置模型精度
+        if not cpu_fallback_mode:
+            transformer.to(dtype=transformer_dtype)
+            vae.to(dtype=dtype)
+            image_encoder.to(dtype=dtype)
+            text_encoder.to(dtype=dtype)
+            text_encoder_2.to(dtype=dtype)
+
+        vae.requires_grad_(False)
+        text_encoder.requires_grad_(False)
+        text_encoder_2.requires_grad_(False)
+        image_encoder.requires_grad_(False)
+        transformer.requires_grad_(False)
+
+        if torch.cuda.is_available() and not cpu_fallback_mode:
+            try:
+                if not high_vram:
+                    # DynamicSwapInstaller is same as huggingface's enable_sequential_offload but 3x faster
+                    DynamicSwapInstaller.install_model(transformer, device=device)
+                    DynamicSwapInstaller.install_model(text_encoder, device=device)
+                else:
+                    text_encoder.to(device)
+                    text_encoder_2.to(device)
+                    image_encoder.to(device)
+                    vae.to(device)
+                    transformer.to(device)
+                print(f"成功将模型移动到{device}设备")
+            except Exception as e:
+                print(f"移动模型到{device}时出错: {e}")
+                print("回退到CPU模式")
+                cpu_fallback_mode = True
+        
+        # 保存到全局变量
+        models = {
+            'text_encoder': text_encoder,
+            'text_encoder_2': text_encoder_2,
+            'tokenizer': tokenizer,
+            'tokenizer_2': tokenizer_2,
+            'vae': vae,
+            'feature_extractor': feature_extractor,
+            'image_encoder': image_encoder,
+            'transformer': transformer
+        }
+        
+        GPU_INITIALIZED = True
+        print(f"模型加载完成，运行模式: {'CPU' if cpu_fallback_mode else 'GPU'}")
+        return models
+    except Exception as e:
+        print(f"加载模型过程中发生错误: {e}")
+        traceback.print_exc()
+        
+        # 记录更详细的错误信息
+        error_info = {
+            "error": str(e),
+            "traceback": traceback.format_exc(),
+            "cuda_available": torch.cuda.is_available(),
+            "device": "cpu" if cpu_fallback_mode else "cuda",
+        }
+        
+        # 保存错误信息到文件，方便排查
+        try:
+            with open(os.path.join(outputs_folder, "error_log.txt"), "w") as f:
+                f.write(str(error_info))
+        except:
+            pass
+            
+        # 返回空字典，允许应用继续尝试运行
+        cpu_fallback_mode = True
+        return {}
+
+
+# 使用Hugging Face Spaces GPU装饰器
+if IN_HF_SPACE and 'spaces' in globals() and GPU_AVAILABLE:
+    try:
+        @spaces.GPU
+        def initialize_models():
+            """在@spaces.GPU装饰器内初始化模型"""
+            global GPU_INITIALIZED
+            try:
+                result = load_models()
+                GPU_INITIALIZED = True
+                return result
+            except Exception as e:
+                print(f"使用spaces.GPU初始化模型时出错: {e}")
+                traceback.print_exc()
+                global cpu_fallback_mode
+                cpu_fallback_mode = True
+                # 不使用装饰器再次尝试
+                return load_models()
+    except Exception as e:
+        print(f"创建spaces.GPU装饰器时出错: {e}")
+        # 如果装饰器出错，直接使用非装饰器版本
+        def initialize_models():
+            return load_models()
+
+
+# 以下函数内部会延迟获取模型
+def get_models():
+    """获取模型，如果尚未加载则加载模型"""
+    global models, GPU_INITIALIZED
+    
+    # 添加模型加载锁，防止并发加载
+    model_loading_key = "__model_loading__"
+    
+    if not models:
+        # 检查是否正在加载模型
+        if model_loading_key in globals():
+            print("模型正在加载中，等待...")
+            # 等待模型加载完成
+            import time
+            start_wait = time.time()
+            while not models and model_loading_key in globals():
+                time.sleep(0.5)
+                # 超过60秒认为加载失败
+                if time.time() - start_wait > 60:
+                    print("等待模型加载超时")
+                    break
+            
+            if models:
+                return models
+            
+        try:
+            # 设置加载标记
+            globals()[model_loading_key] = True
+            
+            if IN_HF_SPACE and 'spaces' in globals() and GPU_AVAILABLE and not cpu_fallback_mode:
+                try:
+                    print("使用@spaces.GPU装饰器加载模型")
+                    models = initialize_models()
+                except Exception as e:
+                    print(f"使用GPU装饰器加载模型失败: {e}")
+                    print("尝试直接加载模型")
+                    models = load_models()
+            else:
+                print("直接加载模型")
+                models = load_models()
+        except Exception as e:
+            print(f"加载模型时发生未预期的错误: {e}")
+            traceback.print_exc()
+            # 确保有一个空字典
+            models = {}
+        finally:
+            # 无论成功与否，都移除加载标记
+            if model_loading_key in globals():
+                del globals()[model_loading_key]
+    
+    return models
+
+
+stream = AsyncStream()
+
+
+@torch.no_grad()
+def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache):
+    global last_update_time
+    last_update_time = time.time()
+    
+    # 限制视频长度不超过5秒
+    total_second_length = min(total_second_length, 5.0)
+    
+    # 获取模型
+    try:
+        models = get_models()
+        if not models:
+            error_msg = "模型加载失败，请检查日志获取详细信息"
+            print(error_msg)
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+        
+        text_encoder = models['text_encoder']
+        text_encoder_2 = models['text_encoder_2']
+        tokenizer = models['tokenizer']
+        tokenizer_2 = models['tokenizer_2']
+        vae = models['vae']
+        feature_extractor = models['feature_extractor']
+        image_encoder = models['image_encoder']
+        transformer = models['transformer']
+    except Exception as e:
+        error_msg = f"获取模型时出错: {e}"
+        print(error_msg)
+        traceback.print_exc()
+        stream.output_queue.push(('error', error_msg))
+        stream.output_queue.push(('end', None))
+        return
+    
+    # 确定设备
+    device = 'cuda' if GPU_AVAILABLE and not cpu_fallback_mode else 'cpu'
+    print(f"使用设备: {device} 进行推理")
+    
+    # 调整参数以适应CPU模式
+    if cpu_fallback_mode:
+        print("CPU模式下使用更精简的参数")
+        # 减小处理大小以加快CPU处理
+        latent_window_size = min(latent_window_size, 5)
+        steps = min(steps, 15)  # 减少步数
+        total_second_length = min(total_second_length, 2.0)  # CPU模式下进一步限制视频长度
+    
+    total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
+    total_latent_sections = int(max(round(total_latent_sections), 1))
+
+    job_id = generate_timestamp()
+    last_output_filename = None
+    history_pixels = None
+    history_latents = None
+    total_generated_latent_frames = 0
+
+    stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
+
+    try:
+        # Clean GPU
+        if not high_vram and not cpu_fallback_mode:
+            try:
+                unload_complete_models(
+                    text_encoder, text_encoder_2, image_encoder, vae, transformer
+                )
+            except Exception as e:
+                print(f"卸载模型时出错: {e}")
+                # 继续执行，不中断流程
+
+        # Text encoding
+        last_update_time = time.time()
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))
+
+        try:
+            if not high_vram and not cpu_fallback_mode:
+                fake_diffusers_current_device(text_encoder, device)
+                load_model_as_complete(text_encoder_2, target_device=device)
+
+            llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+            if cfg == 1:
+                llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
+            else:
+                llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+            llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
+            llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
+        except Exception as e:
+            error_msg = f"文本编码过程出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        # Processing input image
+        last_update_time = time.time()
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))
+
+        try:
+            H, W, C = input_image.shape
+            height, width = find_nearest_bucket(H, W, resolution=640)
+            
+            # 如果是CPU模式，缩小处理尺寸
+            if cpu_fallback_mode:
+                height = min(height, 320)
+                width = min(width, 320)
+                
+            input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
+
+            Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
+
+            input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
+            input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
+        except Exception as e:
+            error_msg = f"图像处理过程出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        # VAE encoding
+        last_update_time = time.time()
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
+
+        try:
+            if not high_vram and not cpu_fallback_mode:
+                load_model_as_complete(vae, target_device=device)
+
+            start_latent = vae_encode(input_image_pt, vae)
+        except Exception as e:
+            error_msg = f"VAE编码过程出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        # CLIP Vision
+        last_update_time = time.time()
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
+
+        try:
+            if not high_vram and not cpu_fallback_mode:
+                load_model_as_complete(image_encoder, target_device=device)
+
+            image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
+            image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
+        except Exception as e:
+            error_msg = f"CLIP Vision编码过程出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        # Dtype
+        try:
+            llama_vec = llama_vec.to(transformer.dtype)
+            llama_vec_n = llama_vec_n.to(transformer.dtype)
+            clip_l_pooler = clip_l_pooler.to(transformer.dtype)
+            clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
+            image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
+        except Exception as e:
+            error_msg = f"数据类型转换出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        # Sampling
+        last_update_time = time.time()
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
+
+        rnd = torch.Generator("cpu").manual_seed(seed)
+        num_frames = latent_window_size * 4 - 3
+
+        try:
+            history_latents = torch.zeros(size=(1, 16, 1 + 2 + 16, height // 8, width // 8), dtype=torch.float32).cpu()
+            history_pixels = None
+            total_generated_latent_frames = 0
+        except Exception as e:
+            error_msg = f"初始化历史状态出错: {e}"
+            print(error_msg)
+            traceback.print_exc()
+            stream.output_queue.push(('error', error_msg))
+            stream.output_queue.push(('end', None))
+            return
+
+        latent_paddings = reversed(range(total_latent_sections))
+
+        if total_latent_sections > 4:
+            # In theory the latent_paddings should follow the above sequence, but it seems that duplicating some
+            # items looks better than expanding it when total_latent_sections > 4
+            # One can try to remove below trick and just
+            # use `latent_paddings = list(reversed(range(total_latent_sections)))` to compare
+            latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0]
+
+        for latent_padding in latent_paddings:
+            last_update_time = time.time()
+            is_last_section = latent_padding == 0
+            latent_padding_size = latent_padding * latent_window_size
+
+            if stream.input_queue.top() == 'end':
+                # 确保在结束时保存当前的视频
+                if history_pixels is not None and total_generated_latent_frames > 0:
+                    try:
+                        output_filename = os.path.join(outputs_folder, f'{job_id}_final_{total_generated_latent_frames}.mp4')
+                        save_bcthw_as_mp4(history_pixels, output_filename, fps=30)
+                        stream.output_queue.push(('file', output_filename))
+                    except Exception as e:
+                        print(f"保存最终视频时出错: {e}")
+                
+                stream.output_queue.push(('end', None))
+                return
+
+            print(f'latent_padding_size = {latent_padding_size}, is_last_section = {is_last_section}')
+
+            try:
+                indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0)
+                clean_latent_indices_pre, blank_indices, latent_indices, clean_latent_indices_post, clean_latent_2x_indices, clean_latent_4x_indices = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1)
+                clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1)
+
+                clean_latents_pre = start_latent.to(history_latents)
+                clean_latents_post, clean_latents_2x, clean_latents_4x = history_latents[:, :, :1 + 2 + 16, :, :].split([1, 2, 16], dim=2)
+                clean_latents = torch.cat([clean_latents_pre, clean_latents_post], dim=2)
+            except Exception as e:
+                error_msg = f"准备采样数据时出错: {e}"
+                print(error_msg)
+                traceback.print_exc()
+                # 尝试继续下一轮迭代而不是完全终止
+                if last_output_filename:
+                    stream.output_queue.push(('file', last_output_filename))
+                continue
+
+            if not high_vram and not cpu_fallback_mode:
+                try:
+                    unload_complete_models()
+                    move_model_to_device_with_memory_preservation(transformer, target_device=device, preserved_memory_gb=gpu_memory_preservation)
+                except Exception as e:
+                    print(f"移动transformer到GPU时出错: {e}")
+                    # 继续执行，可能影响性能但不必终止
+
+            if use_teacache and not cpu_fallback_mode:
+                try:
+                    transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
+                except Exception as e:
+                    print(f"初始化teacache时出错: {e}")
+                    # 禁用teacache并继续
+                    transformer.initialize_teacache(enable_teacache=False)
+            else:
+                transformer.initialize_teacache(enable_teacache=False)
+
+            def callback(d):
+                global last_update_time
+                last_update_time = time.time()
+                
+                try:
+                    # 首先检查是否有停止信号
+                    print(f"【调试】回调函数: 步骤 {d['i']}, 检查是否有停止信号")
+                    try:
+                        queue_top = stream.input_queue.top()
+                        print(f"【调试】回调函数: 队列顶部信号 = {queue_top}")
+                        
+                        if queue_top == 'end':
+                            print("【调试】回调函数: 检测到停止信号，准备中断...")
+                            try:
+                                stream.output_queue.push(('end', None))
+                                print("【调试】回调函数: 成功向输出队列推送end信号")
+                            except Exception as e:
+                                print(f"【调试】回调函数: 向输出队列推送end信号失败: {e}")
+                                
+                            print("【调试】回调函数: 即将抛出KeyboardInterrupt异常")
+                            raise KeyboardInterrupt('用户主动结束任务')
+                    except Exception as e:
+                        print(f"【调试】回调函数: 检查队列顶部信号出错: {e}")
+                        
+                    preview = d['denoised']
+                    preview = vae_decode_fake(preview)
+
+                    preview = (preview * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8)
+                    preview = einops.rearrange(preview, 'b c t h w -> (b h) (t w) c')
+
+                    current_step = d['i'] + 1
+                    percentage = int(100.0 * current_step / steps)
+                    hint = f'Sampling {current_step}/{steps}'
+                    desc = f'Total generated frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / 30) :.2f} seconds (FPS-30). The video is being extended now ...'
+                    stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
+                except KeyboardInterrupt as e:
+                    # 捕获并重新抛出中断异常，确保它能传播到采样函数
+                    print(f"【调试】回调函数: 捕获到KeyboardInterrupt: {e}")
+                    print("【调试】回调函数: 重新抛出中断异常，确保传播到采样函数")
+                    raise
+                except Exception as e:
+                    print(f"【调试】回调函数中出错: {e}")
+                    # 不中断采样过程
+                print(f"【调试】回调函数: 步骤 {d['i']} 完成")
+                return
+
+            try:
+                sampling_start_time = time.time()
+                print(f"开始采样，设备: {device}, 数据类型: {transformer.dtype}, 使用TeaCache: {use_teacache and not cpu_fallback_mode}")
+                
+                try:
+                    print("【调试】开始sample_hunyuan采样流程")
+                    generated_latents = sample_hunyuan(
+                        transformer=transformer,
+                        sampler='unipc',
+                        width=width,
+                        height=height,
+                        frames=num_frames,
+                        real_guidance_scale=cfg,
+                        distilled_guidance_scale=gs,
+                        guidance_rescale=rs,
+                        # shift=3.0,
+                        num_inference_steps=steps,
+                        generator=rnd,
+                        prompt_embeds=llama_vec,
+                        prompt_embeds_mask=llama_attention_mask,
+                        prompt_poolers=clip_l_pooler,
+                        negative_prompt_embeds=llama_vec_n,
+                        negative_prompt_embeds_mask=llama_attention_mask_n,
+                        negative_prompt_poolers=clip_l_pooler_n,
+                        device=device,
+                        dtype=transformer.dtype,
+                        image_embeddings=image_encoder_last_hidden_state,
+                        latent_indices=latent_indices,
+                        clean_latents=clean_latents,
+                        clean_latent_indices=clean_latent_indices,
+                        clean_latents_2x=clean_latents_2x,
+                        clean_latent_2x_indices=clean_latent_2x_indices,
+                        clean_latents_4x=clean_latents_4x,
+                        clean_latent_4x_indices=clean_latent_4x_indices,
+                        callback=callback,
+                    )
+                    
+                    print(f"【调试】采样完成，用时: {time.time() - sampling_start_time:.2f}秒")
+                except KeyboardInterrupt as e:
+                    # 用户主动中断
+                    print(f"【调试】捕获到KeyboardInterrupt: {e}")
+                    print("【调试】用户主动中断采样过程，处理中断逻辑")
+                    
+                    # 如果已经有生���的视频，返回最后生成的视频
+                    if last_output_filename:
+                        print(f"【调试】已有部分生成视频: {last_output_filename}，返回此视频")
+                        stream.output_queue.push(('file', last_output_filename))
+                        error_msg = "用户中断生成过程，但已生成部分视频"
+                    else:
+                        print("【调试】没有部分生成视频，返回中断消息")
+                        error_msg = "用户中断生成过程，未生成视频"
+                    
+                    print(f"【调试】推送错误消息: {error_msg}")
+                    stream.output_queue.push(('error', error_msg))
+                    print("【调试】推送end信号")
+                    stream.output_queue.push(('end', None))
+                    print("【调试】中断处理完成，返回")
+                    return
+            except Exception as e:
+                print(f"采样过程中出错: {e}")
+                traceback.print_exc()
+                
+                # 如果已经有生成的视频，返回最后生成的视频
+                if last_output_filename:
+                    stream.output_queue.push(('file', last_output_filename))
+                    
+                    # 创建错误信息
+                    error_msg = f"采样过程中出错，但已返回部分生成的视频: {e}"
+                    stream.output_queue.push(('error', error_msg))
+                else:
+                    # 如果没有生成的视频，返回错误信息
+                    error_msg = f"采样过程中出错，无法生成视频: {e}"
+                    stream.output_queue.push(('error', error_msg))
+                
+                stream.output_queue.push(('end', None))
+                return
+
+            try:
+                if is_last_section:
+                    generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)
+
+                total_generated_latent_frames += int(generated_latents.shape[2])
+                history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)
+            except Exception as e:
+                error_msg = f"处理生成的潜变量时出错: {e}"
+                print(error_msg)
+                traceback.print_exc()
+                
+                if last_output_filename:
+                    stream.output_queue.push(('file', last_output_filename))
+                stream.output_queue.push(('error', error_msg))
+                stream.output_queue.push(('end', None))
+                return
+
+            if not high_vram and not cpu_fallback_mode:
+                try:
+                    offload_model_from_device_for_memory_preservation(transformer, target_device=device, preserved_memory_gb=8)
+                    load_model_as_complete(vae, target_device=device)
+                except Exception as e:
+                    print(f"管理模型内存时出错: {e}")
+                    # 继续执行
+
+            try:
+                real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :]
+            except Exception as e:
+                error_msg = f"处理历史潜变量时出错: {e}"
+                print(error_msg)
+                
+                if last_output_filename:
+                    stream.output_queue.push(('file', last_output_filename))
+                continue
+
+            try:
+                vae_start_time = time.time()
+                print(f"开始VAE解码，潜变量形状: {real_history_latents.shape}")
+                
+                if history_pixels is None:
+                    history_pixels = vae_decode(real_history_latents, vae).cpu()
+                else:
+                    section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
+                    overlapped_frames = latent_window_size * 4 - 3
+
+                    current_pixels = vae_decode(real_history_latents[:, :, :section_latent_frames], vae).cpu()
+                    history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)
+
+                print(f"VAE解码完成，用时: {time.time() - vae_start_time:.2f}秒")
+                
+                if not high_vram and not cpu_fallback_mode:
+                    try:
+                        unload_complete_models()
+                    except Exception as e:
+                        print(f"卸载模型时出错: {e}")
+
+                output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
+
+                save_start_time = time.time()
+                save_bcthw_as_mp4(history_pixels, output_filename, fps=30)
+                print(f"保存视频完成，用时: {time.time() - save_start_time:.2f}秒")
+
+                print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
+
+                last_output_filename = output_filename
+                stream.output_queue.push(('file', output_filename))
+            except Exception as e:
+                print(f"视频解码或保存过程中出错: {e}")
+                traceback.print_exc()
+                
+                # 如果已经有生成的视频，返回最后生成的视频
+                if last_output_filename:
+                    stream.output_queue.push(('file', last_output_filename))
+                
+                # 记录错误信息
+                error_msg = f"视频解码或保存过程中出错: {e}"
+                stream.output_queue.push(('error', error_msg))
+                
+                # 尝试继续下一次迭代
+                continue
+
+            if is_last_section:
+                break
+    except Exception as e:
+        print(f"【调试】处理过程中出现错误: {e}, 类型: {type(e)}")
+        print(f"【调试】错误详情:")
+        traceback.print_exc()
+        
+        # 检查是否是中断类型异常
+        if isinstance(e, KeyboardInterrupt):
+            print("【调试】捕获到外层KeyboardInterrupt异常")
+
+        if not high_vram and not cpu_fallback_mode:
+            try:
+                print("【调试】尝试卸载模型以释放资源")
+                unload_complete_models(
+                    text_encoder, text_encoder_2, image_encoder, vae, transformer
+                )
+                print("【调试】模型卸载成功")
+            except Exception as unload_error:
+                print(f"【调试】卸载模型时出错: {unload_error}")
+                pass
+        
+        # 如果已经有生成的视频，返回最后生成的视频
+        if last_output_filename:
+            print(f"【调试】外层异常处理: 返回已生成的部分视频 {last_output_filename}")
+            stream.output_queue.push(('file', last_output_filename))
+        else:
+            print("【调试】外层异常处理: 未找到已生成的视频")
+        
+        # 返回错误信息
+        error_msg = f"处理过程中出现错误: {e}"
+        print(f"【调试】外层异常处理: 推送错误信息: {error_msg}")
+        stream.output_queue.push(('error', error_msg))
+
+    # 确保总是返回end信号
+    print("【调试】工作函数结束，推送end信号")
+    stream.output_queue.push(('end', None))
+    return
+
+
+# 使用Hugging Face Spaces GPU装饰器处理进程函数
+if IN_HF_SPACE and 'spaces' in globals():
+    @spaces.GPU
+    def process_with_gpu(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache):
+        global stream
+        assert input_image is not None, 'No input image!'
+
+        # 初始化UI状态
+        yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
+
+        try:
+            stream = AsyncStream()
+
+            # 异步启动worker
+            async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache)
+
+            output_filename = None
+            prev_output_filename = None
+            error_message = None
+
+            # 持续检查worker的输出
+            while True:
+                try:
+                    flag, data = stream.output_queue.next()
+
+                    if flag == 'file':
+                        output_filename = data
+                        prev_output_filename = output_filename
+                        # 清除错误显示，确保文件成功时不显示错误
+                        yield output_filename, gr.update(), gr.update(), '', gr.update(interactive=False), gr.update(interactive=True)
+
+                    if flag == 'progress':
+                        preview, desc, html = data
+                        # 更新进度时不改变错误信息，并确保停止按钮可交互
+                        yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
+                    
+                    if flag == 'error':
+                        error_message = data
+                        print(f"收到错误消息: {error_message}")
+                        # 不立即显示，等待end信号
+
+                    if flag == 'end':
+                        # 如果有最后的视频文件，确保返回
+                        if output_filename is None and prev_output_filename is not None:
+                            output_filename = prev_output_filename
+                        
+                        # 如果有错误消息，创建友好的错误显示
+                        if error_message:
+                            error_html = create_error_html(error_message)
+                            yield output_filename, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        else:
+                            # 确保成功完成时不显示任何错误
+                            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
+                        break
+                except Exception as e:
+                    print(f"处理输出时出错: {e}")
+                    # 检查是否长时间没有更新
+                    current_time = time.time()
+                    if current_time - last_update_time > 60:  # 60秒没有更新，可能卡住了
+                        print(f"处理似乎卡住了，已经 {current_time - last_update_time:.1f} 秒没有更新")
+                        
+                        # 如果有部分生成的视频，返回
+                        if prev_output_filename:
+                            error_html = create_error_html("处理超时，但已生成部分视频", is_timeout=True)
+                            yield prev_output_filename, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        else:
+                            error_html = create_error_html(f"处理超时: {e}", is_timeout=True)
+                            yield None, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        break
+                    
+        except Exception as e:
+            print(f"启动处理时出错: {e}")
+            traceback.print_exc()
+            error_msg = str(e)
+            
+            error_html = create_error_html(error_msg)
+            yield None, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+    
+    process = process_with_gpu
+else:
+    def process(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache):
+        global stream
+        assert input_image is not None, 'No input image!'
+
+        # 初始化UI状态
+        yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
+
+        try:
+            stream = AsyncStream()
+
+            # 异步启动worker
+            async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache)
+
+            output_filename = None
+            prev_output_filename = None
+            error_message = None
+
+            # 持续检查worker的输出
+            while True:
+                try:
+                    flag, data = stream.output_queue.next()
+
+                    if flag == 'file':
+                        output_filename = data
+                        prev_output_filename = output_filename
+                        # 清除错误显示，确保文件成功时不显示错误
+                        yield output_filename, gr.update(), gr.update(), '', gr.update(interactive=False), gr.update(interactive=True)
+
+                    if flag == 'progress':
+                        preview, desc, html = data
+                        # 更新进度时不改变错误信息，并确保停止按钮可交互
+                        yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
+                    
+                    if flag == 'error':
+                        error_message = data
+                        print(f"收到错误消息: {error_message}")
+                        # 不立即显示，等待end信号
+
+                    if flag == 'end':
+                        # 如果有最后的视频文件，确保返回
+                        if output_filename is None and prev_output_filename is not None:
+                            output_filename = prev_output_filename
+                        
+                        # 如果有错误消息，创建友好的错误显示
+                        if error_message:
+                            error_html = create_error_html(error_message)
+                            yield output_filename, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        else:
+                            # 确保成功完成时不显示任何错误
+                            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
+                        break
+                except Exception as e:
+                    print(f"处理输出时出错: {e}")
+                    # 检查是否长时间没有更新
+                    current_time = time.time()
+                    if current_time - last_update_time > 60:  # 60秒没有更新，可能卡住了
+                        print(f"处理似乎卡住了，已经 {current_time - last_update_time:.1f} 秒没有更新")
+                        
+                        # 如果有部分生成的视频，返回
+                        if prev_output_filename:
+                            error_html = create_error_html("处理超时，但已生成部分视频", is_timeout=True)
+                            yield prev_output_filename, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        else:
+                            error_html = create_error_html(f"处理超时: {e}", is_timeout=True)
+                            yield None, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+                        break
+                    
+        except Exception as e:
+            print(f"启动处理时出错: {e}")
+            traceback.print_exc()
+            error_msg = str(e)
+            
+            error_html = create_error_html(error_msg)
+            yield None, gr.update(visible=False), gr.update(), error_html, gr.update(interactive=True), gr.update(interactive=False)
+
+
+def end_process():
+    """停止生成过程函数 - 通过在队列中推送'end'信号来中断生成"""
+    print("【调试】用户点击了停止按钮，发送停止信号...")
+    # 确保stream已初始化
+    if 'stream' in globals() and stream is not None:
+        # 在推送前检查队列状态
+        try:
+            current_top = stream.input_queue.top()
+            print(f"【调试】当前队列顶部信号: {current_top}")
+        except Exception as e:
+            print(f"【调试】检查队列状态出错: {e}")
+            
+        # 推送end信号
+        try:
+            stream.input_queue.push('end')
+            print("【调试】成功推送end信号到队列")
+            
+            # 验证信号是否成功推送
+            try:
+                current_top_after = stream.input_queue.top()
+                print(f"【调试】推送后队列顶部信号: {current_top_after}")
+            except Exception as e:
+                print(f"【调试】验证推送后队列状态出错: {e}")
+                
+        except Exception as e:
+            print(f"【调试】推送end信号到队列失败: {e}")
+    else:
+        print("【调试】警告: stream未初始化，无法发送停止信号")
+    return None
+
+
+quick_prompts = [
+    'The girl dances gracefully, with clear movements, full of charm.',
+    'A character doing some simple body movements.',
+]
+quick_prompts = [[x] for x in quick_prompts]
+
+
+# 创建一个自定义CSS，增加响应式布局支持
+def make_custom_css():
+    progress_bar_css = make_progress_bar_css()
+    
+    responsive_css = """
+    /* 基础响应式设置 */
+    #app-container {
+        max-width: 100%;
+        margin: 0 auto;
+    }
+    
+    /* 语言切换按钮样式 */
+    #language-toggle {
+        position: fixed;
+        top: 10px;
+        right: 10px;
+        z-index: 1000;
+        background-color: rgba(0, 0, 0, 0.7);
+        color: white;
+        border: none;
+        border-radius: 4px;
+        padding: 5px 10px;
+        cursor: pointer;
+        font-size: 14px;
+    }
+    
+    /* 页面标题样式 */
+    h1 {
+        font-size: 2rem;
+        text-align: center;
+        margin-bottom: 1rem;
+    }
+    
+    /* 按钮样式 */
+    .start-btn, .stop-btn {
+        min-height: 45px;
+        font-size: 1rem;
+    }
+    
+    /* 移动设备样式 - 小屏幕 */
+    @media (max-width: 768px) {
+        h1 {
+            font-size: 1.5rem;
+            margin-bottom: 0.5rem;
+        }
+        
+        /* 单列布局 */
+        .mobile-full-width {
+            flex-direction: column !important;
+        }
+        
+        .mobile-full-width > .gr-block {
+            min-width: 100% !important;
+            flex-grow: 1;
+        }
+        
+        /* 调整视频大小 */
+        .video-container {
+            height: auto !important;
+        }
+        
+        /* 调整按钮大小 */
+        .button-container button {
+            min-height: 50px;
+            font-size: 1rem;
+            touch-action: manipulation;
+        }
+        
+        /* 调整滑块 */
+        .slider-container input[type="range"] {
+            height: 30px;
+        }
+    }
+    
+    /* 平板设备样式 */
+    @media (min-width: 769px) and (max-width: 1024px) {
+        .tablet-adjust {
+            width: 48% !important;
+        }
+    }
+    
+    /* 黑暗模式支持 */
+    @media (prefers-color-scheme: dark) {
+        .dark-mode-text {
+            color: #f0f0f0;
+        }
+        
+        .dark-mode-bg {
+            background-color: #2a2a2a;
+        }
+    }
+    
+    /* 增强可访问性 */
+    button, input, select, textarea {
+        font-size: 16px; /* 防止iOS缩放 */
+    }
+    
+    /* 触摸优化 */
+    button, .interactive-element {
+        min-height: 44px;
+        min-width: 44px;
+    }
+    
+    /* 提高对比度 */
+    .high-contrast {
+        color: #fff;
+        background-color: #000;
+    }
+    
+    /* 进度条样式增强 */
+    .progress-container {
+        margin-top: 10px;
+        margin-bottom: 10px;
+    }
+    
+    /* 错误消息样式 */
+    #error-message {
+        color: #ff4444;
+        font-weight: bold;
+        padding: 10px;
+        border-radius: 4px;
+        margin-top: 10px;
+    }
+    
+    /* 确保错误容器正确显示 */
+    .error-message {
+        background-color: rgba(255, 0, 0, 0.1);
+        padding: 10px;
+        border-radius: 4px;
+        margin-top: 10px;
+        border: 1px solid #ffcccc;
+    }
+    
+    /* 处理多语言错误消息 */
+    .error-msg-en, .error-msg-zh {
+        font-weight: bold;
+    }
+    
+    /* 错误图标 */
+    .error-icon {
+        color: #ff4444;
+        font-size: 18px;
+        margin-right: 8px;
+    }
+    
+    /* 确保空错误消息不显示背景和边框 */
+    #error-message:empty {
+        background-color: transparent;
+        border: none;
+        padding: 0;
+        margin: 0;
+    }
+    
+    /* 修复Gradio默认错误显示 */
+    .error {
+        display: none !important;
+    }
+    """
+    
+    # 合并CSS
+    combined_css = progress_bar_css + responsive_css
+    return combined_css
+
+
+css = make_custom_css()
+block = gr.Blocks(css=css).queue()
+with block:
+    # 添加语言切换功能
+    gr.HTML("""
+        <div id="app-container">
+            <button id="language-toggle" onclick="toggleLanguage()">中文/English</button>
+        </div>
+        <script>
+            // 全局变量，存储当前语言
+            window.currentLang = "en";
+            
+            // 语言切换函数
+            function toggleLanguage() {
+                window.currentLang = window.currentLang === "en" ? "zh" : "en";
+                
+                // 获取所有带有data-i18n属性的元素
+                const elements = document.querySelectorAll('[data-i18n]');
+                
+                // 遍历并切换语言
+                elements.forEach(el => {
+                    const key = el.getAttribute('data-i18n');
+                    const translations = {
+                        "en": {
+                            "title": "FramePack - Image to Video Generation",
+                            "upload_image": "Upload Image",
+                            "prompt": "Prompt",
+                            "quick_prompts": "Quick Prompts",
+                            "start_generation": "Generate",
+                            "stop_generation": "Stop",
+                            "use_teacache": "Use TeaCache",
+                            "teacache_info": "Faster speed, but may result in slightly worse finger and hand generation.",
+                            "negative_prompt": "Negative Prompt",
+                            "seed": "Seed",
+                            "video_length": "Video Length (max 5 seconds)",
+                            "latent_window": "Latent Window Size",
+                            "steps": "Inference Steps",
+                            "steps_info": "Changing this value is not recommended.",
+                            "cfg_scale": "CFG Scale",
+                            "distilled_cfg": "Distilled CFG Scale",
+                            "distilled_cfg_info": "Changing this value is not recommended.",
+                            "cfg_rescale": "CFG Rescale",
+                            "gpu_memory": "GPU Memory Preservation (GB) (larger means slower)",
+                            "gpu_memory_info": "Set this to a larger value if you encounter OOM errors. Larger values cause slower speed.",
+                            "next_latents": "Next Latents",
+                            "generated_video": "Generated Video",
+                            "sampling_note": "Note: Due to reversed sampling, ending actions will be generated before starting actions. If the starting action is not in the video, please wait, it will be generated later.",
+                            "error_message": "Error",
+                            "processing_error": "Processing error",
+                            "network_error": "Network connection is unstable, model download timed out. Please try again later.",
+                            "memory_error": "GPU memory insufficient, please try increasing GPU memory preservation value or reduce video length.",
+                            "model_error": "Failed to load model, possibly due to network issues or high server load. Please try again later.",
+                            "partial_video": "Processing error, but partial video has been generated",
+                            "processing_interrupt": "Processing was interrupted, but partial video has been generated"
+                        },
+                        "zh": {
+                            "title": "FramePack - 图像到视频生成",
+                            "upload_image": "上传图像",
+                            "prompt": "提示词",
+                            "quick_prompts": "快速提示词列表",
+                            "start_generation": "开始生成",
+                            "stop_generation": "结束生成",
+                            "use_teacache": "使用TeaCache",
+                            "teacache_info": "速度更快，但可能会使手指和手的生成效果稍差。",
+                            "negative_prompt": "负面提示词",
+                            "seed": "随机种子",
+                            "video_length": "视频长度(最大5秒)",
+                            "latent_window": "潜在窗口大小",
+                            "steps": "推理步数",
+                            "steps_info": "不建议修改此值。",
+                            "cfg_scale": "CFG Scale",
+                            "distilled_cfg": "蒸馏CFG比例",
+                            "distilled_cfg_info": "不建议修改此值。",
+                            "cfg_rescale": "CFG重缩放",
+                            "gpu_memory": "GPU推理保留内存(GB)(值越大速度越慢)",
+                            "gpu_memory_info": "如果出现OOM错误，请将此值设置得更大。值越大，速度越慢。",
+                            "next_latents": "下一批潜变量",
+                            "generated_video": "生成的视频",
+                            "sampling_note": "注意：由于采样是倒序的，结束动作将在开始动作之前生成。如果视频中没有出现起始动作，请继续等待，它将在稍后生成。",
+                            "error_message": "错误信息",
+                            "processing_error": "处理过程出错",
+                            "network_error": "网络连接不稳定，模型下载超时。请稍后再试。",
+                            "memory_error": "GPU内存不足，请尝试增加GPU推理保留内存值或降低视频长度。",
+                            "model_error": "模型加载失败，可能是网络问题或服务器负载过高。请稍后再试。",
+                            "partial_video": "处理过程中出现错误，但已生成部分视频",
+                            "processing_interrupt": "处理过程中断，但已生成部分视频"
+                        }
+                    };
+                    
+                    if (translations[window.currentLang] && translations[window.currentLang][key]) {
+                        // 根据元素类型设置文本
+                        if (el.tagName === 'BUTTON') {
+                            el.textContent = translations[window.currentLang][key];
+                        } else if (el.tagName === 'LABEL') {
+                            el.textContent = translations[window.currentLang][key];
+                        } else {
+                            el.innerHTML = translations[window.currentLang][key];
+                        }
+                    }
+                });
+                
+                // 更新页面上其他元素
+                document.querySelectorAll('.bilingual-label').forEach(el => {
+                    const enText = el.getAttribute('data-en');
+                    const zhText = el.getAttribute('data-zh');
+                    el.textContent = window.currentLang === 'en' ? enText : zhText;
+                });
+                
+                // 处理错误消息容器
+                document.querySelectorAll('[data-lang]').forEach(el => {
+                    el.style.display = el.getAttribute('data-lang') === window.currentLang ? 'block' : 'none';
+                });
+            }
+            
+            // 页面加载后初始化
+            document.addEventListener('DOMContentLoaded', function() {
+                // 添加data-i18n属性到需要国际化的元素
+                setTimeout(() => {
+                    // 给所有标签添加i18n属性
+                    const labelMap = {
+                        "Upload Image": "upload_image",
+                        "上传图像": "upload_image",
+                        "Prompt": "prompt",
+                        "提示词": "prompt",
+                        "Quick Prompts": "quick_prompts",
+                        "快速提示词列表": "quick_prompts",
+                        "Generate": "start_generation", 
+                        "开始生成": "start_generation",
+                        "Stop": "stop_generation",
+                        "结束生成": "stop_generation",
+                        // 添加其他标签映射...
+                    };
+                    
+                    // 处理标签
+                    document.querySelectorAll('label, span, button').forEach(el => {
+                        const text = el.textContent.trim();
+                        if (labelMap[text]) {
+                            el.setAttribute('data-i18n', labelMap[text]);
+                        }
+                    });
+                    
+                    // 添加特定元素的i18n属性
+                    const titleEl = document.querySelector('h1');
+                    if (titleEl) titleEl.setAttribute('data-i18n', 'title');
+                    
+                    // 初始化标签语言
+                    toggleLanguage();
+                }, 1000);
+            });
+        </script>
+    """)
+    
+    # 标题使用data-i18n属性以便JavaScript切换
+    gr.HTML("<h1 data-i18n='title'>FramePack - Image to Video Generation / 图像到视频生成</h1>")
+    
+    # 使用带有mobile-full-width类的响应式行
+    with gr.Row(elem_classes="mobile-full-width"):
+        with gr.Column(scale=1, elem_classes="mobile-full-width"):
+            # 添加双语标签 - 上传图像
+            input_image = gr.Image(
+                sources='upload', 
+                type="numpy", 
+                label="Upload Image / 上传图像", 
+                elem_id="input-image",
+                height=320
+            )
+            
+            # 添加双语标签 - 提示词
+            prompt = gr.Textbox(
+                label="Prompt / 提示词", 
+                value='',
+                elem_id="prompt-input"
+            )
+            
+            # 添加双语标签 - 快速提示词
+            example_quick_prompts = gr.Dataset(
+                samples=quick_prompts, 
+                label='Quick Prompts / 快速提示词列表', 
+                samples_per_page=1000, 
+                components=[prompt]
+            )
+            example_quick_prompts.click(lambda x: x[0], inputs=[example_quick_prompts], outputs=prompt, show_progress=False, queue=False)
+
+            # 按钮添加样式和双语标签
+            with gr.Row(elem_classes="button-container"):
+                start_button = gr.Button(
+                    value="Generate / 开始生成", 
+                    elem_classes="start-btn", 
+                    elem_id="start-button",
+                    variant="primary"
+                )
+                
+                end_button = gr.Button(
+                    value="Stop / 结束生成", 
+                    elem_classes="stop-btn", 
+                    elem_id="stop-button",
+                    interactive=False
+                )
+
+            # 参数设置区域
+            with gr.Group():
+                use_teacache = gr.Checkbox(
+                    label='Use TeaCache / 使用TeaCache', 
+                    value=True, 
+                    info='Faster speed, but may result in slightly worse finger and hand generation. / 速度更快，但可能会使手指和手的生成效果稍差。'
+                )
+
+                n_prompt = gr.Textbox(label="Negative Prompt / 负面提示词", value="", visible=False)  # Not used
+                
+                seed = gr.Number(
+                    label="Seed / 随机种子", 
+                    value=31337, 
+                    precision=0
+                )
+
+                # 添加slider-container类以便CSS触摸优化
+                with gr.Group(elem_classes="slider-container"):
+                    total_second_length = gr.Slider(
+                        label="Video Length (max 5 seconds) / 视频长度(最大5秒)", 
+                        minimum=1, 
+                        maximum=5, 
+                        value=5, 
+                        step=0.1
+                    )
+                    
+                    latent_window_size = gr.Slider(
+                        label="Latent Window Size / 潜在窗口大小", 
+                        minimum=1, 
+                        maximum=33, 
+                        value=9, 
+                        step=1, 
+                        visible=False
+                    )
+                    
+                    steps = gr.Slider(
+                        label="Inference Steps / 推理步数", 
+                        minimum=1, 
+                        maximum=100, 
+                        value=25, 
+                        step=1, 
+                        info='Changing this value is not recommended. / 不建议修改此值。'
+                    )
+
+                    cfg = gr.Slider(
+                        label="CFG Scale", 
+                        minimum=1.0, 
+                        maximum=32.0, 
+                        value=1.0, 
+                        step=0.01, 
+                        visible=False
+                    )
+                    
+                    gs = gr.Slider(
+                        label="Distilled CFG Scale / 蒸馏CFG比例", 
+                        minimum=1.0, 
+                        maximum=32.0, 
+                        value=10.0, 
+                        step=0.01, 
+                        info='Changing this value is not recommended. / 不建议修改此值。'
+                    )
+                    
+                    rs = gr.Slider(
+                        label="CFG Rescale / CFG重缩放", 
+                        minimum=0.0, 
+                        maximum=1.0, 
+                        value=0.0, 
+                        step=0.01, 
+                        visible=False
+                    )
+
+                    gpu_memory_preservation = gr.Slider(
+                        label="GPU Memory (GB) / GPU推理保留内存(GB)", 
+                        minimum=6, 
+                        maximum=128, 
+                        value=6, 
+                        step=0.1, 
+                        info="Set this to a larger value if you encounter OOM errors. Larger values cause slower speed. / 如果出现OOM错误，请将此值设置得更大。值越大，速度越慢。"
+                    )
+
+        # 右侧预览和结果列
+        with gr.Column(scale=1, elem_classes="mobile-full-width"):
+            # 预览图像
+            preview_image = gr.Image(
+                label="Preview / 预览", 
+                height=200, 
+                visible=False,
+                elem_classes="preview-container"
+            )
+            
+            # 视频结果容器
+            result_video = gr.Video(
+                label="Generated Video / 生成的视频", 
+                autoplay=True, 
+                show_share_button=True,  # 添加分享按钮
+                height=512, 
+                loop=True,
+                elem_classes="video-container",
+                elem_id="result-video"
+            )
+            
+            # 双语说明
+            gr.HTML("<div data-i18n='sampling_note' class='note'>Note: Due to reversed sampling, ending actions will be generated before starting actions. If the starting action is not in the video, please wait, it will be generated later.</div>")
+            
+            # 进度指示器
+            with gr.Group(elem_classes="progress-container"):
+                progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+                progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+            
+            # 错误信息区域 - 确保使用HTML组件以支持我们的自定义错误消息格式
+            error_message = gr.HTML('', elem_id='error-message', visible=True)
+    
+    # 处理函数
+    ips = [input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache]
+    
+    # 开始和结束按钮事件
+    start_button.click(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
+    end_button.click(fn=end_process)
+
+
+block.launch() 
+
+# 创建友好的错误显示HTML
+def create_error_html(error_msg, is_timeout=False):
+    """创建双语错误消息HTML"""
+    # 提供更友好的中英文双语错误信息
+    en_msg = ""
+    zh_msg = ""
+    
+    if is_timeout:
+        en_msg = "Processing timed out, but partial video may have been generated" if "部分视频" in error_msg else f"Processing timed out: {error_msg}"
+        zh_msg = "处理超时，但已生成部分视频" if "部分视频" in error_msg else f"处理超时: {error_msg}"
+    elif "模型加载失败" in error_msg:
+        en_msg = "Failed to load models. The Space may be experiencing high traffic or GPU issues."
+        zh_msg = "模型加载失败，可能是Space流量过高或GPU资源不足。"
+    elif "GPU" in error_msg or "CUDA" in error_msg or "内存" in error_msg or "memory" in error_msg:
+        en_msg = "GPU memory insufficient or GPU error. Try increasing GPU memory preservation value or reduce video length."
+        zh_msg = "GPU内存不足或GPU错误，请尝试增加GPU推理保留内存值或降低视频长度。"
+    elif "采样过程中出错" in error_msg:
+        if "部分" in error_msg:
+            en_msg = "Error during sampling process, but partial video has been generated."
+            zh_msg = "采样过程中出错，但已生成部分视频。"
+        else:
+            en_msg = "Error during sampling process. Unable to generate video."
+            zh_msg = "采样过程中出错，无法生成视频。"
+    elif "模型下载超时" in error_msg or "网络连接不稳定" in error_msg or "ReadTimeoutError" in error_msg or "ConnectionError" in error_msg:
+        en_msg = "Network connection is unstable, model download timed out. Please try again later."
+        zh_msg = "网络连接不稳定，模型下载超时。请稍后再试。"
+    elif "VAE" in error_msg or "解码" in error_msg or "decode" in error_msg:
+        en_msg = "Error during video decoding or saving process. Try again with a different seed."
+        zh_msg = "视频解码或保存过程中出错，请尝试使用不同的随机种子。"
+    else:
+        en_msg = f"Processing error: {error_msg}"
+        zh_msg = f"处理过程出错: {error_msg}"
+    
+    # 创建双语错误消息HTML - 添加有用的图标并确保CSS样式适用
+    return f"""
+    <div class="error-message" id="custom-error-container">
+        <div class="error-msg-en" data-lang="en">
+            <span class="error-icon">⚠️</span> {en_msg}
+        </div>
+        <div class="error-msg-zh" data-lang="zh">
+            <span class="error-icon">⚠️</span> {zh_msg}
+        </div>
+    </div>
+    <script>
+        // 根据当前语言显示相应的错误消息
+        (function() {{
+            const errorContainer = document.getElementById('custom-error-container');
+            if (errorContainer) {{
+                const currentLang = window.currentLang || 'en'; // 默认英语
+                const errMsgs = errorContainer.querySelectorAll('[data-lang]');
+                errMsgs.forEach(msg => {{
+                    msg.style.display = msg.getAttribute('data-lang') === currentLang ? 'block' : 'none';
+                }});
+                
+                // 确保Gradio默认错误UI不显示
+                const defaultErrorElements = document.querySelectorAll('.error');
+                defaultErrorElements.forEach(el => {{
+                    el.style.display = 'none';
+                }});
+            }}
+        }})();
+    </script>
+    """