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")
# 如果已经有生成的视频，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>
"""