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+FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
+
+# 设置非交互式安装并避免不必要的包
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=Asia/Shanghai
+
+# 安装基本工具和Python
+RUN apt-get update && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    ffmpeg \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# 设置工作目录
+WORKDIR /app
+
+# 复制需要的文件
+COPY requirements.txt ./
+COPY app.py ./
+COPY setup.sh ./
+COPY README.md ./
+COPY diffusers_helper ./diffusers_helper
+
+# 安装Python依赖
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# 创建需要的目录
+RUN mkdir -p /app/outputs
+RUN mkdir -p /app/hf_download
+
+# 设置权限
+RUN chmod +x setup.sh
+
+# 设置环境变量
+ENV HF_HOME=/app/hf_download
+
+# 运行应用
+CMD ["python3", "app.py"] 

demo_gradio.py

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+
+import os
+import argparse
+
+
+
+import torch
+import gradio as gr
+
+
+import numpy as np
+import einops
+import traceback
+
+from PIL import Image
+from diffusers import AutoencoderKLHunyuanVideo
+from transformers import (
+    LlamaModel, CLIPTextModel,
+    LlamaTokenizerFast, CLIPTokenizer,
+    SiglipImageProcessor, SiglipVisionModel
+)
+
+from diffusers_helper.hf_login import login
+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, 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 (
+    gpu, get_cuda_free_memory_gb, unload_complete_models, load_model_as_complete,
+    DynamicSwapInstaller, move_model_to_device_with_memory_preservation,
+    offload_model_from_device_for_memory_preservation, fake_diffusers_current_device
+)
+from diffusers_helper.clip_vision import hf_clip_vision_encode
+from diffusers_helper.thread_utils import AsyncStream, async_run
+
+
+# --- Args and config ---
+parser = argparse.ArgumentParser()
+parser.add_argument('--share', action='store_true')
+parser.add_argument('--server', type=str, default='0.0.0.0')
+parser.add_argument('--port', type=int, required=False)
+parser.add_argument('--inbrowser', action='store_true')
+args = parser.parse_args()
+
+os.environ['HF_HOME'] = os.path.abspath(
+    os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download'))
+)
+
+print(args)
+
+free_mem_gb = get_cuda_free_memory_gb(gpu)
+high_vram = free_mem_gb > 60
+
+print(f'Free VRAM {free_mem_gb} GB')
+print(f'High-VRAM Mode: {high_vram}')
+
+# --- Load models ---
+text_encoder = LlamaModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder', torch_dtype=torch.float16).cpu()
+text_encoder_2 = CLIPTextModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=torch.float16).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=torch.float16).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=torch.float16).cpu()
+
+transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePackI2V_HY', torch_dtype=torch.bfloat16).cpu()
+
+vae.eval(), text_encoder.eval(), text_encoder_2.eval(), image_encoder.eval(), transformer.eval()
+
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+
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+
+if not high_vram:
+    vae.enable_slicing()
+    vae.enable_tiling()
+
+transformer.high_quality_fp32_output_for_inference = True
+
+
+transformer.to(dtype=torch.bfloat16)
+vae.to(dtype=torch.float16)
+image_encoder.to(dtype=torch.float16)
+text_encoder.to(dtype=torch.float16)
+text_encoder_2.to(dtype=torch.float16)
+
+for model in [vae, text_encoder, text_encoder_2, image_encoder, transformer]:
+    model.requires_grad_(False)
+
+
+
+
+if not high_vram:
+
+    DynamicSwapInstaller.install_model(transformer, device=gpu)
+    DynamicSwapInstaller.install_model(text_encoder, device=gpu)
+else:
+    transformer.to(gpu)
+
+stream = AsyncStream()
+
+outputs_folder = './outputs/'
+os.makedirs(outputs_folder, exist_ok=True)
+
+# --- UI + CSS ---
+def make_progress_bar_css():
+    return """
+    body, .gradio-container {
+        background-color: #000000 !important;
+        color: #FFFFFF !important;
+    }
+    .gr-button, .gr-input, .gr-textbox, .gr-slider, .gr-checkbox {
+        background-color: #1a1a1a !important;
+        color: #ffffff !important;
+        border-color: #444 !important;
+    }
+    .gr-button:hover {
+        background-color: #333 !important;
+    }
+    .gr-markdown {
+        color: #ddd !important;
+    }
+    .gr-image-preview, .gr-video {
+        background-color: #111 !important;
+    }
+    """
+
+def end_process():
+    stream.input_queue.push('end')
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+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!'
+
+    yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
+
+    stream = AsyncStream()
+
+    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
+
+    while True:
+        flag, data = stream.output_queue.next()
+
+        if flag == 'file':
+            output_filename = data
+            yield output_filename, gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True)
+
+        if flag == 'progress':
+            preview, desc, html = data
+            yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
+
+        if flag == 'end':
+            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
+            break
+
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+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]
+
+
+css = make_progress_bar_css()
+
+block = gr.Blocks(css=css).queue()
+with block:
+    gr.Markdown('# FramePack')
+                end_button = gr.Button(value="End Generation", interactive=False)
+
+            with gr.Group():
+                use_teacache = gr.Checkbox(label='Use TeaCache', value=True)
+                n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=False)
+
+                seed = gr.Number(label="Seed", value=31337, precision=0)
+
+                total_second_length = gr.Slider(label="Total Video Length (Seconds)", minimum=1, maximum=120, value=5, step=0.1)
+                latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, visible=False)
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1)
+                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", minimum=1.0, maximum=32.0, value=10.0, step=0.01)
+                rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False)
+                gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB)", minimum=6, maximum=128, value=6, step=0.1)
+
+
+
+        with gr.Column():
+            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
+            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
+            gr.Markdown('Note: The ending actions are generated before the start. Wait for full video.')
+            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+
+    ips = [input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache]
+    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(
+    server_name=args.server,
+    server_port=args.port,

setup.sh

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+#!/bin/bash
+# 创建必要的目录
+mkdir -p hf_download
+mkdir -p outputs
+
+# 如果模型尚未下载，会在首次运行时自动下载
+echo "环境准备完毕，运行 python app.py 启动应用"