app.py

from diffusers_helper.hf_login import login

import os

os.environ['HF_HOME'] = os.path.abspath(os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download')))

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

# 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")
    except ImportError:
        print("未能导入spaces模块，可能不在Hugging Face Space环境中")

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")
    free_mem_gb = 60.0  # 默认在Spaces中使用较高的值
    high_vram = True
    print(f'High-VRAM Mode: {high_vram}')

# 使用models变量存储全局模型引用
models = {}

# 使用加载模型的函数
def load_models():
    global models
    
    print("开始加载模型...")
    
    # 加载模型
    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()

    if not high_vram:
        vae.enable_slicing()
        vae.enable_tiling()

    transformer.high_quality_fp32_output_for_inference = True
    print('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)

    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():
        if not high_vram:
            # DynamicSwapInstaller is same as huggingface's enable_sequential_offload but 3x faster
            DynamicSwapInstaller.install_model(transformer, device=gpu)
            DynamicSwapInstaller.install_model(text_encoder, device=gpu)
        else:
            text_encoder.to(gpu)
            text_encoder_2.to(gpu)
            image_encoder.to(gpu)
            vae.to(gpu)
            transformer.to(gpu)
    
    # 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
    }
    
    return models


# 使用Hugging Face Spaces GPU装饰器
if IN_HF_SPACE and 'spaces' in globals():
    @spaces.GPU
    def initialize_models():
        """在@spaces.GPU装饰器内初始化模型"""
        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
    
    if not models:
        if IN_HF_SPACE and 'spaces' in globals():
            print("使用@spaces.GPU装饰器加载模型")
            models = initialize_models()
        else:
            print("直接加载模型")
            load_models()
    
    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):
    # 获取模型
    models = get_models()
    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']
    
    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()

    stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))

    try:
        # Clean GPU
        if not high_vram:
            unload_complete_models(
                text_encoder, text_encoder_2, image_encoder, vae, transformer
            )

        # Text encoding

        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))

        if not high_vram:
            fake_diffusers_current_device(text_encoder, gpu)  # since we only encode one text - that is one model move and one encode, offload is same time consumption since it is also one load and one encode.
            load_model_as_complete(text_encoder_2, target_device=gpu)

        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)

        # Processing input image

        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))

        H, W, C = input_image.shape
        height, width = find_nearest_bucket(H, W, resolution=640)
        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]

        # VAE encoding

        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))

        if not high_vram:
            load_model_as_complete(vae, target_device=gpu)

        start_latent = vae_encode(input_image_pt, vae)

        # CLIP Vision

        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))

        if not high_vram:
            load_model_as_complete(image_encoder, target_device=gpu)

        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

        # Dtype

        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)

        # Sampling

        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

        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

        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:
            is_last_section = latent_padding == 0
            latent_padding_size = latent_padding * latent_window_size

            if stream.input_queue.top() == 'end':
                stream.output_queue.push(('end', None))
                return

            print(f'latent_padding_size = {latent_padding_size}, is_last_section = {is_last_section}')

            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)

            if not high_vram:
                unload_complete_models()
                move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)

            if use_teacache:
                transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
            else:
                transformer.initialize_teacache(enable_teacache=False)

            def callback(d):
                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')

                if stream.input_queue.top() == 'end':
                    stream.output_queue.push(('end', None))
                    raise KeyboardInterrupt('User ends the task.')

                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))))
                return

            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=gpu,
                dtype=torch.bfloat16,
                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,
            )

            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)

            if not high_vram:
                offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
                load_model_as_complete(vae, target_device=gpu)

            real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :]

            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)

            if not high_vram:
                unload_complete_models()

            output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')

            save_bcthw_as_mp4(history_pixels, output_filename, fps=30)

            print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')

            stream.output_queue.push(('file', output_filename))

            if is_last_section:
                break
    except:
        traceback.print_exc()

        if not high_vram:
            unload_complete_models(
                text_encoder, text_encoder_2, image_encoder, vae, transformer
            )

    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!'

        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
    
    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!'

        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


def end_process():
    stream.input_queue.push('end')


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 - 图像到视频生成')
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(sources='upload', type="numpy", label="上传图像", height=320)
            prompt = gr.Textbox(label="提示词", value='')
            example_quick_prompts = gr.Dataset(samples=quick_prompts, label='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)

            with gr.Row():
                start_button = gr.Button(value="开始生成")
                end_button = gr.Button(value="结束生成", interactive=False)

            with gr.Group():
                use_teacache = gr.Checkbox(label='使用TeaCache', value=True, info='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="负面提示词", value="", visible=False)  # Not used
                seed = gr.Number(label="随机种子", value=31337, precision=0)

                total_second_length = gr.Slider(label="视频长度(秒)", minimum=1, maximum=120, value=5, step=0.1)
                latent_window_size = gr.Slider(label="潜在窗口大小", minimum=1, maximum=33, value=9, step=1, visible=False)  # Should not change
                steps = gr.Slider(label="推理步数", minimum=1, maximum=100, value=25, step=1, info='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)  # Should not change
                gs = gr.Slider(label="蒸馏CFG比例", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='不建议修改此值。')
                rs = gr.Slider(label="CFG重缩放", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False)  # Should not change

                gpu_memory_preservation = gr.Slider(label="GPU推理保留内存(GB)(值越大速度越慢)", minimum=6, maximum=128, value=6, step=0.1, info="如果出现OOM错误，请将此值设置得更大。值越大，速度越慢。")

        with gr.Column():
            preview_image = gr.Image(label="下一批潜变量", height=200, visible=False)
            result_video = gr.Video(label="生成的视频", autoplay=True, show_share_button=False, height=512, loop=True)
            gr.Markdown('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。')
            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()