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
import spaces

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
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


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}')

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/FramePack_F1_I2V_HY_20250503', 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 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)

stream = AsyncStream()

outputs_folder = './outputs/'
os.makedirs(outputs_folder, exist_ok=True)

examples = [
["img_examples/1.png", "The girl dances gracefully, with clear movements, full of charm.",],
["img_examples/2.jpg", "The man dances flamboyantly, swinging his hips and striking bold poses with dramatic flair."],
["img_examples/3.png", "The woman dances elegantly among the blossoms, spinning slowly with flowing sleeves and graceful hand movements."],
]

def generate_examples(input_image, prompt):
    
    t2v=False 
    n_prompt=""
    seed=31337
    total_second_length=5 
    latent_window_size=9 
    steps=25 
    cfg=1.0 
    gs=10.0 
    rs=0.0
    gpu_memory_preservation=6 
    use_teacache=True 
    mp4_crf=16

    global stream
    
    # assert input_image is not None, 'No input image!'
    if t2v:
        default_height, default_width = 640, 640
        input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
        print("No input image provided. Using a blank white 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, mp4_crf)

    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


    
@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, mp4_crf):
    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)

        history_latents = torch.zeros(size=(1, 16, 16 + 2 + 1, height // 8, width // 8), dtype=torch.float32).cpu()
        history_pixels = None

        history_latents = torch.cat([history_latents, start_latent.to(history_latents)], dim=2)
        total_generated_latent_frames = 1

        for section_index in range(total_latent_sections):
            if stream.input_queue.top() == 'end':
                stream.output_queue.push(('end', None))
                return

            print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')

            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

            indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
            clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
            clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)

            clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -sum([16, 2, 1]):, :, :].split([16, 2, 1], dim=2)
            clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)

            generated_latents = sample_hunyuan(
                transformer=transformer,
                sampler='unipc',
                width=width,
                height=height,
                frames=latent_window_size * 4 - 3,
                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,
            )

            total_generated_latent_frames += int(generated_latents.shape[2])
            history_latents = torch.cat([history_latents, generated_latents.to(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
                overlapped_frames = latent_window_size * 4 - 3

                current_pixels = vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
                history_pixels = soft_append_bcthw(history_pixels, current_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, crf=mp4_crf)

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

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

def get_duration(input_image, prompt, t2v, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
    return total_second_length * 60

@spaces.GPU(duration=get_duration)
def process(input_image, prompt, 
            t2v=False, 
            n_prompt="", 
            seed=31337, 
            total_second_length=5, 
            latent_window_size=9, 
            steps=25, 
            cfg=1.0, 
            gs=10.0, 
            rs=0.0, 
            gpu_memory_preservation=6, 
            use_teacache=True, 
            mp4_crf=16
           ):
    global stream
    
    # assert input_image is not None, 'No input image!'
    if t2v:
        default_height, default_width = 640, 640
        input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
        print("No input image provided. Using a blank white image.")
    else:
        composite_rgba_uint8 = input_image["composite"] 

        # rgb_uint8 will be (H, W, 3), dtype uint8
        rgb_uint8 = composite_rgba_uint8[:, :, :3]
        # mask_uint8 will be (H, W), dtype uint8
        mask_uint8 = composite_rgba_uint8[:, :, 3]
    
        # Create background
        h, w = rgb_uint8.shape[:2]
        # White background, (H, W, 3), dtype uint8
        background_uint8 = np.full((h, w, 3), 255, dtype=np.uint8) 
        
        # Normalize mask to range [0.0, 1.0]. 
        alpha_normalized_float32 = mask_uint8.astype(np.float32) / 255.0
        
        # Expand alpha to 3 channels to match RGB images for broadcasting.
        # alpha_mask_float32 will have shape (H, W, 3)
        alpha_mask_float32 = np.stack([alpha_normalized_float32] * 3, axis=2)
        
        # alpha blending
        blended_image_float32 = rgb_uint8.astype(np.float32) * alpha_mask_float32 + \
                                background_uint8.astype(np.float32) * (1.0 - alpha_mask_float32)

        input_image = np.clip(blended_image_float32, 0, 255).astype(np.uint8)
    
    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, mp4_crf)

    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-F1')
    gr.Markdown(f"""### Video diffusion, but feels like image diffusion
*FramePack F1 - a FramePack model that only predicts future frames from history frames*
### *beta* FramePack Fill 🖋️- draw a mask over the input image to inpaint the video output
adapted from the officical code repo [FramePack](https://github.com/lllyasviel/FramePack) by [lllyasviel](lllyasviel/FramePack_F1_I2V_HY_20250503) and [FramePack Studio](https://github.com/colinurbs/FramePack-Studio) 🙌🏻
    """)
    with gr.Row():
        with gr.Column():
            input_image = gr.ImageEditor(type="numpy", label="Image", height=320, brush=gr.Brush(colors=["#ffffff"]))
            prompt = gr.Textbox(label="Prompt", value='')
            t2v = gr.Checkbox(label="do text-to-video", value=False)
            example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Quick List', 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="Start Generation")
                end_button = gr.Button(value="End Generation", interactive=False)

            total_second_length = gr.Slider(label="Total Video Length (Seconds)", minimum=1, maximum=5, value=2, step=0.1)
            with gr.Group():
                with gr.Accordion("Advanced settings", open=False):
                    use_teacache = gr.Checkbox(label='Use TeaCache', value=True, info='Faster speed, but often makes hands and fingers slightly worse.')
    
                    n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=False)  # Not used
                    seed = gr.Number(label="Seed", value=31337, precision=0)
    
                    
                    latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, visible=False)  # Should not change
                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1, info='Changing this value is not recommended.')
    
                    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="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='Changing this value is not recommended.')
                    rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False)  # Should not change
    
                    gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
    
                    mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")

        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)
            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
            progress_bar = gr.HTML('', elem_classes='no-generating-animation')

    gr.HTML('<div style="text-align:center; margin-top:20px;">Share your results and find ideas at the <a href="https://x.com/search?q=framepack&f=live" target="_blank">FramePack Twitter (X) thread</a></div>')

    ips = [input_image, prompt, t2v, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf]
    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)

    # gr.Examples(
    #     examples,
    #     inputs=[input_image, prompt],
    #     outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
    #     fn=generate_examples,
    #     cache_examples=True
    #     )


block.launch(share=True)