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 spaces
import gradio as gr
import torch
import traceback
import einops
import safetensors.torch as sf
import numpy as np
import random
import math
# 20250506 pftq: Added for video input loading
import decord
# 20250506 pftq: Added for progress bars in video_encode
from tqdm import tqdm
# 20250506 pftq: Normalize file paths for Windows compatibility
import pathlib
# 20250506 pftq: for easier to read timestamp
from datetime import datetime
# 20250508 pftq: for saving prompt to mp4 comments metadata
import imageio_ffmpeg
import tempfile
import shutil
import subprocess

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
if torch.cuda.device_count() > 0:
    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
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, HunyuanVideoTransformer3DModel, HunyuanVideoPipeline

high_vram = False
free_mem_gb = 0

if torch.cuda.device_count() > 0:
    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)

def check_parameters(generation_mode, input_image, input_video):
    if generation_mode == "image" and input_image is None:
        raise gr.Error("Please provide an image to extend.")
    if generation_mode == "video" and input_video is None:
        raise gr.Error("Please provide a video to extend.")
    return []

@spaces.GPU()
@torch.no_grad()
def video_encode(video_path, resolution, no_resize, vae, vae_batch_size=16, device="cuda", width=None, height=None):
    """
    Encode a video into latent representations using the VAE.

    Args:
        video_path: Path to the input video file.
        vae: AutoencoderKLHunyuanVideo model.
        height, width: Target resolution for resizing frames.
        vae_batch_size: Number of frames to process per batch.
        device: Device for computation (e.g., "cuda").

    Returns:
        start_latent: Latent of the first frame (for compatibility with original code).
        input_image_np: First frame as numpy array (for CLIP vision encoding).
        history_latents: Latents of all frames (shape: [1, channels, frames, height//8, width//8]).
        fps: Frames per second of the input video.
    """
    # 20250506 pftq: Normalize video path for Windows compatibility
    video_path = str(pathlib.Path(video_path).resolve())
    print(f"Processing video: {video_path}")

    # 20250506 pftq: Check CUDA availability and fallback to CPU if needed
    if device == "cuda" and not torch.cuda.is_available():
        print("CUDA is not available, falling back to CPU")
        device = "cpu"

    try:
        # 20250506 pftq: Load video and get FPS
        print("Initializing VideoReader...")
        vr = decord.VideoReader(video_path)
        fps = vr.get_avg_fps()  # Get input video FPS
        num_real_frames = len(vr)
        print(f"Video loaded: {num_real_frames} frames, FPS: {fps}")

        # Truncate to nearest latent size (multiple of 4)
        latent_size_factor = 4
        num_frames = (num_real_frames // latent_size_factor) * latent_size_factor
        if num_frames != num_real_frames:
            print(f"Truncating video from {num_real_frames} to {num_frames} frames for latent size compatibility")
        num_real_frames = num_frames

        # 20250506 pftq: Read frames
        print("Reading video frames...")
        frames = vr.get_batch(range(num_real_frames)).asnumpy()  # Shape: (num_real_frames, height, width, channels)
        print(f"Frames read: {frames.shape}")

        # 20250506 pftq: Get native video resolution
        native_height, native_width = frames.shape[1], frames.shape[2]
        print(f"Native video resolution: {native_width}x{native_height}")

        # 20250506 pftq: Use native resolution if height/width not specified, otherwise use provided values
        target_height = native_height if height is None else height
        target_width = native_width if width is None else width

        # 20250506 pftq: Adjust to nearest bucket for model compatibility
        if not no_resize:
            target_height, target_width = find_nearest_bucket(target_height, target_width, resolution=resolution)
            print(f"Adjusted resolution: {target_width}x{target_height}")
        else:
            print(f"Using native resolution without resizing: {target_width}x{target_height}")

        # 20250506 pftq: Preprocess frames to match original image processing
        processed_frames = []
        for i, frame in enumerate(frames):
            #print(f"Preprocessing frame {i+1}/{num_frames}")
            frame_np = resize_and_center_crop(frame, target_width=target_width, target_height=target_height)
            processed_frames.append(frame_np)
        processed_frames = np.stack(processed_frames)  # Shape: (num_real_frames, height, width, channels)
        print(f"Frames preprocessed: {processed_frames.shape}")

        # 20250506 pftq: Save first frame for CLIP vision encoding
        input_image_np = processed_frames[0]

        # 20250506 pftq: Convert to tensor and normalize to [-1, 1]
        print("Converting frames to tensor...")
        frames_pt = torch.from_numpy(processed_frames).float() / 127.5 - 1
        frames_pt = frames_pt.permute(0, 3, 1, 2)  # Shape: (num_real_frames, channels, height, width)
        frames_pt = frames_pt.unsqueeze(0)  # Shape: (1, num_real_frames, channels, height, width)
        frames_pt = frames_pt.permute(0, 2, 1, 3, 4)  # Shape: (1, channels, num_real_frames, height, width)
        print(f"Tensor shape: {frames_pt.shape}")

        # 20250507 pftq: Save pixel frames for use in worker
        input_video_pixels = frames_pt.cpu()

        # 20250506 pftq: Move to device
        print(f"Moving tensor to device: {device}")
        frames_pt = frames_pt.to(device)
        print("Tensor moved to device")

        # 20250506 pftq: Move VAE to device
        print(f"Moving VAE to device: {device}")
        vae.to(device)
        print("VAE moved to device")

        # 20250506 pftq: Encode frames in batches
        print(f"Encoding input video frames in VAE batch size {vae_batch_size} (reduce if memory issues here or if forcing video resolution)")
        latents = []
        vae.eval()
        with torch.no_grad():
            for i in tqdm(range(0, frames_pt.shape[2], vae_batch_size), desc="Encoding video frames", mininterval=0.1):
                #print(f"Encoding batch {i//vae_batch_size + 1}: frames {i} to {min(i + vae_batch_size, frames_pt.shape[2])}")
                batch = frames_pt[:, :, i:i + vae_batch_size]  # Shape: (1, channels, batch_size, height, width)
                try:
                    # 20250506 pftq: Log GPU memory before encoding
                    if device == "cuda":
                        free_mem = torch.cuda.memory_allocated() / 1024**3
                        #print(f"GPU memory before encoding: {free_mem:.2f} GB")
                    batch_latent = vae_encode(batch, vae)
                    # 20250506 pftq: Synchronize CUDA to catch issues
                    if device == "cuda":
                        torch.cuda.synchronize()
                        #print(f"GPU memory after encoding: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
                    latents.append(batch_latent)
                    #print(f"Batch encoded, latent shape: {batch_latent.shape}")
                except RuntimeError as e:
                    print(f"Error during VAE encoding: {str(e)}")
                    if device == "cuda" and "out of memory" in str(e).lower():
                        print("CUDA out of memory, try reducing vae_batch_size or using CPU")
                    raise

        # 20250506 pftq: Concatenate latents
        print("Concatenating latents...")
        history_latents = torch.cat(latents, dim=2)  # Shape: (1, channels, frames, height//8, width//8)
        print(f"History latents shape: {history_latents.shape}")

        # 20250506 pftq: Get first frame's latent
        start_latent = history_latents[:, :, :1]  # Shape: (1, channels, 1, height//8, width//8)
        print(f"Start latent shape: {start_latent.shape}")

        # 20250506 pftq: Move VAE back to CPU to free GPU memory
        if device == "cuda":
            vae.to(cpu)
            torch.cuda.empty_cache()
            print("VAE moved back to CPU, CUDA cache cleared")

        return start_latent, input_image_np, history_latents, fps, target_height, target_width, input_video_pixels

    except Exception as e:
        print(f"Error in video_encode: {str(e)}")
        raise

# 20250508 pftq: for saving prompt to mp4 metadata comments
def set_mp4_comments_imageio_ffmpeg(input_file, comments):
    try:
        # Get the path to the bundled FFmpeg binary from imageio-ffmpeg
        ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()

        # Check if input file exists
        if not os.path.exists(input_file):
            print(f"Error: Input file {input_file} does not exist")
            return False

        # Create a temporary file path
        temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False).name

        # FFmpeg command using the bundled binary
        command = [
            ffmpeg_path,                   # Use imageio-ffmpeg's FFmpeg
            '-i', input_file,              # input file
            '-metadata', f'comment={comments}',  # set comment metadata
            '-c:v', 'copy',                # copy video stream without re-encoding
            '-c:a', 'copy',                # copy audio stream without re-encoding
            '-y',                          # overwrite output file if it exists
            temp_file                      # temporary output file
        ]

        # Run the FFmpeg command
        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)

        if result.returncode == 0:
            # Replace the original file with the modified one
            shutil.move(temp_file, input_file)
            print(f"Successfully added comments to {input_file}")
            return True
        else:
            # Clean up temp file if FFmpeg fails
            if os.path.exists(temp_file):
                os.remove(temp_file)
            print(f"Error: FFmpeg failed with message:\n{result.stderr}")
            return False

    except Exception as e:
        # Clean up temp file in case of other errors
        if 'temp_file' in locals() and os.path.exists(temp_file):
            os.remove(temp_file)
        print(f"Error saving prompt to video metadata, ffmpeg may be required: "+str(e))
        return False

@torch.no_grad()
def worker(input_image, prompts, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
    def encode_prompt(prompt, n_prompt):
        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)

        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)
        return [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n]

    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)

        prompt_parameters = []

        for prompt_part in prompts:
            prompt_parameters.append(encode_prompt(prompt_part, n_prompt))

        # 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

        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

        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

        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 len(prompt_parameters) > 0:
                [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters.pop(0)

            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)

            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, generation_mode, n_prompt, randomize_seed, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
    return total_second_length * 60 * (0.7 if use_teacache else 1.3)


@spaces.GPU(duration=get_duration)
def process(input_image, prompt,
            generation_mode="image",
            n_prompt="",
            randomize_seed=True,
            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=False,
            mp4_crf=16
           ):
    global stream

    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return None, None, None, None, None, None

    if randomize_seed:
        seed = random.randint(0, np.iinfo(np.int32).max)

    prompts = prompt.split(";")

    # assert input_image is not None, 'No input image!'
    if generation_mode == "text":
        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, prompts, 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

# 20250506 pftq: Modified worker to accept video input and clean frame count
@spaces.GPU()
@torch.no_grad()
def worker_video(input_video, prompt, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):

    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)

        # 20250506 pftq: Processing input video instead of image
        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Video processing ...'))))

        # 20250506 pftq: Encode video
        #H, W = 640, 640  # Default resolution, will be adjusted
        #height, width = find_nearest_bucket(H, W, resolution=640)
        #start_latent, input_image_np, history_latents, fps = video_encode(input_video, vae, height, width, vae_batch_size=16, device=gpu)
        start_latent, input_image_np, video_latents, fps, height, width, input_video_pixels  = video_encode(input_video, resolution, no_resize, vae, vae_batch_size=vae_batch, device=gpu)

        #Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))

        # 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)

        total_latent_sections = (total_second_length * fps) / (latent_window_size * 4)
        total_latent_sections = int(max(round(total_latent_sections), 1))

        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 frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / fps) :.2f} seconds (FPS-{fps}), Seed: {seed}, Video {idx+1} of {batch}. The video is generating part {section_index+1} of {total_latent_sections}...'
            stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
            return

        for idx in range(batch):
            if batch > 1:
                print(f"Beginning video {idx+1} of {batch} with seed {seed} ")

            #job_id = generate_timestamp()
            job_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+f"_framepackf1-videoinput_{width}-{total_second_length}sec_seed-{seed}_steps-{steps}_distilled-{gs}_cfg-{cfg}" # 20250506 pftq: easier to read timestamp and filename

            # Sampling
            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))

            rnd = torch.Generator("cpu").manual_seed(seed)

            # 20250506 pftq: Initialize history_latents with video latents
            history_latents = video_latents.cpu()
            total_generated_latent_frames = history_latents.shape[2]
            # 20250506 pftq: Initialize history_pixels to fix UnboundLocalError
            history_pixels = None
            previous_video = None

            # 20250507 pftq: hot fix for initial video being corrupted by vae encoding, issue with ghosting because of slight differences
            #history_pixels = input_video_pixels
            #save_bcthw_as_mp4(vae_decode(video_latents, vae).cpu(), os.path.join(outputs_folder, f'{job_id}_input_video.mp4'), fps=fps, crf=mp4_crf) # 20250507 pftq: test fast movement corrupted by vae encoding if vae batch size too low

            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)

                # 20250506 pftq: Use user-specified number of context frames, matching original allocation for num_clean_frames=2
                available_frames = history_latents.shape[2]  # Number of latent frames
                max_pixel_frames = min(latent_window_size * 4 - 3, available_frames * 4)  # Cap at available pixel frames
                adjusted_latent_frames = max(1, (max_pixel_frames + 3) // 4)  # Convert back to latent frames
                # Adjust num_clean_frames to match original behavior: num_clean_frames=2 means 1 frame for clean_latents_1x
                effective_clean_frames = max(0, num_clean_frames - 1) if num_clean_frames > 1 else 0
                effective_clean_frames = min(effective_clean_frames, available_frames - 2) if available_frames > 2 else 0 # 20250507 pftq: changed 1 to 2 for edge case for <=1 sec videos
                num_2x_frames = min(2, max(1, available_frames - effective_clean_frames - 1)) if available_frames > effective_clean_frames + 1 else 0 # 20250507 pftq: subtracted 1 for edge case for <=1 sec videos
                num_4x_frames = min(16, max(1, available_frames - effective_clean_frames - num_2x_frames)) if available_frames > effective_clean_frames + num_2x_frames else 0 # 20250507 pftq: Edge case for <=1 sec

                total_context_frames = num_4x_frames + num_2x_frames + effective_clean_frames
                total_context_frames = min(total_context_frames, available_frames)  # 20250507 pftq: Edge case for <=1 sec videos

                indices = torch.arange(0, sum([1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames])).unsqueeze(0) # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
                clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split(
                    [1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames], dim=1 # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
                )
                clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)

                # 20250506 pftq: Split history_latents dynamically based on available frames
                fallback_frame_count = 2 # 20250507 pftq: Changed 0 to 2 Edge case for <=1 sec videos
                context_frames = clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :]

                if total_context_frames > 0:
                    context_frames = history_latents[:, :, -total_context_frames:, :, :]
                    split_sizes = [num_4x_frames, num_2x_frames, effective_clean_frames]
                    split_sizes = [s for s in split_sizes if s > 0]  # Remove zero sizes
                    if split_sizes:
                        splits = context_frames.split(split_sizes, dim=2)
                        split_idx = 0
                        
                        if num_4x_frames > 0:
                            clean_latents_4x = splits[split_idx]
                            split_idx = 1
                        if clean_latents_4x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
                            clean_latents_4x = torch.cat([clean_latents_4x, clean_latents_4x[:, :, -1:, :, :]], dim=2)[:, :, :2, :, :]
                            
                        if num_2x_frames > 0 and split_idx < len(splits):
                            clean_latents_2x = splits[split_idx]
                            if clean_latents_2x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
                                clean_latents_2x = torch.cat([clean_latents_2x, clean_latents_2x[:, :, -1:, :, :]], dim=2)[:, :, :2, :, :]
                            split_idx += 1
                        elif clean_latents_2x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
                            clean_latents_2x = clean_latents_4x
                            
                        if effective_clean_frames > 0 and split_idx < len(splits):
                            clean_latents_1x = splits[split_idx]

                clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)

                # 20250507 pftq: Fix for <=1 sec videos.
                max_frames = min(latent_window_size * 4 - 3, history_latents.shape[2] * 4)

                generated_latents = sample_hunyuan(
                    transformer=transformer,
                    sampler='unipc',
                    width=width,
                    height=height,
                    frames=max_frames,
                    real_guidance_scale=cfg,
                    distilled_guidance_scale=gs,
                    guidance_rescale=rs,
                    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 = min(latent_window_size * 4 - 3, history_pixels.shape[2])

                  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')

                # 20250506 pftq: Use input video FPS for output
                save_bcthw_as_mp4(history_pixels, output_filename, fps=fps, crf=mp4_crf)
                print(f"Latest video saved: {output_filename}")
                # 20250508 pftq: Save prompt to mp4 metadata comments
                set_mp4_comments_imageio_ffmpeg(output_filename, f"Prompt: {prompt} | Negative Prompt: {n_prompt}");
                print(f"Prompt saved to mp4 metadata comments: {output_filename}")

                # 20250506 pftq: Clean up previous partial files
                if previous_video is not None and os.path.exists(previous_video):
                    try:
                        os.remove(previous_video)
                        print(f"Previous partial video deleted: {previous_video}")
                    except Exception as e:
                        print(f"Error deleting previous partial video {previous_video}: {e}")
                previous_video = output_filename

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

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

            seed = (seed + 1) % np.iinfo(np.int32).max

    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_video(input_video, prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
    return total_second_length * 60 * (0.7 if use_teacache else 2)

# 20250506 pftq: Modified process to pass clean frame count, etc from video_encode
@spaces.GPU(duration=get_duration_video)
def process_video(input_video, prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
    global stream, high_vram

    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return None, None, None, None, None, None

    if randomize_seed:
        seed = random.randint(0, np.iinfo(np.int32).max)

    # 20250506 pftq: Updated assertion for video input
    assert input_video is not None, 'No input video!'

    yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)

    # 20250507 pftq: Even the H100 needs offloading if the video dimensions are 720p or higher
    if high_vram and (no_resize or resolution>640):
        print("Disabling high vram mode due to no resize and/or potentially higher resolution...")
        high_vram = False
        vae.enable_slicing()
        vae.enable_tiling()
        DynamicSwapInstaller.install_model(transformer, device=gpu)
        DynamicSwapInstaller.install_model(text_encoder, device=gpu)

    # 20250508 pftq: automatically set distilled cfg to 1 if cfg is used
    if cfg > 1:
        gs = 1

    stream = AsyncStream()

    # 20250506 pftq: Pass num_clean_frames, vae_batch, etc
    async_run(worker_video, input_video, prompt, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch)

    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)
            yield output_filename, gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True) # 20250506 pftq: Keep refreshing the video in case it got hidden when the tab was in the background

        if flag == 'end':
            yield output_filename, gr.update(visible=False), desc+' Video complete.', '', gr.update(interactive=True), gr.update(interactive=False)
            break

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

timeless_prompt_value = [""]
timed_prompts = {}

def handle_prompt_number_change():
    timed_prompts.clear()
    return []

def handle_timeless_prompt_change(timeless_prompt):
    timeless_prompt_value[0] = timeless_prompt
    return refresh_prompt()

def handle_timed_prompt_change(timed_prompt_id, timed_prompt):
    timed_prompts[timed_prompt_id] = timed_prompt
    return refresh_prompt()

def refresh_prompt():
    dict_values = {k: v for k, v in timed_prompts.items()}
    sorted_dict_values = sorted(dict_values.items(), key=lambda x: x[0])
    array = []
    for sorted_dict_value in sorted_dict_values:
        array.append(timeless_prompt_value[0] + ". " + sorted_dict_value[1])
    print(str(array))
    return ";".join(array)

title_html = """
    <h1><center>FramePack</center></h1>
    <big><center>Generate videos from text/image/video freely, without account, without watermark and download it</center></big>
    <br/>
    <br/>
    
    <p>This space is ready to work on ZeroGPU and GPU and has been tested successfully on ZeroGPU. Please leave a <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack/discussions/new">message in discussion</a> if you encounter issues.</p>
    """

css = make_progress_bar_css()
block = gr.Blocks(css=css).queue()
with block:
    if torch.cuda.device_count() == 0:
        with gr.Row():
            gr.HTML("""
    <p style="background-color: red;"><big><big><big><b>⚠️To use FramePack, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack?duplicate=true">duplicate this space</a> and set a GPU with 30 GB VRAM.</b>

    You can't use FramePack directly here because this space runs on a CPU, which is not enough for FramePack. Please provide <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack/discussions/new">feedback</a> if you have issues.
    </big></big></big></p>
    """)
    gr.HTML(title_html)
    with gr.Row():
        with gr.Column():
            generation_mode = gr.Radio([["Text-to-Video", "text"], ["Image-to-Video", "image"], ["Video-to-Video", "video"]], label="Generation mode", value = "image")
            text_to_video_hint = gr.HTML("I discourage to use the Text-to-Video feature. You should rather generate an image with Flux and use Image-to-Video. You will save time.", visible=False)
            input_image = gr.Image(sources='upload', type="numpy", label="Image", height=320)
            input_video = gr.Video(sources='upload', label="Input Video", height=320, visible=False)
            timeless_prompt = gr.Textbox(label="Timeless prompt", info='Used on the whole duration of the generation', value='', placeholder="The creature starts to move, fast motion, focus motion, consistent arm, consistent position, fixed camera")
            prompt_number = gr.Slider(label="Timed prompt number", minimum=0, maximum=1000, value=0, step=1, info='Not for video extension')
            prompt_number.change(fn=handle_prompt_number_change, inputs=[], outputs=[])

            @gr.render(inputs=prompt_number)
            def show_split(prompt_number):
                for digit in range(prompt_number):
                    timed_prompt_id = gr.Textbox(value="timed_prompt_" + str(digit), visible=False)
                    timed_prompt = gr.Textbox(label="Timed prompt #" + str(digit + 1), elem_id="timed_prompt_" + str(digit), value="")
                    timed_prompt.change(fn=handle_timed_prompt_change, inputs=[timed_prompt_id, timed_prompt], outputs=[final_prompt])

            final_prompt = gr.Textbox(label="Final prompt", value='', info='Use ; to separate in time')
            timeless_prompt.change(fn=handle_timeless_prompt_change, inputs=[timeless_prompt], outputs=[final_prompt])
            total_second_length = gr.Slider(label="Video Length to Generate (seconds)", minimum=1, maximum=120, value=2, step=0.1)

            with gr.Row():
                start_button = gr.Button(value="🎥 Generate", variant="primary")
                start_button_video = gr.Button(value="🎥 Generate", variant="primary", visible=False)
                end_button = gr.Button(value="End Generation", variant="stop", interactive=False, visible=False)

            with gr.Accordion("Advanced settings", open=False):
                with gr.Row():
                    use_teacache = gr.Checkbox(label='Use TeaCache', value=False, info='Faster speed, but often makes hands and fingers slightly worse.')
                    no_resize = gr.Checkbox(label='Force Original Video Resolution (no Resizing) (only for video extension)', value=False, info='Might run out of VRAM (720p requires > 24GB VRAM).')

                n_prompt = gr.Textbox(label="Negative Prompt", value="Missing arm, unrealistic position, blurred, blurry", info='Requires using normal CFG (undistilled) instead of Distilled (set Distilled=1 and CFG > 1).')
                randomize_seed = gr.Checkbox(label='Randomize seed', value=True, info='If checked, the seed is always different')
                seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.int32).max, step=1, randomize=True)

                latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, info='Generate more frames at a time (larger chunks). Less degradation and better blending but higher VRAM cost. Should not change.')
                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1, info='Increase for more quality, especially if using high non-distilled CFG. Changing this value is not recommended.')
                batch = gr.Slider(label="Batch Size (Number of Videos)", minimum=1, maximum=1000, value=1, step=1, info='Generate multiple videos each with a different seed (only for video extension).')

                resolution = gr.Number(label="Resolution (max width or height)", value=640, precision=0, info='Only for video extension')

                # 20250506 pftq: Reduced default distilled guidance scale to improve adherence to input video
                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, info='Use this instead of Distilled for more detail/control + Negative Prompt (make sure Distilled set to 1). Doubles render time. Should not change.')
                gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='Prompt adherence at the cost of less details from the input video, but to a lesser extent than Context Frames; 3=blurred motions& & unsharped, 10=focus motion; 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)  # Should not change


                # 20250506 pftq: Renamed slider to Number of Context Frames and updated description
                num_clean_frames = gr.Slider(label="Number of Context Frames", minimum=2, maximum=10, value=5, step=1, info="Retain more video details but increase memory use. Reduce to 2 if memory issues (only for video extension).")

                default_vae = 32
                if high_vram:
                    default_vae = 128
                elif free_mem_gb>=20:
                    default_vae = 64

                vae_batch = gr.Slider(label="VAE Batch Size for Input Video", minimum=4, maximum=256, value=default_vae, step=4, info="Reduce if running out of memory. Increase for better quality frames during fast motion (only for video extension).")


                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')

    # 20250506 pftq: Updated inputs to include num_clean_frames
    ips = [input_image, final_prompt, generation_mode, n_prompt, randomize_seed, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf]
    ips_video = [input_video, final_prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch]

    start_button.click(fn = check_parameters, inputs = [
        generation_mode, input_image, input_video
    ], outputs = [], queue = False, show_progress = False).success(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
    start_button_video.click(fn = check_parameters, inputs = [
        generation_mode, input_image, input_video
    ], outputs = [], queue = False, show_progress = False).success(fn=process_video, inputs=ips_video, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button])
    end_button.click(fn=end_process)

    gr.Examples(
        examples = [
                [
                    "./img_examples/Example1.png", # input_image
                    "View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
                    "image", # generation_mode
                    "Missing arm, unrealistic position, blurred, blurry", # n_prompt
                    True, # randomize_seed
                    42, # seed
                    1, # total_second_length
                    9, # latent_window_size
                    25, # steps
                    1.0, # cfg
                    10.0, # gs
                    0.0, # rs
                    6, # gpu_memory_preservation
                    False, # use_teacache
                    16 # mp4_crf
                ],
                [
                    "./img_examples/Example1.png", # input_image
                    "A dolphin emerges from the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
                    "image", # generation_mode
                    "Missing arm, unrealistic position, blurred, blurry", # n_prompt
                    True, # randomize_seed
                    42, # seed
                    1, # total_second_length
                    9, # latent_window_size
                    25, # steps
                    1.0, # cfg
                    10.0, # gs
                    0.0, # rs
                    6, # gpu_memory_preservation
                    False, # use_teacache
                    16 # mp4_crf
                ],
                [
                    "./img_examples/Example1.png", # input_image
                    "We are sinking, photorealistic, realistic, intricate details, 8k, insanely detailed",
                    "image", # generation_mode
                    "Missing arm, unrealistic position, blurred, blurry", # n_prompt
                    True, # randomize_seed
                    42, # seed
                    1, # total_second_length
                    9, # latent_window_size
                    25, # steps
                    1.0, # cfg
                    10.0, # gs
                    0.0, # rs
                    6, # gpu_memory_preservation
                    False, # use_teacache
                    16 # mp4_crf
                ],
                [
                    "./img_examples/Example1.png", # input_image
                    "A boat is passing, photorealistic, realistic, intricate details, 8k, insanely detailed",
                    "image", # generation_mode
                    "Missing arm, unrealistic position, blurred, blurry", # n_prompt
                    True, # randomize_seed
                    42, # seed
                    1, # total_second_length
                    9, # latent_window_size
                    25, # steps
                    1.0, # cfg
                    10.0, # gs
                    0.0, # rs
                    6, # gpu_memory_preservation
                    False, # use_teacache
                    16 # mp4_crf
                ],
            ],
        run_on_click = True,
        fn = process,
	    inputs = ips,
        outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
        cache_examples = torch.cuda.device_count() > 0,
    )

    gr.Examples(
        examples = [
                [
                    "./img_examples/Example1.mp4", # input_video
                    "View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
                    "Missing arm, unrealistic position, blurred, blurry", # n_prompt
                    True, # randomize_seed
                    42, # seed
                    1, # batch
                    640, # resolution
                    1, # total_second_length
                    9, # latent_window_size
                    25, # steps
                    1.0, # cfg
                    10.0, # gs
                    0.0, # rs
                    6, # gpu_memory_preservation
                    False, # use_teacache
                    False, # no_resize
                    16, # mp4_crf
                    5, # num_clean_frames
                    default_vae
                ],
            ],
        run_on_click = True,
        fn = process_video,
	    inputs = ips_video,
	    outputs = [result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button],
        cache_examples = torch.cuda.device_count() > 0,
    )

    
    def handle_generation_mode_change(generation_mode_data):
        if generation_mode_data == "text":
            return [gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False)]
        elif generation_mode_data == "image":
            return [gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False)]
        elif generation_mode_data == "video":
            return [gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True)]

    generation_mode.change(
        fn=handle_generation_mode_change,
        inputs=[generation_mode],
        outputs=[text_to_video_hint, input_image, input_video, start_button, start_button_video]
    )

block.launch(mcp_server=False, ssr_mode=False)