app_endframe.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 argparse
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
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

parser = argparse.ArgumentParser()
parser.add_argument('--share', action='store_true')
parser.add_argument("--server", type=str, default='0.0.0.0')
parser.add_argument("--port", type=int, required=False)
parser.add_argument("--inbrowser", action='store_true')
args = parser.parse_args()

print(args)

free_mem_gb = get_cuda_free_memory_gb(gpu)
high_vram = free_mem_gb > 60

print(f'Free VRAM {free_mem_gb} GB')
print(f'High-VRAM Mode: {high_vram}')

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 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)

input_video_debug_value = prompt_debug_value = total_second_length_debug_value = None

# 20250506 pftq: Added function to encode input video frames into latents
@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]
        end_of_input_video_image_np = processed_frames[-1]

        # 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)
        end_of_input_video_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, end_of_input_video_latent, end_of_input_video_image_np

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

    
# 20250507 pftq: New function to encode a single image (end frame)
@torch.no_grad()
def image_encode(image_np, target_width, target_height, vae, image_encoder, feature_extractor, device="cuda"):
    """

    Encode a single image into a latent and compute its CLIP vision embedding.

    

    Args:

        image_np: Input image as numpy array.

        target_width, target_height: Exact resolution to resize the image to (matches start frame).

        vae: AutoencoderKLHunyuanVideo model.

        image_encoder: SiglipVisionModel for CLIP vision encoding.

        feature_extractor: SiglipImageProcessor for preprocessing.

        device: Device for computation (e.g., "cuda").

    

    Returns:

        latent: Latent representation of the image (shape: [1, channels, 1, height//8, width//8]).

        clip_embedding: CLIP vision embedding of the image.

        processed_image_np: Processed image as numpy array (after resizing).

    """
    # 20250507 pftq: Process end frame with exact start frame dimensions
    print("Processing end frame...")
    try:
        print(f"Using exact start frame resolution for end frame: {target_width}x{target_height}")

        # Resize and preprocess image to match start frame
        processed_image_np = resize_and_center_crop(image_np, target_width=target_width, target_height=target_height)

        # Convert to tensor and normalize
        image_pt = torch.from_numpy(processed_image_np).float() / 127.5 - 1
        image_pt = image_pt.permute(2, 0, 1).unsqueeze(0).unsqueeze(2)  # Shape: [1, channels, 1, height, width]
        image_pt = image_pt.to(device)

        # Move VAE to device
        vae.to(device)

        # Encode to latent
        latent = vae_encode(image_pt, vae)
        print(f"image_encode vae output shape: {latent.shape}")

        # Move image encoder to device
        image_encoder.to(device)

        # Compute CLIP vision embedding
        clip_embedding = hf_clip_vision_encode(processed_image_np, feature_extractor, image_encoder).last_hidden_state

        # Move models back to CPU and clear cache
        if device == "cuda":
            vae.to(cpu)
            image_encoder.to(cpu)
            torch.cuda.empty_cache()
            print("VAE and image encoder moved back to CPU, CUDA cache cleared")

        print(f"End latent shape: {latent.shape}")
        return latent, clip_embedding, processed_image_np

    except Exception as e:
        print(f"Error in image_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

# 20250506 pftq: Modified worker to accept video input, and clean frame count
@torch.no_grad()
def worker(input_video, end_frame, end_frame_weight, 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
        start_latent, input_image_np, video_latents, fps, height, width, input_video_pixels, end_of_input_video_latent, end_of_input_video_image_np = 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
        start_embedding = image_encoder_last_hidden_state
        
        end_of_input_video_output = hf_clip_vision_encode(end_of_input_video_image_np, feature_extractor, image_encoder)
        end_of_input_video_last_hidden_state = end_of_input_video_output.last_hidden_state
        end_of_input_video_embedding = end_of_input_video_last_hidden_state

        # 20250507 pftq: Process end frame if provided
        end_latent = None
        end_clip_embedding = None
        if end_frame is not None:
            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'End frame encoding ...'))))
            end_latent, end_clip_embedding, _ = image_encode(
                end_frame, target_width=width, target_height=height, vae=vae,
                image_encoder=image_encoder, feature_extractor=feature_extractor, device=gpu
            )

        # 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)
        end_of_input_video_embedding = end_of_input_video_embedding.to(transformer.dtype)

        # 20250509 pftq: Restored original placement of total_latent_sections after video_encode
        total_latent_sections = (total_second_length * fps) / (latent_window_size * 4)
        total_latent_sections = int(max(round(total_latent_sections), 1))

        for idx in range(batch):
            if batch > 1:
                print(f"Beginning video {idx+1} of {batch} with seed {seed} ")
            
            job_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+f"_framepack-videoinput-endframe_{width}-{total_second_length}sec_seed-{seed}_steps-{steps}_distilled-{gs}_cfg-{cfg}"
            
            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
    
            rnd = torch.Generator("cpu").manual_seed(seed)
    
            history_latents = video_latents.cpu()
            history_pixels = None
            total_generated_latent_frames = 0
            previous_video = None
            
            
            # 20250509 Generate backwards with end frame for better end frame anchoring
            if total_latent_sections > 4:
                latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0]
            else:
                latent_paddings = list(reversed(range(total_latent_sections)))

            for section_index, latent_padding in enumerate(latent_paddings):
                is_start_of_video = latent_padding == 0
                is_end_of_video = latent_padding == latent_paddings[0]
                latent_padding_size = latent_padding * latent_window_size

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

                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):
                    try:
                        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}. Generating part {total_latent_sections - section_index} of {total_latent_sections} backward...'
                        stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
                    except ConnectionResetError as e:
                        print(f"Suppressed ConnectionResetError in callback: {e}")
                    return

                # 20250509 pftq: Dynamic frame allocation like original num_clean_frames, fix split error
                available_frames = video_latents.shape[2] if is_start_of_video else history_latents.shape[2]
                if is_start_of_video:
                    effective_clean_frames = 1 # avoid jumpcuts from input video
                else:
                    effective_clean_frames = max(0, num_clean_frames - 1) if num_clean_frames > 1 else 1
                clean_latent_pre_frames = effective_clean_frames 
                num_2x_frames = min(2, max(1, available_frames - clean_latent_pre_frames - 1)) if available_frames > clean_latent_pre_frames + 1 else 1
                num_4x_frames = min(16, max(1, available_frames - clean_latent_pre_frames - num_2x_frames)) if available_frames > clean_latent_pre_frames + num_2x_frames else 1
                total_context_frames = num_2x_frames + num_4x_frames
                total_context_frames = min(total_context_frames, available_frames - clean_latent_pre_frames)

                # 20250511 pftq: Dynamically adjust post_frames based on clean_latents_post
                post_frames = 1 if is_end_of_video and end_latent is not None else effective_clean_frames  # 20250511 pftq: Single frame for end_latent, otherwise padding causes still image
                indices = torch.arange(0, clean_latent_pre_frames + latent_padding_size + latent_window_size + post_frames + num_2x_frames + num_4x_frames).unsqueeze(0)
                clean_latent_indices_pre, blank_indices, latent_indices, clean_latent_indices_post, clean_latent_2x_indices, clean_latent_4x_indices = indices.split(
                    [clean_latent_pre_frames, latent_padding_size, latent_window_size, post_frames, num_2x_frames, num_4x_frames], dim=1
                )
                clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1)

                # 20250509 pftq: Split context frames dynamically for 2x and 4x only
                context_frames = history_latents[:, :, -(total_context_frames + clean_latent_pre_frames):-clean_latent_pre_frames, :, :] if total_context_frames > 0 else history_latents[:, :, :1, :, :]
                split_sizes = [num_4x_frames, num_2x_frames]
                split_sizes = [s for s in split_sizes if s > 0]
                if split_sizes and context_frames.shape[2] >= sum(split_sizes):
                    splits = context_frames.split(split_sizes, dim=2)
                    split_idx = 0
                    clean_latents_4x = splits[split_idx] if num_4x_frames > 0 else history_latents[:, :, :1, :, :]
                    split_idx += 1 if num_4x_frames > 0 else 0
                    clean_latents_2x = splits[split_idx] if num_2x_frames > 0 and split_idx < len(splits) else history_latents[:, :, :1, :, :]
                else:
                    clean_latents_4x = clean_latents_2x = history_latents[:, :, :1, :, :]

                clean_latents_pre = video_latents[:, :, -min(effective_clean_frames, video_latents.shape[2]):].to(history_latents)  # smoother motion but jumpcuts if end frame is too different, must change clean_latent_pre_frames to effective_clean_frames also
                clean_latents_post = history_latents[:, :, :min(effective_clean_frames, history_latents.shape[2]), :, :] # smoother motion, must change post_frames to effective_clean_frames also
                    
                if is_end_of_video:
                    clean_latents_post = torch.zeros_like(end_of_input_video_latent).to(history_latents)
                
                # 20250509 pftq: handle end frame if available
                if end_latent is not None:
                    #current_end_frame_weight = end_frame_weight * (latent_padding / latent_paddings[0])
                    #current_end_frame_weight = current_end_frame_weight * 0.5 + 0.5
                    current_end_frame_weight = end_frame_weight # changing this over time introduces discontinuity
                    # 20250511 pftq: Removed end frame weight adjustment as it has no effect
                    image_encoder_last_hidden_state = (1 - current_end_frame_weight) * end_of_input_video_embedding + end_clip_embedding * current_end_frame_weight
                    image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
                    
                    # 20250511 pftq: Use end_latent only
                    if is_end_of_video:
                        clean_latents_post = end_latent.to(history_latents)[:, :, :1, :, :]  # Ensure single frame
                    
                # 20250511 pftq: Pad clean_latents_pre to match clean_latent_pre_frames if needed
                if clean_latents_pre.shape[2] < clean_latent_pre_frames:
                    clean_latents_pre = clean_latents_pre.repeat(1, 1, clean_latent_pre_frames // clean_latents_pre.shape[2], 1, 1)
                # 20250511 pftq: Pad clean_latents_post to match post_frames if needed
                if clean_latents_post.shape[2] < post_frames:
                    clean_latents_post = clean_latents_post.repeat(1, 1, post_frames // clean_latents_post.shape[2], 1, 1)
                 
                clean_latents = torch.cat([clean_latents_pre, clean_latents_post], dim=2)

                max_frames = min(latent_window_size * 4 - 3, history_latents.shape[2] * 4)
                print(f"Generating video {idx+1} of {batch} with seed {seed}, part {total_latent_sections - section_index} of {total_latent_sections} backward")
                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,
                )

                if is_start_of_video:
                    generated_latents = torch.cat([video_latents[:, :, -1:].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_start_of_video 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=fps, crf=mp4_crf)
                print(f"Latest video saved: {output_filename}")
                set_mp4_comments_imageio_ffmpeg(output_filename, f"Prompt: {prompt} | Negative Prompt: {n_prompt}")
                print(f"Prompt saved to mp4 metadata comments: {output_filename}")

                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))

                if is_start_of_video:
                    break

            history_pixels = torch.cat([input_video_pixels, history_pixels], dim=2)
            #overlapped_frames = latent_window_size * 4 - 3
            #history_pixels = soft_append_bcthw(input_video_pixels, history_pixels, overlapped_frames)

            output_filename = os.path.join(outputs_folder, f'{job_id}_final.mp4')
            save_bcthw_as_mp4(history_pixels, output_filename, fps=fps, crf=mp4_crf)
            print(f"Final video with input blend saved: {output_filename}")
            set_mp4_comments_imageio_ffmpeg(output_filename, f"Prompt: {prompt} | Negative Prompt: {n_prompt}")
            print(f"Prompt saved to mp4 metadata comments: {output_filename}")
            stream.output_queue.push(('file', output_filename))
    
            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
    
# 20250506 pftq: Modified process to pass clean frame count, etc
def get_duration(

    input_video, end_frame, end_frame_weight, 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 total_second_length_debug_value
    if total_second_length_debug_value is not None:
        return min(total_second_length_debug_value * 60 * 2, 600)
    return total_second_length * 60 * 2

@spaces.GPU(duration=get_duration)
def process(

            input_video, end_frame, end_frame_weight, 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, input_video_debug_value, prompt_debug_value, total_second_length_debug_value

    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 input_video_debug_value is not None or prompt_debug_value is not None or total_second_length_debug_value is not None:
        input_video = input_video_debug_value
        prompt = prompt_debug_value
        total_second_length = total_second_length_debug_value
        input_video_debug_value = prompt_debug_value = total_second_length_debug_value = 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, input_video, end_frame, end_frame_weight, 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')

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(
    max_size=10  # 20250507 pftq: Limit queue size
)
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/SUPIR?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/SUPIR/discussions/new">feedback</a> if you have issues.

    </big></big></big></p>

    """)
    # 20250506 pftq: Updated title to reflect video input functionality
    gr.Markdown('# Framepack with Video Input (Video Extension) + End Frame')
    with gr.Row():
        with gr.Column():
        
            # 20250506 pftq: Changed to Video input from Image
            with gr.Row():
                input_video = gr.Video(sources='upload', label="Input Video", height=320)
                with gr.Column():
                  # 20250507 pftq: Added end_frame + weight
                  end_frame = gr.Image(sources='upload', type="numpy", label="End Frame (Optional) - Reduce context frames if very different from input video or if it is jumpcutting/slowing to still image.", height=320)
                  end_frame_weight = gr.Slider(label="End Frame Weight", minimum=0.0, maximum=1.0, value=1.0, step=0.01, info='Reduce to treat more as a reference image; no effect')
                
            prompt = gr.Textbox(label="Prompt", value='')

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

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

                    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)

                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.')

                resolution = gr.Number(label="Resolution (max width or height)", value=640, precision=0)

                total_second_length = gr.Slider(label="Additional Video Length to Generate (Seconds)", minimum=1, maximum=120, value=5, step=0.1)
                
                # 20250506 pftq: Reduced default distilled guidance scale to improve adherence to input video
                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.')
                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, info='Use instead of Distilled for more detail/control + Negative Prompt (make sure Distilled=1). Doubles render time.')  # Should not change
                rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01)  # Should not change

                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).') 
                
                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1, info='Expensive. Increase for more quality, especially if using high non-distilled CFG.')
                
                # 20250506 pftq: Renamed slider to Number of Context Frames and updated description
                num_clean_frames = gr.Slider(label="Number of Context Frames (Adherence to Video)", minimum=2, maximum=10, value=5, step=1, info="Expensive. Retain more video details. Reduce if memory issues or motion too restricted (jumpcut, ignoring prompt, still).")

                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="Expensive. Increase for better quality frames during fast motion. Reduce if running out of memory")

                latent_window_size = gr.Slider(label="Latent Window Size", minimum=9, maximum=49, value=9, step=1, info='Expensive. Generate more frames at a time (larger chunks). Less degradation but higher VRAM cost.') 

                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.Accordion("Debug", open=False):
                input_video_debug = gr.Video(sources='upload', label="Input Video Debug", height=320)
                prompt_debug = gr.Textbox(label="Prompt Debug", value='')
                total_second_length_debug = gr.Slider(label="Additional Video Length to Generate (Seconds) Debug", minimum=1, maximum=120, value=5, step=0.1)

        with gr.Column():
            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
            progress_bar = gr.HTML('', elem_classes='no-generating-animation')

    with gr.Row(visible=False):
        gr.Examples(
        examples = [
                [
                    "./img_examples/Example1.mp4", # input_video
                    None, # end_frame
                    0.0, # end_frame_weight
                    "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
                    True, # use_teacache
                    False, # no_resize
                    16, # mp4_crf
                    5, # num_clean_frames
                    default_vae
                ],
            ],
        run_on_click = True,
        fn = process,
	    inputs = [input_video, end_frame, end_frame_weight, 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],
	    outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
        cache_examples = True,
    )
        
    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>

    """)

    # 20250506 pftq: Updated inputs to include num_clean_frames
    ips = [input_video, end_frame, end_frame_weight, 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=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
    end_button.click(fn=end_process)

    
    def handle_field_debug_change(input_video_debug_data, prompt_debug_data, total_second_length_debug_data):
        global input_video_debug_value, prompt_debug_value, total_second_length_debug_value
        input_video_debug_value = input_video_debug_data
        prompt_debug_value = prompt_debug_data
        total_second_length_debug_value = total_second_length_debug_data
        return []
    
    input_video_debug.upload(
        fn=handle_field_debug_change,
        inputs=[input_video_debug, prompt_debug, total_second_length_debug],
        outputs=[]
    )
    
    prompt_debug.change(
        fn=handle_field_debug_change,
        inputs=[input_video_debug, prompt_debug, total_second_length_debug],
        outputs=[]
    )
    
    total_second_length_debug.change(
        fn=handle_field_debug_change,
        inputs=[input_video_debug, prompt_debug, total_second_length_debug],
        outputs=[]
    )

block.launch(share=True)