Change model

app_v2v.py

@@ -1,653 +1,653 @@
-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 argparse
-import math
-import decord
-from tqdm import tqdm
-import pathlib
-from datetime import datetime
-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
-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
-
-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 > 80
-
-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()
-
-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
-    "lllyasviel/FramePack_F1_I2V_HY_20250503",
-    quantization_config=quant_config,
-    torch_dtype=torch.bfloat16,
-).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)
-
-@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.
-    """
-    video_path = str(pathlib.Path(video_path).resolve())
-    print(f"Processing video: {video_path}")
-
-    if device == "cuda" and not torch.cuda.is_available():
-        print("CUDA is not available, falling back to CPU")
-        device = "cpu"
-
-    try:
-        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
-
-        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}")
-
-        native_height, native_width = frames.shape[1], frames.shape[2]
-        print(f"Native video resolution: {native_width}x{native_height}")
-    
-        target_height = native_height if height is None else height
-        target_width = native_width if width is None else width
-    
-        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}")
-
-        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}")
-
-        input_image_np = processed_frames[0]
-
-        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}")
-        
-        input_video_pixels = frames_pt.cpu()
-
-        print(f"Moving tensor to device: {device}")
-        frames_pt = frames_pt.to(device)
-        print("Tensor moved to device")
-
-        print(f"Moving VAE to device: {device}")
-        vae.to(device)
-        print("VAE moved to device")
-
-        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:
-                    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)
-                    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
-        
-        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}")
-
-        start_latent = history_latents[:, :, :1]  # Shape: (1, channels, 1, height//8, width//8)
-        print(f"Start latent shape: {start_latent.shape}")
-
-        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
-
-def set_mp4_comments_imageio_ffmpeg(input_file, comments):
-    try:
-        ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
-        
-        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
-        ]
-        
-        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
-
-@spaces.GPU()
-@torch.no_grad()
-def worker(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:
-        if not high_vram:
-            unload_complete_models(
-                text_encoder, text_encoder_2, image_encoder, vae
-            )
-
-        # 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))
-
-        for idx in range(batch):
-            if idx>0:
-                seed = seed + 1
-            
-            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)
-    
-            history_latents = video_latents.cpu()
-            total_generated_latent_frames = history_latents.shape[2]
-            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)
-    
-                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
-    
-                # 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 = history_latents[:, :, -total_context_frames:, :, :] if total_context_frames > 0 else history_latents[:, :, :fallback_frame_count, :, :]  
-                if total_context_frames > 0:
-                    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
-                        clean_latents_4x = splits[split_idx] if num_4x_frames > 0 else history_latents[:, :, :fallback_frame_count, :, :]
-                        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, :, :]
-                        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[:, :, :fallback_frame_count, :, :]
-                        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 if num_2x_frames > 0 else 0
-                        clean_latents_1x = splits[split_idx] if effective_clean_frames > 0 and split_idx < len(splits) else history_latents[:, :, :fallback_frame_count, :, :] 
-                    else:
-                        clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :] 
-                else:
-                    clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :] 
-    
-                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])
-                  
-                  #if section_index == 0: 
-                    #extra_latents = 1  # Add up to 2 extra latent frames for smoother overlap to initial video
-                    #extra_pixel_frames = extra_latents * 4  # Approx. 4 pixel frames per latent
-                    #overlapped_frames = min(overlapped_frames + extra_pixel_frames, history_pixels.shape[2], section_latent_frames * 4)
-
-                  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))
-    except:
-        traceback.print_exc()
-
-        if not high_vram:
-            unload_complete_models(
-                text_encoder, text_encoder_2, image_encoder, vae
-            )
-
-    stream.output_queue.push(('end', None))
-    return
-    
-@spaces.GPU()
-def process(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):
-    global stream, high_vram
-    # 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, 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()
-with block:
-    gr.Markdown('# Framepack F1 (Video Extender)')
-    with gr.Row():
-        with gr.Column():
-            # 20250506 pftq: Changed to Video input from Image
-            input_video = gr.Video(sources='upload', label="Input Video", height=320)
-            prompt = gr.Textbox(label="Prompt", value='')
-            #example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Quick List', samples_per_page=1000, components=[prompt])
-            #example_quick_prompts.click(lambda x: x[0], inputs=[example_quick_prompts], outputs=prompt, show_progress=False, queue=False)
-
-            with gr.Row():
-                start_button = gr.Button(value="Start Generation")
-                end_button = gr.Button(value="End Generation", interactive=False)
-
-            with gr.Group():
-                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)', value=False, info='Might run out of VRAM (720p requires > 24GB VRAM).')
-
-                seed = gr.Number(label="Seed", value=31337, precision=0)
-
-                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, visible=False)
-
-                total_second_length = gr.Slider(label="Additional Video Length to Generate (Seconds)", minimum=1, maximum=120, value=5, step=0.1)
-                
-                gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=3.0, 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, visible=True, info='Use this instead of Distilled for more detail/control + Negative Prompt (make sure Distilled set to 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, visible=False)  # Should not change
-
-                n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=True, 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='Increase for more quality, especially if using high non-distilled CFG.')
-
-                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.")
-                
-                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.")
-
-                latent_window_size = gr.Slider(label="Latent Window Size", minimum=9, maximum=33, value=9, step=1, visible=True, info='Generate more frames at a time (larger chunks). Less degradation and better blending 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.Column():
-            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
-            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
-            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
-            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
-
-    gr.HTML("""
-        <div style="text-align:center; margin-top:20px;">Share your results and find ideas at the <a href="https://x.com/search?q=framepack&f=live" target="_blank">FramePack Twitter (X) thread</a></div>
-    """)
-
-    ips = [input_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]
-    start_button.click(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
-    end_button.click(fn=end_process)
-
-block.launch(ssr_mode=False)
+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 argparse
+import math
+import decord
+from tqdm import tqdm
+import pathlib
+from datetime import datetime
+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
+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
+
+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 > 80
+
+print(f'Free VRAM {free_mem_gb} GB')
+print(f'High-VRAM Mode: {high_vram}')
+
+
+
+text_encoder = LlamaModel.from_pretrained("Fabrice-TIERCELIN/HunyuanVideo", subfolder='text_encoder', torch_dtype=torch.float16).cpu()
+text_encoder_2 = CLIPTextModel.from_pretrained("Fabrice-TIERCELIN/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=torch.float16).cpu()
+tokenizer = LlamaTokenizerFast.from_pretrained("Fabrice-TIERCELIN/HunyuanVideo", subfolder='tokenizer')
+tokenizer_2 = CLIPTokenizer.from_pretrained("Fabrice-TIERCELIN/HunyuanVideo", subfolder='tokenizer_2')
+vae = AutoencoderKLHunyuanVideo.from_pretrained("Fabrice-TIERCELIN/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()
+
+quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
+transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
+    "lllyasviel/FramePack_F1_I2V_HY_20250503",
+    quantization_config=quant_config,
+    torch_dtype=torch.bfloat16,
+).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)
+
+@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.
+    """
+    video_path = str(pathlib.Path(video_path).resolve())
+    print(f"Processing video: {video_path}")
+
+    if device == "cuda" and not torch.cuda.is_available():
+        print("CUDA is not available, falling back to CPU")
+        device = "cpu"
+
+    try:
+        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
+
+        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}")
+
+        native_height, native_width = frames.shape[1], frames.shape[2]
+        print(f"Native video resolution: {native_width}x{native_height}")
+    
+        target_height = native_height if height is None else height
+        target_width = native_width if width is None else width
+    
+        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}")
+
+        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}")
+
+        input_image_np = processed_frames[0]
+
+        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}")
+        
+        input_video_pixels = frames_pt.cpu()
+
+        print(f"Moving tensor to device: {device}")
+        frames_pt = frames_pt.to(device)
+        print("Tensor moved to device")
+
+        print(f"Moving VAE to device: {device}")
+        vae.to(device)
+        print("VAE moved to device")
+
+        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:
+                    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)
+                    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
+        
+        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}")
+
+        start_latent = history_latents[:, :, :1]  # Shape: (1, channels, 1, height//8, width//8)
+        print(f"Start latent shape: {start_latent.shape}")
+
+        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
+
+def set_mp4_comments_imageio_ffmpeg(input_file, comments):
+    try:
+        ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
+        
+        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
+        ]
+        
+        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
+
+@spaces.GPU()
+@torch.no_grad()
+def worker(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:
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae
+            )
+
+        # 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))
+
+        for idx in range(batch):
+            if idx>0:
+                seed = seed + 1
+            
+            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)
+    
+            history_latents = video_latents.cpu()
+            total_generated_latent_frames = history_latents.shape[2]
+            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)
+    
+                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
+    
+                # 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 = history_latents[:, :, -total_context_frames:, :, :] if total_context_frames > 0 else history_latents[:, :, :fallback_frame_count, :, :]  
+                if total_context_frames > 0:
+                    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
+                        clean_latents_4x = splits[split_idx] if num_4x_frames > 0 else history_latents[:, :, :fallback_frame_count, :, :]
+                        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, :, :]
+                        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[:, :, :fallback_frame_count, :, :]
+                        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 if num_2x_frames > 0 else 0
+                        clean_latents_1x = splits[split_idx] if effective_clean_frames > 0 and split_idx < len(splits) else history_latents[:, :, :fallback_frame_count, :, :] 
+                    else:
+                        clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :] 
+                else:
+                    clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :] 
+    
+                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])
+                  
+                  #if section_index == 0: 
+                    #extra_latents = 1  # Add up to 2 extra latent frames for smoother overlap to initial video
+                    #extra_pixel_frames = extra_latents * 4  # Approx. 4 pixel frames per latent
+                    #overlapped_frames = min(overlapped_frames + extra_pixel_frames, history_pixels.shape[2], section_latent_frames * 4)
+
+                  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))
+    except:
+        traceback.print_exc()
+
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae
+            )
+
+    stream.output_queue.push(('end', None))
+    return
+    
+@spaces.GPU()
+def process(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):
+    global stream, high_vram
+    # 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, 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()
+with block:
+    gr.Markdown('# Framepack F1 (Video Extender)')
+    with gr.Row():
+        with gr.Column():
+            # 20250506 pftq: Changed to Video input from Image
+            input_video = gr.Video(sources='upload', label="Input Video", height=320)
+            prompt = gr.Textbox(label="Prompt", value='')
+            #example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Quick List', samples_per_page=1000, components=[prompt])
+            #example_quick_prompts.click(lambda x: x[0], inputs=[example_quick_prompts], outputs=prompt, show_progress=False, queue=False)
+
+            with gr.Row():
+                start_button = gr.Button(value="Start Generation")
+                end_button = gr.Button(value="End Generation", interactive=False)
+
+            with gr.Group():
+                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)', value=False, info='Might run out of VRAM (720p requires > 24GB VRAM).')
+
+                seed = gr.Number(label="Seed", value=31337, precision=0)
+
+                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, visible=False)
+
+                total_second_length = gr.Slider(label="Additional Video Length to Generate (Seconds)", minimum=1, maximum=120, value=5, step=0.1)
+                
+                gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=3.0, 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, visible=True, info='Use this instead of Distilled for more detail/control + Negative Prompt (make sure Distilled set to 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, visible=False)  # Should not change
+
+                n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=True, 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='Increase for more quality, especially if using high non-distilled CFG.')
+
+                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.")
+                
+                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.")
+
+                latent_window_size = gr.Slider(label="Latent Window Size", minimum=9, maximum=33, value=9, step=1, visible=True, info='Generate more frames at a time (larger chunks). Less degradation and better blending 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.Column():
+            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
+            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
+            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+
+    gr.HTML("""
+        <div style="text-align:center; margin-top:20px;">Share your results and find ideas at the <a href="https://x.com/search?q=framepack&f=live" target="_blank">FramePack Twitter (X) thread</a></div>
+    """)
+
+    ips = [input_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]
+    start_button.click(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
+    end_button.click(fn=end_process)
+
+block.launch(ssr_mode=False)