This Pull Request upgrades to the modern AI FramePack

.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Example1.mp4 filter=lfs diff=lfs merge=lfs -text
+img_examples/Example1.mp4 filter=lfs diff=lfs merge=lfs -text
+img_examples/Example1.png filter=lfs diff=lfs merge=lfs -text
+img_examples/Example2.webp filter=lfs diff=lfs merge=lfs -text
+img_examples/Example3.jpg filter=lfs diff=lfs merge=lfs -text
+img_examples/Example4.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,21 @@
----
-title: Openai Sora
-emoji: 🚀🎥
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 4.31.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Generate incredible videos using Openai Sora
----
-
+---
+title: FramePack/HunyuanVideo
+emoji: 🎥
+colorFrom: pink
+colorTo: gray
+sdk: gradio
+sdk_version: 5.29.1
+app_file: app.py
+license: apache-2.0
+short_description: Text-to-Video/Image-to-Video/Video extender (timed prompt)
+tags:
+  - Image-to-Video
+  - Image-2-Video
+  - Img-to-Vid
+  - Img-2-Vid
+  - language models
+  - LLMs
+suggested_hardware: zero-a10g
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,52 +1,1393 @@
+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')))
+
+try:
+    import spaces
+except:
+    print("Not on HuggingFace")
 import gradio as gr
-import spaces
 import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-import cv2
+import traceback
+import einops
+import safetensors.torch as sf
 import numpy as np
+import random
+import time
+import math
+# 20250506 pftq: Added for video input loading
+import decord
+# 20250506 pftq: Added for progress bars in video_encode
+from tqdm import tqdm
+# 20250506 pftq: Normalize file paths for Windows compatibility
+import pathlib
+# 20250506 pftq: for easier to read timestamp
+from datetime import datetime
+# 20250508 pftq: for saving prompt to mp4 comments metadata
+import imageio_ffmpeg
+import tempfile
+import shutil
+import subprocess
+
+from PIL import Image
+from diffusers import AutoencoderKLHunyuanVideo
+from transformers import LlamaModel, CLIPTextModel, LlamaTokenizerFast, CLIPTokenizer
+from diffusers_helper.hunyuan import encode_prompt_conds, vae_decode, vae_encode, vae_decode_fake
+from diffusers_helper.utils import save_bcthw_as_mp4, crop_or_pad_yield_mask, soft_append_bcthw, resize_and_center_crop, state_dict_weighted_merge, state_dict_offset_merge, generate_timestamp
+from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
+from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
+if torch.cuda.device_count() > 0:
+    from diffusers_helper.memory import cpu, gpu, get_cuda_free_memory_gb, move_model_to_device_with_memory_preservation, offload_model_from_device_for_memory_preservation, fake_diffusers_current_device, DynamicSwapInstaller, unload_complete_models, load_model_as_complete
+from diffusers_helper.thread_utils import AsyncStream, async_run
+from diffusers_helper.gradio.progress_bar import make_progress_bar_css, make_progress_bar_html
+from transformers import SiglipImageProcessor, SiglipVisionModel
+from diffusers_helper.clip_vision import hf_clip_vision_encode
+from diffusers_helper.bucket_tools import find_nearest_bucket
+from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, HunyuanVideoTransformer3DModel, HunyuanVideoPipeline
+import pillow_heif
+
+pillow_heif.register_heif_opener()
+
+high_vram = False
+free_mem_gb = 0
+
+if torch.cuda.device_count() > 0:
+    free_mem_gb = get_cuda_free_memory_gb(gpu)
+    high_vram = free_mem_gb > 60
+
+    print(f'Free VRAM {free_mem_gb} GB')
+    print(f'High-VRAM Mode: {high_vram}')
+    
+    text_encoder = LlamaModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder', torch_dtype=torch.float16).cpu()
+    text_encoder_2 = CLIPTextModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='text_encoder_2', torch_dtype=torch.float16).cpu()
+    tokenizer = LlamaTokenizerFast.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer')
+    tokenizer_2 = CLIPTokenizer.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='tokenizer_2')
+    vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder='vae', torch_dtype=torch.float16).cpu()
+
+    feature_extractor = SiglipImageProcessor.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='feature_extractor')
+    image_encoder = SiglipVisionModel.from_pretrained("lllyasviel/flux_redux_bfl", subfolder='image_encoder', torch_dtype=torch.float16).cpu()
+
+    transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePack_F1_I2V_HY_20250503', torch_dtype=torch.bfloat16).cpu()
+
+    vae.eval()
+    text_encoder.eval()
+    text_encoder_2.eval()
+    image_encoder.eval()
+    transformer.eval()
+
+    if not high_vram:
+        vae.enable_slicing()
+        vae.enable_tiling()
+
+    transformer.high_quality_fp32_output_for_inference = True
+    print('transformer.high_quality_fp32_output_for_inference = True')
+
+    transformer.to(dtype=torch.bfloat16)
+    vae.to(dtype=torch.float16)
+    image_encoder.to(dtype=torch.float16)
+    text_encoder.to(dtype=torch.float16)
+    text_encoder_2.to(dtype=torch.float16)
+
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    text_encoder_2.requires_grad_(False)
+    image_encoder.requires_grad_(False)
+    transformer.requires_grad_(False)
+
+    if not high_vram:
+        # DynamicSwapInstaller is same as huggingface's enable_sequential_offload but 3x faster
+        DynamicSwapInstaller.install_model(transformer, device=gpu)
+        DynamicSwapInstaller.install_model(text_encoder, device=gpu)
+    else:
+        text_encoder.to(gpu)
+        text_encoder_2.to(gpu)
+        image_encoder.to(gpu)
+        vae.to(gpu)
+        transformer.to(gpu)
+
+stream = AsyncStream()
+
+outputs_folder = './outputs/'
+os.makedirs(outputs_folder, exist_ok=True)
+
+default_local_storage = {
+        "generation-mode": "image",
+    }
+
+@torch.no_grad()
+def video_encode(video_path, resolution, no_resize, vae, vae_batch_size=16, device="cuda", width=None, height=None):
+    """
+    Encode a video into latent representations using the VAE.
+
+    Args:
+        video_path: Path to the input video file.
+        vae: AutoencoderKLHunyuanVideo model.
+        height, width: Target resolution for resizing frames.
+        vae_batch_size: Number of frames to process per batch.
+        device: Device for computation (e.g., "cuda").
+
+    Returns:
+        start_latent: Latent of the first frame (for compatibility with original code).
+        input_image_np: First frame as numpy array (for CLIP vision encoding).
+        history_latents: Latents of all frames (shape: [1, channels, frames, height//8, width//8]).
+        fps: Frames per second of the input video.
+    """
+    # 20250506 pftq: Normalize video path for Windows compatibility
+    video_path = str(pathlib.Path(video_path).resolve())
+    print(f"Processing video: {video_path}")
+
+    # 20250506 pftq: Check CUDA availability and fallback to CPU if needed
+    if device == "cuda" and not torch.cuda.is_available():
+        print("CUDA is not available, falling back to CPU")
+        device = "cpu"
+
+    try:
+        # 20250506 pftq: Load video and get FPS
+        print("Initializing VideoReader...")
+        vr = decord.VideoReader(video_path)
+        fps = vr.get_avg_fps()  # Get input video FPS
+        num_real_frames = len(vr)
+        print(f"Video loaded: {num_real_frames} frames, FPS: {fps}")
+
+        # Truncate to nearest latent size (multiple of 4)
+        latent_size_factor = 4
+        num_frames = (num_real_frames // latent_size_factor) * latent_size_factor
+        if num_frames != num_real_frames:
+            print(f"Truncating video from {num_real_frames} to {num_frames} frames for latent size compatibility")
+        num_real_frames = num_frames
+
+        # 20250506 pftq: Read frames
+        print("Reading video frames...")
+        frames = vr.get_batch(range(num_real_frames)).asnumpy()  # Shape: (num_real_frames, height, width, channels)
+        print(f"Frames read: {frames.shape}")
+
+        # 20250506 pftq: Get native video resolution
+        native_height, native_width = frames.shape[1], frames.shape[2]
+        print(f"Native video resolution: {native_width}x{native_height}")
+
+        # 20250506 pftq: Use native resolution if height/width not specified, otherwise use provided values
+        target_height = native_height if height is None else height
+        target_width = native_width if width is None else width
+
+        # 20250506 pftq: Adjust to nearest bucket for model compatibility
+        if not no_resize:
+            target_height, target_width = find_nearest_bucket(target_height, target_width, resolution=resolution)
+            print(f"Adjusted resolution: {target_width}x{target_height}")
+        else:
+            print(f"Using native resolution without resizing: {target_width}x{target_height}")
+
+        # 20250506 pftq: Preprocess frames to match original image processing
+        processed_frames = []
+        for i, frame in enumerate(frames):
+            #print(f"Preprocessing frame {i+1}/{num_frames}")
+            frame_np = resize_and_center_crop(frame, target_width=target_width, target_height=target_height)
+            processed_frames.append(frame_np)
+        processed_frames = np.stack(processed_frames)  # Shape: (num_real_frames, height, width, channels)
+        print(f"Frames preprocessed: {processed_frames.shape}")
+
+        # 20250506 pftq: Save first frame for CLIP vision encoding
+        input_image_np = processed_frames[0]
+
+        # 20250506 pftq: Convert to tensor and normalize to [-1, 1]
+        print("Converting frames to tensor...")
+        frames_pt = torch.from_numpy(processed_frames).float() / 127.5 - 1
+        frames_pt = frames_pt.permute(0, 3, 1, 2)  # Shape: (num_real_frames, channels, height, width)
+        frames_pt = frames_pt.unsqueeze(0)  # Shape: (1, num_real_frames, channels, height, width)
+        frames_pt = frames_pt.permute(0, 2, 1, 3, 4)  # Shape: (1, channels, num_real_frames, height, width)
+        print(f"Tensor shape: {frames_pt.shape}")
+
+        # 20250507 pftq: Save pixel frames for use in worker
+        input_video_pixels = frames_pt.cpu()
+
+        # 20250506 pftq: Move to device
+        print(f"Moving tensor to device: {device}")
+        frames_pt = frames_pt.to(device)
+        print("Tensor moved to device")
+
+        # 20250506 pftq: Move VAE to device
+        print(f"Moving VAE to device: {device}")
+        vae.to(device)
+        print("VAE moved to device")
+
+        # 20250506 pftq: Encode frames in batches
+        print(f"Encoding input video frames in VAE batch size {vae_batch_size} (reduce if memory issues here or if forcing video resolution)")
+        latents = []
+        vae.eval()
+        with torch.no_grad():
+            for i in tqdm(range(0, frames_pt.shape[2], vae_batch_size), desc="Encoding video frames", mininterval=0.1):
+                #print(f"Encoding batch {i//vae_batch_size + 1}: frames {i} to {min(i + vae_batch_size, frames_pt.shape[2])}")
+                batch = frames_pt[:, :, i:i + vae_batch_size]  # Shape: (1, channels, batch_size, height, width)
+                try:
+                    # 20250506 pftq: Log GPU memory before encoding
+                    if device == "cuda":
+                        free_mem = torch.cuda.memory_allocated() / 1024**3
+                        #print(f"GPU memory before encoding: {free_mem:.2f} GB")
+                    batch_latent = vae_encode(batch, vae)
+                    # 20250506 pftq: Synchronize CUDA to catch issues
+                    if device == "cuda":
+                        torch.cuda.synchronize()
+                        #print(f"GPU memory after encoding: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
+                    latents.append(batch_latent)
+                    #print(f"Batch encoded, latent shape: {batch_latent.shape}")
+                except RuntimeError as e:
+                    print(f"Error during VAE encoding: {str(e)}")
+                    if device == "cuda" and "out of memory" in str(e).lower():
+                        print("CUDA out of memory, try reducing vae_batch_size or using CPU")
+                    raise
+
+        # 20250506 pftq: Concatenate latents
+        print("Concatenating latents...")
+        history_latents = torch.cat(latents, dim=2)  # Shape: (1, channels, frames, height//8, width//8)
+        print(f"History latents shape: {history_latents.shape}")
+
+        # 20250506 pftq: Get first frame's latent
+        start_latent = history_latents[:, :, :1]  # Shape: (1, channels, 1, height//8, width//8)
+        print(f"Start latent shape: {start_latent.shape}")
+
+        # 20250506 pftq: Move VAE back to CPU to free GPU memory
+        if device == "cuda":
+            vae.to(cpu)
+            torch.cuda.empty_cache()
+            print("VAE moved back to CPU, CUDA cache cleared")
+
+        return start_latent, input_image_np, history_latents, fps, target_height, target_width, input_video_pixels
+
+    except Exception as e:
+        print(f"Error in video_encode: {str(e)}")
+        raise
+
+# 20250508 pftq: for saving prompt to mp4 metadata comments
+def set_mp4_comments_imageio_ffmpeg(input_file, comments):
+    try:
+        # Get the path to the bundled FFmpeg binary from imageio-ffmpeg
+        ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
+
+        # Check if input file exists
+        if not os.path.exists(input_file):
+            print(f"Error: Input file {input_file} does not exist")
+            return False
+
+        # Create a temporary file path
+        temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False).name
+
+        # FFmpeg command using the bundled binary
+        command = [
+            ffmpeg_path,                   # Use imageio-ffmpeg's FFmpeg
+            '-i', input_file,              # input file
+            '-metadata', f'comment={comments}',  # set comment metadata
+            '-c:v', 'copy',                # copy video stream without re-encoding
+            '-c:a', 'copy',                # copy audio stream without re-encoding
+            '-y',                          # overwrite output file if it exists
+            temp_file                      # temporary output file
+        ]
+
+        # Run the FFmpeg command
+        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+
+        if result.returncode == 0:
+            # Replace the original file with the modified one
+            shutil.move(temp_file, input_file)
+            print(f"Successfully added comments to {input_file}")
+            return True
+        else:
+            # Clean up temp file if FFmpeg fails
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+            print(f"Error: FFmpeg failed with message:\n{result.stderr}")
+            return False
+
+    except Exception as e:
+        # Clean up temp file in case of other errors
+        if 'temp_file' in locals() and os.path.exists(temp_file):
+            os.remove(temp_file)
+        print(f"Error saving prompt to video metadata, ffmpeg may be required: "+str(e))
+        return False
+
+@torch.no_grad()
+def worker(input_image, image_position, prompts, n_prompt, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf):
+    is_last_frame = (image_position == 100)
+    def encode_prompt(prompt, n_prompt):
+        llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+        if cfg == 1:
+            llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
+        else:
+            llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+        llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
+        llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
+
+        llama_vec = llama_vec.to(transformer.dtype)
+        llama_vec_n = llama_vec_n.to(transformer.dtype)
+        clip_l_pooler = clip_l_pooler.to(transformer.dtype)
+        clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
+        return [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n]
+
+    total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
+    total_latent_sections = int(max(round(total_latent_sections), 1))
+
+    job_id = generate_timestamp()
+
+    stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
+
+    try:
+        # Clean GPU
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae, transformer
+            )
+
+        # Text encoding
+
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))
+
+        if not high_vram:
+            fake_diffusers_current_device(text_encoder, gpu)  # since we only encode one text - that is one model move and one encode, offload is same time consumption since it is also one load and one encode.
+            load_model_as_complete(text_encoder_2, target_device=gpu)
+
+        prompt_parameters = []
+
+        for prompt_part in prompts:
+            prompt_parameters.append(encode_prompt(prompt_part, n_prompt))
+
+        # Processing input image
+
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))
+
+        H, W, C = input_image.shape
+        height, width = find_nearest_bucket(H, W, resolution=resolution)
+        
+        def get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram):
+            input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
+            
+            #Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
+            
+            input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
+            input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
+            
+            # VAE encoding
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(vae, target_device=gpu)
+            
+            start_latent = vae_encode(input_image_pt, vae)
+            
+            # CLIP Vision
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(image_encoder, target_device=gpu)
+
+            image_encoder_last_hidden_state = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder).last_hidden_state
+                
+            return [start_latent, image_encoder_last_hidden_state]
+            
+        [start_latent, image_encoder_last_hidden_state] = get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram)
+
+        # Dtype
+
+        image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
+
+        # Sampling
+
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
+
+        rnd = torch.Generator("cpu").manual_seed(seed)
+
+        history_latents = torch.zeros(size=(1, 16, 16 + 2 + 1, height // 8, width // 8), dtype=torch.float32).cpu()
+        start_latent = start_latent.to(history_latents)
+        history_pixels = None
+
+        history_latents = torch.cat([start_latent, history_latents] if is_last_frame else [history_latents, start_latent], dim=2)
+        total_generated_latent_frames = 1
+
+        if enable_preview:
+            def callback(d):
+                preview = d['denoised']
+                preview = vae_decode_fake(preview)
+			    
+                preview = (preview * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8)
+                preview = einops.rearrange(preview, 'b c t h w -> (b h) (t w) c')
+			    
+                if stream.input_queue.top() == 'end':
+                    stream.output_queue.push(('end', None))
+                    raise KeyboardInterrupt('User ends the task.')
+			    
+                current_step = d['i'] + 1
+                percentage = int(100.0 * current_step / steps)
+                hint = f'Sampling {current_step}/{steps}'
+                desc = f'Total generated frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / 30) :.2f} seconds (FPS-30), Resolution: {height}px * {width}px. The video is being extended now ...'
+                stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
+                return
+        else:
+            def callback(d):
+                return
+
+        indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
+        if is_last_frame:
+            latent_indices, clean_latent_1x_indices, clean_latent_2x_indices, clean_latent_4x_indices, clean_latent_indices_start = indices.split([latent_window_size, 1, 2, 16, 1], dim=1)
+            clean_latent_indices = torch.cat([clean_latent_1x_indices, clean_latent_indices_start], dim=1)
+        else:
+            clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
+            clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
+
+        def post_process(generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, section_index, total_latent_sections, outputs_folder, mp4_crf, stream):
+            total_generated_latent_frames += int(generated_latents.shape[2])
+            history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2) if is_last_frame else 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)
+
+            if history_pixels is None:
+                real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :] if is_last_frame else history_latents[:, :, -total_generated_latent_frames:, :, :]
+                history_pixels = vae_decode(real_history_latents, vae).cpu()
+            else:
+                section_latent_frames = latent_window_size * 2
+                overlapped_frames = latent_window_size * 4 - 3
+
+                if is_last_frame:
+                    real_history_latents = history_latents[:, :, :min(section_latent_frames, total_generated_latent_frames), :, :]
+                    history_pixels = soft_append_bcthw(vae_decode(real_history_latents, vae).cpu(), history_pixels, overlapped_frames)
+                else:
+                    real_history_latents = history_latents[:, :, -min(section_latent_frames, total_generated_latent_frames):, :, :]
+                    history_pixels = soft_append_bcthw(history_pixels, vae_decode(real_history_latents, vae).cpu(), overlapped_frames)
+
+            if not high_vram:
+                unload_complete_models()
+
+            if enable_preview or section_index == (0 if is_last_frame else (total_latent_sections - 1)):
+                output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
+
+                save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=mp4_crf)
+
+                print(f'Decoded. Current latent shape pixel shape {history_pixels.shape}')
+
+                stream.output_queue.push(('file', output_filename))
+            return [total_generated_latent_frames, history_latents, history_pixels]
+
+        for section_index in range(total_latent_sections - 1, -1, -1) if is_last_frame else range(total_latent_sections):
+            if stream.input_queue.top() == 'end':
+                stream.output_queue.push(('end', None))
+                return
+
+            print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')
+
+            if len(prompt_parameters) > 0:
+                [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters.pop((len(prompt_parameters) - 1) if is_last_frame else 0)
+
+            if not high_vram:
+                unload_complete_models()
+                move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
+
+            if use_teacache:
+                transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
+            else:
+                transformer.initialize_teacache(enable_teacache=False)
+
+            if is_last_frame:
+                clean_latents_1x, clean_latents_2x, clean_latents_4x = history_latents[:, :, :sum([1, 2, 16]), :, :].split([1, 2, 16], dim=2)
+                clean_latents = torch.cat([clean_latents_1x, start_latent], dim=2)
+            else:
+                clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -sum([16, 2, 1]):, :, :].split([16, 2, 1], dim=2)
+                clean_latents = torch.cat([start_latent, clean_latents_1x], dim=2)
+
+            generated_latents = sample_hunyuan(
+                transformer=transformer,
+                sampler='unipc',
+                width=width,
+                height=height,
+                frames=latent_window_size * 4 - 3,
+                real_guidance_scale=cfg,
+                distilled_guidance_scale=gs,
+                guidance_rescale=rs,
+                # shift=3.0,
+                num_inference_steps=steps,
+                generator=rnd,
+                prompt_embeds=llama_vec,
+                prompt_embeds_mask=llama_attention_mask,
+                prompt_poolers=clip_l_pooler,
+                negative_prompt_embeds=llama_vec_n,
+                negative_prompt_embeds_mask=llama_attention_mask_n,
+                negative_prompt_poolers=clip_l_pooler_n,
+                device=gpu,
+                dtype=torch.bfloat16,
+                image_embeddings=image_encoder_last_hidden_state,
+                latent_indices=latent_indices,
+                clean_latents=clean_latents,
+                clean_latent_indices=clean_latent_indices,
+                clean_latents_2x=clean_latents_2x,
+                clean_latent_2x_indices=clean_latent_2x_indices,
+                clean_latents_4x=clean_latents_4x,
+                clean_latent_4x_indices=clean_latent_4x_indices,
+                callback=callback,
+            )
+
+            [total_generated_latent_frames, history_latents, history_pixels] = post_process(generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, section_index, total_latent_sections, outputs_folder, mp4_crf, stream)
+    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 worker to accept video input and clean frame count
+@torch.no_grad()
+def worker_video(input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
+    def encode_prompt(prompt, n_prompt):
+        llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+        if cfg == 1:
+            llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
+        else:
+            llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
+
+        llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
+        llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
+
+        llama_vec = llama_vec.to(transformer.dtype)
+        llama_vec_n = llama_vec_n.to(transformer.dtype)
+        clip_l_pooler = clip_l_pooler.to(transformer.dtype)
+        clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
+        return [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n]
+
+    stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
+
+    try:
+        # Clean GPU
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae, transformer
+            )
+
+        # Text encoding
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Text encoding ...'))))
+
+        if not high_vram:
+            fake_diffusers_current_device(text_encoder, gpu)  # since we only encode one text - that is one model move and one encode, offload is same time consumption since it is also one load and one encode.
+            load_model_as_complete(text_encoder_2, target_device=gpu)
+
+        prompt_parameters = []
+
+        for prompt_part in prompts:
+            prompt_parameters.append(encode_prompt(prompt_part, n_prompt))
+
+        # 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 = video_encode(input_video, resolution, no_resize, vae, vae_batch_size=vae_batch, device=gpu)[:6]
+        start_latent = start_latent.to(dtype=torch.float32).cpu()
+        video_latents = video_latents.cpu()
 
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
-
-@spaces.GPU(duration=250)
-def generate(prompt, num_inference_steps, num_frames):
-    video_frames = pipe(prompt, num_inference_steps=num_inference_steps, num_frames=num_frames).frames[0]
-    video_path = export_to_video(video_frames, fps=10)
-    return video_path
-
-prompt = gr.Textbox(label="Enter prompt to generate a video", info="Based on this prompt ai will generate a video")
-description="""
-🚀 This is **unofficial** demo of Openai's Sora that haven't been released yet.\n
-✔ This space made using [ali-vilab/text-to-video-ms-1.7b](https://huggingface.co/ali-vilab/text-to-video-ms-1.7b)\n
-⌚ Estimated generation time is **150 seconds**\n
-🎁 Space is running on ZeroGPU, if you want faster generation, duplicate space and choose faster GPU
+        # CLIP Vision
+        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
+
+        if not high_vram:
+            load_model_as_complete(image_encoder, target_device=gpu)
+
+        image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
+        image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
+
+        # Dtype
+        image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
+
+        total_latent_sections = (total_second_length * fps) / (latent_window_size * 4)
+        total_latent_sections = int(max(round(total_latent_sections), 1))
+
+        if enable_preview:
+            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}), Resolution: {height}px * {width}px, 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
+        else:
+            def callback(d):
+                return
+
+        def compute_latent(history_latents, latent_window_size, num_clean_frames, start_latent):
+            # 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)
+            effective_clean_frames = min(effective_clean_frames, available_frames - 2) if available_frames > 2 else 0 # 20250507 pftq: changed 1 to 2 for edge case for <=1 sec videos
+            num_2x_frames = min(2, max(1, available_frames - effective_clean_frames - 1)) if available_frames > effective_clean_frames + 1 else 0 # 20250507 pftq: subtracted 1 for edge case for <=1 sec videos
+            num_4x_frames = min(16, max(1, available_frames - effective_clean_frames - num_2x_frames)) if available_frames > effective_clean_frames + num_2x_frames else 0 # 20250507 pftq: Edge case for <=1 sec
+
+            total_context_frames = num_4x_frames + num_2x_frames + effective_clean_frames
+            total_context_frames = min(total_context_frames, available_frames)  # 20250507 pftq: Edge case for <=1 sec videos
+
+            indices = torch.arange(0, sum([1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames])).unsqueeze(0) # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
+            clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split(
+                [1, num_4x_frames, num_2x_frames, effective_clean_frames, adjusted_latent_frames], dim=1 # 20250507 pftq: latent_window_size to adjusted_latent_frames for edge case for <=1 sec videos
+            )
+            clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
+
+            # 20250506 pftq: Split history_latents dynamically based on available frames
+            fallback_frame_count = 2 # 20250507 pftq: Changed 0 to 2 Edge case for <=1 sec videos
+            context_frames = clean_latents_4x = clean_latents_2x = clean_latents_1x = history_latents[:, :, :fallback_frame_count, :, :]
+
+            if total_context_frames > 0:
+                context_frames = history_latents[:, :, -total_context_frames:, :, :]
+                split_sizes = [num_4x_frames, num_2x_frames, effective_clean_frames]
+                split_sizes = [s for s in split_sizes if s > 0]  # Remove zero sizes
+                if split_sizes:
+                    splits = context_frames.split(split_sizes, dim=2)
+                    split_idx = 0
+                    
+                    if num_4x_frames > 0:
+                        clean_latents_4x = splits[split_idx]
+                        split_idx = 1
+                    if clean_latents_4x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
+                        print("Edge case for <=1 sec videos 4x")
+                        clean_latents_4x = clean_latents_4x.expand(-1, -1, 2, -1, -1)
+                        
+                    if num_2x_frames > 0 and split_idx < len(splits):
+                        clean_latents_2x = splits[split_idx]
+                        if clean_latents_2x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
+                            print("Edge case for <=1 sec videos 2x")
+                            clean_latents_2x = clean_latents_2x.expand(-1, -1, 2, -1, -1)
+                        split_idx += 1
+                    elif clean_latents_2x.shape[2] < 2:  # 20250507 pftq: edge case for <=1 sec videos
+                        clean_latents_2x = clean_latents_4x
+                        
+                    if effective_clean_frames > 0 and split_idx < len(splits):
+                        clean_latents_1x = splits[split_idx]
+
+            clean_latents = torch.cat([start_latent, 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)
+            return [max_frames, clean_latents, clean_latents_2x, clean_latents_4x, latent_indices, clean_latents, clean_latent_indices, clean_latent_2x_indices, clean_latent_4x_indices]
+
+        for idx in range(batch):
+            if batch > 1:
+                print(f"Beginning video {idx+1} of {batch} with seed {seed} ")
+
+            #job_id = generate_timestamp()
+            job_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+f"_framepackf1-videoinput_{width}-{total_second_length}sec_seed-{seed}_steps-{steps}_distilled-{gs}_cfg-{cfg}" # 20250506 pftq: easier to read timestamp and filename
+
+            # Sampling
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
+
+            rnd = torch.Generator("cpu").manual_seed(seed)
+
+            # 20250506 pftq: Initialize history_latents with video latents
+            history_latents = video_latents
+            total_generated_latent_frames = history_latents.shape[2]
+            # 20250506 pftq: Initialize history_pixels to fix UnboundLocalError
+            history_pixels = None
+            previous_video = None
+
+            for section_index in range(total_latent_sections):
+                if stream.input_queue.top() == 'end':
+                    stream.output_queue.push(('end', None))
+                    return
+
+                print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')
+
+                if len(prompt_parameters) > 0:
+                    [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters.pop(0)
+
+                if not high_vram:
+                    unload_complete_models()
+                    move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
+
+                if use_teacache:
+                    transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
+                else:
+                    transformer.initialize_teacache(enable_teacache=False)
+
+                [max_frames, clean_latents, clean_latents_2x, clean_latents_4x, latent_indices, clean_latents, clean_latent_indices, clean_latent_2x_indices, clean_latent_4x_indices] = compute_latent(history_latents, latent_window_size, num_clean_frames, start_latent)
+
+                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)
+
+                if history_pixels is None:
+                    real_history_latents = history_latents[:, :, -total_generated_latent_frames:, :, :]
+                    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])
+
+                    real_history_latents = history_latents[:, :, -min(total_generated_latent_frames, section_latent_frames):, :, :]
+                    history_pixels = soft_append_bcthw(history_pixels, vae_decode(real_history_latents, vae).cpu(), overlapped_frames)
+
+                if not high_vram:
+                    unload_complete_models()
+
+                if enable_preview or section_index == total_latent_sections - 1:
+                    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: {prompts} | Negative Prompt: {n_prompt}");
+                    print(f"Prompt saved to mp4 metadata comments: {output_filename}")
+				    
+                    # 20250506 pftq: Clean up previous partial files
+                    if previous_video is not None and os.path.exists(previous_video):
+                        try:
+                            os.remove(previous_video)
+                            print(f"Previous partial video deleted: {previous_video}")
+                        except Exception as e:
+                            print(f"Error deleting previous partial video {previous_video}: {e}")
+                    previous_video = output_filename
+				    
+                    print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
+				    
+                    stream.output_queue.push(('file', output_filename))
+
+            seed = (seed + 1) % np.iinfo(np.int32).max
+
+    except:
+        traceback.print_exc()
+
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae, transformer
+            )
+
+    stream.output_queue.push(('end', None))
+    return
+
+def get_duration(input_image, image_position, prompts, generation_mode, n_prompt, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf):
+    return total_second_length * 60 * (0.9 if use_teacache else 1.5) * (1 + ((steps - 25) / 100))
+
+# Remove this decorator if you run on local
+@spaces.GPU(duration=get_duration)
+def process_on_gpu(input_image,
+            image_position=0,
+            prompts=[""],
+            generation_mode="image",
+            n_prompt="",
+            seed=31337,
+            resolution=640,
+            total_second_length=5,
+            latent_window_size=9,
+            steps=25,
+            cfg=1.0,
+            gs=10.0,
+            rs=0.0,
+            gpu_memory_preservation=6,
+            enable_preview=True,
+            use_teacache=False,
+            mp4_crf=16
+           ):
+    start = time.time()
+    global stream
+    stream = AsyncStream()
+
+    async_run(worker, input_image, image_position, prompts, n_prompt, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf)
+
+    output_filename = None
+
+    while True:
+        flag, data = stream.output_queue.next()
+
+        if flag == 'file':
+            output_filename = data
+            yield gr.update(value=output_filename, label="Previewed Frames"), gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True), gr.update()
+
+        if flag == 'progress':
+            preview, desc, html = data
+            yield gr.update(label="Previewed Frames"), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True), gr.update()
+
+        if flag == 'end':
+            end = time.time()
+            secondes = int(end - start)
+            minutes = math.floor(secondes / 60)
+            secondes = secondes - (minutes * 60)
+            hours = math.floor(minutes / 60)
+            minutes = minutes - (hours * 60)
+            yield gr.update(value=output_filename, label="Finished Frames"), gr.update(visible=False), gr.update(), "The process has lasted " + \
+            ((str(hours) + " h, ") if hours != 0 else "") + \
+            ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
+            str(secondes) + " sec. " + \
+            "You can upscale the result with RIFE. To make all your generated scenes consistent, you can then apply a face swap on the main character. If you do not see the generated video above, the process may have failed. See the logs for more information. If you see an error like ''NVML_SUCCESS == r INTERNAL ASSERT FAILED'', you probably haven't enough VRAM. Test an example or other options to compare. You can share your inputs to the original space or set your space in public for a peer review.", gr.update(interactive=True), gr.update(interactive=False), gr.update(visible = False)
+            break
+
+def process(input_image,
+            image_position=0,
+            prompt="",
+            generation_mode="image",
+            n_prompt="",
+            randomize_seed=True,
+            seed=31337,
+            resolution=640,
+            total_second_length=5,
+            latent_window_size=9,
+            steps=25,
+            cfg=1.0,
+            gs=10.0,
+            rs=0.0,
+            gpu_memory_preservation=6,
+            enable_preview=True,
+            use_teacache=False,
+            mp4_crf=16
+           ):
+
+    if torch.cuda.device_count() == 0:
+        gr.Warning('Set this space to GPU config to make it work.')
+        yield gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible = False)
+        return
+
+    if randomize_seed:
+        seed = random.randint(0, np.iinfo(np.int32).max)
+
+    prompts = prompt.split(";")
+
+    # assert input_image is not None, 'No input image!'
+    if generation_mode == "text":
+        default_height, default_width = 640, 640
+        input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
+        print("No input image provided. Using a blank white image.")
+
+    yield gr.update(label="Previewed Frames"), None, '', '', gr.update(interactive=False), gr.update(interactive=True), gr.update()
+
+    yield from process_on_gpu(input_image,
+            image_position,
+            prompts,
+            generation_mode,
+            n_prompt,
+            seed,
+            resolution,
+            total_second_length,
+            latent_window_size,
+            steps,
+            cfg,
+            gs,
+            rs,
+            gpu_memory_preservation,
+            enable_preview,
+            use_teacache,
+            mp4_crf
+           )
+
+def get_duration_video(input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
+    return total_second_length * 60 * (1.5 if use_teacache else 2.5) * (1 + ((steps - 25) / 100))
+
+# Remove this decorator if you run on local
+@spaces.GPU(duration=get_duration_video)
+def process_video_on_gpu(input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
+    start = time.time()
+    global stream
+    stream = AsyncStream()
+
+    # 20250506 pftq: Pass num_clean_frames, vae_batch, etc
+    async_run(worker_video, input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, 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 gr.update(value=output_filename, label="Previewed Frames"), gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True), gr.update()
+
+        if flag == 'progress':
+            preview, desc, html = data
+            yield gr.update(label="Previewed Frames"), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True), gr.update() # 20250506 pftq: Keep refreshing the video in case it got hidden when the tab was in the background
+
+        if flag == 'end':
+            end = time.time()
+            secondes = int(end - start)
+            minutes = math.floor(secondes / 60)
+            secondes = secondes - (minutes * 60)
+            hours = math.floor(minutes / 60)
+            minutes = minutes - (hours * 60)
+            yield gr.update(value=output_filename, label="Finished Frames"), gr.update(visible=False), desc + \
+            " The process has lasted " + \
+            ((str(hours) + " h, ") if hours != 0 else "") + \
+            ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
+            str(secondes) + " sec. " + \
+            " You can upscale the result with RIFE. To make all your generated scenes consistent, you can then apply a face swap on the main character. If you do not see the generated video above, the process may have failed. See the logs for more information. If you see an error like ''NVML_SUCCESS == r INTERNAL ASSERT FAILED'', you probably haven't enough VRAM. Test an example or other options to compare. You can share your inputs to the original space or set your space in public for a peer review.", '', gr.update(interactive=True), gr.update(interactive=False), gr.update(visible = False)
+            break
+
+def process_video(input_video, prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch):
+    global high_vram
+
+    if torch.cuda.device_count() == 0:
+        gr.Warning('Set this space to GPU config to make it work.')
+        yield gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible = False)
+        return
+
+    if randomize_seed:
+        seed = random.randint(0, np.iinfo(np.int32).max)
+
+    prompts = prompt.split(";")
+
+    # 20250506 pftq: Updated assertion for video input
+    assert input_video is not None, 'No input video!'
+
+    yield gr.update(label="Previewed Frames"), None, '', '', gr.update(interactive=False), gr.update(interactive=True), gr.update()
+
+    # 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
+
+    yield from process_video_on_gpu(input_video, prompts, n_prompt, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch)
+
+def end_process():
+    stream.input_queue.push('end')
+
+timeless_prompt_value = [""]
+timed_prompts = {}
+
+def handle_prompt_number_change():
+    timed_prompts.clear()
+    return []
+
+def handle_timeless_prompt_change(timeless_prompt):
+    timeless_prompt_value[0] = timeless_prompt
+    return refresh_prompt()
+
+def handle_timed_prompt_change(timed_prompt_id, timed_prompt):
+    timed_prompts[timed_prompt_id] = timed_prompt
+    return refresh_prompt()
+
+def refresh_prompt():
+    dict_values = {k: v for k, v in timed_prompts.items()}
+    sorted_dict_values = sorted(dict_values.items(), key=lambda x: x[0])
+    array = []
+    for sorted_dict_value in sorted_dict_values:
+        if timeless_prompt_value[0] is not None and len(timeless_prompt_value[0]) and sorted_dict_value[1] is not None and len(sorted_dict_value[1]):
+            array.append(timeless_prompt_value[0] + ". " + sorted_dict_value[1])
+        else:
+            array.append(timeless_prompt_value[0] + sorted_dict_value[1])
+    print(str(array))
+    return ";".join(array)
+
+title_html = """
+    <h1><center>FramePack</center></h1>
+    <big><center>Generate videos from text/image/video freely, without account, without watermark and download it</center></big>
+    <br/>
+    
+    <p>This space is ready to work on ZeroGPU and GPU and has been tested successfully on ZeroGPU. Please leave a <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack/discussions/new">message in discussion</a> if you encounter issues.</p>
+    """
+
+js = """
+function createGradioAnimation() {
+    window.addEventListener("beforeunload", function (e) {
+        if (document.getElementById('end-button') && !document.getElementById('end-button').disabled) {
+            var confirmationMessage = 'A process is still running. '
+                                    + 'If you leave before saving, your changes will be lost.';
+    
+            (e || window.event).returnValue = confirmationMessage;
+        }
+        return confirmationMessage;
+    });
+    return 'Animation created';
+}
 """
-num_inference_steps=gr.Slider(8, 128, step=1, value=24, label="Num Inference Steps", info="More steps then better quality")
-num_frames=gr.Slider(8, 1000, step=1, value=200, label="Num of Frames", info="It is duration of video")
-
-interface = gr.Interface(
-  generate,
-  inputs=[prompt],
-  additional_inputs=[num_inference_steps, num_frames],
-  examples=[
-      ["Astronaut riding a horse", 60, 100], 
-      ["Darth vader surfing in waves", 30, 200], 
-      ["A house in the woods in ocean", 70, 100], 
-      ["A car in the forest", 70, 100],
-      ["A house firing", 60, 150],
-      ["A plane firing and falling down", 100, 20],
-      ["Campfire", 50, 50],
-      ["Zombie apocalypse", 100, 20],
-      ["A New Yourk City", 100, 20],
-      ["A man running in beautiufl forest", 100, 20],
-      ["A cup of tea with fog", 100, 20]
-  ],
-  outputs="video",
-  title="Openai Sora (Unofficial)",
-  description=description,
-  cache_examples=False,
-  theme="soft"
-).launch()
+
+css = make_progress_bar_css()
+block = gr.Blocks(css=css, js=js).queue()
+with block:
+    if torch.cuda.device_count() == 0:
+        with gr.Row():
+            gr.HTML("""
+    <p style="background-color: red;"><big><big><big><b>⚠️To use FramePack, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack?duplicate=true">duplicate this space</a> and set a GPU with 30 GB VRAM.</b>
+
+    You can't use FramePack directly here because this space runs on a CPU, which is not enough for FramePack. Please provide <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/FramePack/discussions/new">feedback</a> if you have issues.
+    </big></big></big></p>
+    """)
+    gr.HTML(title_html)
+    local_storage = gr.BrowserState(default_local_storage)
+    with gr.Row():
+        with gr.Column():
+            generation_mode = gr.Radio([["Text-to-Video", "text"], ["Image-to-Video", "image"], ["Video Extension", "video"]], elem_id="generation-mode", label="Generation mode", value = "image")
+            text_to_video_hint = gr.HTML("I discourage to use the Text-to-Video feature. You should rather generate an image with Flux and use Image-to-Video. You will save time.")
+            input_image = gr.Image(sources='upload', type="numpy", label="Image", height=320)
+            image_position = gr.Slider(label="Image position", minimum=0, maximum=100, value=0, step=100, info='0=Video start; 100=Video end (lower quality)')
+            input_video = gr.Video(sources='upload', label="Input Video", height=320)
+            timeless_prompt = gr.Textbox(label="Timeless prompt", info='Used on the whole duration of the generation', value='', placeholder="The creature starts to move, fast motion, fixed camera, focus motion, consistent arm, consistent position, mute colors, insanely detailed")
+            prompt_number = gr.Slider(label="Timed prompt number", minimum=0, maximum=1000, value=0, step=1, info='Prompts will automatically appear')
+
+            @gr.render(inputs=prompt_number)
+            def show_split(prompt_number):
+                for digit in range(prompt_number):
+                    timed_prompt_id = gr.Textbox(value="timed_prompt_" + str(digit), visible=False)
+                    timed_prompt = gr.Textbox(label="Timed prompt #" + str(digit + 1), elem_id="timed_prompt_" + str(digit), value="")
+                    timed_prompt.change(fn=handle_timed_prompt_change, inputs=[timed_prompt_id, timed_prompt], outputs=[final_prompt])
+
+            final_prompt = gr.Textbox(label="Final prompt", value='', info='Use ; to separate in time; beware to write to stop the previous action')
+            prompt_hint = gr.HTML("Video extension barely follows the prompt; to force to follow the prompt, you have to set the Distilled CFG Scale to 3.0 and the Context Frames to 2 but the video quality will be poor.")
+            total_second_length = gr.Slider(label="Video Length to Generate (seconds)", minimum=1, maximum=120, value=2, step=0.1)
+
+            with gr.Row():
+                start_button = gr.Button(value="🎥 Generate", variant="primary")
+                start_button_video = gr.Button(value="🎥 Generate", variant="primary")
+                end_button = gr.Button(elem_id="end-button", value="End Generation", variant="stop", interactive=False)
+
+            with gr.Accordion("Advanced settings", open=False):
+                enable_preview = gr.Checkbox(label='Enable preview', value=True, info='Display a preview around each second generated but it costs 2 sec. for each second generated.')
+                use_teacache = gr.Checkbox(label='Use TeaCache', value=False, info='Faster speed and no break in brightness, but often makes hands and fingers slightly worse.')
+
+                n_prompt = gr.Textbox(label="Negative Prompt", value="Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", info='Requires using normal CFG (undistilled) instead of Distilled (set Distilled=1 and CFG > 1).')
+
+                latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, info='Generate more frames at a time (larger chunks). Less degradation and better blending but higher VRAM cost. Should not change.')
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30, step=1, info='Increase for more quality, especially if using high non-distilled CFG. If your animation has very few motion, you may have brutal brightness change; this can be fixed increasing the steps.')
+
+                with gr.Row():
+                    no_resize = gr.Checkbox(label='Force Original Video Resolution (no Resizing)', value=False, info='Might run out of VRAM (720p requires > 24GB VRAM).')
+                    resolution = gr.Dropdown([
+                        ["409,600 px (working)", 640],
+                        ["451,584 px (working)", 672],
+                        ["495,616 px (VRAM pb on HF)", 704],
+                        ["589,824 px (not tested)", 768],
+                        ["692,224 px (not tested)", 832],
+                        ["746,496 px (not tested)", 864],
+                        ["921,600 px (not tested)", 960]
+                    ], value=672, label="Resolution (width x height)", info="Do not affect the generation time")
+
+                # 20250506 pftq: Reduced default distilled guidance scale to improve adherence to input video
+                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, info='Use this instead of Distilled for more detail/control + Negative Prompt (make sure Distilled set to 1). Doubles render time. Should not change.')
+                gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='Prompt adherence at the cost of less details from the input video, but to a lesser extent than Context Frames; 3=follow the prompt but blurred motions & unsharped, 10=focus motion; changing this value is not recommended')
+                rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, info='Should not change')
+
+
+                # 20250506 pftq: Renamed slider to Number of Context Frames and updated description
+                num_clean_frames = gr.Slider(label="Number of Context Frames", minimum=2, maximum=10, value=5, step=1, info="Retain more video details but increase memory use. Reduce to 2 to avoid memory issues or to give more weight to the prompt.")
+
+                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.")
+
+
+                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. ")
+                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.')
+                with gr.Row():
+                    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)
+
+        with gr.Column():
+            warning = gr.HTML(value = "<center><big>Your computer must <u>not</u> enter into standby mode.</big><br/>On Chrome, you can force to keep a tab alive in <code>chrome://discards/</code></center>", visible = False)
+            result_video = gr.Video(label="Generated Frames", autoplay=True, show_share_button=False, height=512, loop=True)
+            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
+            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+
+    # 20250506 pftq: Updated inputs to include num_clean_frames
+    ips = [input_image, image_position, final_prompt, generation_mode, n_prompt, randomize_seed, seed, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, mp4_crf]
+    ips_video = [input_video, final_prompt, n_prompt, randomize_seed, seed, batch, resolution, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, enable_preview, use_teacache, no_resize, mp4_crf, num_clean_frames, vae_batch]
+
+    gr.Examples(
+        label = "Examples from text",
+        examples = [
+                [
+                    None, # input_image
+                    0, # image_position
+                    "Overcrowed street in Japan, photorealistic, realistic, intricate details, 8k, insanely detailed",
+                    "text", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    1, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    False, # use_teacache
+                    16 # mp4_crf
+                ]
+            ],
+        run_on_click = True,
+        fn = process,
+	    inputs = ips,
+        outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
+        cache_examples = False,
+    )
+
+    gr.Examples(
+        label = "Examples from image",
+        examples = [
+                [
+                    "./img_examples/Example1.png", # input_image
+                    0, # image_position
+                    "A dolphin emerges from the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
+                    "image", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    1, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    True, # use_teacache
+                    16 # mp4_crf
+                ],
+                [
+                    "./img_examples/Example2.webp", # input_image
+                    0, # image_position
+                    "A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The man talks and the woman listens; A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The woman talks, the man stops talking and the man listens; A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The woman talks and the man listens",
+                    "image", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    2, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    True, # use_teacache
+                    16 # mp4_crf
+                ],
+                [
+                    "./img_examples/Example2.webp", # input_image
+                    0, # image_position
+                    "A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The woman talks and the man listens; A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The man talks, the woman stops talking and the woman listens A man on the left and a woman on the right face each other ready to start a conversation, large space between the persons, full view, full-length view, 3D, pixar, 3D render, CGI. The man talks and the woman listens",
+                    "image", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    2, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    True, # use_teacache
+                    16 # mp4_crf
+                ],
+                [
+                    "./img_examples/Example3.jpg", # input_image
+                    0, # image_position
+                    "A boy is walking to the right, full view, full-length view, cartoon",
+                    "image", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    1, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    True, # use_teacache
+                    16 # mp4_crf
+                ],
+                [
+                    "./img_examples/Example4.webp", # input_image
+                    100, # image_position
+                    "A building starting to explode, photorealistic, realisitc, 8k, insanely detailed",
+                    "image", # generation_mode
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    672, # resolution
+                    1, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    False, # use_teacache
+                    16 # mp4_crf
+                ]
+            ],
+        run_on_click = True,
+        fn = process,
+	    inputs = ips,
+        outputs = [result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
+        cache_examples = False,
+    )
+
+    gr.Examples(
+        label = "Examples from video",
+        examples = [
+                [
+                    "./img_examples/Example1.mp4", # input_video
+                    "View of the sea as far as the eye can see, from the seaside, a piece of land is barely visible on the horizon at the middle, the sky is radiant, reflections of the sun in the water, photorealistic, realistic, intricate details, 8k, insanely detailed",
+                    "Missing arm, unrealistic position, impossible contortion, visible bone, muscle contraction, blurred, blurry", # n_prompt
+                    True, # randomize_seed
+                    42, # seed
+                    1, # batch
+                    672, # resolution
+                    1, # total_second_length
+                    9, # latent_window_size
+                    30, # steps
+                    1.0, # cfg
+                    10.0, # gs
+                    0.0, # rs
+                    6, # gpu_memory_preservation
+                    False, # enable_preview
+                    True, # use_teacache
+                    False, # no_resize
+                    16, # mp4_crf
+                    5, # num_clean_frames
+                    default_vae
+                ]
+            ],
+        run_on_click = True,
+        fn = process_video,
+	    inputs = ips_video,
+	    outputs = [result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button],
+        cache_examples = False,
+    )
+
+    def save_preferences(preferences, value):
+        preferences["generation-mode"] = value
+        return preferences
+    
+    def load_preferences(saved_prefs):
+        saved_prefs = init_preferences(saved_prefs)
+        return saved_prefs["generation-mode"]
+    
+    def init_preferences(saved_prefs):
+        if saved_prefs is None:
+            saved_prefs = default_local_storage
+        return saved_prefs
+    
+    def check_parameters(generation_mode, input_image, input_video):
+        if generation_mode == "image" and input_image is None:
+            raise gr.Error("Please provide an image to extend.")
+        if generation_mode == "video" and input_video is None:
+            raise gr.Error("Please provide a video to extend.")
+        return [gr.update(interactive=True), gr.update(visible = True)]
+    
+    def handle_generation_mode_change(generation_mode_data):
+        if generation_mode_data == "text":
+            return [gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False)]
+        elif generation_mode_data == "image":
+            return [gr.update(visible = False), gr.update(visible = True), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = False)]
+        elif generation_mode_data == "video":
+            return [gr.update(visible = False), gr.update(visible = False), gr.update(visible = False), gr.update(visible = True), gr.update(visible = False), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True), gr.update(visible = True)]
+
+    prompt_number.change(fn=handle_prompt_number_change, inputs=[], outputs=[])
+    timeless_prompt.change(fn=handle_timeless_prompt_change, inputs=[timeless_prompt], outputs=[final_prompt])
+    start_button.click(fn = check_parameters, inputs = [
+        generation_mode, input_image, input_video
+    ], outputs = [end_button, warning], queue = False, show_progress = False).success(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button, warning], scroll_to_output = True)
+    start_button_video.click(fn = check_parameters, inputs = [
+        generation_mode, input_image, input_video
+    ], outputs = [end_button, warning], queue = False, show_progress = False).success(fn=process_video, inputs=ips_video, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button_video, end_button, warning], scroll_to_output = True)
+    end_button.click(fn=end_process)
+
+    generation_mode.change(fn = save_preferences, inputs = [
+        local_storage,
+        generation_mode,
+    ], outputs = [
+       local_storage
+    ])
+
+    generation_mode.change(
+        fn=handle_generation_mode_change,
+        inputs=[generation_mode],
+        outputs=[text_to_video_hint, image_position, input_image, input_video, start_button, start_button_video, no_resize, batch, num_clean_frames, vae_batch, prompt_hint]
+    )
+
+    # Update display when the page loads
+    block.load(
+        fn=handle_generation_mode_change, inputs = [
+        generation_mode
+    ], outputs = [
+       text_to_video_hint, image_position, input_image, input_video, start_button, start_button_video, no_resize, batch, num_clean_frames, vae_batch, prompt_hint
+    ]
+    )
+
+    # Load saved preferences when the page loads
+    block.load(
+        fn=load_preferences, inputs = [
+        local_storage
+    ], outputs = [
+       generation_mode
+    ]
+    )
+
+block.launch(mcp_server=True, ssr_mode=False)

app_endframe.py

@@ -0,0 +1,822 @@
+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)
+
+# 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):
+    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
+
+    if torch.cuda.device_count() == 0:
+        gr.Warning('Set this space to GPU config to make it work.')
+        return None, None, None, None, None, None
+
+    if randomize_seed:
+        seed = random.randint(0, np.iinfo(np.int32).max)
+
+    # 20250506 pftq: Updated assertion for video input
+    assert input_video is not None, 'No input video!'
+
+    yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
+
+    # 20250507 pftq: Even the H100 needs offloading if the video dimensions are 720p or higher
+    if high_vram and (no_resize or resolution>640):
+        print("Disabling high vram mode due to no resize and/or potentially higher resolution...")
+        high_vram = False
+        vae.enable_slicing()
+        vae.enable_tiling()
+        DynamicSwapInstaller.install_model(transformer, device=gpu)
+        DynamicSwapInstaller.install_model(text_encoder, device=gpu)
+        
+    # 20250508 pftq: automatically set distilled cfg to 1 if cfg is used
+    if cfg > 1:
+        gs = 1
+
+    stream = AsyncStream()
+
+    # 20250506 pftq: Pass num_clean_frames, vae_batch, etc
+    async_run(worker, 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.Column():
+            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
+            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
+            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+
+    # 20250506 pftq: Updated inputs to include num_clean_frames
+    ips = [input_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)
+
+block.launch(share=True)

diffusers_helper/bucket_tools.py

@@ -0,0 +1,103 @@
+bucket_options = {
+    640: [
+        (416, 960),
+        (448, 864),
+        (480, 832),
+        (512, 768),
+        (544, 704),
+        (576, 672),
+        (608, 640),
+        (640, 608),
+        (672, 576),
+        (704, 544),
+        (768, 512),
+        (832, 480),
+        (864, 448),
+        (960, 416),
+    ],
+    672: [
+        (480, 864),
+        (512, 832),
+        (544, 768),
+        (576, 704),
+        (608, 672),
+        (640, 640),
+        (672, 608),
+        (704, 576),
+        (768, 544),
+        (832, 512),
+        (864, 480),
+    ],
+    704: [
+        (480, 960),
+        (512, 864),
+        (544, 832),
+        (576, 768),
+        (608, 704),
+        (640, 672),
+        (672, 640),
+        (704, 608),
+        (768, 576),
+        (832, 544),
+        (864, 512),
+        (960, 480),
+    ],
+    768: [
+        (512, 960),
+        (544, 864),
+        (576, 832),
+        (608, 768),
+        (640, 704),
+        (672, 672),
+        (704, 640),
+        (768, 608),
+        (832, 576),
+        (864, 544),
+        (960, 512),
+    ],
+    832: [
+        (544, 960),
+        (576, 864),
+        (608, 832),
+        (640, 768),
+        (672, 704),
+        (704, 672),
+        (768, 640),
+        (832, 608),
+        (864, 576),
+        (960, 544),
+    ],
+    864: [
+        (576, 960),
+        (608, 864),
+        (640, 832),
+        (672, 768),
+        (704, 704),
+        (768, 672),
+        (832, 640),
+        (864, 608),
+        (960, 576),
+    ],
+    960: [
+        (608, 960),
+        (640, 864),
+        (672, 832),
+        (704, 768),
+        (768, 704),
+        (832, 672),
+        (864, 640),
+        (960, 608),
+    ],
+}
+
+
+def find_nearest_bucket(h, w, resolution=640):
+    min_metric = float('inf')
+    best_bucket = None
+    for (bucket_h, bucket_w) in bucket_options[resolution]:
+        metric = abs(h * bucket_w - w * bucket_h)
+        if metric <= min_metric:
+            min_metric = metric
+            best_bucket = (bucket_h, bucket_w)
+    print("The resolution of the generated video will be " + str(best_bucket))
+    return best_bucket

diffusers_helper/clip_vision.py

@@ -0,0 +1,12 @@
+import numpy as np
+
+
+def hf_clip_vision_encode(image, feature_extractor, image_encoder):
+    assert isinstance(image, np.ndarray)
+    assert image.ndim == 3 and image.shape[2] == 3
+    assert image.dtype == np.uint8
+
+    preprocessed = feature_extractor.preprocess(images=image, return_tensors="pt").to(device=image_encoder.device, dtype=image_encoder.dtype)
+    image_encoder_output = image_encoder(**preprocessed)
+
+    return image_encoder_output

diffusers_helper/dit_common.py

@@ -0,0 +1,53 @@
+import torch
+import accelerate.accelerator
+
+from diffusers.models.normalization import RMSNorm, LayerNorm, FP32LayerNorm, AdaLayerNormContinuous
+
+
+accelerate.accelerator.convert_outputs_to_fp32 = lambda x: x
+
+
+def LayerNorm_forward(self, x):
+    return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps).to(x)
+
+
+LayerNorm.forward = LayerNorm_forward
+torch.nn.LayerNorm.forward = LayerNorm_forward
+
+
+def FP32LayerNorm_forward(self, x):
+    origin_dtype = x.dtype
+    return torch.nn.functional.layer_norm(
+        x.float(),
+        self.normalized_shape,
+        self.weight.float() if self.weight is not None else None,
+        self.bias.float() if self.bias is not None else None,
+        self.eps,
+    ).to(origin_dtype)
+
+
+FP32LayerNorm.forward = FP32LayerNorm_forward
+
+
+def RMSNorm_forward(self, hidden_states):
+    input_dtype = hidden_states.dtype
+    variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+
+    if self.weight is None:
+        return hidden_states.to(input_dtype)
+
+    return hidden_states.to(input_dtype) * self.weight.to(input_dtype)
+
+
+RMSNorm.forward = RMSNorm_forward
+
+
+def AdaLayerNormContinuous_forward(self, x, conditioning_embedding):
+    emb = self.linear(self.silu(conditioning_embedding))
+    scale, shift = emb.chunk(2, dim=1)
+    x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+    return x
+
+
+AdaLayerNormContinuous.forward = AdaLayerNormContinuous_forward

diffusers_helper/gradio/progress_bar.py

@@ -0,0 +1,86 @@
+progress_html = '''
+<div class="loader-container">
+  <div class="loader"></div>
+  <div class="progress-container">
+    <progress value="*number*" max="100"></progress>
+  </div>
+  <span>*text*</span>
+</div>
+'''
+
+css = '''
+.loader-container {
+  display: flex; /* Use flex to align items horizontally */
+  align-items: center; /* Center items vertically within the container */
+  white-space: nowrap; /* Prevent line breaks within the container */
+}
+
+.loader {
+  border: 8px solid #f3f3f3; /* Light grey */
+  border-top: 8px solid #3498db; /* Blue */
+  border-radius: 50%;
+  width: 30px;
+  height: 30px;
+  animation: spin 2s linear infinite;
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/* Style the progress bar */
+progress {
+  appearance: none; /* Remove default styling */
+  height: 20px; /* Set the height of the progress bar */
+  border-radius: 5px; /* Round the corners of the progress bar */
+  background-color: #f3f3f3; /* Light grey background */
+  width: 100%;
+  vertical-align: middle !important;
+}
+
+/* Style the progress bar container */
+.progress-container {
+  margin-left: 20px;
+  margin-right: 20px;
+  flex-grow: 1; /* Allow the progress container to take up remaining space */
+}
+
+/* Set the color of the progress bar fill */
+progress::-webkit-progress-value {
+  background-color: #3498db; /* Blue color for the fill */
+}
+
+progress::-moz-progress-bar {
+  background-color: #3498db; /* Blue color for the fill in Firefox */
+}
+
+/* Style the text on the progress bar */
+progress::after {
+  content: attr(value '%'); /* Display the progress value followed by '%' */
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  color: white; /* Set text color */
+  font-size: 14px; /* Set font size */
+}
+
+/* Style other texts */
+.loader-container > span {
+  margin-left: 5px; /* Add spacing between the progress bar and the text */
+}
+
+.no-generating-animation > .generating {
+  display: none !important;
+}
+
+'''
+
+
+def make_progress_bar_html(number, text):
+    return progress_html.replace('*number*', str(number)).replace('*text*', text)
+
+
+def make_progress_bar_css():
+    return css

diffusers_helper/hf_login.py

@@ -0,0 +1,21 @@
+import os
+
+
+def login(token):
+    from huggingface_hub import login
+    import time
+
+    while True:
+        try:
+            login(token)
+            print('HF login ok.')
+            break
+        except Exception as e:
+            print(f'HF login failed: {e}. Retrying')
+            time.sleep(0.5)
+
+
+hf_token = os.environ.get('HF_TOKEN', None)
+
+if hf_token is not None:
+    login(hf_token)

diffusers_helper/hunyuan.py

@@ -0,0 +1,111 @@
+import torch
+
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import DEFAULT_PROMPT_TEMPLATE
+from diffusers_helper.utils import crop_or_pad_yield_mask
+
+
+@torch.no_grad()
+def encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2, max_length=256):
+    assert isinstance(prompt, str)
+
+    prompt = [prompt]
+
+    # LLAMA
+
+    prompt_llama = [DEFAULT_PROMPT_TEMPLATE["template"].format(p) for p in prompt]
+    crop_start = DEFAULT_PROMPT_TEMPLATE["crop_start"]
+
+    llama_inputs = tokenizer(
+        prompt_llama,
+        padding="max_length",
+        max_length=max_length + crop_start,
+        truncation=True,
+        return_tensors="pt",
+        return_length=False,
+        return_overflowing_tokens=False,
+        return_attention_mask=True,
+    )
+
+    llama_input_ids = llama_inputs.input_ids.to(text_encoder.device)
+    llama_attention_mask = llama_inputs.attention_mask.to(text_encoder.device)
+    llama_attention_length = int(llama_attention_mask.sum())
+
+    llama_outputs = text_encoder(
+        input_ids=llama_input_ids,
+        attention_mask=llama_attention_mask,
+        output_hidden_states=True,
+    )
+
+    llama_vec = llama_outputs.hidden_states[-3][:, crop_start:llama_attention_length]
+    # llama_vec_remaining = llama_outputs.hidden_states[-3][:, llama_attention_length:]
+    llama_attention_mask = llama_attention_mask[:, crop_start:llama_attention_length]
+
+    assert torch.all(llama_attention_mask.bool())
+
+    # CLIP
+
+    clip_l_input_ids = tokenizer_2(
+        prompt,
+        padding="max_length",
+        max_length=77,
+        truncation=True,
+        return_overflowing_tokens=False,
+        return_length=False,
+        return_tensors="pt",
+    ).input_ids
+    clip_l_pooler = text_encoder_2(clip_l_input_ids.to(text_encoder_2.device), output_hidden_states=False).pooler_output
+
+    return llama_vec, clip_l_pooler
+
+
+@torch.no_grad()
+def vae_decode_fake(latents):
+    latent_rgb_factors = [
+        [-0.0395, -0.0331, 0.0445],
+        [0.0696, 0.0795, 0.0518],
+        [0.0135, -0.0945, -0.0282],
+        [0.0108, -0.0250, -0.0765],
+        [-0.0209, 0.0032, 0.0224],
+        [-0.0804, -0.0254, -0.0639],
+        [-0.0991, 0.0271, -0.0669],
+        [-0.0646, -0.0422, -0.0400],
+        [-0.0696, -0.0595, -0.0894],
+        [-0.0799, -0.0208, -0.0375],
+        [0.1166, 0.1627, 0.0962],
+        [0.1165, 0.0432, 0.0407],
+        [-0.2315, -0.1920, -0.1355],
+        [-0.0270, 0.0401, -0.0821],
+        [-0.0616, -0.0997, -0.0727],
+        [0.0249, -0.0469, -0.1703]
+    ]  # From comfyui
+
+    latent_rgb_factors_bias = [0.0259, -0.0192, -0.0761]
+
+    weight = torch.tensor(latent_rgb_factors, device=latents.device, dtype=latents.dtype).transpose(0, 1)[:, :, None, None, None]
+    bias = torch.tensor(latent_rgb_factors_bias, device=latents.device, dtype=latents.dtype)
+
+    images = torch.nn.functional.conv3d(latents, weight, bias=bias, stride=1, padding=0, dilation=1, groups=1)
+    images = images.clamp(0.0, 1.0)
+
+    return images
+
+
+@torch.no_grad()
+def vae_decode(latents, vae, image_mode=False):
+    latents = latents / vae.config.scaling_factor
+
+    if image_mode:
+        latents = latents.to(device=vae.device, dtype=vae.dtype).unbind(2)
+        image = [vae.decode(l.unsqueeze(2)).sample for l in latents]
+        image = torch.cat(image, dim=2)
+    else:
+        image = vae.decode(latents.to(device=vae.device, dtype=vae.dtype)).sample
+
+    return image
+
+
+@torch.no_grad()
+def vae_encode(image, vae):
+    latents = vae.encode(image.to(device=vae.device, dtype=vae.dtype)).latent_dist.sample()
+    latents = latents * vae.config.scaling_factor
+    return latents

diffusers_helper/k_diffusion/uni_pc_fm.py

@@ -0,0 +1,141 @@
+# Better Flow Matching UniPC by Lvmin Zhang
+# (c) 2025
+# CC BY-SA 4.0
+# Attribution-ShareAlike 4.0 International Licence
+
+
+import torch
+
+from tqdm.auto import trange
+
+
+def expand_dims(v, dims):
+    return v[(...,) + (None,) * (dims - 1)]
+
+
+class FlowMatchUniPC:
+    def __init__(self, model, extra_args, variant='bh1'):
+        self.model = model
+        self.variant = variant
+        self.extra_args = extra_args
+
+    def model_fn(self, x, t):
+        return self.model(x, t, **self.extra_args)
+
+    def update_fn(self, x, model_prev_list, t_prev_list, t, order):
+        assert order <= len(model_prev_list)
+        dims = x.dim()
+
+        t_prev_0 = t_prev_list[-1]
+        lambda_prev_0 = - torch.log(t_prev_0)
+        lambda_t = - torch.log(t)
+        model_prev_0 = model_prev_list[-1]
+
+        h = lambda_t - lambda_prev_0
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            t_prev_i = t_prev_list[-(i + 1)]
+            model_prev_i = model_prev_list[-(i + 1)]
+            lambda_prev_i = - torch.log(t_prev_i)
+            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
+            rks.append(rk)
+            D1s.append((model_prev_i - model_prev_0) / rk)
+
+        rks.append(1.)
+        rks = torch.tensor(rks, device=x.device)
+
+        R = []
+        b = []
+
+        hh = -h[0]
+        h_phi_1 = torch.expm1(hh)
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.variant == 'bh1':
+            B_h = hh
+        elif self.variant == 'bh2':
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError('Bad variant!')
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= (i + 1)
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=x.device)
+
+        use_predictor = len(D1s) > 0
+
+        if use_predictor:
+            D1s = torch.stack(D1s, dim=1)
+            if order == 2:
+                rhos_p = torch.tensor([0.5], device=b.device)
+            else:
+                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+        else:
+            D1s = None
+            rhos_p = None
+
+        if order == 1:
+            rhos_c = torch.tensor([0.5], device=b.device)
+        else:
+            rhos_c = torch.linalg.solve(R, b)
+
+        x_t_ = expand_dims(t / t_prev_0, dims) * x - expand_dims(h_phi_1, dims) * model_prev_0
+
+        if use_predictor:
+            pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0]))
+        else:
+            pred_res = 0
+
+        x_t = x_t_ - expand_dims(B_h, dims) * pred_res
+        model_t = self.model_fn(x_t, t)
+
+        if D1s is not None:
+            corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0]))
+        else:
+            corr_res = 0
+
+        D1_t = (model_t - model_prev_0)
+        x_t = x_t_ - expand_dims(B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+
+        return x_t, model_t
+
+    def sample(self, x, sigmas, callback=None, disable_pbar=False):
+        order = min(3, len(sigmas) - 2)
+        model_prev_list, t_prev_list = [], []
+        for i in trange(len(sigmas) - 1, disable=disable_pbar):
+            vec_t = sigmas[i].expand(x.shape[0])
+
+            if i == 0:
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+            elif i < order:
+                init_order = i
+                x, model_x = self.update_fn(x, model_prev_list, t_prev_list, vec_t, init_order)
+                model_prev_list.append(model_x)
+                t_prev_list.append(vec_t)
+            else:
+                x, model_x = self.update_fn(x, model_prev_list, t_prev_list, vec_t, order)
+                model_prev_list.append(model_x)
+                t_prev_list.append(vec_t)
+
+            model_prev_list = model_prev_list[-order:]
+            t_prev_list = t_prev_list[-order:]
+
+            if callback is not None:
+                callback({'x': x, 'i': i, 'denoised': model_prev_list[-1]})
+
+        return model_prev_list[-1]
+
+
+def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'):
+    assert variant in ['bh1', 'bh2']
+    return FlowMatchUniPC(model, extra_args=extra_args, variant=variant).sample(noise, sigmas=sigmas, callback=callback, disable_pbar=disable)

diffusers_helper/k_diffusion/wrapper.py

@@ -0,0 +1,51 @@
+import torch
+
+
+def append_dims(x, target_dims):
+    return x[(...,) + (None,) * (target_dims - x.ndim)]
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=1.0):
+    if guidance_rescale == 0:
+        return noise_cfg
+
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1.0 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+def fm_wrapper(transformer, t_scale=1000.0):
+    def k_model(x, sigma, **extra_args):
+        dtype = extra_args['dtype']
+        cfg_scale = extra_args['cfg_scale']
+        cfg_rescale = extra_args['cfg_rescale']
+        concat_latent = extra_args['concat_latent']
+
+        original_dtype = x.dtype
+        sigma = sigma.float()
+
+        x = x.to(dtype)
+        timestep = (sigma * t_scale).to(dtype)
+
+        if concat_latent is None:
+            hidden_states = x
+        else:
+            hidden_states = torch.cat([x, concat_latent.to(x)], dim=1)
+
+        pred_positive = transformer(hidden_states=hidden_states, timestep=timestep, return_dict=False, **extra_args['positive'])[0].float()
+
+        if cfg_scale == 1.0:
+            pred_negative = torch.zeros_like(pred_positive)
+        else:
+            pred_negative = transformer(hidden_states=hidden_states, timestep=timestep, return_dict=False, **extra_args['negative'])[0].float()
+
+        pred_cfg = pred_negative + cfg_scale * (pred_positive - pred_negative)
+        pred = rescale_noise_cfg(pred_cfg, pred_positive, guidance_rescale=cfg_rescale)
+
+        x0 = x.float() - pred.float() * append_dims(sigma, x.ndim)
+
+        return x0.to(dtype=original_dtype)
+
+    return k_model

diffusers_helper/memory.py

@@ -0,0 +1,134 @@
+# By lllyasviel
+
+
+import torch
+
+
+cpu = torch.device('cpu')
+gpu = torch.device(f'cuda:{torch.cuda.current_device()}')
+gpu_complete_modules = []
+
+
+class DynamicSwapInstaller:
+    @staticmethod
+    def _install_module(module: torch.nn.Module, **kwargs):
+        original_class = module.__class__
+        module.__dict__['forge_backup_original_class'] = original_class
+
+        def hacked_get_attr(self, name: str):
+            if '_parameters' in self.__dict__:
+                _parameters = self.__dict__['_parameters']
+                if name in _parameters:
+                    p = _parameters[name]
+                    if p is None:
+                        return None
+                    if p.__class__ == torch.nn.Parameter:
+                        return torch.nn.Parameter(p.to(**kwargs), requires_grad=p.requires_grad)
+                    else:
+                        return p.to(**kwargs)
+            if '_buffers' in self.__dict__:
+                _buffers = self.__dict__['_buffers']
+                if name in _buffers:
+                    return _buffers[name].to(**kwargs)
+            return super(original_class, self).__getattr__(name)
+
+        module.__class__ = type('DynamicSwap_' + original_class.__name__, (original_class,), {
+            '__getattr__': hacked_get_attr,
+        })
+
+        return
+
+    @staticmethod
+    def _uninstall_module(module: torch.nn.Module):
+        if 'forge_backup_original_class' in module.__dict__:
+            module.__class__ = module.__dict__.pop('forge_backup_original_class')
+        return
+
+    @staticmethod
+    def install_model(model: torch.nn.Module, **kwargs):
+        for m in model.modules():
+            DynamicSwapInstaller._install_module(m, **kwargs)
+        return
+
+    @staticmethod
+    def uninstall_model(model: torch.nn.Module):
+        for m in model.modules():
+            DynamicSwapInstaller._uninstall_module(m)
+        return
+
+
+def fake_diffusers_current_device(model: torch.nn.Module, target_device: torch.device):
+    if hasattr(model, 'scale_shift_table'):
+        model.scale_shift_table.data = model.scale_shift_table.data.to(target_device)
+        return
+
+    for k, p in model.named_modules():
+        if hasattr(p, 'weight'):
+            p.to(target_device)
+            return
+
+
+def get_cuda_free_memory_gb(device=None):
+    if device is None:
+        device = gpu
+
+    memory_stats = torch.cuda.memory_stats(device)
+    bytes_active = memory_stats['active_bytes.all.current']
+    bytes_reserved = memory_stats['reserved_bytes.all.current']
+    bytes_free_cuda, _ = torch.cuda.mem_get_info(device)
+    bytes_inactive_reserved = bytes_reserved - bytes_active
+    bytes_total_available = bytes_free_cuda + bytes_inactive_reserved
+    return bytes_total_available / (1024 ** 3)
+
+
+def move_model_to_device_with_memory_preservation(model, target_device, preserved_memory_gb=0):
+    print(f'Moving {model.__class__.__name__} to {target_device} with preserved memory: {preserved_memory_gb} GB')
+
+    for m in model.modules():
+        if get_cuda_free_memory_gb(target_device) <= preserved_memory_gb:
+            torch.cuda.empty_cache()
+            return
+
+        if hasattr(m, 'weight'):
+            m.to(device=target_device)
+
+    model.to(device=target_device)
+    torch.cuda.empty_cache()
+    return
+
+
+def offload_model_from_device_for_memory_preservation(model, target_device, preserved_memory_gb=0):
+    print(f'Offloading {model.__class__.__name__} from {target_device} to preserve memory: {preserved_memory_gb} GB')
+
+    for m in model.modules():
+        if get_cuda_free_memory_gb(target_device) >= preserved_memory_gb:
+            torch.cuda.empty_cache()
+            return
+
+        if hasattr(m, 'weight'):
+            m.to(device=cpu)
+
+    model.to(device=cpu)
+    torch.cuda.empty_cache()
+    return
+
+
+def unload_complete_models(*args):
+    for m in gpu_complete_modules + list(args):
+        m.to(device=cpu)
+        print(f'Unloaded {m.__class__.__name__} as complete.')
+
+    gpu_complete_modules.clear()
+    torch.cuda.empty_cache()
+    return
+
+
+def load_model_as_complete(model, target_device, unload=True):
+    if unload:
+        unload_complete_models()
+
+    model.to(device=target_device)
+    print(f'Loaded {model.__class__.__name__} to {target_device} as complete.')
+
+    gpu_complete_modules.append(model)
+    return

diffusers_helper/models/hunyuan_video_packed.py

@@ -0,0 +1,1035 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import einops
+import torch.nn as nn
+import numpy as np
+
+from diffusers.loaders import FromOriginalModelMixin
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.utils import logging
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps, PixArtAlphaTextProjection
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers_helper.dit_common import LayerNorm
+from diffusers_helper.utils import zero_module
+
+
+enabled_backends = []
+
+if torch.backends.cuda.flash_sdp_enabled():
+    enabled_backends.append("flash")
+if torch.backends.cuda.math_sdp_enabled():
+    enabled_backends.append("math")
+if torch.backends.cuda.mem_efficient_sdp_enabled():
+    enabled_backends.append("mem_efficient")
+if torch.backends.cuda.cudnn_sdp_enabled():
+    enabled_backends.append("cudnn")
+
+print("Currently enabled native sdp backends:", enabled_backends)
+
+try:
+    # raise NotImplementedError
+    from xformers.ops import memory_efficient_attention as xformers_attn_func
+    print('Xformers is installed!')
+except:
+    print('Xformers is not installed!')
+    xformers_attn_func = None
+
+try:
+    # raise NotImplementedError
+    from flash_attn import flash_attn_varlen_func, flash_attn_func
+    print('Flash Attn is installed!')
+except:
+    print('Flash Attn is not installed!')
+    flash_attn_varlen_func = None
+    flash_attn_func = None
+
+try:
+    # raise NotImplementedError
+    from sageattention import sageattn_varlen, sageattn
+    print('Sage Attn is installed!')
+except:
+    print('Sage Attn is not installed!')
+    sageattn_varlen = None
+    sageattn = None
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def pad_for_3d_conv(x, kernel_size):
+    b, c, t, h, w = x.shape
+    pt, ph, pw = kernel_size
+    pad_t = (pt - (t % pt)) % pt
+    pad_h = (ph - (h % ph)) % ph
+    pad_w = (pw - (w % pw)) % pw
+    return torch.nn.functional.pad(x, (0, pad_w, 0, pad_h, 0, pad_t), mode='replicate')
+
+
+def center_down_sample_3d(x, kernel_size):
+    # pt, ph, pw = kernel_size
+    # cp = (pt * ph * pw) // 2
+    # xp = einops.rearrange(x, 'b c (t pt) (h ph) (w pw) -> (pt ph pw) b c t h w', pt=pt, ph=ph, pw=pw)
+    # xc = xp[cp]
+    # return xc
+    return torch.nn.functional.avg_pool3d(x, kernel_size, stride=kernel_size)
+
+
+def get_cu_seqlens(text_mask, img_len):
+    batch_size = text_mask.shape[0]
+    text_len = text_mask.sum(dim=1)
+    max_len = text_mask.shape[1] + img_len
+
+    cu_seqlens = torch.zeros([2 * batch_size + 1], dtype=torch.int32, device="cuda")
+
+    for i in range(batch_size):
+        s = text_len[i] + img_len
+        s1 = i * max_len + s
+        s2 = (i + 1) * max_len
+        cu_seqlens[2 * i + 1] = s1
+        cu_seqlens[2 * i + 2] = s2
+
+    return cu_seqlens
+
+
+def apply_rotary_emb_transposed(x, freqs_cis):
+    cos, sin = freqs_cis.unsqueeze(-2).chunk(2, dim=-1)
+    x_real, x_imag = x.unflatten(-1, (-1, 2)).unbind(-1)
+    x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+    out = x.float() * cos + x_rotated.float() * sin
+    out = out.to(x)
+    return out
+
+
+def attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv):
+    if cu_seqlens_q is None and cu_seqlens_kv is None and max_seqlen_q is None and max_seqlen_kv is None:
+        if sageattn is not None:
+            x = sageattn(q, k, v, tensor_layout='NHD')
+            return x
+
+        if flash_attn_func is not None:
+            x = flash_attn_func(q, k, v)
+            return x
+
+        if xformers_attn_func is not None:
+            x = xformers_attn_func(q, k, v)
+            return x
+
+        x = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).transpose(1, 2)
+        return x
+
+    B, L, H, C = q.shape
+
+    q = q.flatten(0, 1)
+    k = k.flatten(0, 1)
+    v = v.flatten(0, 1)
+
+    if sageattn_varlen is not None:
+        x = sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
+    elif flash_attn_varlen_func is not None:
+        x = flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
+    else:
+        raise NotImplementedError('No Attn Installed!')
+
+    x = x.unflatten(0, (B, L))
+
+    return x
+
+
+class HunyuanAttnProcessorFlashAttnDouble:
+    def __call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb):
+        cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv = attention_mask
+
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1))
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        query = apply_rotary_emb_transposed(query, image_rotary_emb)
+        key = apply_rotary_emb_transposed(key, image_rotary_emb)
+
+        encoder_query = attn.add_q_proj(encoder_hidden_states)
+        encoder_key = attn.add_k_proj(encoder_hidden_states)
+        encoder_value = attn.add_v_proj(encoder_hidden_states)
+
+        encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
+        encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
+        encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
+
+        encoder_query = attn.norm_added_q(encoder_query)
+        encoder_key = attn.norm_added_k(encoder_key)
+
+        query = torch.cat([query, encoder_query], dim=1)
+        key = torch.cat([key, encoder_key], dim=1)
+        value = torch.cat([value, encoder_value], dim=1)
+
+        hidden_states = attn_varlen_func(query, key, value, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
+        hidden_states = hidden_states.flatten(-2)
+
+        txt_length = encoder_hidden_states.shape[1]
+        hidden_states, encoder_hidden_states = hidden_states[:, :-txt_length], hidden_states[:, -txt_length:]
+
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanAttnProcessorFlashAttnSingle:
+    def __call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, image_rotary_emb):
+        cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv = attention_mask
+
+        hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
+
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(2, (attn.heads, -1))
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        txt_length = encoder_hidden_states.shape[1]
+
+        query = torch.cat([apply_rotary_emb_transposed(query[:, :-txt_length], image_rotary_emb), query[:, -txt_length:]], dim=1)
+        key = torch.cat([apply_rotary_emb_transposed(key[:, :-txt_length], image_rotary_emb), key[:, -txt_length:]], dim=1)
+
+        hidden_states = attn_varlen_func(query, key, value, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
+        hidden_states = hidden_states.flatten(-2)
+
+        hidden_states, encoder_hidden_states = hidden_states[:, :-txt_length], hidden_states[:, -txt_length:]
+
+        return hidden_states, encoder_hidden_states
+
+
+class CombinedTimestepGuidanceTextProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, pooled_projection_dim):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")
+
+    def forward(self, timestep, guidance, pooled_projection):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype))
+
+        guidance_proj = self.time_proj(guidance)
+        guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=pooled_projection.dtype))
+
+        time_guidance_emb = timesteps_emb + guidance_emb
+
+        pooled_projections = self.text_embedder(pooled_projection)
+        conditioning = time_guidance_emb + pooled_projections
+
+        return conditioning
+
+
+class CombinedTimestepTextProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, pooled_projection_dim):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")
+
+    def forward(self, timestep, pooled_projection):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype))
+
+        pooled_projections = self.text_embedder(pooled_projection)
+
+        conditioning = timesteps_emb + pooled_projections
+
+        return conditioning
+
+
+class HunyuanVideoAdaNorm(nn.Module):
+    def __init__(self, in_features: int, out_features: Optional[int] = None) -> None:
+        super().__init__()
+
+        out_features = out_features or 2 * in_features
+        self.linear = nn.Linear(in_features, out_features)
+        self.nonlinearity = nn.SiLU()
+
+    def forward(
+        self, temb: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        temb = self.linear(self.nonlinearity(temb))
+        gate_msa, gate_mlp = temb.chunk(2, dim=-1)
+        gate_msa, gate_mlp = gate_msa.unsqueeze(1), gate_mlp.unsqueeze(1)
+        return gate_msa, gate_mlp
+
+
+class HunyuanVideoIndividualTokenRefinerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            bias=attention_bias,
+        )
+
+        self.norm2 = LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.ff = FeedForward(hidden_size, mult=mlp_width_ratio, activation_fn="linear-silu", dropout=mlp_drop_rate)
+
+        self.norm_out = HunyuanVideoAdaNorm(hidden_size, 2 * hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        norm_hidden_states = self.norm1(hidden_states)
+
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=None,
+            attention_mask=attention_mask,
+        )
+
+        gate_msa, gate_mlp = self.norm_out(temb)
+        hidden_states = hidden_states + attn_output * gate_msa
+
+        ff_output = self.ff(self.norm2(hidden_states))
+        hidden_states = hidden_states + ff_output * gate_mlp
+
+        return hidden_states
+
+
+class HunyuanVideoIndividualTokenRefiner(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.refiner_blocks = nn.ModuleList(
+            [
+                HunyuanVideoIndividualTokenRefinerBlock(
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    mlp_width_ratio=mlp_width_ratio,
+                    mlp_drop_rate=mlp_drop_rate,
+                    attention_bias=attention_bias,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> None:
+        self_attn_mask = None
+        if attention_mask is not None:
+            batch_size = attention_mask.shape[0]
+            seq_len = attention_mask.shape[1]
+            attention_mask = attention_mask.to(hidden_states.device).bool()
+            self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, seq_len).expand(-1, -1, seq_len, -1)
+            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+            self_attn_mask[:, :, :, 0] = True
+
+        for block in self.refiner_blocks:
+            hidden_states = block(hidden_states, temb, self_attn_mask)
+
+        return hidden_states
+
+
+class HunyuanVideoTokenRefiner(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        num_layers: int,
+        mlp_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        attention_bias: bool = True,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
+            embedding_dim=hidden_size, pooled_projection_dim=in_channels
+        )
+        self.proj_in = nn.Linear(in_channels, hidden_size, bias=True)
+        self.token_refiner = HunyuanVideoIndividualTokenRefiner(
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            num_layers=num_layers,
+            mlp_width_ratio=mlp_ratio,
+            mlp_drop_rate=mlp_drop_rate,
+            attention_bias=attention_bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+        if attention_mask is None:
+            pooled_projections = hidden_states.mean(dim=1)
+        else:
+            original_dtype = hidden_states.dtype
+            mask_float = attention_mask.float().unsqueeze(-1)
+            pooled_projections = (hidden_states * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+            pooled_projections = pooled_projections.to(original_dtype)
+
+        temb = self.time_text_embed(timestep, pooled_projections)
+        hidden_states = self.proj_in(hidden_states)
+        hidden_states = self.token_refiner(hidden_states, temb, attention_mask)
+
+        return hidden_states
+
+
+class HunyuanVideoRotaryPosEmbed(nn.Module):
+    def __init__(self, rope_dim, theta):
+        super().__init__()
+        self.DT, self.DY, self.DX = rope_dim
+        self.theta = theta
+
+    @torch.no_grad()
+    def get_frequency(self, dim, pos):
+        T, H, W = pos.shape
+        freqs = 1.0 / (self.theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=pos.device)[: (dim // 2)] / dim))
+        freqs = torch.outer(freqs, pos.reshape(-1)).unflatten(-1, (T, H, W)).repeat_interleave(2, dim=0)
+        return freqs.cos(), freqs.sin()
+
+    @torch.no_grad()
+    def forward_inner(self, frame_indices, height, width, device):
+        GT, GY, GX = torch.meshgrid(
+            frame_indices.to(device=device, dtype=torch.float32),
+            torch.arange(0, height, device=device, dtype=torch.float32),
+            torch.arange(0, width, device=device, dtype=torch.float32),
+            indexing="ij"
+        )
+
+        FCT, FST = self.get_frequency(self.DT, GT)
+        FCY, FSY = self.get_frequency(self.DY, GY)
+        FCX, FSX = self.get_frequency(self.DX, GX)
+
+        result = torch.cat([FCT, FCY, FCX, FST, FSY, FSX], dim=0)
+
+        return result.to(device)
+
+    @torch.no_grad()
+    def forward(self, frame_indices, height, width, device):
+        frame_indices = frame_indices.unbind(0)
+        results = [self.forward_inner(f, height, width, device) for f in frame_indices]
+        results = torch.stack(results, dim=0)
+        return results
+
+
+class AdaLayerNormZero(nn.Module):
+    def __init__(self, embedding_dim: int, norm_type="layer_norm", bias=True):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        emb = emb.unsqueeze(-2)
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=-1)
+        x = self.norm(x) * (1 + scale_msa) + shift_msa
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class AdaLayerNormZeroSingle(nn.Module):
+    def __init__(self, embedding_dim: int, norm_type="layer_norm", bias=True):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, 3 * embedding_dim, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        emb = emb.unsqueeze(-2)
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=-1)
+        x = self.norm(x) * (1 + scale_msa) + shift_msa
+        return x, gate_msa
+
+
+class AdaLayerNormContinuous(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        conditioning_embedding_dim: int,
+        elementwise_affine=True,
+        eps=1e-5,
+        bias=True,
+        norm_type="layer_norm",
+    ):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
+        emb = emb.unsqueeze(-2)
+        emb = self.linear(self.silu(emb))
+        scale, shift = emb.chunk(2, dim=-1)
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class HunyuanVideoSingleTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float = 4.0,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+        mlp_dim = int(hidden_size * mlp_ratio)
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            bias=True,
+            processor=HunyuanAttnProcessorFlashAttnSingle(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+            pre_only=True,
+        )
+
+        self.norm = AdaLayerNormZeroSingle(hidden_size, norm_type="layer_norm")
+        self.proj_mlp = nn.Linear(hidden_size, mlp_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(hidden_size + mlp_dim, hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.shape[1]
+        hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
+
+        residual = hidden_states
+
+        # 1. Input normalization
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+        norm_hidden_states, norm_encoder_hidden_states = (
+            norm_hidden_states[:, :-text_seq_length, :],
+            norm_hidden_states[:, -text_seq_length:, :],
+        )
+
+        # 2. Attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+        attn_output = torch.cat([attn_output, context_attn_output], dim=1)
+
+        # 3. Modulation and residual connection
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states = gate * self.proj_out(hidden_states)
+        hidden_states = hidden_states + residual
+
+        hidden_states, encoder_hidden_states = (
+            hidden_states[:, :-text_seq_length, :],
+            hidden_states[:, -text_seq_length:, :],
+        )
+        return hidden_states, encoder_hidden_states
+
+
+class HunyuanVideoTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_ratio: float,
+        qk_norm: str = "rms_norm",
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+
+        self.norm1 = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
+        self.norm1_context = AdaLayerNormZero(hidden_size, norm_type="layer_norm")
+
+        self.attn = Attention(
+            query_dim=hidden_size,
+            cross_attention_dim=None,
+            added_kv_proj_dim=hidden_size,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=hidden_size,
+            context_pre_only=False,
+            bias=True,
+            processor=HunyuanAttnProcessorFlashAttnDouble(),
+            qk_norm=qk_norm,
+            eps=1e-6,
+        )
+
+        self.norm2 = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
+
+        self.norm2_context = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff_context = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 1. Input normalization
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(encoder_hidden_states, emb=temb)
+
+        # 2. Joint attention
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            attention_mask=attention_mask,
+            image_rotary_emb=freqs_cis,
+        )
+
+        # 3. Modulation and residual connection
+        hidden_states = hidden_states + attn_output * gate_msa
+        encoder_hidden_states = encoder_hidden_states + context_attn_output * c_gate_msa
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+
+        norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp) + c_shift_mlp
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+
+        hidden_states = hidden_states + gate_mlp * ff_output
+        encoder_hidden_states = encoder_hidden_states + c_gate_mlp * context_ff_output
+
+        return hidden_states, encoder_hidden_states
+
+
+class ClipVisionProjection(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.up = nn.Linear(in_channels, out_channels * 3)
+        self.down = nn.Linear(out_channels * 3, out_channels)
+
+    def forward(self, x):
+        projected_x = self.down(nn.functional.silu(self.up(x)))
+        return projected_x
+
+
+class HunyuanVideoPatchEmbed(nn.Module):
+    def __init__(self, patch_size, in_chans, embed_dim):
+        super().__init__()
+        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+
+class HunyuanVideoPatchEmbedForCleanLatents(nn.Module):
+    def __init__(self, inner_dim):
+        super().__init__()
+        self.proj = nn.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+        self.proj_2x = nn.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4))
+        self.proj_4x = nn.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8))
+
+    @torch.no_grad()
+    def initialize_weight_from_another_conv3d(self, another_layer):
+        weight = another_layer.weight.detach().clone()
+        bias = another_layer.bias.detach().clone()
+
+        sd = {
+            'proj.weight': weight.clone(),
+            'proj.bias': bias.clone(),
+            'proj_2x.weight': einops.repeat(weight, 'b c t h w -> b c (t tk) (h hk) (w wk)', tk=2, hk=2, wk=2) / 8.0,
+            'proj_2x.bias': bias.clone(),
+            'proj_4x.weight': einops.repeat(weight, 'b c t h w -> b c (t tk) (h hk) (w wk)', tk=4, hk=4, wk=4) / 64.0,
+            'proj_4x.bias': bias.clone(),
+        }
+
+        sd = {k: v.clone() for k, v in sd.items()}
+
+        self.load_state_dict(sd)
+        return
+
+
+class HunyuanVideoTransformer3DModelPacked(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        num_attention_heads: int = 24,
+        attention_head_dim: int = 128,
+        num_layers: int = 20,
+        num_single_layers: int = 40,
+        num_refiner_layers: int = 2,
+        mlp_ratio: float = 4.0,
+        patch_size: int = 2,
+        patch_size_t: int = 1,
+        qk_norm: str = "rms_norm",
+        guidance_embeds: bool = True,
+        text_embed_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        rope_theta: float = 256.0,
+        rope_axes_dim: Tuple[int] = (16, 56, 56),
+        has_image_proj=False,
+        image_proj_dim=1152,
+        has_clean_x_embedder=False,
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        # 1. Latent and condition embedders
+        self.x_embedder = HunyuanVideoPatchEmbed((patch_size_t, patch_size, patch_size), in_channels, inner_dim)
+        self.context_embedder = HunyuanVideoTokenRefiner(
+            text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
+        )
+        self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings(inner_dim, pooled_projection_dim)
+
+        self.clean_x_embedder = None
+        self.image_projection = None
+
+        # 2. RoPE
+        self.rope = HunyuanVideoRotaryPosEmbed(rope_axes_dim, rope_theta)
+
+        # 3. Dual stream transformer blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                HunyuanVideoTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 4. Single stream transformer blocks
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                HunyuanVideoSingleTransformerBlock(
+                    num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
+                )
+                for _ in range(num_single_layers)
+            ]
+        )
+
+        # 5. Output projection
+        self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(inner_dim, patch_size_t * patch_size * patch_size * out_channels)
+
+        self.inner_dim = inner_dim
+        self.use_gradient_checkpointing = False
+        self.enable_teacache = False
+
+        if has_image_proj:
+            self.install_image_projection(image_proj_dim)
+
+        if has_clean_x_embedder:
+            self.install_clean_x_embedder()
+
+        self.high_quality_fp32_output_for_inference = False
+
+    def install_image_projection(self, in_channels):
+        self.image_projection = ClipVisionProjection(in_channels=in_channels, out_channels=self.inner_dim)
+        self.config['has_image_proj'] = True
+        self.config['image_proj_dim'] = in_channels
+
+    def install_clean_x_embedder(self):
+        self.clean_x_embedder = HunyuanVideoPatchEmbedForCleanLatents(self.inner_dim)
+        self.config['has_clean_x_embedder'] = True
+
+    def enable_gradient_checkpointing(self):
+        self.use_gradient_checkpointing = True
+        print('self.use_gradient_checkpointing = True')
+
+    def disable_gradient_checkpointing(self):
+        self.use_gradient_checkpointing = False
+        print('self.use_gradient_checkpointing = False')
+
+    def initialize_teacache(self, enable_teacache=True, num_steps=25, rel_l1_thresh=0.15):
+        self.enable_teacache = enable_teacache
+        self.cnt = 0
+        self.num_steps = num_steps
+        self.rel_l1_thresh = rel_l1_thresh  # 0.1 for 1.6x speedup, 0.15 for 2.1x speedup
+        self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = None
+        self.previous_residual = None
+        self.teacache_rescale_func = np.poly1d([7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02])
+
+    def gradient_checkpointing_method(self, block, *args):
+        if self.use_gradient_checkpointing:
+            result = torch.utils.checkpoint.checkpoint(block, *args, use_reentrant=False)
+        else:
+            result = block(*args)
+        return result
+
+    def process_input_hidden_states(
+            self,
+            latents, latent_indices=None,
+            clean_latents=None, clean_latent_indices=None,
+            clean_latents_2x=None, clean_latent_2x_indices=None,
+            clean_latents_4x=None, clean_latent_4x_indices=None
+    ):
+        hidden_states = self.gradient_checkpointing_method(self.x_embedder.proj, latents)
+        B, C, T, H, W = hidden_states.shape
+
+        if latent_indices is None:
+            latent_indices = torch.arange(0, T).unsqueeze(0).expand(B, -1)
+
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        rope_freqs = self.rope(frame_indices=latent_indices, height=H, width=W, device=hidden_states.device)
+        rope_freqs = rope_freqs.flatten(2).transpose(1, 2)
+
+        if clean_latents is not None and clean_latent_indices is not None:
+            clean_latents = clean_latents.to(hidden_states)
+            clean_latents = self.gradient_checkpointing_method(self.clean_x_embedder.proj, clean_latents)
+            clean_latents = clean_latents.flatten(2).transpose(1, 2)
+
+            clean_latent_rope_freqs = self.rope(frame_indices=clean_latent_indices, height=H, width=W, device=clean_latents.device)
+            clean_latent_rope_freqs = clean_latent_rope_freqs.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([clean_latents, hidden_states], dim=1)
+            rope_freqs = torch.cat([clean_latent_rope_freqs, rope_freqs], dim=1)
+
+        if clean_latents_2x is not None and clean_latent_2x_indices is not None:
+            clean_latents_2x = clean_latents_2x.to(hidden_states)
+            clean_latents_2x = pad_for_3d_conv(clean_latents_2x, (2, 4, 4))
+            clean_latents_2x = self.gradient_checkpointing_method(self.clean_x_embedder.proj_2x, clean_latents_2x)
+            clean_latents_2x = clean_latents_2x.flatten(2).transpose(1, 2)
+
+            clean_latent_2x_rope_freqs = self.rope(frame_indices=clean_latent_2x_indices, height=H, width=W, device=clean_latents_2x.device)
+            clean_latent_2x_rope_freqs = pad_for_3d_conv(clean_latent_2x_rope_freqs, (2, 2, 2))
+            clean_latent_2x_rope_freqs = center_down_sample_3d(clean_latent_2x_rope_freqs, (2, 2, 2))
+            clean_latent_2x_rope_freqs = clean_latent_2x_rope_freqs.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([clean_latents_2x, hidden_states], dim=1)
+            rope_freqs = torch.cat([clean_latent_2x_rope_freqs, rope_freqs], dim=1)
+
+        if clean_latents_4x is not None and clean_latent_4x_indices is not None:
+            clean_latents_4x = clean_latents_4x.to(hidden_states)
+            clean_latents_4x = pad_for_3d_conv(clean_latents_4x, (4, 8, 8))
+            clean_latents_4x = self.gradient_checkpointing_method(self.clean_x_embedder.proj_4x, clean_latents_4x)
+            clean_latents_4x = clean_latents_4x.flatten(2).transpose(1, 2)
+
+            clean_latent_4x_rope_freqs = self.rope(frame_indices=clean_latent_4x_indices, height=H, width=W, device=clean_latents_4x.device)
+            clean_latent_4x_rope_freqs = pad_for_3d_conv(clean_latent_4x_rope_freqs, (4, 4, 4))
+            clean_latent_4x_rope_freqs = center_down_sample_3d(clean_latent_4x_rope_freqs, (4, 4, 4))
+            clean_latent_4x_rope_freqs = clean_latent_4x_rope_freqs.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([clean_latents_4x, hidden_states], dim=1)
+            rope_freqs = torch.cat([clean_latent_4x_rope_freqs, rope_freqs], dim=1)
+
+        return hidden_states, rope_freqs
+
+    def forward(
+            self,
+            hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance,
+            latent_indices=None,
+            clean_latents=None, clean_latent_indices=None,
+            clean_latents_2x=None, clean_latent_2x_indices=None,
+            clean_latents_4x=None, clean_latent_4x_indices=None,
+            image_embeddings=None,
+            attention_kwargs=None, return_dict=True
+    ):
+
+        if attention_kwargs is None:
+            attention_kwargs = {}
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p, p_t = self.config['patch_size'], self.config['patch_size_t']
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p
+        post_patch_width = width // p
+        original_context_length = post_patch_num_frames * post_patch_height * post_patch_width
+
+        hidden_states, rope_freqs = self.process_input_hidden_states(hidden_states, latent_indices, clean_latents, clean_latent_indices, clean_latents_2x, clean_latent_2x_indices, clean_latents_4x, clean_latent_4x_indices)
+
+        temb = self.gradient_checkpointing_method(self.time_text_embed, timestep, guidance, pooled_projections)
+        encoder_hidden_states = self.gradient_checkpointing_method(self.context_embedder, encoder_hidden_states, timestep, encoder_attention_mask)
+
+        if self.image_projection is not None:
+            assert image_embeddings is not None, 'You must use image embeddings!'
+            extra_encoder_hidden_states = self.gradient_checkpointing_method(self.image_projection, image_embeddings)
+            extra_attention_mask = torch.ones((batch_size, extra_encoder_hidden_states.shape[1]), dtype=encoder_attention_mask.dtype, device=encoder_attention_mask.device)
+
+            # must cat before (not after) encoder_hidden_states, due to attn masking
+            encoder_hidden_states = torch.cat([extra_encoder_hidden_states, encoder_hidden_states], dim=1)
+            encoder_attention_mask = torch.cat([extra_attention_mask, encoder_attention_mask], dim=1)
+
+        if batch_size == 1:
+            # When batch size is 1, we do not need any masks or var-len funcs since cropping is mathematically same to what we want
+            # If they are not same, then their impls are wrong. Ours are always the correct one.
+            text_len = encoder_attention_mask.sum().item()
+            encoder_hidden_states = encoder_hidden_states[:, :text_len]
+            attention_mask = None, None, None, None
+        else:
+            img_seq_len = hidden_states.shape[1]
+            txt_seq_len = encoder_hidden_states.shape[1]
+
+            cu_seqlens_q = get_cu_seqlens(encoder_attention_mask, img_seq_len)
+            cu_seqlens_kv = cu_seqlens_q
+            max_seqlen_q = img_seq_len + txt_seq_len
+            max_seqlen_kv = max_seqlen_q
+
+            attention_mask = cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv
+
+        if self.enable_teacache:
+            modulated_inp = self.transformer_blocks[0].norm1(hidden_states, emb=temb)[0]
+
+            if self.cnt == 0 or self.cnt == self.num_steps-1:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+            else:
+                curr_rel_l1 = ((modulated_inp - self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()
+                self.accumulated_rel_l1_distance += self.teacache_rescale_func(curr_rel_l1)
+                should_calc = self.accumulated_rel_l1_distance >= self.rel_l1_thresh
+
+                if should_calc:
+                    self.accumulated_rel_l1_distance = 0
+
+            self.previous_modulated_input = modulated_inp
+            self.cnt += 1
+
+            if self.cnt == self.num_steps:
+                self.cnt = 0
+
+            if not should_calc:
+                hidden_states = hidden_states + self.previous_residual
+            else:
+                ori_hidden_states = hidden_states.clone()
+
+                for block_id, block in enumerate(self.transformer_blocks):
+                    hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
+                        block,
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        attention_mask,
+                        rope_freqs
+                    )
+
+                for block_id, block in enumerate(self.single_transformer_blocks):
+                    hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
+                        block,
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        attention_mask,
+                        rope_freqs
+                    )
+
+                self.previous_residual = hidden_states - ori_hidden_states
+        else:
+            for block_id, block in enumerate(self.transformer_blocks):
+                hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask,
+                    rope_freqs
+                )
+
+            for block_id, block in enumerate(self.single_transformer_blocks):
+                hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    attention_mask,
+                    rope_freqs
+                )
+
+        hidden_states = self.gradient_checkpointing_method(self.norm_out, hidden_states, temb)
+
+        hidden_states = hidden_states[:, -original_context_length:, :]
+
+        if self.high_quality_fp32_output_for_inference:
+            hidden_states = hidden_states.to(dtype=torch.float32)
+            if self.proj_out.weight.dtype != torch.float32:
+                self.proj_out.to(dtype=torch.float32)
+
+        hidden_states = self.gradient_checkpointing_method(self.proj_out, hidden_states)
+
+        hidden_states = einops.rearrange(hidden_states, 'b (t h w) (c pt ph pw) -> b c (t pt) (h ph) (w pw)',
+                                         t=post_patch_num_frames, h=post_patch_height, w=post_patch_width,
+                                         pt=p_t, ph=p, pw=p)
+
+        if return_dict:
+            return Transformer2DModelOutput(sample=hidden_states)
+
+        return hidden_states,

diffusers_helper/pipelines/k_diffusion_hunyuan.py

@@ -0,0 +1,120 @@
+import torch
+import math
+
+from diffusers_helper.k_diffusion.uni_pc_fm import sample_unipc
+from diffusers_helper.k_diffusion.wrapper import fm_wrapper
+from diffusers_helper.utils import repeat_to_batch_size
+
+
+def flux_time_shift(t, mu=1.15, sigma=1.0):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def calculate_flux_mu(context_length, x1=256, y1=0.5, x2=4096, y2=1.15, exp_max=7.0):
+    k = (y2 - y1) / (x2 - x1)
+    b = y1 - k * x1
+    mu = k * context_length + b
+    mu = min(mu, math.log(exp_max))
+    return mu
+
+
+def get_flux_sigmas_from_mu(n, mu):
+    sigmas = torch.linspace(1, 0, steps=n + 1)
+    sigmas = flux_time_shift(sigmas, mu=mu)
+    return sigmas
+
+
+@torch.inference_mode()
+def sample_hunyuan(
+        transformer,
+        sampler='unipc',
+        initial_latent=None,
+        concat_latent=None,
+        strength=1.0,
+        width=512,
+        height=512,
+        frames=16,
+        real_guidance_scale=1.0,
+        distilled_guidance_scale=6.0,
+        guidance_rescale=0.0,
+        shift=None,
+        num_inference_steps=25,
+        batch_size=None,
+        generator=None,
+        prompt_embeds=None,
+        prompt_embeds_mask=None,
+        prompt_poolers=None,
+        negative_prompt_embeds=None,
+        negative_prompt_embeds_mask=None,
+        negative_prompt_poolers=None,
+        dtype=torch.bfloat16,
+        device=None,
+        negative_kwargs=None,
+        callback=None,
+        **kwargs,
+):
+    device = device or transformer.device
+
+    if batch_size is None:
+        batch_size = int(prompt_embeds.shape[0])
+
+    latents = torch.randn((batch_size, 16, (frames + 3) // 4, height // 8, width // 8), generator=generator, device=generator.device).to(device=device, dtype=torch.float32)
+
+    B, C, T, H, W = latents.shape
+    seq_length = T * H * W // 4
+
+    if shift is None:
+        mu = calculate_flux_mu(seq_length, exp_max=7.0)
+    else:
+        mu = math.log(shift)
+
+    sigmas = get_flux_sigmas_from_mu(num_inference_steps, mu).to(device)
+
+    k_model = fm_wrapper(transformer)
+
+    if initial_latent is not None:
+        sigmas = sigmas * strength
+        first_sigma = sigmas[0].to(device=device, dtype=torch.float32)
+        initial_latent = initial_latent.to(device=device, dtype=torch.float32)
+        latents = initial_latent.float() * (1.0 - first_sigma) + latents.float() * first_sigma
+
+    if concat_latent is not None:
+        concat_latent = concat_latent.to(latents)
+
+    distilled_guidance = torch.tensor([distilled_guidance_scale * 1000.0] * batch_size).to(device=device, dtype=dtype)
+
+    prompt_embeds = repeat_to_batch_size(prompt_embeds, batch_size)
+    prompt_embeds_mask = repeat_to_batch_size(prompt_embeds_mask, batch_size)
+    prompt_poolers = repeat_to_batch_size(prompt_poolers, batch_size)
+    negative_prompt_embeds = repeat_to_batch_size(negative_prompt_embeds, batch_size)
+    negative_prompt_embeds_mask = repeat_to_batch_size(negative_prompt_embeds_mask, batch_size)
+    negative_prompt_poolers = repeat_to_batch_size(negative_prompt_poolers, batch_size)
+    concat_latent = repeat_to_batch_size(concat_latent, batch_size)
+
+    sampler_kwargs = dict(
+        dtype=dtype,
+        cfg_scale=real_guidance_scale,
+        cfg_rescale=guidance_rescale,
+        concat_latent=concat_latent,
+        positive=dict(
+            pooled_projections=prompt_poolers,
+            encoder_hidden_states=prompt_embeds,
+            encoder_attention_mask=prompt_embeds_mask,
+            guidance=distilled_guidance,
+            **kwargs,
+        ),
+        negative=dict(
+            pooled_projections=negative_prompt_poolers,
+            encoder_hidden_states=negative_prompt_embeds,
+            encoder_attention_mask=negative_prompt_embeds_mask,
+            guidance=distilled_guidance,
+            **(kwargs if negative_kwargs is None else {**kwargs, **negative_kwargs}),
+        )
+    )
+
+    if sampler == 'unipc':
+        results = sample_unipc(k_model, latents, sigmas, extra_args=sampler_kwargs, disable=False, callback=callback)
+    else:
+        raise NotImplementedError(f'Sampler {sampler} is not supported.')
+
+    return results

diffusers_helper/thread_utils.py

@@ -0,0 +1,76 @@
+import time
+
+from threading import Thread, Lock
+
+
+class Listener:
+    task_queue = []
+    lock = Lock()
+    thread = None
+    
+    @classmethod
+    def _process_tasks(cls):
+        while True:
+            task = None
+            with cls.lock:
+                if cls.task_queue:
+                    task = cls.task_queue.pop(0)
+                    
+            if task is None:
+                time.sleep(0.001)
+                continue
+                
+            func, args, kwargs = task
+            try:
+                func(*args, **kwargs)
+            except Exception as e:
+                print(f"Error in listener thread: {e}")
+    
+    @classmethod
+    def add_task(cls, func, *args, **kwargs):
+        with cls.lock:
+            cls.task_queue.append((func, args, kwargs))
+
+        if cls.thread is None:
+            cls.thread = Thread(target=cls._process_tasks, daemon=True)
+            cls.thread.start()
+
+
+def async_run(func, *args, **kwargs):
+    Listener.add_task(func, *args, **kwargs)
+
+
+class FIFOQueue:
+    def __init__(self):
+        self.queue = []
+        self.lock = Lock()
+
+    def push(self, item):
+        with self.lock:
+            self.queue.append(item)
+
+    def pop(self):
+        with self.lock:
+            if self.queue:
+                return self.queue.pop(0)
+            return None
+
+    def top(self):
+        with self.lock:
+            if self.queue:
+                return self.queue[0]
+            return None
+
+    def next(self):
+        while True:
+            with self.lock:
+                if self.queue:
+                    return self.queue.pop(0)
+
+            time.sleep(0.001)
+
+
+class AsyncStream:
+    def __init__(self):
+        self.input_queue = FIFOQueue()
+        self.output_queue = FIFOQueue()

diffusers_helper/utils.py

@@ -0,0 +1,613 @@
+import os
+import cv2
+import json
+import random
+import glob
+import torch
+import einops
+import numpy as np
+import datetime
+import torchvision
+
+import safetensors.torch as sf
+from PIL import Image
+
+
+def min_resize(x, m):
+    if x.shape[0] < x.shape[1]:
+        s0 = m
+        s1 = int(float(m) / float(x.shape[0]) * float(x.shape[1]))
+    else:
+        s0 = int(float(m) / float(x.shape[1]) * float(x.shape[0]))
+        s1 = m
+    new_max = max(s1, s0)
+    raw_max = max(x.shape[0], x.shape[1])
+    if new_max < raw_max:
+        interpolation = cv2.INTER_AREA
+    else:
+        interpolation = cv2.INTER_LANCZOS4
+    y = cv2.resize(x, (s1, s0), interpolation=interpolation)
+    return y
+
+
+def d_resize(x, y):
+    H, W, C = y.shape
+    new_min = min(H, W)
+    raw_min = min(x.shape[0], x.shape[1])
+    if new_min < raw_min:
+        interpolation = cv2.INTER_AREA
+    else:
+        interpolation = cv2.INTER_LANCZOS4
+    y = cv2.resize(x, (W, H), interpolation=interpolation)
+    return y
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    if target_height == image.shape[0] and target_width == image.shape[1]:
+        return image
+
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+
+def resize_and_center_crop_pytorch(image, target_width, target_height):
+    B, C, H, W = image.shape
+
+    if H == target_height and W == target_width:
+        return image
+
+    scale_factor = max(target_width / W, target_height / H)
+    resized_width = int(round(W * scale_factor))
+    resized_height = int(round(H * scale_factor))
+
+    resized = torch.nn.functional.interpolate(image, size=(resized_height, resized_width), mode='bilinear', align_corners=False)
+
+    top = (resized_height - target_height) // 2
+    left = (resized_width - target_width) // 2
+    cropped = resized[:, :, top:top + target_height, left:left + target_width]
+
+    return cropped
+
+
+def resize_without_crop(image, target_width, target_height):
+    if target_height == image.shape[0] and target_width == image.shape[1]:
+        return image
+
+    pil_image = Image.fromarray(image)
+    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
+    return np.array(resized_image)
+
+
+def just_crop(image, w, h):
+    if h == image.shape[0] and w == image.shape[1]:
+        return image
+
+    original_height, original_width = image.shape[:2]
+    k = min(original_height / h, original_width / w)
+    new_width = int(round(w * k))
+    new_height = int(round(h * k))
+    x_start = (original_width - new_width) // 2
+    y_start = (original_height - new_height) // 2
+    cropped_image = image[y_start:y_start + new_height, x_start:x_start + new_width]
+    return cropped_image
+
+
+def write_to_json(data, file_path):
+    temp_file_path = file_path + ".tmp"
+    with open(temp_file_path, 'wt', encoding='utf-8') as temp_file:
+        json.dump(data, temp_file, indent=4)
+    os.replace(temp_file_path, file_path)
+    return
+
+
+def read_from_json(file_path):
+    with open(file_path, 'rt', encoding='utf-8') as file:
+        data = json.load(file)
+    return data
+
+
+def get_active_parameters(m):
+    return {k: v for k, v in m.named_parameters() if v.requires_grad}
+
+
+def cast_training_params(m, dtype=torch.float32):
+    result = {}
+    for n, param in m.named_parameters():
+        if param.requires_grad:
+            param.data = param.to(dtype)
+            result[n] = param
+    return result
+
+
+def separate_lora_AB(parameters, B_patterns=None):
+    parameters_normal = {}
+    parameters_B = {}
+
+    if B_patterns is None:
+        B_patterns = ['.lora_B.', '__zero__']
+
+    for k, v in parameters.items():
+        if any(B_pattern in k for B_pattern in B_patterns):
+            parameters_B[k] = v
+        else:
+            parameters_normal[k] = v
+
+    return parameters_normal, parameters_B
+
+
+def set_attr_recursive(obj, attr, value):
+    attrs = attr.split(".")
+    for name in attrs[:-1]:
+        obj = getattr(obj, name)
+    setattr(obj, attrs[-1], value)
+    return
+
+
+def print_tensor_list_size(tensors):
+    total_size = 0
+    total_elements = 0
+
+    if isinstance(tensors, dict):
+        tensors = tensors.values()
+
+    for tensor in tensors:
+        total_size += tensor.nelement() * tensor.element_size()
+        total_elements += tensor.nelement()
+
+    total_size_MB = total_size / (1024 ** 2)
+    total_elements_B = total_elements / 1e9
+
+    print(f"Total number of tensors: {len(tensors)}")
+    print(f"Total size of tensors: {total_size_MB:.2f} MB")
+    print(f"Total number of parameters: {total_elements_B:.3f} billion")
+    return
+
+
+@torch.no_grad()
+def batch_mixture(a, b=None, probability_a=0.5, mask_a=None):
+    batch_size = a.size(0)
+
+    if b is None:
+        b = torch.zeros_like(a)
+
+    if mask_a is None:
+        mask_a = torch.rand(batch_size) < probability_a
+
+    mask_a = mask_a.to(a.device)
+    mask_a = mask_a.reshape((batch_size,) + (1,) * (a.dim() - 1))
+    result = torch.where(mask_a, a, b)
+    return result
+
+
+@torch.no_grad()
+def zero_module(module):
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+@torch.no_grad()
+def supress_lower_channels(m, k, alpha=0.01):
+    data = m.weight.data.clone()
+
+    assert int(data.shape[1]) >= k
+
+    data[:, :k] = data[:, :k] * alpha
+    m.weight.data = data.contiguous().clone()
+    return m
+
+
+def freeze_module(m):
+    if not hasattr(m, '_forward_inside_frozen_module'):
+        m._forward_inside_frozen_module = m.forward
+    m.requires_grad_(False)
+    m.forward = torch.no_grad()(m.forward)
+    return m
+
+
+def get_latest_safetensors(folder_path):
+    safetensors_files = glob.glob(os.path.join(folder_path, '*.safetensors'))
+
+    if not safetensors_files:
+        raise ValueError('No file to resume!')
+
+    latest_file = max(safetensors_files, key=os.path.getmtime)
+    latest_file = os.path.abspath(os.path.realpath(latest_file))
+    return latest_file
+
+
+def generate_random_prompt_from_tags(tags_str, min_length=3, max_length=32):
+    tags = tags_str.split(', ')
+    tags = random.sample(tags, k=min(random.randint(min_length, max_length), len(tags)))
+    prompt = ', '.join(tags)
+    return prompt
+
+
+def interpolate_numbers(a, b, n, round_to_int=False, gamma=1.0):
+    numbers = a + (b - a) * (np.linspace(0, 1, n) ** gamma)
+    if round_to_int:
+        numbers = np.round(numbers).astype(int)
+    return numbers.tolist()
+
+
+def uniform_random_by_intervals(inclusive, exclusive, n, round_to_int=False):
+    edges = np.linspace(0, 1, n + 1)
+    points = np.random.uniform(edges[:-1], edges[1:])
+    numbers = inclusive + (exclusive - inclusive) * points
+    if round_to_int:
+        numbers = np.round(numbers).astype(int)
+    return numbers.tolist()
+
+
+def soft_append_bcthw(history, current, overlap=0):
+    if overlap <= 0:
+        return torch.cat([history, current], dim=2)
+
+    assert history.shape[2] >= overlap, f"History length ({history.shape[2]}) must be >= overlap ({overlap})"
+    assert current.shape[2] >= overlap, f"Current length ({current.shape[2]}) must be >= overlap ({overlap})"
+    
+    weights = torch.linspace(1, 0, overlap, dtype=history.dtype, device=history.device).view(1, 1, -1, 1, 1)
+    blended = weights * history[:, :, -overlap:] + (1 - weights) * current[:, :, :overlap]
+    output = torch.cat([history[:, :, :-overlap], blended, current[:, :, overlap:]], dim=2)
+
+    return output.to(history)
+
+
+def save_bcthw_as_mp4(x, output_filename, fps=10, crf=0):
+    b, c, t, h, w = x.shape
+
+    per_row = b
+    for p in [6, 5, 4, 3, 2]:
+        if b % p == 0:
+            per_row = p
+            break
+
+    os.makedirs(os.path.dirname(os.path.abspath(os.path.realpath(output_filename))), exist_ok=True)
+    x = torch.clamp(x.float(), -1., 1.) * 127.5 + 127.5
+    x = x.detach().cpu().to(torch.uint8)
+    x = einops.rearrange(x, '(m n) c t h w -> t (m h) (n w) c', n=per_row)
+    torchvision.io.write_video(output_filename, x, fps=fps, video_codec='libx264', options={'crf': str(int(crf))})
+    return x
+
+
+def save_bcthw_as_png(x, output_filename):
+    os.makedirs(os.path.dirname(os.path.abspath(os.path.realpath(output_filename))), exist_ok=True)
+    x = torch.clamp(x.float(), -1., 1.) * 127.5 + 127.5
+    x = x.detach().cpu().to(torch.uint8)
+    x = einops.rearrange(x, 'b c t h w -> c (b h) (t w)')
+    torchvision.io.write_png(x, output_filename)
+    return output_filename
+
+
+def save_bchw_as_png(x, output_filename):
+    os.makedirs(os.path.dirname(os.path.abspath(os.path.realpath(output_filename))), exist_ok=True)
+    x = torch.clamp(x.float(), -1., 1.) * 127.5 + 127.5
+    x = x.detach().cpu().to(torch.uint8)
+    x = einops.rearrange(x, 'b c h w -> c h (b w)')
+    torchvision.io.write_png(x, output_filename)
+    return output_filename
+
+
+def add_tensors_with_padding(tensor1, tensor2):
+    if tensor1.shape == tensor2.shape:
+        return tensor1 + tensor2
+
+    shape1 = tensor1.shape
+    shape2 = tensor2.shape
+
+    new_shape = tuple(max(s1, s2) for s1, s2 in zip(shape1, shape2))
+
+    padded_tensor1 = torch.zeros(new_shape)
+    padded_tensor2 = torch.zeros(new_shape)
+
+    padded_tensor1[tuple(slice(0, s) for s in shape1)] = tensor1
+    padded_tensor2[tuple(slice(0, s) for s in shape2)] = tensor2
+
+    result = padded_tensor1 + padded_tensor2
+    return result
+
+
+def print_free_mem():
+    torch.cuda.empty_cache()
+    free_mem, total_mem = torch.cuda.mem_get_info(0)
+    free_mem_mb = free_mem / (1024 ** 2)
+    total_mem_mb = total_mem / (1024 ** 2)
+    print(f"Free memory: {free_mem_mb:.2f} MB")
+    print(f"Total memory: {total_mem_mb:.2f} MB")
+    return
+
+
+def print_gpu_parameters(device, state_dict, log_count=1):
+    summary = {"device": device, "keys_count": len(state_dict)}
+
+    logged_params = {}
+    for i, (key, tensor) in enumerate(state_dict.items()):
+        if i >= log_count:
+            break
+        logged_params[key] = tensor.flatten()[:3].tolist()
+
+    summary["params"] = logged_params
+
+    print(str(summary))
+    return
+
+
+def visualize_txt_as_img(width, height, text, font_path='font/DejaVuSans.ttf', size=18):
+    from PIL import Image, ImageDraw, ImageFont
+
+    txt = Image.new("RGB", (width, height), color="white")
+    draw = ImageDraw.Draw(txt)
+    font = ImageFont.truetype(font_path, size=size)
+
+    if text == '':
+        return np.array(txt)
+
+    # Split text into lines that fit within the image width
+    lines = []
+    words = text.split()
+    current_line = words[0]
+
+    for word in words[1:]:
+        line_with_word = f"{current_line} {word}"
+        if draw.textbbox((0, 0), line_with_word, font=font)[2] <= width:
+            current_line = line_with_word
+        else:
+            lines.append(current_line)
+            current_line = word
+
+    lines.append(current_line)
+
+    # Draw the text line by line
+    y = 0
+    line_height = draw.textbbox((0, 0), "A", font=font)[3]
+
+    for line in lines:
+        if y + line_height > height:
+            break  # stop drawing if the next line will be outside the image
+        draw.text((0, y), line, fill="black", font=font)
+        y += line_height
+
+    return np.array(txt)
+
+
+def blue_mark(x):
+    x = x.copy()
+    c = x[:, :, 2]
+    b = cv2.blur(c, (9, 9))
+    x[:, :, 2] = ((c - b) * 16.0 + b).clip(-1, 1)
+    return x
+
+
+def green_mark(x):
+    x = x.copy()
+    x[:, :, 2] = -1
+    x[:, :, 0] = -1
+    return x
+
+
+def frame_mark(x):
+    x = x.copy()
+    x[:64] = -1
+    x[-64:] = -1
+    x[:, :8] = 1
+    x[:, -8:] = 1
+    return x
+
+
+@torch.inference_mode()
+def pytorch2numpy(imgs):
+    results = []
+    for x in imgs:
+        y = x.movedim(0, -1)
+        y = y * 127.5 + 127.5
+        y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
+        results.append(y)
+    return results
+
+
+@torch.inference_mode()
+def numpy2pytorch(imgs):
+    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.5 - 1.0
+    h = h.movedim(-1, 1)
+    return h
+
+
+@torch.no_grad()
+def duplicate_prefix_to_suffix(x, count, zero_out=False):
+    if zero_out:
+        return torch.cat([x, torch.zeros_like(x[:count])], dim=0)
+    else:
+        return torch.cat([x, x[:count]], dim=0)
+
+
+def weighted_mse(a, b, weight):
+    return torch.mean(weight.float() * (a.float() - b.float()) ** 2)
+
+
+def clamped_linear_interpolation(x, x_min, y_min, x_max, y_max, sigma=1.0):
+    x = (x - x_min) / (x_max - x_min)
+    x = max(0.0, min(x, 1.0))
+    x = x ** sigma
+    return y_min + x * (y_max - y_min)
+
+
+def expand_to_dims(x, target_dims):
+    return x.view(*x.shape, *([1] * max(0, target_dims - x.dim())))
+
+
+def repeat_to_batch_size(tensor: torch.Tensor, batch_size: int):
+    if tensor is None:
+        return None
+
+    first_dim = tensor.shape[0]
+
+    if first_dim == batch_size:
+        return tensor
+
+    if batch_size % first_dim != 0:
+        raise ValueError(f"Cannot evenly repeat first dim {first_dim} to match batch_size {batch_size}.")
+
+    repeat_times = batch_size // first_dim
+
+    return tensor.repeat(repeat_times, *[1] * (tensor.dim() - 1))
+
+
+def dim5(x):
+    return expand_to_dims(x, 5)
+
+
+def dim4(x):
+    return expand_to_dims(x, 4)
+
+
+def dim3(x):
+    return expand_to_dims(x, 3)
+
+
+def crop_or_pad_yield_mask(x, length):
+    B, F, C = x.shape
+    device = x.device
+    dtype = x.dtype
+
+    if F < length:
+        y = torch.zeros((B, length, C), dtype=dtype, device=device)
+        mask = torch.zeros((B, length), dtype=torch.bool, device=device)
+        y[:, :F, :] = x
+        mask[:, :F] = True
+        return y, mask
+
+    return x[:, :length, :], torch.ones((B, length), dtype=torch.bool, device=device)
+
+
+def extend_dim(x, dim, minimal_length, zero_pad=False):
+    original_length = int(x.shape[dim])
+
+    if original_length >= minimal_length:
+        return x
+
+    if zero_pad:
+        padding_shape = list(x.shape)
+        padding_shape[dim] = minimal_length - original_length
+        padding = torch.zeros(padding_shape, dtype=x.dtype, device=x.device)
+    else:
+        idx = (slice(None),) * dim + (slice(-1, None),) + (slice(None),) * (len(x.shape) - dim - 1)
+        last_element = x[idx]
+        padding = last_element.repeat_interleave(minimal_length - original_length, dim=dim)
+
+    return torch.cat([x, padding], dim=dim)
+
+
+def lazy_positional_encoding(t, repeats=None):
+    if not isinstance(t, list):
+        t = [t]
+
+    from diffusers.models.embeddings import get_timestep_embedding
+
+    te = torch.tensor(t)
+    te = get_timestep_embedding(timesteps=te, embedding_dim=256, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=1.0)
+
+    if repeats is None:
+        return te
+
+    te = te[:, None, :].expand(-1, repeats, -1)
+
+    return te
+
+
+def state_dict_offset_merge(A, B, C=None):
+    result = {}
+    keys = A.keys()
+
+    for key in keys:
+        A_value = A[key]
+        B_value = B[key].to(A_value)
+
+        if C is None:
+            result[key] = A_value + B_value
+        else:
+            C_value = C[key].to(A_value)
+            result[key] = A_value + B_value - C_value
+
+    return result
+
+
+def state_dict_weighted_merge(state_dicts, weights):
+    if len(state_dicts) != len(weights):
+        raise ValueError("Number of state dictionaries must match number of weights")
+
+    if not state_dicts:
+        return {}
+
+    total_weight = sum(weights)
+
+    if total_weight == 0:
+        raise ValueError("Sum of weights cannot be zero")
+
+    normalized_weights = [w / total_weight for w in weights]
+
+    keys = state_dicts[0].keys()
+    result = {}
+
+    for key in keys:
+        result[key] = state_dicts[0][key] * normalized_weights[0]
+
+        for i in range(1, len(state_dicts)):
+            state_dict_value = state_dicts[i][key].to(result[key])
+            result[key] += state_dict_value * normalized_weights[i]
+
+    return result
+
+
+def group_files_by_folder(all_files):
+    grouped_files = {}
+
+    for file in all_files:
+        folder_name = os.path.basename(os.path.dirname(file))
+        if folder_name not in grouped_files:
+            grouped_files[folder_name] = []
+        grouped_files[folder_name].append(file)
+
+    list_of_lists = list(grouped_files.values())
+    return list_of_lists
+
+
+def generate_timestamp():
+    now = datetime.datetime.now()
+    timestamp = now.strftime('%y%m%d_%H%M%S')
+    milliseconds = f"{int(now.microsecond / 1000):03d}"
+    random_number = random.randint(0, 9999)
+    return f"{timestamp}_{milliseconds}_{random_number}"
+
+
+def write_PIL_image_with_png_info(image, metadata, path):
+    from PIL.PngImagePlugin import PngInfo
+
+    png_info = PngInfo()
+    for key, value in metadata.items():
+        png_info.add_text(key, value)
+
+    image.save(path, "PNG", pnginfo=png_info)
+    return image
+
+
+def torch_safe_save(content, path):
+    torch.save(content, path + '_tmp')
+    os.replace(path + '_tmp', path)
+    return path
+
+
+def move_optimizer_to_device(optimizer, device):
+    for state in optimizer.state.values():
+        for k, v in state.items():
+            if isinstance(v, torch.Tensor):
+                state[k] = v.to(device)

img_examples/Example1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a906a1d14d1699f67ca54865c7aa5857e55246f4ec63bbaf3edcf359e73bebd1
+size 240647

requirements.txt

@@ -1,6 +1,24 @@
-diffusers
-transformers
-torch
-accelerate
-opencv-python
-numpy
+accelerate==1.7.0
+diffusers==0.33.1
+transformers==4.52.4
+sentencepiece==0.2.0
+pillow==11.2.1
+av==12.1.0
+numpy==1.26.2
+scipy==1.12.0
+requests==2.32.4
+torchsde==0.2.6
+torch>=2.0.0
+torchvision
+torchaudio
+einops
+opencv-contrib-python
+safetensors
+huggingface_hub
+decord
+imageio_ffmpeg==0.6.0
+sageattention==1.0.6
+xformers==0.0.29.post3
+bitsandbytes==0.46.0
+pillow-heif==0.22.0
+spaces[security]