Update app.py

app.py

@@ -1,13 +1,7 @@
-#############################################
-# from diffusers_helper.hf_login import login
-# 필요시 HF 로그인 사용 (주석 해제 후)
-#############################################
 
 import os
 
-os.environ['HF_HOME'] = os.path.abspath(
-    os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download'))
-)
+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
@@ -16,1028 +10,137 @@ import einops
 import safetensors.torch as sf
 import numpy as np
 import math
-import time
-
-# Hugging Face Spaces 환경 인지 확인
-IN_HF_SPACE = os.environ.get('SPACE_ID') is not None
-
-# --------- 번역 딕셔너리(영어 고정) ---------
-translations = {
-    "en": {
-        "title": "FramePack - Image to Video Generation",
-        "upload_image": "Upload Image",
-        "prompt": "Prompt",
-        "quick_prompts": "Quick Prompts",
-        "start_generation": "Generate",
-        "stop_generation": "Stop",
-        "use_teacache": "Use TeaCache",
-        "teacache_info": "Faster speed, but may result in slightly worse finger and hand generation.",
-        "negative_prompt": "Negative Prompt",
-        "seed": "Seed",
-        # 최대 4초로 UI 표기 수정
-        "video_length": "Video Length (max 4 seconds)",
-        "latent_window": "Latent Window Size",
-        "steps": "Inference Steps",
-        "steps_info": "Changing this value is not recommended.",
-        "cfg_scale": "CFG Scale",
-        "distilled_cfg": "Distilled CFG Scale",
-        "distilled_cfg_info": "Changing this value is not recommended.",
-        "cfg_rescale": "CFG Rescale",
-        "gpu_memory": "GPU Memory Preservation (GB) (larger means slower)",
-        "gpu_memory_info": "Set this to a larger value if you encounter OOM errors. Larger values cause slower speed.",
-        "next_latents": "Next Latents",
-        "generated_video": "Generated Video",
-        "sampling_note": "Note: The model predicts future frames from past frames. If the start action isn't immediately visible, please wait for more frames.",
-        "error_message": "Error",
-        "processing_error": "Processing error",
-        "network_error": "Network connection is unstable, model download timed out. Please try again later.",
-        "memory_error": "GPU memory insufficient, please try increasing GPU memory preservation value or reduce video length.",
-        "model_error": "Failed to load model, possibly due to network issues or high server load. Please try again later.",
-        "partial_video": "Processing error, but partial video has been generated",
-        "processing_interrupt": "Processing was interrupted, but partial video has been generated"
-    }
-}
-
-def get_translation(key):
-    return translations["en"].get(key, key)
-
-#############################################
-# diffusers_helper 관련 임포트
-#############################################
+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 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.utils import (
-    generate_timestamp,
-    save_bcthw_as_mp4,
-    resize_and_center_crop,
-    crop_or_pad_yield_mask,
-    soft_append_bcthw
-)
-from diffusers_helper.bucket_tools import find_nearest_bucket
-from diffusers_helper.hunyuan import (
-    encode_prompt_conds, vae_encode, vae_decode, vae_decode_fake
-)
+from transformers import SiglipImageProcessor, SiglipVisionModel
 from diffusers_helper.clip_vision import hf_clip_vision_encode
-from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
-from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
-
-from diffusers import AutoencoderKLHunyuanVideo
-from transformers import (
-    LlamaModel, CLIPTextModel,
-    LlamaTokenizerFast, CLIPTokenizer,
-    SiglipVisionModel, SiglipImageProcessor
-)
-
-#############################################
-# GPU 체크
-#############################################
-GPU_AVAILABLE = torch.cuda.is_available()
-free_mem_gb = 0.0
-high_vram = False
-if GPU_AVAILABLE:
-    try:
-        free_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
-        high_vram = (free_mem_gb > 60)
-    except:
-        pass
-print(f"GPU Available: {GPU_AVAILABLE}, free_mem_gb={free_mem_gb}, high_vram={high_vram}")
-
-cpu_fallback_mode = not GPU_AVAILABLE
-last_update_time = time.time()
-
-#############################################
-# 모델 로드 (전역)
-#############################################
-text_encoder = None
-text_encoder_2 = None
-tokenizer = None
-tokenizer_2 = None
-vae = None
-feature_extractor = None
-image_encoder = None
-transformer = None
-
-# 아래 로직은 질문에 제시된 '두 번째 코드'의 모델 로드 부분을 거의 그대로 사용
-def load_global_models():
-    global text_encoder, text_encoder_2, tokenizer, tokenizer_2
-    global vae, feature_extractor, image_encoder, transformer
-    global cpu_fallback_mode
-
-    # 이미 로드되었으면 패스
-    if transformer is not None:
-        return
-
-    # GPU 메모리 정보
-    device = gpu if GPU_AVAILABLE else cpu
-
-    # diffusers_helper.memory.get_cuda_free_memory_gb(gpu)로 더 정확히 구해도 됨
-    print("Loading models...")
-
-    # ======== 실 코드: 두 번째 예시 기준 =========
-    # (1) 하이브리드 (if high_vram -> GPU로 로드, 아니면 CPU + DynamicSwap)
-
-    # 반드시 float16, bfloat16로 로드
-    text_encoder_local = LlamaModel.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='text_encoder',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    text_encoder_2_local = CLIPTextModel.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='text_encoder_2',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    tokenizer_local = LlamaTokenizerFast.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='tokenizer'
-    )
-    tokenizer_2_local = CLIPTokenizer.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='tokenizer_2'
-    )
-
-    vae_local = AutoencoderKLHunyuanVideo.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='vae',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    feature_extractor_local = SiglipImageProcessor.from_pretrained(
-        "lllyasviel/flux_redux_bfl", subfolder='feature_extractor'
-    )
-    image_encoder_local = SiglipVisionModel.from_pretrained(
-        "lllyasviel/flux_redux_bfl",
-        subfolder='image_encoder',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    # FramePack_F1_I2V_HY_20250503 (bfloat16)
-    transformer_local = HunyuanVideoTransformer3DModelPacked.from_pretrained(
-        'lllyasviel/FramePack_F1_I2V_HY_20250503',
-        torch_dtype=torch.bfloat16
-    ).cpu()
-
-    # eval & dtype
-    vae_local.eval()
-    text_encoder_local.eval()
-    text_encoder_2_local.eval()
-    image_encoder_local.eval()
-    transformer_local.eval()
-
-    # VAE slicing for low VRAM
-    if not high_vram:
-        vae_local.enable_slicing()
-        vae_local.enable_tiling()
-
-    # 오프로드용
-    transformer_local.high_quality_fp32_output_for_inference = True
-    transformer_local.to(dtype=torch.bfloat16)
-    vae_local.to(dtype=torch.float16)
-    image_encoder_local.to(dtype=torch.float16)
-    text_encoder_local.to(dtype=torch.float16)
-    text_encoder_2_local.to(dtype=torch.float16)
-
-    # requires_grad_(False)
-    for m in [vae_local, text_encoder_local, text_encoder_2_local, image_encoder_local, transformer_local]:
-        m.requires_grad_(False)
-
-    # GPU 모드 & VRAM 많으면 전부 GPU
-    # 그렇지 않으면 DynamicSwap
-    if GPU_AVAILABLE:
-        if not high_vram:
-            DynamicSwapInstaller.install_model(transformer_local, device=gpu)
-            DynamicSwapInstaller.install_model(text_encoder_local, device=gpu)
-        else:
-            text_encoder_local.to(gpu)
-            text_encoder_2_local.to(gpu)
-            image_encoder_local.to(gpu)
-            vae_local.to(gpu)
-            transformer_local.to(gpu)
-    else:
-        cpu_fallback_mode = True
-
-    # 글로벌에 할당
-    print("Model loaded.")
-    text_encoder = text_encoder_local
-    text_encoder_2 = text_encoder_2_local
-    tokenizer = tokenizer_local
-    tokenizer_2 = tokenizer_2_local
-    vae = vae_local
-    feature_extractor = feature_extractor_local
-    image_encoder = image_encoder_local
-    transformer = transformer_local
-
-#############################################
-# Worker 로직 (두 번째 코드) 그대로
-#############################################
-stream = AsyncStream()
-
-outputs_folder = './outputs/'
-os.makedirs(outputs_folder, exist_ok=True)
-
-@torch.no_grad()
-def worker(
-    input_image, prompt, n_prompt, seed,
-    total_second_length, latent_window_size, steps,
-    cfg, gs, rs, gpu_memory_preservation, use_teacache
-):
-    """
-    실제 샘플링 로직 (두 번째 코드 기반)
-    """
-    load_global_models()  # 모델 로딩
-    global text_encoder, text_encoder_2, tokenizer, tokenizer_2
-    global vae, feature_extractor, image_encoder, transformer
-    global last_update_time
-
-    # 최대 4초로 고정
-    total_second_length = min(total_second_length, 4.0)
-
-    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:
-        # GPU 적을 경우 Unload
-        if not high_vram and GPU_AVAILABLE:
-            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 and GPU_AVAILABLE:
-            fake_diffusers_current_device(text_encoder, gpu)
-            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 == 1.0:
-            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_mask = crop_or_pad_yield_mask(llama_vec, length=512)
-        llama_vec_n, llama_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
-
-        # Image processing
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))
-
-        H, W, C = input_image.shape
-        height, width = find_nearest_bucket(H, W, resolution=640)
-
-        if cpu_fallback_mode:
-            height = min(height, 320)
-            width = min(width, 320)
-
-        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 encode
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
-
-        if not high_vram and GPU_AVAILABLE:
-            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 and GPU_AVAILABLE:
-            load_model_as_complete(image_encoder, target_device=gpu)
-        image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
-        image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
-
-        # dtype
-        llama_vec = llama_vec.to(transformer.dtype)
-        llama_vec_n = llama_vec_n.to(transformer.dtype)
-        clip_l_pooler = clip_l_pooler.to(transformer.dtype)
-        clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
-        image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
-
-        # Start sampling
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
-
-        rnd = torch.Generator("cpu").manual_seed(seed)
-
-        # 초기 history latents
-        history_latents = torch.zeros(size=(1, 16, 16 + 2 + 1, height // 8, width // 8), dtype=torch.float32).cpu()
-        history_pixels = None
-
-        # start_latent 붙이기
-        history_latents = torch.cat([history_latents, start_latent.to(history_latents)], dim=2)
-        total_generated_latent_frames = 1
-
-        for section_index in range(total_latent_sections):
-            if stream.input_queue.top() == 'end':
-                stream.output_queue.push(('end', None))
-                return
-
-            print(f'Section {section_index+1}/{total_latent_sections}')
-
-            if not high_vram and GPU_AVAILABLE:
-                unload_complete_models()
-                move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
-
-            # teacache
-            if use_teacache:
-                transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
-            else:
-                transformer.initialize_teacache(enable_teacache=False)
-
-            def callback(d):
-                preview = d['denoised']
-                preview = vae_decode_fake(preview)
-                preview = (preview * 255.0).detach().cpu().numpy().clip(0, 255).astype(np.uint8)
-                preview = einops.rearrange(preview, 'b c t h w -> (b h) (t w) c')
+from diffusers_helper.bucket_tools import find_nearest_bucket
 
-                if stream.input_queue.top() == 'end':
-                    stream.output_queue.push(('end', None))
-                    raise KeyboardInterrupt('User stops generation.')
 
-                current_step = d['i'] + 1
-                percentage = int(100.0 * current_step / steps)
-                hint = f'Sampling {current_step}/{steps}'
-                desc = f'Section {section_index+1}/{total_latent_sections}'
-                stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
-                return
+free_mem_gb = get_cuda_free_memory_gb(gpu)
+high_vram = free_mem_gb > 60
 
-            # indices
-            frames_per_section = latent_window_size * 4 - 3
-            indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
-            (
-                clean_latent_indices_start,
-                clean_latent_4x_indices,
-                clean_latent_2x_indices,
-                clean_latent_1x_indices,
-                latent_indices
-            ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
+print(f'Free VRAM {free_mem_gb} GB')
+print(f'High-VRAM Mode: {high_vram}')
 
-            clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
+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()
 
-            clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -19:, :, :].split([16, 2, 1], dim=2)
-            clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)
+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()
 
-            try:
-                generated_latents = sample_hunyuan(
-                    transformer=transformer,
-                    sampler='unipc',
-                    width=width,
-                    height=height,
-                    frames=frames_per_section,
-                    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_mask,
-                    prompt_poolers=clip_l_pooler,
-                    negative_prompt_embeds=llama_vec_n,
-                    negative_prompt_embeds_mask=llama_mask_n,
-                    negative_prompt_poolers=clip_l_pooler_n,
-                    device=gpu if GPU_AVAILABLE else cpu,
-                    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
-                )
-            except KeyboardInterrupt:
-                print("User cancelled.")
-                stream.output_queue.push(('end', None))
-                return
-            except Exception as e:
-                traceback.print_exc()
-                stream.output_queue.push(('end', None))
-                return
+transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained('lllyasviel/FramePack_F1_I2V_HY_20250503', torch_dtype=torch.bfloat16).cpu()
 
-            total_generated_latent_frames += generated_latents.shape[2]
-            history_latents = torch.cat([history_latents, generated_latents.to(history_latents)], dim=2)
+vae.eval()
+text_encoder.eval()
+text_encoder_2.eval()
+image_encoder.eval()
+transformer.eval()
 
-            if not high_vram and GPU_AVAILABLE:
-                offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
-                load_model_as_complete(vae, target_device=gpu)
+if not high_vram:
+    vae.enable_slicing()
+    vae.enable_tiling()
 
-            real_history_latents = history_latents[:, :, -total_generated_latent_frames:, :, :]
+transformer.high_quality_fp32_output_for_inference = True
+print('transformer.high_quality_fp32_output_for_inference = True')
 
-            if history_pixels is None:
-                history_pixels = vae_decode(real_history_latents, vae).cpu()
-            else:
-                section_latent_frames = latent_window_size * 2
-                overlapped_frames = frames_per_section
-                current_pixels = vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
-                history_pixels = soft_append_bcthw(history_pixels, current_pixels, overlapped_frames)
+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)
 
-            if not high_vram and GPU_AVAILABLE:
-                unload_complete_models()
+vae.requires_grad_(False)
+text_encoder.requires_grad_(False)
+text_encoder_2.requires_grad_(False)
+image_encoder.requires_grad_(False)
+transformer.requires_grad_(False)
 
-            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=16)  # CRF=16
+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.output_queue.push(('file', output_filename))
+stream = AsyncStream()
 
-    except:
-        traceback.print_exc()
-        if not high_vram and GPU_AVAILABLE:
-            unload_complete_models(text_encoder, text_encoder_2, image_encoder, vae, transformer)
+outputs_folder = './outputs/'
+os.makedirs(outputs_folder, exist_ok=True)
 
-    stream.output_queue.push(('end', None))
-    return
+examples = [
+["img_examples/1.png", "The girl dances gracefully, with clear movements, full of charm.",],
+["img_examples/2.jpg", "The man dances flamboyantly, swinging his hips and striking bold poses with dramatic flair."],
+["img_examples/3.png", "The woman dances elegantly among the blossoms, spinning slowly with flowing sleeves and graceful hand movements."],
+]
 
-def end_process():
-    """
-    중단 요청
-    """
-    global stream
-    stream.input_queue.push('end')
+def generate_examples(input_image, prompt):
+    
+    t2v=False 
+    n_prompt=""
+    seed=31337
+    total_second_length=5 
+    latent_window_size=9 
+    steps=25 
+    cfg=1.0 
+    gs=10.0 
+    rs=0.0
+    gpu_memory_preservation=6 
+    use_teacache=True 
+    mp4_crf=16
 
-# Gradio에서 이 worker 함수를 비동기로 호출
-def process(
-    input_image, prompt, n_prompt, seed,
-    total_second_length, latent_window_size, steps,
-    cfg, gs, rs, gpu_memory_preservation, use_teacache
-):
     global stream
-    if input_image is None:
-        raise ValueError("No input image provided.")
+    
+    # assert input_image is not None, 'No input image!'
+    if t2v:
+        default_height, default_width = 640, 640
+        input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
+        print("No input image provided. Using a blank white image.") 
 
-    yield None, None, "", "", gr.update(interactive=False), gr.update(interactive=True)
+    yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
 
     stream = AsyncStream()
-    async_run(
-        worker,
-        input_image, prompt, n_prompt, seed,
-        total_second_length, latent_window_size, steps,
-        cfg, gs, rs, gpu_memory_preservation, use_teacache
-    )
+
+    async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf)
 
     output_filename = None
-    prev_filename = None
-    error_message = None
 
     while True:
         flag, data = stream.output_queue.next()
+
         if flag == 'file':
             output_filename = data
-            prev_filename = output_filename
-            yield output_filename, gr.update(), gr.update(), "", gr.update(interactive=False), gr.update(interactive=True)
+            yield output_filename, gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True)
 
-        elif flag == 'progress':
+        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)
 
-        elif flag == 'error':
-            error_message = data
-            print(f"Error: {error_message}")
-
-        elif flag == 'end':
-            if output_filename is None and prev_filename:
-                output_filename = prev_filename
-            # 에러가 있었으면 에러 표시
-            if error_message:
-                yield (
-                    output_filename,  # 마지막 파일 (또는 None)
-                    gr.update(visible=False),
-                    gr.update(),
-                    f"<div style='color:red;'>{error_message}</div>",
-                    gr.update(interactive=True),
-                    gr.update(interactive=False)
-                )
-            else:
-                yield (
-                    output_filename, gr.update(visible=False), gr.update(), "", gr.update(interactive=True), gr.update(interactive=False)
-                )
+        if flag == 'end':
+            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
             break
 
-# UI CSS
-def make_custom_css():
-    base_progress_css = make_progress_bar_css()
-    pastel_css = """
-    body {
-        background: #faf9ff !important; 
-        font-family: "Noto Sans", sans-serif;
-    }
-    #app-container {
-        max-width: 1200px;
-        margin: 0 auto;
-        padding: 1rem;
-        position: relative;
-    }
-    #app-container h1 {
-        color: #5F5AA2;
-        margin-bottom: 1.2rem;
-        font-weight: 700;
-        text-shadow: 1px 1px 2px #bbb;
-    }
-    .gr-panel {
-        background: #ffffffcc;
-        border: 1px solid #e1dff0;
-        border-radius: 8px;
-        padding: 1rem;
-        box-shadow: 0 1px 3px rgba(0,0,0,0.1);
-    }
-    .button-container button {
-        min-height: 45px;
-        font-size: 1rem;
-        font-weight: 600;
-        border-radius: 6px;
-    }
-    .button-container button#start-button {
-        background-color: #A289E3 !important;
-        color: #fff !important;
-        border: 1px solid #a58de2;
-    }
-    .button-container button#stop-button {
-        background-color: #F48A9B !important;
-        color: #fff !important;
-        border: 1px solid #f18fa0;
-    }
-    .button-container button:hover {
-        filter: brightness(0.95);
-    }
-    .preview-container, .video-container {
-        border: 1px solid #ded9f2;
-        border-radius: 8px;
-        overflow: hidden;
-    }
-    .progress-container {
-        margin-top: 15px;
-        margin-bottom: 15px;
-    }
-    .error-message {
-        background-color: #FFF5F5;
-        border: 1px solid #FED7D7;
-        color: #E53E3E;
-        padding: 10px;
-        border-radius: 4px;
-        margin-top: 10px;
-        font-weight: 500;
-    }
-    @media (max-width: 768px) {
-        #app-container {
-            padding: 0.5rem;
-        }
-        .mobile-full-width {
-            flex-direction: column !important;
-        }
-        .mobile-full-width > .gr-block {
-            width: 100% !important;
-        }
-    }
-    """
-    return base_progress_css + pastel_css
-
-css = make_custom_css()
-
-# 샘플 프롬프트
-quick_prompts = [
-    ["The girl dances gracefully, with clear movements, full of charm."],
-    ["A character doing some simple body movements."]
-]
-
-# Gradio UI
-block = gr.Blocks(css=css).queue()
-with block:
-    gr.HTML("<div id='app-container'><h1>FramePack - Image to Video Generation</h1></div>")
-
-    with gr.Row(elem_classes="mobile-full-width"):
-        # 왼쪽
-        with gr.Column(scale=1, elem_classes="gr-panel"):
-            input_image = gr.Image(
-                label=get_translation("upload_image"),
-                type="numpy",
-                height=320
-            )
-            prompt = gr.Textbox(
-                label=get_translation("prompt"),
-                value=''
-            )
-
-            example_quick_prompts = gr.Dataset(
-                samples=quick_prompts,
-                label=get_translation("quick_prompts"),
-                samples_per_page=1000,
-                components=[prompt]
-            )
-            example_quick_prompts.click(
-                fn=lambda x: x[0],
-                inputs=[example_quick_prompts],
-                outputs=prompt,
-                show_progress=False,
-                queue=False
-            )
-
-        # 오른쪽
-        with gr.Column(scale=1, elem_classes="gr-panel"):
-            with gr.Row(elem_classes="button-container"):
-                start_button = gr.Button(
-                    value=get_translation("start_generation"),
-                    elem_id="start-button",
-                    variant="primary"
-                )
-                stop_button = gr.Button(
-                    value=get_translation("stop_generation"),
-                    elem_id="stop-button",
-                    interactive=False
-                )
-
-            result_video = gr.Video(
-                label=get_translation("generated_video"),
-                autoplay=True,
-                loop=True,
-                height=320,
-                elem_classes="video-container"
-            )
-            preview_image = gr.Image(
-                label=get_translation("next_latents"),
-                visible=False,
-                height=150,
-                elem_classes="preview-container"
-            )
-            gr.Markdown(get_translation("sampling_note"))
-
-            with gr.Group(elem_classes="progress-container"):
-                progress_desc = gr.Markdown('')
-                progress_bar = gr.HTML('')
-
-            error_message = gr.HTML('', visible=True)
-
-    # Advanced
-    with gr.Accordion("Advanced Settings", open=False, elem_classes="gr-panel"):
-        use_teacache = gr.Checkbox(
-            label=get_translation("use_teacache"),
-            value=True,
-            info=get_translation("teacache_info")
-        )
-        n_prompt = gr.Textbox(label=get_translation("negative_prompt"), value="", visible=False)
-        seed = gr.Number(
-            label=get_translation("seed"),
-            value=31337,
-            precision=0
-        )
-        # 기본 2초, 최대 4초
-        total_second_length = gr.Slider(
-            label=get_translation("video_length"),
-            minimum=1,
-            maximum=4,
-            value=2,
-            step=0.1
-        )
-        latent_window_size = gr.Slider(
-            label=get_translation("latent_window"),
-            minimum=1,
-            maximum=33,
-            value=9,
-            step=1,
-            visible=False
-        )
-        steps = gr.Slider(
-            label=get_translation("steps"),
-            minimum=1,
-            maximum=100,
-            value=25,
-            step=1,
-            info=get_translation("steps_info")
-        )
-        cfg = gr.Slider(
-            label=get_translation("cfg_scale"),
-            minimum=1.0,
-            maximum=32.0,
-            value=1.0,
-            step=0.01,
-            visible=False
-        )
-        gs = gr.Slider(
-            label=get_translation("distilled_cfg"),
-            minimum=1.0,
-            maximum=32.0,
-            value=10.0,
-            step=0.01,
-            info=get_translation("distilled_cfg_info")
-        )
-        rs = gr.Slider(
-            label=get_translation("cfg_rescale"),
-            minimum=0.0,
-            maximum=1.0,
-            value=0.0,
-            step=0.01,
-            visible=False
-        )
-        gpu_memory_preservation = gr.Slider(
-            label=get_translation("gpu_memory"),
-            minimum=6,
-            maximum=128,
-            value=6,
-            step=0.1,
-            info=get_translation("gpu_memory_info")
-        )
-
-    # 버튼 처리
-    inputs_list = [
-        input_image, prompt, n_prompt, seed,
-        total_second_length, latent_window_size, steps,
-        cfg, gs, rs, gpu_memory_preservation, use_teacache
-    ]
-    start_button.click(
-        fn=process,
-        inputs=inputs_list,
-        outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, stop_button]
-    )
-    stop_button.click(fn=end_process)
-
-block.launch()
-#############################################
-# from diffusers_helper.hf_login import login
-# 필요시 HF 로그인 사용 (주석 해제 후)
-#############################################
-
-import os
-
-os.environ['HF_HOME'] = os.path.abspath(
-    os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download'))
-)
-
-import gradio as gr
-import torch
-import traceback
-import einops
-import safetensors.torch as sf
-import numpy as np
-import math
-import time
-
-# Hugging Face Spaces 환경 인지 확인
-IN_HF_SPACE = os.environ.get('SPACE_ID') is not None
-
-# --------- 번역 딕셔너리(영어 고정) ---------
-translations = {
-    "en": {
-        "title": "FramePack - Image to Video Generation",
-        "upload_image": "Upload Image",
-        "prompt": "Prompt",
-        "quick_prompts": "Quick Prompts",
-        "start_generation": "Generate",
-        "stop_generation": "Stop",
-        "use_teacache": "Use TeaCache",
-        "teacache_info": "Faster speed, but may result in slightly worse finger and hand generation.",
-        "negative_prompt": "Negative Prompt",
-        "seed": "Seed",
-        # 최대 4초로 UI 표기 수정
-        "video_length": "Video Length (max 4 seconds)",
-        "latent_window": "Latent Window Size",
-        "steps": "Inference Steps",
-        "steps_info": "Changing this value is not recommended.",
-        "cfg_scale": "CFG Scale",
-        "distilled_cfg": "Distilled CFG Scale",
-        "distilled_cfg_info": "Changing this value is not recommended.",
-        "cfg_rescale": "CFG Rescale",
-        "gpu_memory": "GPU Memory Preservation (GB) (larger means slower)",
-        "gpu_memory_info": "Set this to a larger value if you encounter OOM errors. Larger values cause slower speed.",
-        "next_latents": "Next Latents",
-        "generated_video": "Generated Video",
-        "sampling_note": "Note: The model predicts future frames from past frames. If the start action isn't immediately visible, please wait for more frames.",
-        "error_message": "Error",
-        "processing_error": "Processing error",
-        "network_error": "Network connection is unstable, model download timed out. Please try again later.",
-        "memory_error": "GPU memory insufficient, please try increasing GPU memory preservation value or reduce video length.",
-        "model_error": "Failed to load model, possibly due to network issues or high server load. Please try again later.",
-        "partial_video": "Processing error, but partial video has been generated",
-        "processing_interrupt": "Processing was interrupted, but partial video has been generated"
-    }
-}
-
-def get_translation(key):
-    return translations["en"].get(key, key)
-
-#############################################
-# diffusers_helper 관련 임포트
-#############################################
-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 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.utils import (
-    generate_timestamp,
-    save_bcthw_as_mp4,
-    resize_and_center_crop,
-    crop_or_pad_yield_mask,
-    soft_append_bcthw
-)
-from diffusers_helper.bucket_tools import find_nearest_bucket
-from diffusers_helper.hunyuan import (
-    encode_prompt_conds, vae_encode, vae_decode, vae_decode_fake
-)
-from diffusers_helper.clip_vision import hf_clip_vision_encode
-from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
-from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
-
-from diffusers import AutoencoderKLHunyuanVideo
-from transformers import (
-    LlamaModel, CLIPTextModel,
-    LlamaTokenizerFast, CLIPTokenizer,
-    SiglipVisionModel, SiglipImageProcessor
-)
-
-#############################################
-# GPU 체크
-#############################################
-GPU_AVAILABLE = torch.cuda.is_available()
-free_mem_gb = 0.0
-high_vram = False
-if GPU_AVAILABLE:
-    try:
-        free_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
-        high_vram = (free_mem_gb > 60)
-    except:
-        pass
-print(f"GPU Available: {GPU_AVAILABLE}, free_mem_gb={free_mem_gb}, high_vram={high_vram}")
-
-cpu_fallback_mode = not GPU_AVAILABLE
-last_update_time = time.time()
-
-#############################################
-# 모델 로드 (전역)
-#############################################
-text_encoder = None
-text_encoder_2 = None
-tokenizer = None
-tokenizer_2 = None
-vae = None
-feature_extractor = None
-image_encoder = None
-transformer = None
-
-# 아래 로직은 질문에 제시된 '두 번째 코드'의 모델 로드 부분을 거의 그대로 사용
-def load_global_models():
-    global text_encoder, text_encoder_2, tokenizer, tokenizer_2
-    global vae, feature_extractor, image_encoder, transformer
-    global cpu_fallback_mode
-
-    # 이미 로드되었으면 패스
-    if transformer is not None:
-        return
-
-    # GPU 메모리 정보
-    device = gpu if GPU_AVAILABLE else cpu
-
-    # diffusers_helper.memory.get_cuda_free_memory_gb(gpu)로 더 정확히 구해도 됨
-    print("Loading models...")
-
-    # ======== 실 코드: 두 번째 예시 기준 =========
-    # (1) 하이브리드 (if high_vram -> GPU로 로드, 아니면 CPU + DynamicSwap)
-
-    # 반드시 float16, bfloat16로 로드
-    text_encoder_local = LlamaModel.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='text_encoder',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    text_encoder_2_local = CLIPTextModel.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='text_encoder_2',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    tokenizer_local = LlamaTokenizerFast.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='tokenizer'
-    )
-    tokenizer_2_local = CLIPTokenizer.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='tokenizer_2'
-    )
-
-    vae_local = AutoencoderKLHunyuanVideo.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo",
-        subfolder='vae',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    feature_extractor_local = SiglipImageProcessor.from_pretrained(
-        "lllyasviel/flux_redux_bfl", subfolder='feature_extractor'
-    )
-    image_encoder_local = SiglipVisionModel.from_pretrained(
-        "lllyasviel/flux_redux_bfl",
-        subfolder='image_encoder',
-        torch_dtype=torch.float16
-    ).cpu()
-
-    # FramePack_F1_I2V_HY_20250503 (bfloat16)
-    transformer_local = HunyuanVideoTransformer3DModelPacked.from_pretrained(
-        'lllyasviel/FramePack_F1_I2V_HY_20250503',
-        torch_dtype=torch.bfloat16
-    ).cpu()
-
-    # eval & dtype
-    vae_local.eval()
-    text_encoder_local.eval()
-    text_encoder_2_local.eval()
-    image_encoder_local.eval()
-    transformer_local.eval()
-
-    # VAE slicing for low VRAM
-    if not high_vram:
-        vae_local.enable_slicing()
-        vae_local.enable_tiling()
-
-    # 오프로드용
-    transformer_local.high_quality_fp32_output_for_inference = True
-    transformer_local.to(dtype=torch.bfloat16)
-    vae_local.to(dtype=torch.float16)
-    image_encoder_local.to(dtype=torch.float16)
-    text_encoder_local.to(dtype=torch.float16)
-    text_encoder_2_local.to(dtype=torch.float16)
-
-    # requires_grad_(False)
-    for m in [vae_local, text_encoder_local, text_encoder_2_local, image_encoder_local, transformer_local]:
-        m.requires_grad_(False)
-
-    # GPU 모드 & VRAM 많으면 전부 GPU
-    # 그렇지 않으면 DynamicSwap
-    if GPU_AVAILABLE:
-        if not high_vram:
-            DynamicSwapInstaller.install_model(transformer_local, device=gpu)
-            DynamicSwapInstaller.install_model(text_encoder_local, device=gpu)
-        else:
-            text_encoder_local.to(gpu)
-            text_encoder_2_local.to(gpu)
-            image_encoder_local.to(gpu)
-            vae_local.to(gpu)
-            transformer_local.to(gpu)
-    else:
-        cpu_fallback_mode = True
-
-    # 글로벌에 할당
-    print("Model loaded.")
-    text_encoder = text_encoder_local
-    text_encoder_2 = text_encoder_2_local
-    tokenizer = tokenizer_local
-    tokenizer_2 = tokenizer_2_local
-    vae = vae_local
-    feature_extractor = feature_extractor_local
-    image_encoder = image_encoder_local
-    transformer = transformer_local
-
-#############################################
-# Worker 로직 (두 번째 코드) 그대로
-#############################################
-stream = AsyncStream()
-
-outputs_folder = './outputs/'
-os.makedirs(outputs_folder, exist_ok=True)
 
+    
 @torch.no_grad()
-def worker(
-    input_image, prompt, n_prompt, seed,
-    total_second_length, latent_window_size, steps,
-    cfg, gs, rs, gpu_memory_preservation, use_teacache
-):
-    """
-    실제 샘플링 로직 (두 번째 코드 기반)
-    """
-    load_global_models()  # 모델 로딩
-    global text_encoder, text_encoder_2, tokenizer, tokenizer_2
-    global vae, feature_extractor, image_encoder, transformer
-    global last_update_time
-
-    # 최대 4초로 고정
-    total_second_length = min(total_second_length, 4.0)
-
+def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
     total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
     total_latent_sections = int(max(round(total_latent_sections), 1))
 
@@ -1046,38 +149,36 @@ def worker(
     stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))
 
     try:
-        # GPU 적을 경우 Unload
-        if not high_vram and GPU_AVAILABLE:
+        # 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 and GPU_AVAILABLE:
-            fake_diffusers_current_device(text_encoder, gpu)
+        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 == 1.0:
+
+        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_mask = crop_or_pad_yield_mask(llama_vec, length=512)
-        llama_vec_n, llama_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
+        llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
+        llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
+
+        # Processing input image
 
-        # Image processing
         stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Image processing ...'))))
 
         H, W, C = input_image.shape
         height, width = find_nearest_bucket(H, W, resolution=640)
-
-        if cpu_fallback_mode:
-            height = min(height, 320)
-            width = min(width, 320)
-
         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'))
@@ -1085,38 +186,42 @@ def worker(
         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 encode
+        # VAE encoding
+
         stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
 
-        if not high_vram and GPU_AVAILABLE:
+        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 and GPU_AVAILABLE:
+        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
+        # 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)
 
-        # Start sampling
+        # Sampling
+
         stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Start sampling ...'))))
 
         rnd = torch.Generator("cpu").manual_seed(seed)
 
-        # 초기 history latents
         history_latents = torch.zeros(size=(1, 16, 16 + 2 + 1, height // 8, width // 8), dtype=torch.float32).cpu()
         history_pixels = None
 
-        # start_latent 붙이기
         history_latents = torch.cat([history_latents, start_latent.to(history_latents)], dim=2)
         total_generated_latent_frames = 1
 
@@ -1125,13 +230,12 @@ def worker(
                 stream.output_queue.push(('end', None))
                 return
 
-            print(f'Section {section_index+1}/{total_latent_sections}')
+            print(f'section_index = {section_index}, total_latent_sections = {total_latent_sections}')
 
-            if not high_vram and GPU_AVAILABLE:
+            if not high_vram:
                 unload_complete_models()
                 move_model_to_device_with_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=gpu_memory_preservation)
 
-            # teacache
             if use_teacache:
                 transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
             else:
@@ -1140,79 +244,63 @@ def worker(
             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 stops generation.')
+                    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'Section {section_index+1}/{total_latent_sections}'
+                desc = f'Total generated frames: {int(max(0, total_generated_latent_frames * 4 - 3))}, Video length: {max(0, (total_generated_latent_frames * 4 - 3) / 30) :.2f} seconds (FPS-30). The video is being extended now ...'
                 stream.output_queue.push(('progress', (preview, desc, make_progress_bar_html(percentage, hint))))
                 return
 
-            # indices
-            frames_per_section = latent_window_size * 4 - 3
             indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
-            (
-                clean_latent_indices_start,
-                clean_latent_4x_indices,
-                clean_latent_2x_indices,
-                clean_latent_1x_indices,
-                latent_indices
-            ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
-
+            clean_latent_indices_start, clean_latent_4x_indices, clean_latent_2x_indices, clean_latent_1x_indices, latent_indices = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
             clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)
 
-            clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -19:, :, :].split([16, 2, 1], dim=2)
+            clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents[:, :, -sum([16, 2, 1]):, :, :].split([16, 2, 1], dim=2)
             clean_latents = torch.cat([start_latent.to(history_latents), clean_latents_1x], dim=2)
 
-            try:
-                generated_latents = sample_hunyuan(
-                    transformer=transformer,
-                    sampler='unipc',
-                    width=width,
-                    height=height,
-                    frames=frames_per_section,
-                    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_mask,
-                    prompt_poolers=clip_l_pooler,
-                    negative_prompt_embeds=llama_vec_n,
-                    negative_prompt_embeds_mask=llama_mask_n,
-                    negative_prompt_poolers=clip_l_pooler_n,
-                    device=gpu if GPU_AVAILABLE else cpu,
-                    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
-                )
-            except KeyboardInterrupt:
-                print("User cancelled.")
-                stream.output_queue.push(('end', None))
-                return
-            except Exception as e:
-                traceback.print_exc()
-                stream.output_queue.push(('end', None))
-                return
+            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 += generated_latents.shape[2]
+            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 and GPU_AVAILABLE:
+            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)
 
@@ -1222,325 +310,178 @@ def worker(
                 history_pixels = vae_decode(real_history_latents, vae).cpu()
             else:
                 section_latent_frames = latent_window_size * 2
-                overlapped_frames = frames_per_section
+                overlapped_frames = latent_window_size * 4 - 3
+
                 current_pixels = vae_decode(real_history_latents[:, :, -section_latent_frames:], vae).cpu()
                 history_pixels = soft_append_bcthw(history_pixels, current_pixels, overlapped_frames)
 
-            if not high_vram and GPU_AVAILABLE:
+            if not high_vram:
                 unload_complete_models()
 
             output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
-            save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=16)  # CRF=16
 
-            stream.output_queue.push(('file', output_filename))
+            save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=mp4_crf)
 
+            print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
+
+            stream.output_queue.push(('file', output_filename))
     except:
         traceback.print_exc()
-        if not high_vram and GPU_AVAILABLE:
-            unload_complete_models(text_encoder, text_encoder_2, image_encoder, vae, transformer)
+
+        if not high_vram:
+            unload_complete_models(
+                text_encoder, text_encoder_2, image_encoder, vae, transformer
+            )
 
     stream.output_queue.push(('end', None))
     return
 
-def end_process():
-    """
-    중단 요청
-    """
+def get_duration(input_image, prompt, t2v, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
+    return total_second_length * 60
+
+@spaces.GPU(duration=get_duration)
+def process(input_image, prompt, 
+            t2v=False, 
+            n_prompt="", 
+            seed=31337, 
+            total_second_length=5, 
+            latent_window_size=9, 
+            steps=25, 
+            cfg=1.0, 
+            gs=10.0, 
+            rs=0.0, 
+            gpu_memory_preservation=6, 
+            use_teacache=True, 
+            mp4_crf=16
+           ):
     global stream
-    stream.input_queue.push('end')
-
-# Gradio에서 이 worker 함수를 비동기로 호출
-def process(
-    input_image, prompt, n_prompt, seed,
-    total_second_length, latent_window_size, steps,
-    cfg, gs, rs, gpu_memory_preservation, use_teacache
-):
-    global stream
-    if input_image is None:
-        raise ValueError("No input image provided.")
-
-    yield None, None, "", "", gr.update(interactive=False), gr.update(interactive=True)
+    
+    # assert input_image is not None, 'No input image!'
+    if t2v:
+        default_height, default_width = 640, 640
+        input_image = np.ones((default_height, default_width, 3), dtype=np.uint8) * 255
+        print("No input image provided. Using a blank white image.")
+    else:
+        composite_rgba_uint8 = input_image["composite"] 
+
+        # rgb_uint8 will be (H, W, 3), dtype uint8
+        rgb_uint8 = composite_rgba_uint8[:, :, :3]
+        # mask_uint8 will be (H, W), dtype uint8
+        mask_uint8 = composite_rgba_uint8[:, :, 3]
+    
+        # Create background
+        h, w = rgb_uint8.shape[:2]
+        # White background, (H, W, 3), dtype uint8
+        background_uint8 = np.full((h, w, 3), 255, dtype=np.uint8) 
+        
+        # Normalize mask to range [0.0, 1.0]. 
+        alpha_normalized_float32 = mask_uint8.astype(np.float32) / 255.0
+        
+        # Expand alpha to 3 channels to match RGB images for broadcasting.
+        # alpha_mask_float32 will have shape (H, W, 3)
+        alpha_mask_float32 = np.stack([alpha_normalized_float32] * 3, axis=2)
+        
+        # alpha blending
+        blended_image_float32 = rgb_uint8.astype(np.float32) * alpha_mask_float32 + \
+                                background_uint8.astype(np.float32) * (1.0 - alpha_mask_float32)
+
+        input_image = np.clip(blended_image_float32, 0, 255).astype(np.uint8)
+    
+    yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
 
     stream = AsyncStream()
-    async_run(
-        worker,
-        input_image, prompt, n_prompt, seed,
-        total_second_length, latent_window_size, steps,
-        cfg, gs, rs, gpu_memory_preservation, use_teacache
-    )
+
+    async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf)
 
     output_filename = None
-    prev_filename = None
-    error_message = None
 
     while True:
         flag, data = stream.output_queue.next()
+
         if flag == 'file':
             output_filename = data
-            prev_filename = output_filename
-            yield output_filename, gr.update(), gr.update(), "", gr.update(interactive=False), gr.update(interactive=True)
+            yield output_filename, gr.update(), gr.update(), gr.update(), gr.update(interactive=False), gr.update(interactive=True)
 
-        elif flag == 'progress':
+        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)
 
-        elif flag == 'error':
-            error_message = data
-            print(f"Error: {error_message}")
-
-        elif flag == 'end':
-            if output_filename is None and prev_filename:
-                output_filename = prev_filename
-            # 에러가 있었으면 에러 표시
-            if error_message:
-                yield (
-                    output_filename,  # 마지막 파일 (또는 None)
-                    gr.update(visible=False),
-                    gr.update(),
-                    f"<div style='color:red;'>{error_message}</div>",
-                    gr.update(interactive=True),
-                    gr.update(interactive=False)
-                )
-            else:
-                yield (
-                    output_filename, gr.update(visible=False), gr.update(), "", gr.update(interactive=True), gr.update(interactive=False)
-                )
+        if flag == 'end':
+            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
             break
 
-# UI CSS
-def make_custom_css():
-    base_progress_css = make_progress_bar_css()
-    pastel_css = """
-    body {
-        background: #faf9ff !important; 
-        font-family: "Noto Sans", sans-serif;
-    }
-    #app-container {
-        max-width: 1200px;
-        margin: 0 auto;
-        padding: 1rem;
-        position: relative;
-    }
-    #app-container h1 {
-        color: #5F5AA2;
-        margin-bottom: 1.2rem;
-        font-weight: 700;
-        text-shadow: 1px 1px 2px #bbb;
-    }
-    .gr-panel {
-        background: #ffffffcc;
-        border: 1px solid #e1dff0;
-        border-radius: 8px;
-        padding: 1rem;
-        box-shadow: 0 1px 3px rgba(0,0,0,0.1);
-    }
-    .button-container button {
-        min-height: 45px;
-        font-size: 1rem;
-        font-weight: 600;
-        border-radius: 6px;
-    }
-    .button-container button#start-button {
-        background-color: #A289E3 !important;
-        color: #fff !important;
-        border: 1px solid #a58de2;
-    }
-    .button-container button#stop-button {
-        background-color: #F48A9B !important;
-        color: #fff !important;
-        border: 1px solid #f18fa0;
-    }
-    .button-container button:hover {
-        filter: brightness(0.95);
-    }
-    .preview-container, .video-container {
-        border: 1px solid #ded9f2;
-        border-radius: 8px;
-        overflow: hidden;
-    }
-    .progress-container {
-        margin-top: 15px;
-        margin-bottom: 15px;
-    }
-    .error-message {
-        background-color: #FFF5F5;
-        border: 1px solid #FED7D7;
-        color: #E53E3E;
-        padding: 10px;
-        border-radius: 4px;
-        margin-top: 10px;
-        font-weight: 500;
-    }
-    @media (max-width: 768px) {
-        #app-container {
-            padding: 0.5rem;
-        }
-        .mobile-full-width {
-            flex-direction: column !important;
-        }
-        .mobile-full-width > .gr-block {
-            width: 100% !important;
-        }
-    }
-    """
-    return base_progress_css + pastel_css
-
-css = make_custom_css()
-
-# 샘플 프롬프트
+
+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."]
+    '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]
 
-# Gradio UI
+
+css = make_progress_bar_css()
 block = gr.Blocks(css=css).queue()
 with block:
-    gr.HTML("<div id='app-container'><h1>FramePack - Image to Video Generation</h1></div>")
-
-    with gr.Row(elem_classes="mobile-full-width"):
-        # 왼쪽
-        with gr.Column(scale=1, elem_classes="gr-panel"):
-            input_image = gr.Image(
-                label=get_translation("upload_image"),
-                type="numpy",
-                height=320
-            )
-            prompt = gr.Textbox(
-                label=get_translation("prompt"),
-                value=''
-            )
-
-            example_quick_prompts = gr.Dataset(
-                samples=quick_prompts,
-                label=get_translation("quick_prompts"),
-                samples_per_page=1000,
-                components=[prompt]
-            )
-            example_quick_prompts.click(
-                fn=lambda x: x[0],
-                inputs=[example_quick_prompts],
-                outputs=prompt,
-                show_progress=False,
-                queue=False
-            )
-
-        # 오른쪽
-        with gr.Column(scale=1, elem_classes="gr-panel"):
-            with gr.Row(elem_classes="button-container"):
-                start_button = gr.Button(
-                    value=get_translation("start_generation"),
-                    elem_id="start-button",
-                    variant="primary"
-                )
-                stop_button = gr.Button(
-                    value=get_translation("stop_generation"),
-                    elem_id="stop-button",
-                    interactive=False
-                )
-
-            result_video = gr.Video(
-                label=get_translation("generated_video"),
-                autoplay=True,
-                loop=True,
-                height=320,
-                elem_classes="video-container"
-            )
-            preview_image = gr.Image(
-                label=get_translation("next_latents"),
-                visible=False,
-                height=150,
-                elem_classes="preview-container"
-            )
-            gr.Markdown(get_translation("sampling_note"))
-
-            with gr.Group(elem_classes="progress-container"):
-                progress_desc = gr.Markdown('')
-                progress_bar = gr.HTML('')
-
-            error_message = gr.HTML('', visible=True)
-
-    # Advanced
-    with gr.Accordion("Advanced Settings", open=False, elem_classes="gr-panel"):
-        use_teacache = gr.Checkbox(
-            label=get_translation("use_teacache"),
-            value=True,
-            info=get_translation("teacache_info")
-        )
-        n_prompt = gr.Textbox(label=get_translation("negative_prompt"), value="", visible=False)
-        seed = gr.Number(
-            label=get_translation("seed"),
-            value=31337,
-            precision=0
-        )
-        # 기본 2초, 최대 4초
-        total_second_length = gr.Slider(
-            label=get_translation("video_length"),
-            minimum=1,
-            maximum=4,
-            value=2,
-            step=0.1
-        )
-        latent_window_size = gr.Slider(
-            label=get_translation("latent_window"),
-            minimum=1,
-            maximum=33,
-            value=9,
-            step=1,
-            visible=False
-        )
-        steps = gr.Slider(
-            label=get_translation("steps"),
-            minimum=1,
-            maximum=100,
-            value=25,
-            step=1,
-            info=get_translation("steps_info")
-        )
-        cfg = gr.Slider(
-            label=get_translation("cfg_scale"),
-            minimum=1.0,
-            maximum=32.0,
-            value=1.0,
-            step=0.01,
-            visible=False
-        )
-        gs = gr.Slider(
-            label=get_translation("distilled_cfg"),
-            minimum=1.0,
-            maximum=32.0,
-            value=10.0,
-            step=0.01,
-            info=get_translation("distilled_cfg_info")
-        )
-        rs = gr.Slider(
-            label=get_translation("cfg_rescale"),
-            minimum=0.0,
-            maximum=1.0,
-            value=0.0,
-            step=0.01,
-            visible=False
-        )
-        gpu_memory_preservation = gr.Slider(
-            label=get_translation("gpu_memory"),
-            minimum=6,
-            maximum=128,
-            value=6,
-            step=0.1,
-            info=get_translation("gpu_memory_info")
-        )
-
-    # 버튼 처리
-    inputs_list = [
-        input_image, prompt, n_prompt, seed,
-        total_second_length, latent_window_size, steps,
-        cfg, gs, rs, gpu_memory_preservation, use_teacache
-    ]
-    start_button.click(
-        fn=process,
-        inputs=inputs_list,
-        outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, stop_button]
-    )
-    stop_button.click(fn=end_process)
-
-block.launch()
+    gr.Markdown('# FramePack-F1')
+    gr.Markdown(f"""### Video diffusion, but feels like image diffusion
+*FramePack F1 - a FramePack model that only predicts future frames from history frames*
+### *beta* FramePack Fill 🖋️- draw a mask over the input image to inpaint the video output
+adapted from the officical code repo [FramePack](https://github.com/lllyasviel/FramePack) by [lllyasviel](lllyasviel/FramePack_F1_I2V_HY_20250503) and [FramePack Studio](https://github.com/colinurbs/FramePack-Studio) 🙌🏻
+    """)
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.ImageEditor(type="numpy", label="Image", height=320, brush=gr.Brush(colors=["#ffffff"]))
+            prompt = gr.Textbox(label="Prompt", value='')
+            t2v = gr.Checkbox(label="do text-to-video", value=False)
+            example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Quick List', samples_per_page=1000, components=[prompt])
+            example_quick_prompts.click(lambda x: x[0], inputs=[example_quick_prompts], outputs=prompt, show_progress=False, queue=False)
+
+            with gr.Row():
+                start_button = gr.Button(value="Start Generation")
+                end_button = gr.Button(value="End Generation", interactive=False)
+
+            total_second_length = gr.Slider(label="Total Video Length (Seconds)", minimum=1, maximum=5, value=2, step=0.1)
+            with gr.Group():
+                with gr.Accordion("Advanced settings", open=False):
+                    use_teacache = gr.Checkbox(label='Use TeaCache', value=True, info='Faster speed, but often makes hands and fingers slightly worse.')
+    
+                    n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=False)  # Not used
+                    seed = gr.Number(label="Seed", value=31337, precision=0)
+    
+                    
+                    latent_window_size = gr.Slider(label="Latent Window Size", minimum=1, maximum=33, value=9, step=1, visible=False)  # Should not change
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1, info='Changing this value is not recommended.')
+    
+                    cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=1.0, step=0.01, visible=False)  # Should not change
+                    gs = gr.Slider(label="Distilled CFG Scale", minimum=1.0, maximum=32.0, value=10.0, step=0.01, info='Changing this value is not recommended.')
+                    rs = gr.Slider(label="CFG Re-Scale", minimum=0.0, maximum=1.0, value=0.0, step=0.01, visible=False)  # Should not change
+    
+                    gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
+    
+                    mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")
+
+        with gr.Column():
+            preview_image = gr.Image(label="Next Latents", height=200, visible=False)
+            result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
+            progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
+            progress_bar = gr.HTML('', elem_classes='no-generating-animation')
+
+    gr.HTML('<div style="text-align:center; margin-top:20px;">Share your results and find ideas at the <a href="https://x.com/search?q=framepack&f=live" target="_blank">FramePack Twitter (X) thread</a></div>')
+
+    ips = [input_image, prompt, t2v, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf]
+    start_button.click(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
+    end_button.click(fn=end_process)
+
+    # gr.Examples(
+    #     examples,
+    #     inputs=[input_image, prompt],
+    #     outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button],
+    #     fn=generate_examples,
+    #     cache_examples=True
+    #     )
+
+
+block.launch(share=True)