Update app.py

app.py

@@ -11,476 +11,92 @@ import tempfile
 from PIL import Image
 from huggingface_hub import hf_hub_download
 import shutil
-
-from inference import (
-    create_ltx_video_pipeline,
-    create_latent_upsampler,
-    load_image_to_tensor_with_resize_and_crop,
-    seed_everething,
-    get_device,
-    calculate_padding,
-    load_media_file
-)
-from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXMultiScalePipeline, LTXVideoPipeline
-from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
-
-config_file_path = "configs/ltxv-13b-0.9.7-distilled.yaml"
-with open(config_file_path, "r") as file:
-    PIPELINE_CONFIG_YAML = yaml.safe_load(file)
-
-LTX_REPO = "Lightricks/LTX-Video"
-MAX_IMAGE_SIZE = PIPELINE_CONFIG_YAML.get("max_resolution", 1280)
-MAX_NUM_FRAMES = 257
-
-FPS = 30.0 
-
-# --- Global variables for loaded models ---
-pipeline_instance = None
-latent_upsampler_instance = None
-models_dir = "downloaded_models_gradio_cpu_init"
-Path(models_dir).mkdir(parents=True, exist_ok=True)
-
-print("Downloading models (if not present)...")
-distilled_model_actual_path = hf_hub_download(
-    repo_id=LTX_REPO,
-    filename=PIPELINE_CONFIG_YAML["checkpoint_path"],
-    local_dir=models_dir,
-    local_dir_use_symlinks=False
-)
-PIPELINE_CONFIG_YAML["checkpoint_path"] = distilled_model_actual_path
-print(f"Distilled model path: {distilled_model_actual_path}")
-
-SPATIAL_UPSCALER_FILENAME = PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"]
-spatial_upscaler_actual_path = hf_hub_download(
-    repo_id=LTX_REPO,
-    filename=SPATIAL_UPSCALER_FILENAME,
-    local_dir=models_dir,
-    local_dir_use_symlinks=False
-)
-PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"] = spatial_upscaler_actual_path
-print(f"Spatial upscaler model path: {spatial_upscaler_actual_path}")
-
-print("Creating LTX Video pipeline on CPU...")
-pipeline_instance = create_ltx_video_pipeline(
-    ckpt_path=PIPELINE_CONFIG_YAML["checkpoint_path"],
-    precision=PIPELINE_CONFIG_YAML["precision"],
-    text_encoder_model_name_or_path=PIPELINE_CONFIG_YAML["text_encoder_model_name_or_path"],
-    sampler=PIPELINE_CONFIG_YAML["sampler"],
-    device="cpu",
-    enhance_prompt=False,
-    prompt_enhancer_image_caption_model_name_or_path=PIPELINE_CONFIG_YAML["prompt_enhancer_image_caption_model_name_or_path"],
-    prompt_enhancer_llm_model_name_or_path=PIPELINE_CONFIG_YAML["prompt_enhancer_llm_model_name_or_path"],
-)
-print("LTX Video pipeline created on CPU.")
-
-if PIPELINE_CONFIG_YAML.get("spatial_upscaler_model_path"):
-    print("Creating latent upsampler on CPU...")
-    latent_upsampler_instance = create_latent_upsampler(
-        PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"],
-        device="cpu"
-    )
-    print("Latent upsampler created on CPU.")
-
-target_inference_device = "cuda"
-print(f"Target inference device: {target_inference_device}")
-pipeline_instance.to(target_inference_device)
-if latent_upsampler_instance: 
-    latent_upsampler_instance.to(target_inference_device)
-
-
-# --- Helper function for dimension calculation ---
-MIN_DIM_SLIDER = 256  # As defined in the sliders minimum attribute
-TARGET_FIXED_SIDE = 768 # Desired fixed side length as per requirement
-
-def calculate_new_dimensions(orig_w, orig_h):
-    """
-    Calculates new dimensions for height and width sliders based on original media dimensions.
-    Ensures one side is TARGET_FIXED_SIDE, the other is scaled proportionally,
-    both are multiples of 32, and within [MIN_DIM_SLIDER, MAX_IMAGE_SIZE].
-    """
-    if orig_w == 0 or orig_h == 0:
-        # Default to TARGET_FIXED_SIDE square if original dimensions are invalid
-        return int(TARGET_FIXED_SIDE), int(TARGET_FIXED_SIDE)
-
-    if orig_w >= orig_h:  # Landscape or square
-        new_h = TARGET_FIXED_SIDE
-        aspect_ratio = orig_w / orig_h
-        new_w_ideal = new_h * aspect_ratio
-        
-        # Round to nearest multiple of 32
-        new_w = round(new_w_ideal / 32) * 32
-        
-        # Clamp to [MIN_DIM_SLIDER, MAX_IMAGE_SIZE]
-        new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))
-        # Ensure new_h is also clamped (TARGET_FIXED_SIDE should be within these bounds if configured correctly)
-        new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE)) 
-    else:  # Portrait
-        new_w = TARGET_FIXED_SIDE
-        aspect_ratio = orig_h / orig_w # Use H/W ratio for portrait scaling
-        new_h_ideal = new_w * aspect_ratio
-        
-        # Round to nearest multiple of 32
-        new_h = round(new_h_ideal / 32) * 32
-        
-        # Clamp to [MIN_DIM_SLIDER, MAX_IMAGE_SIZE]
-        new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE))
-        # Ensure new_w is also clamped
-        new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))
-
-    return int(new_h), int(new_w)
-
-def get_duration(prompt, negative_prompt, input_image_filepath, input_video_filepath,
-             height_ui, width_ui, mode,
-             duration_ui, # Removed ui_steps
-             ui_frames_to_use,
-             seed_ui, randomize_seed, ui_guidance_scale, improve_texture_flag,
-             progress):
-    if duration_ui > 7:
-        return 75
-    else:
-        return 60
-
-@spaces.GPU(duration=get_duration)
-def generate(prompt, negative_prompt, input_image_filepath, input_video_filepath,
-             height_ui, width_ui, mode,
-             duration_ui, 
-             ui_frames_to_use,
-             seed_ui, randomize_seed, ui_guidance_scale, improve_texture_flag,
-             progress=gr.Progress(track_tqdm=True)):
-
-    if randomize_seed:
-        seed_ui = random.randint(0, 2**32 - 1)
-    seed_everething(int(seed_ui))
-    
-    target_frames_ideal = duration_ui * FPS
-    target_frames_rounded = round(target_frames_ideal)
-    if target_frames_rounded < 1: 
-        target_frames_rounded = 1
-    
-    n_val = round((float(target_frames_rounded) - 1.0) / 8.0)
-    actual_num_frames = int(n_val * 8 + 1)
-
-    actual_num_frames = max(9, actual_num_frames)
-    actual_num_frames = min(MAX_NUM_FRAMES, actual_num_frames)
-    
-    actual_height = int(height_ui)
-    actual_width = int(width_ui)
-
-    height_padded = ((actual_height - 1) // 32 + 1) * 32
-    width_padded = ((actual_width - 1) // 32 + 1) * 32
-    num_frames_padded = ((actual_num_frames - 2) // 8 + 1) * 8 + 1 
-    if num_frames_padded != actual_num_frames:
-        print(f"Warning: actual_num_frames ({actual_num_frames}) and num_frames_padded ({num_frames_padded}) differ. Using num_frames_padded for pipeline.")
-    
-    padding_values = calculate_padding(actual_height, actual_width, height_padded, width_padded)
-
-    call_kwargs = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "height": height_padded,
-        "width": width_padded,
-        "num_frames": num_frames_padded, 
-        "frame_rate": int(FPS), 
-        "generator": torch.Generator(device=target_inference_device).manual_seed(int(seed_ui)),
-        "output_type": "pt", 
-        "conditioning_items": None,
-        "media_items": None,
-        "decode_timestep": PIPELINE_CONFIG_YAML["decode_timestep"],
-        "decode_noise_scale": PIPELINE_CONFIG_YAML["decode_noise_scale"],
-        "stochastic_sampling": PIPELINE_CONFIG_YAML["stochastic_sampling"],
-        "image_cond_noise_scale": 0.15,
-        "is_video": True,
-        "vae_per_channel_normalize": True,
-        "mixed_precision": (PIPELINE_CONFIG_YAML["precision"] == "mixed_precision"),
-        "offload_to_cpu": False,
-        "enhance_prompt": False,
-    }
-
-    stg_mode_str = PIPELINE_CONFIG_YAML.get("stg_mode", "attention_values")
-    if stg_mode_str.lower() in ["stg_av", "attention_values"]:
-        call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.AttentionValues
-    elif stg_mode_str.lower() in ["stg_as", "attention_skip"]:
-        call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.AttentionSkip
-    elif stg_mode_str.lower() in ["stg_r", "residual"]:
-        call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.Residual
-    elif stg_mode_str.lower() in ["stg_t", "transformer_block"]:
-        call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.TransformerBlock
-    else:
-        raise ValueError(f"Invalid stg_mode: {stg_mode_str}")
-
-    if mode == "image-to-video" and input_image_filepath:
-        try:
-            media_tensor = load_image_to_tensor_with_resize_and_crop(
-                input_image_filepath, actual_height, actual_width
-            )
-            media_tensor = torch.nn.functional.pad(media_tensor, padding_values)
-            call_kwargs["conditioning_items"] = [ConditioningItem(media_tensor.to(target_inference_device), 0, 1.0)]
-        except Exception as e:
-            print(f"Error loading image {input_image_filepath}: {e}")
-            raise gr.Error(f"Could not load image: {e}")
-    elif mode == "video-to-video" and input_video_filepath:
-        try:
-            call_kwargs["media_items"] = load_media_file(
-                media_path=input_video_filepath,
-                height=actual_height, 
-                width=actual_width,
-                max_frames=int(ui_frames_to_use), 
-                padding=padding_values
-            ).to(target_inference_device)
-        except Exception as e:
-            print(f"Error loading video {input_video_filepath}: {e}")
-            raise gr.Error(f"Could not load video: {e}")
-
-    print(f"Moving models to {target_inference_device} for inference (if not already there)...")
-    
-    active_latent_upsampler = None
-    if improve_texture_flag and latent_upsampler_instance:
-        active_latent_upsampler = latent_upsampler_instance
-
-    result_images_tensor = None
-    if improve_texture_flag:
-        if not active_latent_upsampler:
-            raise gr.Error("Spatial upscaler model not loaded or improve_texture not selected, cannot use multi-scale.")
-        
-        multi_scale_pipeline_obj = LTXMultiScalePipeline(pipeline_instance, active_latent_upsampler)
-        
-        first_pass_args = PIPELINE_CONFIG_YAML.get("first_pass", {}).copy()
-        first_pass_args["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML
-        # num_inference_steps will be derived from len(timesteps) in the pipeline
-        first_pass_args.pop("num_inference_steps", None)
-
-
-        second_pass_args = PIPELINE_CONFIG_YAML.get("second_pass", {}).copy()
-        second_pass_args["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML
-        # num_inference_steps will be derived from len(timesteps) in the pipeline
-        second_pass_args.pop("num_inference_steps", None)
-        
-        multi_scale_call_kwargs = call_kwargs.copy()
-        multi_scale_call_kwargs.update({
-            "downscale_factor": PIPELINE_CONFIG_YAML["downscale_factor"],
-            "first_pass": first_pass_args,
-            "second_pass": second_pass_args,
-        })
-        
-        print(f"Calling multi-scale pipeline (eff. HxW: {actual_height}x{actual_width}, Frames: {actual_num_frames} -> Padded: {num_frames_padded}) on {target_inference_device}")
-        result_images_tensor = multi_scale_pipeline_obj(**multi_scale_call_kwargs).images
-    else:
-        single_pass_call_kwargs = call_kwargs.copy()
-        first_pass_config_from_yaml = PIPELINE_CONFIG_YAML.get("first_pass", {})
-
-        single_pass_call_kwargs["timesteps"] = first_pass_config_from_yaml.get("timesteps")
-        single_pass_call_kwargs["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML
-        single_pass_call_kwargs["stg_scale"] = first_pass_config_from_yaml.get("stg_scale")
-        single_pass_call_kwargs["rescaling_scale"] = first_pass_config_from_yaml.get("rescaling_scale")
-        single_pass_call_kwargs["skip_block_list"] = first_pass_config_from_yaml.get("skip_block_list")
-        
-        # Remove keys that might conflict or are not used in single pass / handled by above
-        single_pass_call_kwargs.pop("num_inference_steps", None) 
-        single_pass_call_kwargs.pop("first_pass", None) 
-        single_pass_call_kwargs.pop("second_pass", None)
-        single_pass_call_kwargs.pop("downscale_factor", None)
-        
-        print(f"Calling base pipeline (padded HxW: {height_padded}x{width_padded}, Frames: {actual_num_frames} -> Padded: {num_frames_padded}) on {target_inference_device}")
-        result_images_tensor = pipeline_instance(**single_pass_call_kwargs).images
-
-    if result_images_tensor is None:
-        raise gr.Error("Generation failed.")
-
-    pad_left, pad_right, pad_top, pad_bottom = padding_values
-    slice_h_end = -pad_bottom if pad_bottom > 0 else None
-    slice_w_end = -pad_right if pad_right > 0 else None
-    
-    result_images_tensor = result_images_tensor[
-        :, :, :actual_num_frames, pad_top:slice_h_end, pad_left:slice_w_end
-    ]
-
-    video_np = result_images_tensor[0].permute(1, 2, 3, 0).cpu().float().numpy()
-    
-    video_np = np.clip(video_np, 0, 1) 
-    video_np = (video_np * 255).astype(np.uint8)
-
-    temp_dir = tempfile.mkdtemp()
-    timestamp = random.randint(10000,99999)
-    output_video_path = os.path.join(temp_dir, f"output_{timestamp}.mp4")
-    
-    try:
-        with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], macro_block_size=1) as video_writer:
-            for frame_idx in range(video_np.shape[0]):
-                progress(frame_idx / video_np.shape[0], desc="Saving video")
-                video_writer.append_data(video_np[frame_idx])
-    except Exception as e:
-        print(f"Error saving video with macro_block_size=1: {e}")
-        try:
-            with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], format='FFMPEG', codec='libx264', quality=8) as video_writer:
-                 for frame_idx in range(video_np.shape[0]):
-                    progress(frame_idx / video_np.shape[0], desc="Saving video (fallback ffmpeg)")
-                    video_writer.append_data(video_np[frame_idx])
-        except Exception as e2:
-            print(f"Fallback video saving error: {e2}")
-            raise gr.Error(f"Failed to save video: {e2}")
-            
-    return output_video_path, seed_ui
-
-def update_task_image():
-    return "image-to-video"
-
-def update_task_text():
-    return "text-to-video"
-
-def update_task_video():
-    return "video-to-video"
-
-# --- Gradio UI Definition ---
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 900px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("# LTX Video 0.9.7 Distilled")
-    gr.Markdown("Fast high quality video generation. [Model](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-distilled.safetensors) [GitHub](https://github.com/Lightricks/LTX-Video) [Diffusers](https://huggingface.co/Lightricks/LTX-Video-0.9.7-distilled#diffusers-🧨)")
-    
-    with gr.Row():
-        with gr.Column():
-            with gr.Tab("image-to-video") as image_tab:
-                video_i_hidden = gr.Textbox(label="video_i", visible=False, value=None)
-                image_i2v = gr.Image(label="Input Image", type="filepath", sources=["upload", "webcam", "clipboard"])
-                i2v_prompt = gr.Textbox(label="Prompt", value="The creature from the image starts to move", lines=3)
-                i2v_button = gr.Button("Generate Image-to-Video", variant="primary")
-            with gr.Tab("text-to-video") as text_tab:
-                image_n_hidden = gr.Textbox(label="image_n", visible=False, value=None)
-                video_n_hidden = gr.Textbox(label="video_n", visible=False, value=None)
-                t2v_prompt = gr.Textbox(label="Prompt", value="A majestic dragon flying over a medieval castle", lines=3)
-                t2v_button = gr.Button("Generate Text-to-Video", variant="primary")
-            with gr.Tab("video-to-video", visible=False) as video_tab:
-                image_v_hidden = gr.Textbox(label="image_v", visible=False, value=None)
-                video_v2v = gr.Video(label="Input Video", sources=["upload", "webcam"]) # type defaults to filepath
-                frames_to_use = gr.Slider(label="Frames to use from input video", minimum=9, maximum=MAX_NUM_FRAMES, value=9, step=8, info="Number of initial frames to use for conditioning/transformation. Must be N*8+1.")
-                v2v_prompt = gr.Textbox(label="Prompt", value="Change the style to cinematic anime", lines=3)
-                v2v_button = gr.Button("Generate Video-to-Video", variant="primary")
-
-            duration_input = gr.Slider(
-                label="Video Duration (seconds)", 
-                minimum=0.3, 
-                maximum=8.5, 
-                value=2,  
-                step=0.1, 
-                info=f"Target video duration (0.3s to 8.5s)"
-            )
-            improve_texture = gr.Checkbox(label="Improve Texture (multi-scale)", value=True, info="Uses a two-pass generation for better quality, but is slower. Recommended for final output.")
-
-        with gr.Column():
-            output_video = gr.Video(label="Generated Video", interactive=False)
-            # gr.DeepLinkButton()
-
-    with gr.Accordion("Advanced settings", open=False):
-        mode = gr.Dropdown(["text-to-video", "image-to-video", "video-to-video"], label="task", value="image-to-video", visible=False)
-        negative_prompt_input = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion, blurry, jittery, distorted", lines=2)
-        with gr.Row():
-            seed_input = gr.Number(label="Seed", value=42, precision=0, minimum=0, maximum=2**32-1)
-            randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)
-        with gr.Row():
-            guidance_scale_input = gr.Slider(label="Guidance Scale (CFG)", minimum=1.0, maximum=10.0, value=PIPELINE_CONFIG_YAML.get("first_pass", {}).get("guidance_scale", 1.0), step=0.1, info="Controls how much the prompt influences the output. Higher values = stronger influence.")
-        with gr.Row():
-            height_input = gr.Slider(label="Height", value=512, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE, info="Must be divisible by 32.")
-            width_input = gr.Slider(label="Width", value=704, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE, info="Must be divisible by 32.")
-
-
-    # --- Event handlers for updating dimensions on upload ---
-    def handle_image_upload_for_dims(image_filepath, current_h, current_w):
-        if not image_filepath:  # Image cleared or no image initially
-            # Keep current slider values if image is cleared or no input
-            return gr.update(value=current_h), gr.update(value=current_w)
-        try:
-            img = Image.open(image_filepath)
-            orig_w, orig_h = img.size
-            new_h, new_w = calculate_new_dimensions(orig_w, orig_h)
-            return gr.update(value=new_h), gr.update(value=new_w)
-        except Exception as e:
-            print(f"Error processing image for dimension update: {e}")
-            # Keep current slider values on error
-            return gr.update(value=current_h), gr.update(value=current_w)
-
-    def handle_video_upload_for_dims(video_filepath, current_h, current_w):
-        if not video_filepath:  # Video cleared or no video initially
-            return gr.update(value=current_h), gr.update(value=current_w)
-        try:
-            # Ensure video_filepath is a string for os.path.exists and imageio
-            video_filepath_str = str(video_filepath) 
-            if not os.path.exists(video_filepath_str):
-                print(f"Video file path does not exist for dimension update: {video_filepath_str}")
-                return gr.update(value=current_h), gr.update(value=current_w)
-
-            orig_w, orig_h = -1, -1
-            with imageio.get_reader(video_filepath_str) as reader:
-                meta = reader.get_meta_data()
-                if 'size' in meta:
-                    orig_w, orig_h = meta['size']
-                else:
-                    # Fallback: read first frame if 'size' not in metadata
-                    try:
-                        first_frame = reader.get_data(0)
-                        # Shape is (h, w, c) for frames
-                        orig_h, orig_w = first_frame.shape[0], first_frame.shape[1]
-                    except Exception as e_frame:
-                        print(f"Could not get video size from metadata or first frame: {e_frame}")
-                        return gr.update(value=current_h), gr.update(value=current_w)
-            
-            if orig_w == -1 or orig_h == -1: # If dimensions couldn't be determined
-                 print(f"Could not determine dimensions for video: {video_filepath_str}")
-                 return gr.update(value=current_h), gr.update(value=current_w)
-
-            new_h, new_w = calculate_new_dimensions(orig_w, orig_h)
-            return gr.update(value=new_h), gr.update(value=new_w)
-        except Exception as e:
-            # Log type of video_filepath for debugging if it's not a path-like string
-            print(f"Error processing video for dimension update: {e} (Path: {video_filepath}, Type: {type(video_filepath)})")
-            return gr.update(value=current_h), gr.update(value=current_w)
-
-    
-    image_i2v.upload(
-        fn=handle_image_upload_for_dims,
-        inputs=[image_i2v, height_input, width_input],
-        outputs=[height_input, width_input]
-    )
-    video_v2v.upload(
-        fn=handle_video_upload_for_dims,
-        inputs=[video_v2v, height_input, width_input],
-        outputs=[height_input, width_input]
+from diffusers import LTXImageToVideoPipeline  # o LTXConditionPipeline según versión
+
+# -------------------------
+# 📦 Descargar y cargar modelo
+# -------------------------
+MODEL_ID = "Lightricks/LTX-Video"
+CHECKPOINT_FILE = "ltxv-2b-0.9.6-distilled.safetensors"
+
+local_ckpt = hf_hub_download(repo_id=MODEL_ID, filename=CHECKPOINT_FILE, cache_dir="./models")
+pipe = LTXImageToVideoPipeline.from_pretrained(
+    MODEL_ID, revision="main", safety_checker=None, torch_dtype=torch.bfloat16
+).to("cuda")
+
+# -------------------------
+# 🔧 Funciones de generación
+# -------------------------
+def txt2vid(prompt, height, width, num_frames, steps, seed=None):
+    seed = seed or random.randint(0, 2**32 - 1)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    out = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        num_inference_steps=steps,
+        generator=generator
     )
-
-    image_tab.select(
-        fn=update_task_image,
-        outputs=[mode]
+    vid = out.videos[0]
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    imageio.mimwrite(tmp.name, vid, fps=25)
+    return tmp.name
+
+def img2vid(image, prompt, height, width, num_frames, steps, seed=None):
+    img = Image.fromarray(image)
+    cond_vid = imageio.mimwrite(
+        tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name,
+        [np.array(img)] * 1, fps=1
     )
-    text_tab.select(
-        fn=update_task_text,
-        outputs=[mode]
+    seed = seed or random.randint(0, 2**32 - 1)
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    out = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        num_inference_steps=steps,
+        generator=generator,
+        conditioning_media_paths=[cond_vid],
+        conditioning_start_frames=[0]
     )
-    
-    t2v_inputs = [t2v_prompt, negative_prompt_input, image_n_hidden, video_n_hidden,
-                  height_input, width_input, mode,
-                  duration_input, frames_to_use, 
-                  seed_input, randomize_seed_input, guidance_scale_input, improve_texture]
-    
-    i2v_inputs = [i2v_prompt, negative_prompt_input, image_i2v, video_i_hidden,
-                  height_input, width_input, mode,
-                  duration_input, frames_to_use, 
-                  seed_input, randomize_seed_input, guidance_scale_input, improve_texture]
-
-    v2v_inputs = [v2v_prompt, negative_prompt_input, image_v_hidden, video_v2v,
-                  height_input, width_input, mode,
-                  duration_input, frames_to_use, 
-                  seed_input, randomize_seed_input, guidance_scale_input, improve_texture]
+    vid = out.videos[0]
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    imageio.mimwrite(tmp.name, vid, fps=25)
+    return tmp.name
 
-    t2v_button.click(fn=generate, inputs=t2v_inputs, outputs=[output_video, seed_input], api_name="text_to_video")
-    i2v_button.click(fn=generate, inputs=i2v_inputs, outputs=[output_video, seed_input], api_name="image_to_video")
-    v2v_button.click(fn=generate, inputs=v2v_inputs, outputs=[output_video, seed_input], api_name="video_to_video")
+# -------------------------
+# 🎨 Interfaz Gradio
+# -------------------------
+css = """body { background-color:#111; color:#eee } .gradio-container { max-width:800px; }"""
 
-if __name__ == "__main__":
-    if os.path.exists(models_dir) and os.path.isdir(models_dir):
-        print(f"Model directory: {Path(models_dir).resolve()}")
-    
-    demo.queue().launch(debug=True, share=False, mcp_server=True)
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# LTX‑Video 2B Distilled (Gratuito)")
+
+    with gr.Tab("Text → Video"):
+        t_prompt = gr.Textbox(label="Prompt", value="A serene landscape at sunrise")
+        t_h = gr.Slider(128, 720, value=512, step=32, label="Height")
+        t_w = gr.Slider(128, 1280, value=768, step=32, label="Width")
+        t_f = gr.Slider(9, 257, value=65, step=8, label="Num Frames")
+        t_s = gr.Slider(4, 16, value=8, step=1, label="Steps")
+        t_seed = gr.Number(label="Seed (opcional)", value=0)
+        t_btn = gr.Button("Generate")
+        t_out = gr.Video()
+        t_btn.click(fn=txt2vid, inputs=[t_prompt, t_h, t_w, t_f, t_s, t_seed], outputs=t_out)
+
+    with gr.Tab("Image → Video"):
+        i_img = gr.Image(type="numpy")
+        i_prompt = gr.Textbox(label="Prompt", value="A cute fox in the snow")
+        i_h = gr.Slider(128, 720, value=512, step=32, label="Height")
+        i_w = gr.Slider(128, 1280, value=768, step=32, label="Width")
+        i_f = gr.Slider(9, 257, value=65, step=8, label="Num Frames")
+        i_s = gr.Slider(4, 16, value=8, step=1, label="Steps")
+        i_seed = gr.Number(label="Seed (opcional)", value=0)
+        i_btn = gr.Button("Generate")
+        i_out = gr.Video()
+        i_btn.click(fn=img2vid, inputs=[i_img, i_prompt, i_h, i_w, i_f, i_s, i_seed], outputs=i_out)
+
+    gr.Markdown("**Modelo:** ltxv‑2b‑0.9.6‑distilled • resolución múltiplo de 32, frames múltiplo de 8+1 :contentReference[oaicite:1]{index=1}")
+
+demo.launch()