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app (4).py

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+import gradio as gr
+import torch
+import spaces
+import numpy as np
+import random
+import os
+import yaml
+from pathlib import Path
+import imageio
+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-0.9.8-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=None, input_video_filepath=None,
+             height_ui=512, width_ui=704, mode="text-to-video",
+             duration_ui=2.0, 
+             ui_frames_to_use=9,
+             seed_ui=42, randomize_seed=True, ui_guidance_scale=3.0, improve_texture_flag=True,
+             progress=gr.Progress(track_tqdm=True)):
+    """
+    Generate high-quality videos using LTX Video model with support for text-to-video, image-to-video, and video-to-video modes.
+
+    Args:
+        prompt (str): Text description of the desired video content. Required for all modes.
+        negative_prompt (str): Text describing what to avoid in the generated video. Optional, can be empty string.
+        input_image_filepath (str or None): Path to input image file. Required for image-to-video mode, None for other modes.
+        input_video_filepath (str or None): Path to input video file. Required for video-to-video mode, None for other modes.
+        height_ui (int): Height of the output video in pixels, must be divisible by 32. Default: 512.
+        width_ui (int): Width of the output video in pixels, must be divisible by 32. Default: 704.
+        mode (str): Generation mode. Required. One of "text-to-video", "image-to-video", or "video-to-video". Default: "text-to-video".
+        duration_ui (float): Duration of the output video in seconds. Range: 0.3 to 8.5. Default: 2.0.
+        ui_frames_to_use (int): Number of frames to use from input video. Only used in video-to-video mode. Must be N*8+1. Default: 9.
+        seed_ui (int): Random seed for reproducible generation. Range: 0 to 2^32-1. Default: 42.
+        randomize_seed (bool): Whether to use a random seed instead of seed_ui. Default: True.
+        ui_guidance_scale (float): CFG scale controlling prompt influence. Range: 1.0 to 10.0. Higher values = stronger prompt influence. Default: 3.0.
+        improve_texture_flag (bool): Whether to use multi-scale generation for better texture quality. Slower but higher quality. Default: True.
+        progress (gr.Progress): Progress tracker for the generation process. Optional, used for UI updates.
+
+    Returns:
+        tuple: A tuple containing (output_video_path, used_seed) where output_video_path is the path to the generated video file and used_seed is the actual seed used for generation.
+    """
+
+    # Validate mode-specific required parameters
+    if mode == "image-to-video":
+        if not input_image_filepath:
+            raise gr.Error("input_image_filepath is required for image-to-video mode")
+    elif mode == "video-to-video":
+        if not input_video_filepath:
+            raise gr.Error("input_video_filepath is required for video-to-video mode")
+    elif mode == "text-to-video":
+        # No additional file inputs required for text-to-video
+        pass
+    else:
+        raise gr.Error(f"Invalid mode: {mode}. Must be one of: text-to-video, image-to-video, video-to-video")
+
+    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 0.9.8 13B Distilled")
+    gr.Markdown("Fast high quality video generation.**Update (17/07):** now with the new v0.9.8 for improved prompt understanding and detail generation" )
+    gr.Markdown("[Model](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.8-distilled.safetensors) [GitHub](https://github.com/Lightricks/LTX-Video) [Diffusers](https://huggingface.co/Lightricks/LTX-Video-0.9.8-13B-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,visible=False, 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(visible=False):
+            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]
+    )
+
+    image_tab.select(
+        fn=update_task_image,
+        outputs=[mode]
+    )
+    text_tab.select(
+        fn=update_task_text,
+        outputs=[mode]
+    )
+    
+    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]
+
+    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")
+
+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)

start (4).sh

@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+tree -L 4 /app
+tree -L 4 /data
+
+echo "🚀 Iniciando o script de setup e lançamento do LTX-Video..."
+echo "Usuário atual: $(whoami)"
+
+# Define as variáveis de ambiente que o LTXServer irá consumir
+export HF_HOME="${HF_HOME:-/data/.cache/huggingface}"
+export OUTPUT_ROOT="${OUTPUT_ROOT:-/app/outputs/ltx}"
+export LTXV_FRAME_LOG_EVERY=8
+export LTXV_DEBUG=1
+
+
+# --- Garante que Diretórios Existam ---
+mkdir -p "$OUTPUT_ROOT" "$HF_HOME"
+
+
+# 1) Builder (garante Apex/Flash e deps CUDA)
+#echo "🛠️  Iniciando o builder.sh para compilar/instalar dependências CUDA..."
+#if [ -f "/app/builder.sh" ]; then
+#  /bin/bash /app/builder.sh
+#  echo "✅ Builder finalizado."
+#else
+#  echo "⚠️  Aviso: builder.sh não encontrado. Pulando etapa de compilação de dependências."
+#fi
+
+python setup.py
+
+cp -rfv /app/LTX-Video/ /data/
+
+export OUTPUT_ROOT="${OUTPUT_ROOT:-/app/outputs}"
+export INPUT_ROOT="${INPUT_ROOT:-/app/inputs}"
+
+mkdir -p "$OUTPUT_ROOT" "$INPUT_ROOT"
+echo "[aduc][start] Verificando ambiente como usuário: $(whoami)"
+
+# Env da UI
+export GRADIO_SERVER_NAME="0.0.0.0"
+export GRADIO_SERVER_PORT="${PORT:-7860}"
+export GRADIO_ENABLE_QUEUE="True"
+
+echo "[ltx][start] Lançando app_ltx.py..."
+# Executa diretamente o python.
+exec python app_ltx.py