system1-developer/ltx-video-distilled
0
1import gradio as gr2import torch3import spaces4import numpy as np5import random6import os7import yaml8from pathlib import Path9import imageio10import tempfile11from PIL import Image12from huggingface_hub import hf_hub_download13import shutil14 15from inference import (16 create_ltx_video_pipeline,17 create_latent_upsampler,18 load_image_to_tensor_with_resize_and_crop,19 seed_everething,20 get_device,21 calculate_padding,22 load_media_file23)24from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXMultiScalePipeline, LTXVideoPipeline25from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy26 27config_file_path = "configs/ltxv-13b-0.9.8-distilled.yaml"28with open(config_file_path, "r") as file:29 PIPELINE_CONFIG_YAML = yaml.safe_load(file)30 31LTX_REPO = "Lightricks/LTX-Video"32MAX_IMAGE_SIZE = PIPELINE_CONFIG_YAML.get("max_resolution", 1280)33MAX_NUM_FRAMES = 25734 35FPS = 30.0 36 37# --- Global variables for loaded models ---38pipeline_instance = None39latent_upsampler_instance = None40models_dir = "downloaded_models_gradio_cpu_init"41Path(models_dir).mkdir(parents=True, exist_ok=True)42 43print("Downloading models (if not present)...")44distilled_model_actual_path = hf_hub_download(45 repo_id=LTX_REPO,46 filename=PIPELINE_CONFIG_YAML["checkpoint_path"],47 local_dir=models_dir,48 local_dir_use_symlinks=False49)50PIPELINE_CONFIG_YAML["checkpoint_path"] = distilled_model_actual_path51print(f"Distilled model path: {distilled_model_actual_path}")52 53SPATIAL_UPSCALER_FILENAME = PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"]54spatial_upscaler_actual_path = hf_hub_download(55 repo_id=LTX_REPO,56 filename=SPATIAL_UPSCALER_FILENAME,57 local_dir=models_dir,58 local_dir_use_symlinks=False59)60PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"] = spatial_upscaler_actual_path61print(f"Spatial upscaler model path: {spatial_upscaler_actual_path}")62 63print("Creating LTX Video pipeline on CPU...")64pipeline_instance = create_ltx_video_pipeline(65 ckpt_path=PIPELINE_CONFIG_YAML["checkpoint_path"],66 precision=PIPELINE_CONFIG_YAML["precision"],67 text_encoder_model_name_or_path=PIPELINE_CONFIG_YAML["text_encoder_model_name_or_path"],68 sampler=PIPELINE_CONFIG_YAML["sampler"],69 device="cpu",70 enhance_prompt=False,71 prompt_enhancer_image_caption_model_name_or_path=PIPELINE_CONFIG_YAML["prompt_enhancer_image_caption_model_name_or_path"],72 prompt_enhancer_llm_model_name_or_path=PIPELINE_CONFIG_YAML["prompt_enhancer_llm_model_name_or_path"],73)74print("LTX Video pipeline created on CPU.")75 76if PIPELINE_CONFIG_YAML.get("spatial_upscaler_model_path"):77 print("Creating latent upsampler on CPU...")78 latent_upsampler_instance = create_latent_upsampler(79 PIPELINE_CONFIG_YAML["spatial_upscaler_model_path"],80 device="cpu"81 )82 print("Latent upsampler created on CPU.")83 84target_inference_device = "cuda"85print(f"Target inference device: {target_inference_device}")86pipeline_instance.to(target_inference_device)87if latent_upsampler_instance: 88 latent_upsampler_instance.to(target_inference_device)89 90 91# --- Helper function for dimension calculation ---92MIN_DIM_SLIDER = 256 # As defined in the sliders minimum attribute93TARGET_FIXED_SIDE = 768 # Desired fixed side length as per requirement94 95def calculate_new_dimensions(orig_w, orig_h):96 """97 Calculates new dimensions for height and width sliders based on original media dimensions.98 Ensures one side is TARGET_FIXED_SIDE, the other is scaled proportionally,99 both are multiples of 32, and within [MIN_DIM_SLIDER, MAX_IMAGE_SIZE].100 """101 if orig_w == 0 or orig_h == 0:102 # Default to TARGET_FIXED_SIDE square if original dimensions are invalid103 return int(TARGET_FIXED_SIDE), int(TARGET_FIXED_SIDE)104 105 if orig_w >= orig_h: # Landscape or square106 new_h = TARGET_FIXED_SIDE107 aspect_ratio = orig_w / orig_h108 new_w_ideal = new_h * aspect_ratio109 110 # Round to nearest multiple of 32111 new_w = round(new_w_ideal / 32) * 32112 113 # Clamp to [MIN_DIM_SLIDER, MAX_IMAGE_SIZE]114 new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))115 # Ensure new_h is also clamped (TARGET_FIXED_SIDE should be within these bounds if configured correctly)116 new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE)) 117 else: # Portrait118 new_w = TARGET_FIXED_SIDE119 aspect_ratio = orig_h / orig_w # Use H/W ratio for portrait scaling120 new_h_ideal = new_w * aspect_ratio121 122 # Round to nearest multiple of 32123 new_h = round(new_h_ideal / 32) * 32124 125 # Clamp to [MIN_DIM_SLIDER, MAX_IMAGE_SIZE]126 new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE))127 # Ensure new_w is also clamped128 new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))129 130 return int(new_h), int(new_w)131 132def get_duration(prompt, negative_prompt, input_image_filepath, input_video_filepath,133 height_ui, width_ui, mode,134 duration_ui, # Removed ui_steps135 ui_frames_to_use,136 seed_ui, randomize_seed, ui_guidance_scale, improve_texture_flag,137 progress):138 if duration_ui > 7:139 return 75140 else:141 return 60142 143@spaces.GPU(duration=get_duration, size="xlarge")144def generate(prompt, negative_prompt, input_image_filepath=None, input_video_filepath=None,145 height_ui=512, width_ui=704, mode="text-to-video",146 duration_ui=2.0, 147 ui_frames_to_use=9,148 seed_ui=42, randomize_seed=True, ui_guidance_scale=3.0, improve_texture_flag=True,149 progress=gr.Progress(track_tqdm=True)):150 """151 Generate high-quality videos using LTX Video model with support for text-to-video, image-to-video, and video-to-video modes.152 153 Args:154 prompt (str): Text description of the desired video content. Required for all modes.155 negative_prompt (str): Text describing what to avoid in the generated video. Optional, can be empty string.156 input_image_filepath (str or None): Path to input image file. Required for image-to-video mode, None for other modes.157 input_video_filepath (str or None): Path to input video file. Required for video-to-video mode, None for other modes.158 height_ui (int): Height of the output video in pixels, must be divisible by 32. Default: 512.159 width_ui (int): Width of the output video in pixels, must be divisible by 32. Default: 704.160 mode (str): Generation mode. Required. One of "text-to-video", "image-to-video", or "video-to-video". Default: "text-to-video".161 duration_ui (float): Duration of the output video in seconds. Range: 0.3 to 8.5. Default: 2.0.162 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: 9.163 seed_ui (int): Random seed for reproducible generation. Range: 0 to 2^32-1. Default: 42.164 randomize_seed (bool): Whether to use a random seed instead of seed_ui. Default: True.165 ui_guidance_scale (float): CFG scale controlling prompt influence. Range: 1.0 to 10.0. Higher values = stronger prompt influence. Default: 3.0.166 improve_texture_flag (bool): Whether to use multi-scale generation for better texture quality. Slower but higher quality. Default: True.167 progress (gr.Progress): Progress tracker for the generation process. Optional, used for UI updates.168 169 Returns:170 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.171 """172 173 # Validate mode-specific required parameters174 if mode == "image-to-video":175 if not input_image_filepath:176 raise gr.Error("input_image_filepath is required for image-to-video mode")177 elif mode == "video-to-video":178 if not input_video_filepath:179 raise gr.Error("input_video_filepath is required for video-to-video mode")180 elif mode == "text-to-video":181 # No additional file inputs required for text-to-video182 pass183 else:184 raise gr.Error(f"Invalid mode: {mode}. Must be one of: text-to-video, image-to-video, video-to-video")185 186 if randomize_seed:187 seed_ui = random.randint(0, 2**32 - 1)188 seed_everething(int(seed_ui))189 190 target_frames_ideal = duration_ui * FPS191 target_frames_rounded = round(target_frames_ideal)192 if target_frames_rounded < 1: 193 target_frames_rounded = 1194 195 n_val = round((float(target_frames_rounded) - 1.0) / 8.0)196 actual_num_frames = int(n_val * 8 + 1)197 198 actual_num_frames = max(9, actual_num_frames)199 actual_num_frames = min(MAX_NUM_FRAMES, actual_num_frames)200 201 actual_height = int(height_ui)202 actual_width = int(width_ui)203 204 height_padded = ((actual_height - 1) // 32 + 1) * 32205 width_padded = ((actual_width - 1) // 32 + 1) * 32206 num_frames_padded = ((actual_num_frames - 2) // 8 + 1) * 8 + 1 207 if num_frames_padded != actual_num_frames:208 print(f"Warning: actual_num_frames ({actual_num_frames}) and num_frames_padded ({num_frames_padded}) differ. Using num_frames_padded for pipeline.")209 210 padding_values = calculate_padding(actual_height, actual_width, height_padded, width_padded)211 212 call_kwargs = {213 "prompt": prompt,214 "negative_prompt": negative_prompt,215 "height": height_padded,216 "width": width_padded,217 "num_frames": num_frames_padded, 218 "frame_rate": int(FPS), 219 "generator": torch.Generator(device=target_inference_device).manual_seed(int(seed_ui)),220 "output_type": "pt", 221 "conditioning_items": None,222 "media_items": None,223 "decode_timestep": PIPELINE_CONFIG_YAML["decode_timestep"],224 "decode_noise_scale": PIPELINE_CONFIG_YAML["decode_noise_scale"],225 "stochastic_sampling": PIPELINE_CONFIG_YAML["stochastic_sampling"],226 "image_cond_noise_scale": 0.15,227 "is_video": True,228 "vae_per_channel_normalize": True,229 "mixed_precision": (PIPELINE_CONFIG_YAML["precision"] == "mixed_precision"),230 "offload_to_cpu": False,231 "enhance_prompt": False,232 }233 234 stg_mode_str = PIPELINE_CONFIG_YAML.get("stg_mode", "attention_values")235 if stg_mode_str.lower() in ["stg_av", "attention_values"]:236 call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.AttentionValues237 elif stg_mode_str.lower() in ["stg_as", "attention_skip"]:238 call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.AttentionSkip239 elif stg_mode_str.lower() in ["stg_r", "residual"]:240 call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.Residual241 elif stg_mode_str.lower() in ["stg_t", "transformer_block"]:242 call_kwargs["skip_layer_strategy"] = SkipLayerStrategy.TransformerBlock243 else:244 raise ValueError(f"Invalid stg_mode: {stg_mode_str}")245 246 if mode == "image-to-video" and input_image_filepath:247 try:248 media_tensor = load_image_to_tensor_with_resize_and_crop(249 input_image_filepath, actual_height, actual_width250 )251 media_tensor = torch.nn.functional.pad(media_tensor, padding_values)252 call_kwargs["conditioning_items"] = [ConditioningItem(media_tensor.to(target_inference_device), 0, 1.0)]253 except Exception as e:254 print(f"Error loading image {input_image_filepath}: {e}")255 raise gr.Error(f"Could not load image: {e}")256 elif mode == "video-to-video" and input_video_filepath:257 try:258 call_kwargs["media_items"] = load_media_file(259 media_path=input_video_filepath,260 height=actual_height, 261 width=actual_width,262 max_frames=int(ui_frames_to_use), 263 padding=padding_values264 ).to(target_inference_device)265 except Exception as e:266 print(f"Error loading video {input_video_filepath}: {e}")267 raise gr.Error(f"Could not load video: {e}")268 269 print(f"Moving models to {target_inference_device} for inference (if not already there)...")270 271 active_latent_upsampler = None272 if improve_texture_flag and latent_upsampler_instance:273 active_latent_upsampler = latent_upsampler_instance274 275 result_images_tensor = None276 if improve_texture_flag:277 if not active_latent_upsampler:278 raise gr.Error("Spatial upscaler model not loaded or improve_texture not selected, cannot use multi-scale.")279 280 multi_scale_pipeline_obj = LTXMultiScalePipeline(pipeline_instance, active_latent_upsampler)281 282 first_pass_args = PIPELINE_CONFIG_YAML.get("first_pass", {}).copy()283 first_pass_args["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML284 # num_inference_steps will be derived from len(timesteps) in the pipeline285 first_pass_args.pop("num_inference_steps", None)286 287 288 second_pass_args = PIPELINE_CONFIG_YAML.get("second_pass", {}).copy()289 second_pass_args["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML290 # num_inference_steps will be derived from len(timesteps) in the pipeline291 second_pass_args.pop("num_inference_steps", None)292 293 multi_scale_call_kwargs = call_kwargs.copy()294 multi_scale_call_kwargs.update({295 "downscale_factor": PIPELINE_CONFIG_YAML["downscale_factor"],296 "first_pass": first_pass_args,297 "second_pass": second_pass_args,298 })299 300 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}")301 result_images_tensor = multi_scale_pipeline_obj(**multi_scale_call_kwargs).images302 else:303 single_pass_call_kwargs = call_kwargs.copy()304 first_pass_config_from_yaml = PIPELINE_CONFIG_YAML.get("first_pass", {})305 306 single_pass_call_kwargs["timesteps"] = first_pass_config_from_yaml.get("timesteps")307 single_pass_call_kwargs["guidance_scale"] = float(ui_guidance_scale) # UI overrides YAML308 single_pass_call_kwargs["stg_scale"] = first_pass_config_from_yaml.get("stg_scale")309 single_pass_call_kwargs["rescaling_scale"] = first_pass_config_from_yaml.get("rescaling_scale")310 single_pass_call_kwargs["skip_block_list"] = first_pass_config_from_yaml.get("skip_block_list")311 312 # Remove keys that might conflict or are not used in single pass / handled by above313 single_pass_call_kwargs.pop("num_inference_steps", None) 314 single_pass_call_kwargs.pop("first_pass", None) 315 single_pass_call_kwargs.pop("second_pass", None)316 single_pass_call_kwargs.pop("downscale_factor", None)317 318 print(f"Calling base pipeline (padded HxW: {height_padded}x{width_padded}, Frames: {actual_num_frames} -> Padded: {num_frames_padded}) on {target_inference_device}")319 result_images_tensor = pipeline_instance(**single_pass_call_kwargs).images320 321 if result_images_tensor is None:322 raise gr.Error("Generation failed.")323 324 pad_left, pad_right, pad_top, pad_bottom = padding_values325 slice_h_end = -pad_bottom if pad_bottom > 0 else None326 slice_w_end = -pad_right if pad_right > 0 else None327 328 result_images_tensor = result_images_tensor[329 :, :, :actual_num_frames, pad_top:slice_h_end, pad_left:slice_w_end330 ]331 332 video_np = result_images_tensor[0].permute(1, 2, 3, 0).cpu().float().numpy()333 334 video_np = np.clip(video_np, 0, 1) 335 video_np = (video_np * 255).astype(np.uint8)336 337 temp_dir = tempfile.mkdtemp()338 timestamp = random.randint(10000,99999)339 output_video_path = os.path.join(temp_dir, f"output_{timestamp}.mp4")340 341 try:342 with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], macro_block_size=1) as video_writer:343 for frame_idx in range(video_np.shape[0]):344 progress(frame_idx / video_np.shape[0], desc="Saving video")345 video_writer.append_data(video_np[frame_idx])346 except Exception as e:347 print(f"Error saving video with macro_block_size=1: {e}")348 try:349 with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], format='FFMPEG', codec='libx264', quality=8) as video_writer:350 for frame_idx in range(video_np.shape[0]):351 progress(frame_idx / video_np.shape[0], desc="Saving video (fallback ffmpeg)")352 video_writer.append_data(video_np[frame_idx])353 except Exception as e2:354 print(f"Fallback video saving error: {e2}")355 raise gr.Error(f"Failed to save video: {e2}")356 357 return output_video_path, seed_ui358 359def update_task_image():360 return "image-to-video"361 362def update_task_text():363 return "text-to-video"364 365def update_task_video():366 return "video-to-video"367 368# --- Gradio UI Definition ---369css="""370#col-container {371 margin: 0 auto;372 max-width: 900px;373}374"""375 376with gr.Blocks(css=css) as demo:377 gr.Markdown("# LTX Video 0.9.8 13B Distilled")378 gr.Markdown("Fast high quality video generation.**Update (17/07):** now with the new v0.9.8 for improved prompt understanding and detail generation" )379 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-🧨)")380 with gr.Row():381 with gr.Column():382 with gr.Tab("image-to-video") as image_tab:383 video_i_hidden = gr.Textbox(label="video_i", visible=False, value=None)384 image_i2v = gr.Image(label="Input Image", type="filepath", sources=["upload", "webcam", "clipboard"])385 i2v_prompt = gr.Textbox(label="Prompt", value="The creature from the image starts to move", lines=3)386 i2v_button = gr.Button("Generate Image-to-Video", variant="primary")387 with gr.Tab("text-to-video") as text_tab:388 image_n_hidden = gr.Textbox(label="image_n", visible=False, value=None)389 video_n_hidden = gr.Textbox(label="video_n", visible=False, value=None)390 t2v_prompt = gr.Textbox(label="Prompt", value="A majestic dragon flying over a medieval castle", lines=3)391 t2v_button = gr.Button("Generate Text-to-Video", variant="primary")392 with gr.Tab("video-to-video", visible=False) as video_tab:393 image_v_hidden = gr.Textbox(label="image_v", visible=False, value=None)394 video_v2v = gr.Video(label="Input Video", sources=["upload", "webcam"]) # type defaults to filepath395 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.")396 v2v_prompt = gr.Textbox(label="Prompt", value="Change the style to cinematic anime", lines=3)397 v2v_button = gr.Button("Generate Video-to-Video", variant="primary")398 399 duration_input = gr.Slider(400 label="Video Duration (seconds)", 401 minimum=0.3, 402 maximum=8.5, 403 value=2, 404 step=0.1, 405 info=f"Target video duration (0.3s to 8.5s)"406 )407 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.")408 409 with gr.Column():410 output_video = gr.Video(label="Generated Video", interactive=False)411 # gr.DeepLinkButton()412 413 with gr.Accordion("Advanced settings", open=False):414 mode = gr.Dropdown(["text-to-video", "image-to-video", "video-to-video"], label="task", value="image-to-video", visible=False)415 negative_prompt_input = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion, blurry, jittery, distorted", lines=2)416 with gr.Row():417 seed_input = gr.Number(label="Seed", value=42, precision=0, minimum=0, maximum=2**32-1)418 randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)419 with gr.Row(visible=False):420 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.")421 with gr.Row():422 height_input = gr.Slider(label="Height", value=512, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE, info="Must be divisible by 32.")423 width_input = gr.Slider(label="Width", value=704, step=32, minimum=MIN_DIM_SLIDER, maximum=MAX_IMAGE_SIZE, info="Must be divisible by 32.")424 425 426 # --- Event handlers for updating dimensions on upload ---427 def handle_image_upload_for_dims(image_filepath, current_h, current_w):428 if not image_filepath: # Image cleared or no image initially429 # Keep current slider values if image is cleared or no input430 return gr.update(value=current_h), gr.update(value=current_w)431 try:432 img = Image.open(image_filepath)433 orig_w, orig_h = img.size434 new_h, new_w = calculate_new_dimensions(orig_w, orig_h)435 return gr.update(value=new_h), gr.update(value=new_w)436 except Exception as e:437 print(f"Error processing image for dimension update: {e}")438 # Keep current slider values on error439 return gr.update(value=current_h), gr.update(value=current_w)440 441 def handle_video_upload_for_dims(video_filepath, current_h, current_w):442 if not video_filepath: # Video cleared or no video initially443 return gr.update(value=current_h), gr.update(value=current_w)444 try:445 # Ensure video_filepath is a string for os.path.exists and imageio446 video_filepath_str = str(video_filepath) 447 if not os.path.exists(video_filepath_str):448 print(f"Video file path does not exist for dimension update: {video_filepath_str}")449 return gr.update(value=current_h), gr.update(value=current_w)450 451 orig_w, orig_h = -1, -1452 with imageio.get_reader(video_filepath_str) as reader:453 meta = reader.get_meta_data()454 if 'size' in meta:455 orig_w, orig_h = meta['size']456 else:457 # Fallback: read first frame if 'size' not in metadata458 try:459 first_frame = reader.get_data(0)460 # Shape is (h, w, c) for frames461 orig_h, orig_w = first_frame.shape[0], first_frame.shape[1]462 except Exception as e_frame:463 print(f"Could not get video size from metadata or first frame: {e_frame}")464 return gr.update(value=current_h), gr.update(value=current_w)465 466 if orig_w == -1 or orig_h == -1: # If dimensions couldn't be determined467 print(f"Could not determine dimensions for video: {video_filepath_str}")468 return gr.update(value=current_h), gr.update(value=current_w)469 470 new_h, new_w = calculate_new_dimensions(orig_w, orig_h)471 return gr.update(value=new_h), gr.update(value=new_w)472 except Exception as e:473 # Log type of video_filepath for debugging if it's not a path-like string474 print(f"Error processing video for dimension update: {e} (Path: {video_filepath}, Type: {type(video_filepath)})")475 return gr.update(value=current_h), gr.update(value=current_w)476 477 478 image_i2v.upload(479 fn=handle_image_upload_for_dims,480 inputs=[image_i2v, height_input, width_input],481 outputs=[height_input, width_input]482 )483 video_v2v.upload(484 fn=handle_video_upload_for_dims,485 inputs=[video_v2v, height_input, width_input],486 outputs=[height_input, width_input]487 )488 489 image_tab.select(490 fn=update_task_image,491 outputs=[mode]492 )493 text_tab.select(494 fn=update_task_text,495 outputs=[mode]496 )497 498 t2v_inputs = [t2v_prompt, negative_prompt_input, image_n_hidden, video_n_hidden,499 height_input, width_input, mode,500 duration_input, frames_to_use, 501 seed_input, randomize_seed_input, guidance_scale_input, improve_texture]502 503 i2v_inputs = [i2v_prompt, negative_prompt_input, image_i2v, video_i_hidden,504 height_input, width_input, mode,505 duration_input, frames_to_use, 506 seed_input, randomize_seed_input, guidance_scale_input, improve_texture]507 508 v2v_inputs = [v2v_prompt, negative_prompt_input, image_v_hidden, video_v2v,509 height_input, width_input, mode,510 duration_input, frames_to_use, 511 seed_input, randomize_seed_input, guidance_scale_input, improve_texture]512 513 t2v_button.click(fn=generate, inputs=t2v_inputs, outputs=[output_video, seed_input], api_name="text_to_video")514 i2v_button.click(fn=generate, inputs=i2v_inputs, outputs=[output_video, seed_input], api_name="image_to_video")515 v2v_button.click(fn=generate, inputs=v2v_inputs, outputs=[output_video, seed_input], api_name="video_to_video")516 517if __name__ == "__main__":518 if os.path.exists(models_dir) and os.path.isdir(models_dir):519 print(f"Model directory: {Path(models_dir).resolve()}")520 521 demo.queue().launch(debug=True, share=False, mcp_server=True)