import argparse import os import random from datetime import datetime from pathlib import Path from diffusers.utils import logging from typing import Optional, List, Union import yaml import imageio import json import numpy as np import torch import cv2 from safetensors import safe_open from PIL import Image # New Import for Tethering from gradio_client import Client, handle_file from types import SimpleNamespace from transformers import ( T5EncoderModel, T5Tokenizer, AutoModelForCausalLM, AutoProcessor, AutoTokenizer, ) from huggingface_hub import hf_hub_download from ltx_video.models.autoencoders.causal_video_autoencoder import ( CausalVideoAutoencoder, ) from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier from ltx_video.models.transformers.transformer3d import Transformer3DModel from ltx_video.pipelines.pipeline_ltx_video import ( ConditioningItem, LTXVideoPipeline, LTXMultiScalePipeline, ) from ltx_video.schedulers.rf import RectifiedFlowScheduler from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy from ltx_video.models.autoencoders.latent_upsampler import LatentUpsampler import ltx_video.pipelines.crf_compressor as crf_compressor MAX_HEIGHT = 720 MAX_WIDTH = 1280 MAX_NUM_FRAMES = 257 logger = logging.get_logger("LTX-Video") def get_total_gpu_memory(): if torch.cuda.is_available(): total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3) return total_memory return 0 def get_device(): if torch.cuda.is_available(): return "cuda" elif torch.backends.mps.is_available(): return "mps" return "cpu" def load_image_to_tensor_with_resize_and_crop( image_input: Union[str, Image.Image], target_height: int = 512, target_width: int = 768, just_crop: bool = False, ) -> torch.Tensor: """Load and process an image into a tensor.""" if isinstance(image_input, str): image = Image.open(image_input).convert("RGB") elif isinstance(image_input, Image.Image): image = image_input else: raise ValueError("image_input must be either a file path or a PIL Image object") input_width, input_height = image.size aspect_ratio_target = target_width / target_height aspect_ratio_frame = input_width / input_height if aspect_ratio_frame > aspect_ratio_target: new_width = int(input_height * aspect_ratio_target) new_height = input_height x_start = (input_width - new_width) // 2 y_start = 0 else: new_width = input_width new_height = int(input_width / aspect_ratio_target) x_start = 0 y_start = (input_height - new_height) // 2 image = image.crop((x_start, y_start, x_start + new_width, y_start + new_height)) if not just_crop: image = image.resize((target_width, target_height)) image = np.array(image) image = cv2.GaussianBlur(image, (3, 3), 0) frame_tensor = torch.from_numpy(image).float() frame_tensor = crf_compressor.compress(frame_tensor / 255.0) * 255.0 frame_tensor = frame_tensor.permute(2, 0, 1) frame_tensor = (frame_tensor / 127.5) - 1.0 return frame_tensor.unsqueeze(0).unsqueeze(2) def calculate_padding( source_height: int, source_width: int, target_height: int, target_width: int ) -> tuple[int, int, int, int]: pad_height = target_height - source_height pad_width = target_width - source_width pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left padding = (pad_left, pad_right, pad_top, pad_bottom) return padding def convert_prompt_to_filename(text: str, max_len: int = 20) -> str: clean_text = "".join( char.lower() for char in text if char.isalpha() or char.isspace() ) words = clean_text.split() result = [] current_length = 0 for word in words: new_length = current_length + len(word) if new_length <= max_len: result.append(word) current_length += len(word) else: break return "-".join(result) def get_unique_filename( base: str, ext: str, prompt: str, seed: int, resolution: tuple[int, int, int], dir: Path, endswith=None, index_range=1000, ) -> Path: base_filename = f"{base}_{convert_prompt_to_filename(prompt, max_len=30)}_{seed}_{resolution[0]}x{resolution[1]}x{resolution[2]}" for i in range(index_range): filename = dir / f"{base_filename}_{i}{endswith if endswith else ''}{ext}" if not os.path.exists(filename): return filename raise FileExistsError( f"Could not find a unique filename after {index_range} attempts." ) def seed_everething(seed: int): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) if torch.backends.mps.is_available(): torch.mps.manual_seed(seed) def main(): parser = argparse.ArgumentParser( description="Load models from separate directories and run the pipeline." ) # New Argument for Worker parser.add_argument( "--worker_url", type=str, default=None, help="HuggingFace Space URL for the VAE/TextEncoder Worker (e.g., 'user/space')", ) parser.add_argument( "--hf_token", type=str, default=None, help="HuggingFace Token if the worker space is private", ) # Existing Args parser.add_argument("--output_path", type=str, default=None) parser.add_argument("--seed", type=int, default=171198) parser.add_argument("--num_images_per_prompt", type=int, default=1) parser.add_argument("--image_cond_noise_scale", type=float, default=0.15) parser.add_argument("--height", type=int, default=704) parser.add_argument("--width", type=int, default=1216) parser.add_argument("--num_frames", type=int, default=121) parser.add_argument("--frame_rate", type=int, default=30) parser.add_argument("--device", default=None) parser.add_argument("--pipeline_config", type=str, default="configs/ltxv-13b-0.9.7-dev.yaml") parser.add_argument("--prompt", type=str, help="Text prompt to guide generation") parser.add_argument("--negative_prompt", type=str, default="worst quality, inconsistent motion, blurry, jittery, distorted") parser.add_argument("--offload_to_cpu", action="store_true") parser.add_argument("--input_media_path", type=str, default=None) parser.add_argument("--conditioning_media_paths", type=str, nargs="*") parser.add_argument("--conditioning_strengths", type=float, nargs="*") parser.add_argument("--conditioning_start_frames", type=int, nargs="*") args = parser.parse_args() logger.warning(f"Running generation with arguments: {args}") infer(**vars(args)) # --- Robust Import for Downsample3D --- # --- Robust Import for Downsample3D --- Downsample3D = None try: from ltx_video.models.autoencoders.video_autoencoder import Downsample3D except ImportError: try: from ltx_video.models.autoencoders.vae_encode import Downsample3D except ImportError: pass if Downsample3D is None: class Downsample3D(torch.nn.Module): def __init__(self, *args, **kwargs): super().__init__() def create_ltx_video_pipeline( ckpt_path: str, precision: str, text_encoder_model_name_or_path: str, sampler: Optional[str] = None, device: Optional[str] = None, enhance_prompt: bool = False, prompt_enhancer_image_caption_model_name_or_path: Optional[str] = None, prompt_enhancer_llm_model_name_or_path: Optional[str] = None, use_worker: bool = False, ) -> LTXVideoPipeline: ckpt_path = Path(ckpt_path) # 1. Load Transformer try: transformer = Transformer3DModel.from_pretrained(ckpt_path, subfolder="transformer") except OSError: print("Fallback: Downloading Transformer config...") t_conf = hf_hub_download("Lightricks/LTX-Video", subfolder="transformer", filename="config.json") transformer = Transformer3DModel.from_pretrained(os.path.dirname(t_conf)) # 2. Load Scheduler if sampler == "from_checkpoint" or not sampler: try: scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path) except Exception: print("Fallback: Downloading Scheduler config...") s_path = hf_hub_download(repo_id="Lightricks/LTX-Video", subfolder="scheduler", filename="scheduler_config.json") scheduler = RectifiedFlowScheduler.from_pretrained(os.path.dirname(s_path)) else: scheduler = RectifiedFlowScheduler( sampler=("Uniform" if sampler.lower() == "uniform" else "LinearQuadratic") ) # 3. Handle VAE (Worker vs Local) if use_worker: print("--- WORKER MODE: Creating Structure-Aware Mock VAE (CUDA) ---") try: vae_config_path = hf_hub_download(repo_id="Lightricks/LTX-Video", subfolder="vae", filename="config.json") with open(vae_config_path, "r") as f: config_dict = json.load(f) # --- MOCK CLASSES --- class MockBlock: def __init__(self, has_downsample=False): self.downsample = None if has_downsample: try: self.downsample = Downsample3D(dims=3, in_channels=1, out_channels=1) except: self.downsample = Downsample3D(3, 1, 1) class MockEncoder: def __init__(self, config_dict): self.down_blocks = [] down_block_types = config_dict.get("down_block_types", ["DownEncoderBlock3D"]*4) for block_type in down_block_types: has_down = "DownEncoderBlock3D" in block_type self.down_blocks.append(MockBlock(has_downsample=has_down)) class MockVAE(CausalVideoAutoencoder): def __init__(self, config_dict): # 1. Initialize nn.Module structure torch.nn.Module.__init__(self) self._mock_config = SimpleNamespace(**config_dict) if not hasattr(self._mock_config, "patch_size"): self._mock_config.patch_size = 1 self.encoder = MockEncoder(config_dict) self._mock_dtype = torch.bfloat16 self._mock_device = torch.device("cuda") self.use_slicing = False self.use_tiling = False # --- FIX: Register missing VAE statistical buffers --- # LTX uses these for multi-scale normalization. # We use Identity values (0 and 1) to let data pass through unchanged. # Shape is [128] because LTX latents have 128 channels. self.register_buffer("mean_of_means", torch.zeros(128, dtype=torch.float32)) self.register_buffer("std_of_means", torch.ones(128, dtype=torch.float32)) # Properties @property def config(self): return self._mock_config @property def dtype(self): return self._mock_dtype @property def device(self): return self._mock_device @property def spatial_downscale_factor(self): return 32 @property def temporal_downscale_factor(self): return 8 # Passthrough Encode def encode(self, x): class MockDistribution: def sample(self, generator=None): return x def mode(self): return x return SimpleNamespace(latent_dist=MockDistribution()) vae = MockVAE(config_dict) print("✅ Structure-Aware Mock VAE created.") except Exception as e: print(f"CRITICAL: Failed to create Mock VAE: {e}") raise e text_encoder = None tokenizer = None else: # Standard Local Load vae = CausalVideoAutoencoder.from_pretrained(ckpt_path, subfolder="vae") text_encoder = T5EncoderModel.from_pretrained(text_encoder_model_name_or_path, subfolder="text_encoder") tokenizer = T5Tokenizer.from_pretrained(text_encoder_model_name_or_path, subfolder="tokenizer") # 4. Final Assembly patchifier = SymmetricPatchifier(patch_size=1) transformer = transformer.to(device) if not use_worker: vae = vae.to(device).to(torch.bfloat16) text_encoder = text_encoder.to(device).to(torch.bfloat16) # Prompt Enhancer (Standard) if enhance_prompt and not use_worker: prompt_enhancer_image_caption_model = AutoModelForCausalLM.from_pretrained( prompt_enhancer_image_caption_model_name_or_path, trust_remote_code=True ) prompt_enhancer_image_caption_processor = AutoProcessor.from_pretrained( prompt_enhancer_image_caption_model_name_or_path, trust_remote_code=True ) prompt_enhancer_llm_model = AutoModelForCausalLM.from_pretrained( prompt_enhancer_llm_model_name_or_path, torch_dtype="bfloat16", ) prompt_enhancer_llm_tokenizer = AutoTokenizer.from_pretrained( prompt_enhancer_llm_model_name_or_path, ) else: prompt_enhancer_image_caption_model = None prompt_enhancer_image_caption_processor = None prompt_enhancer_llm_model = None prompt_enhancer_llm_tokenizer = None if precision == "bfloat16" and transformer.dtype != torch.bfloat16: transformer = transformer.to(torch.bfloat16) submodel_dict = { "transformer": transformer, "patchifier": patchifier, "text_encoder": text_encoder, "tokenizer": tokenizer, "scheduler": scheduler, "vae": vae, "prompt_enhancer_image_caption_model": prompt_enhancer_image_caption_model, "prompt_enhancer_image_caption_processor": prompt_enhancer_image_caption_processor, "prompt_enhancer_llm_model": prompt_enhancer_llm_model, "prompt_enhancer_llm_tokenizer": prompt_enhancer_llm_tokenizer, } pipeline = LTXVideoPipeline(**submodel_dict) if not use_worker: pipeline = pipeline.to(device) else: pipeline.transformer = pipeline.transformer.to(device) return pipeline def create_latent_upsampler(latent_upsampler_model_path: str, device: str): latent_upsampler = LatentUpsampler.from_pretrained(latent_upsampler_model_path) latent_upsampler.to(device) latent_upsampler.eval() return latent_upsampler def infer( output_path: Optional[str], seed: int, pipeline_config: str, image_cond_noise_scale: float, height: Optional[int], width: Optional[int], num_frames: int, frame_rate: int, prompt: str, negative_prompt: str, offload_to_cpu: bool, worker_url: Optional[str] = None, # New Arg hf_token: Optional[str] = None, # New Arg input_media_path: Optional[str] = None, conditioning_media_paths: Optional[List[str]] = None, conditioning_strengths: Optional[List[float]] = None, conditioning_start_frames: Optional[List[int]] = None, device: Optional[str] = None, **kwargs, ): # Setup Client if Worker URL is present worker_client = None if worker_url: print(f"Connecting to Worker Space: {worker_url}") worker_client = Client(worker_url, hf_token=hf_token) if not os.path.isfile(pipeline_config): raise ValueError(f"Pipeline config file {pipeline_config} does not exist") with open(pipeline_config, "r") as f: pipeline_config = yaml.safe_load(f) models_dir = "MODEL_DIR" ltxv_model_name_or_path = pipeline_config["checkpoint_path"] if not os.path.isfile(ltxv_model_name_or_path): ltxv_model_path = hf_hub_download( repo_id="Lightricks/LTX-Video", filename=ltxv_model_name_or_path, local_dir=models_dir, repo_type="model", ) else: ltxv_model_path = ltxv_model_name_or_path spatial_upscaler_model_name_or_path = pipeline_config.get("spatial_upscaler_model_path") if spatial_upscaler_model_name_or_path and not os.path.isfile(spatial_upscaler_model_name_or_path): spatial_upscaler_model_path = hf_hub_download( repo_id="Lightricks/LTX-Video", filename=spatial_upscaler_model_name_or_path, local_dir=models_dir, repo_type="model", ) else: spatial_upscaler_model_path = spatial_upscaler_model_name_or_path if kwargs.get("input_image_path", None): logger.warning("Please use conditioning_media_paths instead of input_image_path.") assert not conditioning_media_paths and not conditioning_start_frames conditioning_media_paths = [kwargs["input_image_path"]] conditioning_start_frames = [0] if conditioning_media_paths: if not conditioning_strengths: conditioning_strengths = [1.0] * len(conditioning_media_paths) if not conditioning_start_frames: raise ValueError("If `conditioning_media_paths` is provided, `conditioning_start_frames` must also be provided") if len(conditioning_media_paths) != len(conditioning_strengths) or len(conditioning_media_paths) != len(conditioning_start_frames): raise ValueError("`conditioning_media_paths`, `conditioning_strengths`, and `conditioning_start_frames` must have the same length") if any(s < 0 or s > 1 for s in conditioning_strengths): raise ValueError("All conditioning strengths must be between 0 and 1") if any(f < 0 or f >= num_frames for f in conditioning_start_frames): raise ValueError(f"All conditioning start frames must be between 0 and {num_frames-1}") seed_everething(seed) # CPU Offload Logic if offload_to_cpu and not torch.cuda.is_available(): logger.warning("offload_to_cpu is set to True, but offloading will not occur since the model is already running on CPU.") offload_to_cpu = False else: offload_to_cpu = offload_to_cpu and get_total_gpu_memory() < 30 output_dir = Path(output_path) if output_path else Path(f"outputs/{datetime.today().strftime('%Y-%m-%d')}") output_dir.mkdir(parents=True, exist_ok=True) height_padded = ((height - 1) // 32 + 1) * 32 width_padded = ((width - 1) // 32 + 1) * 32 num_frames_padded = ((num_frames - 2) // 8 + 1) * 8 + 1 padding = calculate_padding(height, width, height_padded, width_padded) prompt_enhancement_words_threshold = pipeline_config["prompt_enhancement_words_threshold"] prompt_word_count = len(prompt.split()) enhance_prompt = (prompt_enhancement_words_threshold > 0 and prompt_word_count < prompt_enhancement_words_threshold) if prompt_enhancement_words_threshold > 0 and not enhance_prompt: logger.info(f"Prompt has {prompt_word_count} words, which exceeds the threshold. Prompt enhancement disabled.") precision = pipeline_config["precision"] text_encoder_model_name_or_path = pipeline_config["text_encoder_model_name_or_path"] sampler = pipeline_config["sampler"] prompt_enhancer_image_caption_model_name_or_path = pipeline_config["prompt_enhancer_image_caption_model_name_or_path"] prompt_enhancer_llm_model_name_or_path = pipeline_config["prompt_enhancer_llm_model_name_or_path"] # Create Pipeline (Passing use_worker flag) pipeline = create_ltx_video_pipeline( ckpt_path=ltxv_model_path, precision=precision, text_encoder_model_name_or_path=text_encoder_model_name_or_path, sampler=sampler, device=kwargs.get("device", get_device()), enhance_prompt=enhance_prompt, prompt_enhancer_image_caption_model_name_or_path=prompt_enhancer_image_caption_model_name_or_path, prompt_enhancer_llm_model_name_or_path=prompt_enhancer_llm_model_name_or_path, use_worker=(worker_client is not None) ) if pipeline_config.get("pipeline_type", None) == "multi-scale": if not spatial_upscaler_model_path: raise ValueError("spatial upscaler model path is missing") latent_upsampler = create_latent_upsampler(spatial_upscaler_model_path, pipeline.device) pipeline = LTXMultiScalePipeline(pipeline, latent_upsampler=latent_upsampler) media_item = None if input_media_path: media_item = load_media_file( media_path=input_media_path, height=height, width=width, max_frames=num_frames_padded, padding=padding, ) conditioning_items = ( prepare_conditioning( conditioning_media_paths=conditioning_media_paths, conditioning_strengths=conditioning_strengths, conditioning_start_frames=conditioning_start_frames, height=height, width=width, num_frames=num_frames, padding=padding, pipeline=pipeline, ) if conditioning_media_paths else None ) stg_mode = pipeline_config.get("stg_mode", "attention_values") del pipeline_config["stg_mode"] if stg_mode.lower() in ["stg_av", "attention_values"]: skip_layer_strategy = SkipLayerStrategy.AttentionValues elif stg_mode.lower() in ["stg_as", "attention_skip"]: skip_layer_strategy = SkipLayerStrategy.AttentionSkip elif stg_mode.lower() in ["stg_r", "residual"]: skip_layer_strategy = SkipLayerStrategy.Residual elif stg_mode.lower() in ["stg_t", "transformer_block"]: skip_layer_strategy = SkipLayerStrategy.TransformerBlock else: raise ValueError(f"Invalid spatiotemporal guidance mode: {stg_mode}") device = device or get_device() generator = torch.Generator(device=device).manual_seed(seed) # --- PREPARE INPUTS --- sample_kwargs = {} if worker_client: print("1. Requesting Text Embeddings from Worker...") embeds_path = worker_client.predict( prompt=prompt, negative_prompt=negative_prompt, api_name="/encode_prompt" ) embeds_data = torch.load(embeds_path, map_location="cuda") # Explicitly pass embeddings to pipeline sample_kwargs["prompt_embeds"] = embeds_data["prompt_embeds"].to(dtype=torch.bfloat16) sample_kwargs["negative_prompt_embeds"] = embeds_data["negative_prompt_embeds"].to(dtype=torch.bfloat16) sample_kwargs["prompt_attention_mask"] = embeds_data["attention_mask"].to(dtype=torch.bfloat16) sample_kwargs["negative_prompt_attention_mask"] = embeds_data["negative_attention_mask"].to(dtype=torch.bfloat16) else: # Standard Local Text Encoding sample_kwargs["prompt"] = prompt sample_kwargs["negative_prompt"] = negative_prompt sample_kwargs["prompt_attention_mask"] = None sample_kwargs["negative_prompt_attention_mask"] = None # --- RUN PIPELINE --- print("2. Running Transformer Inference...") # Decide output type based on worker presence desired_output_type = "latent" if worker_client else "pt" pipeline_output = pipeline( **pipeline_config, skip_layer_strategy=skip_layer_strategy, generator=generator, output_type=desired_output_type, # <--- CRITICAL SWITCH callback_on_step_end=None, height=height_padded, width=width_padded, num_frames=num_frames_padded, frame_rate=frame_rate, media_items=media_item, conditioning_items=conditioning_items, is_video=True, vae_per_channel_normalize=True, image_cond_noise_scale=image_cond_noise_scale, mixed_precision=(precision == "mixed_precision"), offload_to_cpu=offload_to_cpu, device=device, enhance_prompt=enhance_prompt, **sample_kwargs ) # --- POST PROCESSING --- if worker_client: # TETHERED PATH latents = pipeline_output.frames print("3. Sending Latents to Worker for Decode...") temp_path = "/tmp/temp_latents.pt" torch.save(latents.cpu(), temp_path) # Get final video path from worker video_result_path = worker_client.predict( latent_file_path=handle_file(temp_path), api_name="/decode_latents" ) # Copy the worker's result to our local output directory import shutil final_output_filename = get_unique_filename( f"video_output", ".mp4", prompt=prompt, seed=seed, resolution=(height, width, num_frames), dir=output_dir, ) shutil.copy(video_result_path, final_output_filename) logger.warning(f"Output saved to {final_output_filename}") else: # STANDARD PATH (Local Decode) images = pipeline_output.images (pad_left, pad_right, pad_top, pad_bottom) = padding pad_bottom = -pad_bottom pad_right = -pad_right if pad_bottom == 0: pad_bottom = images.shape[3] if pad_right == 0: pad_right = images.shape[4] images = images[:, :, :num_frames, pad_top:pad_bottom, pad_left:pad_right] for i in range(images.shape[0]): video_np = images[i].permute(1, 2, 3, 0).cpu().float().numpy() video_np = (video_np * 255).astype(np.uint8) fps = frame_rate height, width = video_np.shape[1:3] if video_np.shape[0] == 1: output_filename = get_unique_filename( f"image_output_{i}", ".png", prompt=prompt, seed=seed, resolution=(height, width, num_frames), dir=output_dir, ) imageio.imwrite(output_filename, video_np[0], quality=100) else: output_filename = get_unique_filename( f"video_output_{i}", ".mp4", prompt=prompt, seed=seed, resolution=(height, width, num_frames), dir=output_dir, ) with imageio.get_writer(output_filename, fps=fps, quality=10) as video: for frame in video_np: video.append_data(frame) logger.warning(f"Output saved to {output_filename}") def prepare_conditioning( conditioning_media_paths: List[str], conditioning_strengths: List[float], conditioning_start_frames: List[int], height: int, width: int, num_frames: int, padding: tuple[int, int, int, int], pipeline: LTXVideoPipeline, ) -> Optional[List[ConditioningItem]]: """Prepare conditioning items based on input media paths and their parameters.""" conditioning_items = [] for path, strength, start_frame in zip( conditioning_media_paths, conditioning_strengths, conditioning_start_frames ): num_input_frames = orig_num_input_frames = get_media_num_frames(path) if hasattr(pipeline, "trim_conditioning_sequence") and callable( getattr(pipeline, "trim_conditioning_sequence") ): num_input_frames = pipeline.trim_conditioning_sequence( start_frame, orig_num_input_frames, num_frames ) if num_input_frames < orig_num_input_frames: logger.warning( f"Trimming conditioning video {path} from {orig_num_input_frames} to {num_input_frames} frames." ) media_tensor = load_media_file( media_path=path, height=height, width=width, max_frames=num_input_frames, padding=padding, just_crop=True, ) conditioning_items.append(ConditioningItem(media_tensor, start_frame, strength)) return conditioning_items def get_media_num_frames(media_path: str) -> int: is_video = any( media_path.lower().endswith(ext) for ext in [".mp4", ".avi", ".mov", ".mkv"] ) num_frames = 1 if is_video: reader = imageio.get_reader(media_path) num_frames = reader.count_frames() reader.close() return num_frames def load_media_file( media_path: str, height: int, width: int, max_frames: int, padding: tuple[int, int, int, int], just_crop: bool = False, ) -> torch.Tensor: is_video = any( media_path.lower().endswith(ext) for ext in [".mp4", ".avi", ".mov", ".mkv"] ) if is_video: reader = imageio.get_reader(media_path) num_input_frames = min(reader.count_frames(), max_frames) frames = [] for i in range(num_input_frames): frame = Image.fromarray(reader.get_data(i)) frame_tensor = load_image_to_tensor_with_resize_and_crop( frame, height, width, just_crop=just_crop ) frame_tensor = torch.nn.functional.pad(frame_tensor, padding) frames.append(frame_tensor) reader.close() media_tensor = torch.cat(frames, dim=2) else: # Input image media_tensor = load_image_to_tensor_with_resize_and_crop( media_path, height, width, just_crop=just_crop ) media_tensor = torch.nn.functional.pad(media_tensor, padding) return media_tensor if __name__ == "__main__": main()