""" Cosmos Transfer2.5 Inference World-to-world translation with multiple control inputs """ import torch import time from typing import Optional, Union, List, Dict from PIL import Image from pathlib import Path import numpy as np from .loaders import load_transfer_pipeline, get_device_info from .utils_video import save_video, load_video_frames, extract_edges, extract_depth_map # Supported control types CONTROL_TYPES = ["blur", "edge", "depth", "segmentation"] def transfer_video( input_video: Union[str, List[Image.Image]], prompt: str, control_type: str = "blur", negative_prompt: str = "", num_inference_steps: int = 30, guidance_scale: float = 7.0, controlnet_conditioning_scale: float = 1.0, seed: int = 42, output_path: Optional[str] = None ) -> dict: """ Transfer video to new domain/style (World-to-world translation) Args: input_video: Input video path or frames prompt: Text description of target domain/style control_type: Type of control signal ("blur", "edge", "depth", "segmentation") negative_prompt: What to avoid num_inference_steps: Diffusion steps guidance_scale: CFG scale controlnet_conditioning_scale: How strongly to follow control signal seed: Random seed output_path: Path to save output Returns: dict with video_path, frames, metadata """ start_time = time.time() if control_type not in CONTROL_TYPES: raise ValueError(f"control_type must be one of {CONTROL_TYPES}") # Load video frames if isinstance(input_video, str): input_frames = load_video_frames(input_video) else: input_frames = input_video # Extract control signal based on type control_frames = prepare_control_frames(input_frames, control_type) pipe = load_transfer_pipeline() generator = torch.Generator(device="cuda").manual_seed(seed) device_info = get_device_info() print(f"\n=== Transfer2.5 Video Transfer ===") print(f"Control type: {control_type}") print(f"Input frames: {len(input_frames)}") print(f"Prompt: {prompt[:100]}...") print(f"Steps: {num_inference_steps}, CFG: {guidance_scale}, Seed: {seed}") print(f"Device: {device_info['name']}") output = pipe( prompt=prompt, negative_prompt=negative_prompt, control_video=control_frames, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, generator=generator ) frames = output.frames[0] inference_time = time.time() - start_time if output_path is None: output_path = f"output_transfer_{control_type}_seed{seed}.mp4" save_video(frames, output_path, fps=16) result = { "video_path": output_path, "control_type": control_type, "input_frames": len(input_frames), "output_frames": len(frames), "inference_time_s": round(inference_time, 2), "seed": seed, "prompt": prompt, "device": device_info['name'] } print(f"Video saved: {output_path} ({len(frames)} frames in {inference_time:.2f}s)") return result def prepare_control_frames( frames: List[Image.Image], control_type: str ) -> List[Image.Image]: """ Prepare control signal frames from input video Args: frames: Input video frames control_type: Type of control to extract Returns: List of control frames """ print(f"Extracting {control_type} control signal from {len(frames)} frames...") if control_type == "blur": # Gaussian blur for structure preservation from PIL import ImageFilter return [f.filter(ImageFilter.GaussianBlur(radius=10)) for f in frames] elif control_type == "edge": # Canny edge detection return [extract_edges(f) for f in frames] elif control_type == "depth": # Depth estimation (simplified - uses gradient as proxy) return [extract_depth_map(f) for f in frames] elif control_type == "segmentation": # For segmentation, we'd need a separate model # Using edge detection as fallback print("Note: Using edge detection as segmentation proxy") return [extract_edges(f) for f in frames] else: raise ValueError(f"Unknown control type: {control_type}") def transfer_style( input_video: Union[str, List[Image.Image]], source_style: str, target_style: str, control_type: str = "blur", num_inference_steps: int = 30, seed: int = 42, output_path: Optional[str] = None ) -> dict: """ Transfer video from source style/domain to target style/domain Common style transfers: - "day" -> "night" - "sunny" -> "rainy" - "clear" -> "foggy" - "urban" -> "rural" Args: input_video: Input video source_style: Description of source style target_style: Description of target style control_type: Control signal type num_inference_steps: Diffusion steps seed: Random seed output_path: Output path Returns: dict with video_path and metadata """ prompt = f"Transform from {source_style} to {target_style}. High quality, realistic, smooth motion." negative_prompt = f"{source_style}, low quality, blurry, artifacts, flickering" return transfer_video( input_video=input_video, prompt=prompt, control_type=control_type, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, seed=seed, output_path=output_path ) def batch_transfer_styles( input_video: Union[str, List[Image.Image]], style_pairs: List[tuple], control_type: str = "blur", num_inference_steps: int = 30, seed: int = 42 ) -> List[dict]: """ Apply multiple style transfers to the same input video Args: input_video: Input video style_pairs: List of (source_style, target_style) tuples control_type: Control signal type num_inference_steps: Diffusion steps seed: Base random seed Returns: List of result dicts """ results = [] # Load frames once if isinstance(input_video, str): frames = load_video_frames(input_video) else: frames = input_video for i, (source, target) in enumerate(style_pairs): print(f"\n=== Style Transfer {i+1}/{len(style_pairs)}: {source} -> {target} ===") output_path = f"output_transfer_{source}_to_{target}_seed{seed}.mp4" result = transfer_style( input_video=frames, source_style=source, target_style=target, control_type=control_type, num_inference_steps=num_inference_steps, seed=seed + i, # Different seed for each output_path=output_path ) results.append(result) return results