import tempfile import imageio import os import torch import logging import argparse import json import numpy as np import torch.nn.functional as F from pathlib import Path from omegaconf import OmegaConf from torch.utils.data import Dataset from transformers import CLIPTextModel, CLIPTokenizer from ddiffusers import AutoencoderKL, DDIMScheduler from einops import rearrange from genphoto.pipelines.pipeline_animation import GenPhotoPipeline from genphoto.models.unet import UNet3DConditionModelCameraCond from genphoto.models.camera_adaptor import CameraCameraEncoder, CameraAdaptor from genphoto.utils.util import save_videos_grid logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def create_focal_length_embedding(focal_length_values, target_height, target_width, base_focal_length=24.0, sensor_height=24.0, sensor_width=36.0): device = 'cpu' focal_length_values = focal_length_values.to(device) f = focal_length_values.shape[0] # Number of frames # Convert constants to tensors to perform operations with focal_length_values sensor_width = torch.tensor(sensor_width, device=device) sensor_height = torch.tensor(sensor_height, device=device) base_focal_length = torch.tensor(base_focal_length, device=device) # Calculate the FOV for the base focal length (min_focal_length) base_fov_x = 2.0 * torch.atan(sensor_width * 0.5 / base_focal_length) base_fov_y = 2.0 * torch.atan(sensor_height * 0.5 / base_focal_length) # Calculate the FOV for each focal length in focal_length_values target_fov_x = 2.0 * torch.atan(sensor_width * 0.5 / focal_length_values) target_fov_y = 2.0 * torch.atan(sensor_height * 0.5 / focal_length_values) # Calculate crop ratio: how much of the image is cropped at the current focal length crop_ratio_xs = target_fov_x / base_fov_x # Crop ratio for horizontal axis crop_ratio_ys = target_fov_y / base_fov_y # Crop ratio for vertical axis # Get the center of the image center_h, center_w = target_height // 2, target_width // 2 # Initialize a mask tensor with zeros on CPU focal_length_embedding = torch.zeros((f, 3, target_height, target_width), dtype=torch.float32) # Shape [f, 3, H, W] # Fill the center region with 1 based on the calculated crop dimensions for i in range(f): # Crop dimensions calculated using rounded float values crop_h = torch.round(crop_ratio_ys[i] * target_height).int().item() # Rounded cropped height for the current frame # print('crop_h', crop_h) crop_w = torch.round(crop_ratio_xs[i] * target_width).int().item() # Rounded cropped width for the current frame # Ensure the cropped dimensions are within valid bounds crop_h = max(1, min(target_height, crop_h)) crop_w = max(1, min(target_width, crop_w)) # Set the center region of the focal_length embedding to 1 for the current frame focal_length_embedding[i, :, center_h - crop_h // 2: center_h + crop_h // 2, center_w - crop_w // 2: center_w + crop_w // 2] = 1.0 return focal_length_embedding class Camera_Embedding(Dataset): def __init__(self, focal_length_values, tokenizer, text_encoder, device, sample_size=[256, 384]): self.focal_length_values = focal_length_values.to(device) self.tokenizer = tokenizer self.text_encoder = text_encoder self.device = device self.sample_size = sample_size def load(self): if len(self.focal_length_values) != 5: raise ValueError("Expected 5 focal_length values") # Generate prompts for each focal length value and append focal_length information to caption prompts = [] for fl in self.focal_length_values: prompt = f"" prompts.append(prompt) # Tokenize prompts and encode to get embeddings with torch.no_grad(): prompt_ids = self.tokenizer( prompts, max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ).input_ids.to(self.device) encoder_hidden_states = self.text_encoder(input_ids=prompt_ids).last_hidden_state # Shape: (f, sequence_length, hidden_size) # Calculate differences between consecutive embeddings (ignoring sequence_length) differences = [] for i in range(1, encoder_hidden_states.size(0)): diff = encoder_hidden_states[i] - encoder_hidden_states[i - 1] diff = diff.unsqueeze(0) differences.append(diff) # Add the difference between the last and the first embedding final_diff = encoder_hidden_states[-1] - encoder_hidden_states[0] final_diff = final_diff.unsqueeze(0) differences.append(final_diff) # Concatenate differences along the batch dimension (f-1) concatenated_differences = torch.cat(differences, dim=0) frame = concatenated_differences.size(0) concatenated_differences = torch.cat(differences, dim=0) pad_length = 128 - concatenated_differences.size(1) if pad_length > 0: # Pad along the second dimension (77 -> 128), pad only on the right side concatenated_differences_padded = F.pad(concatenated_differences, (0, 0, 0, pad_length)) ccl_embedding = concatenated_differences_padded.reshape(frame, self.sample_size[0], self.sample_size[1]) ccl_embedding = ccl_embedding.unsqueeze(1) ccl_embedding = ccl_embedding.expand(-1, 3, -1, -1) ccl_embedding = ccl_embedding.to(self.device) focal_length_embedding = create_focal_length_embedding(self.focal_length_values, self.sample_size[0], self.sample_size[1]).to(self.device) camera_embedding = torch.cat((focal_length_embedding, ccl_embedding), dim=1) return camera_embedding def load_models(cfg): device = "cuda" if torch.cuda.is_available() else "cpu" noise_scheduler = DDIMScheduler(**OmegaConf.to_container(cfg.noise_scheduler_kwargs)) vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_path, subfolder="vae").to(device) vae.requires_grad_(False) tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_path, subfolder="tokenizer") text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_path, subfolder="text_encoder").to(device) text_encoder.requires_grad_(False) unet = UNet3DConditionModelCameraCond.from_pretrained_2d( cfg.pretrained_model_path, subfolder=cfg.unet_subfolder, unet_additional_kwargs=cfg.unet_additional_kwargs ).to(device) unet.requires_grad_(False) camera_encoder = CameraCameraEncoder(**cfg.camera_encoder_kwargs).to(device) camera_encoder.requires_grad_(False) camera_adaptor = CameraAdaptor(unet, camera_encoder) camera_adaptor.requires_grad_(False) camera_adaptor.to(device) logger.info("Setting the attention processors") unet.set_all_attn_processor( add_spatial_lora=cfg.lora_ckpt is not None, add_motion_lora=cfg.motion_lora_rank > 0, lora_kwargs={"lora_rank": cfg.lora_rank, "lora_scale": cfg.lora_scale}, motion_lora_kwargs={"lora_rank": cfg.motion_lora_rank, "lora_scale": cfg.motion_lora_scale}, **cfg.attention_processor_kwargs ) if cfg.lora_ckpt is not None: print(f"Loading the lora checkpoint from {cfg.lora_ckpt}") lora_checkpoints = torch.load(cfg.lora_ckpt, map_location=unet.device) if 'lora_state_dict' in lora_checkpoints.keys(): lora_checkpoints = lora_checkpoints['lora_state_dict'] _, lora_u = unet.load_state_dict(lora_checkpoints, strict=False) assert len(lora_u) == 0 print(f'Loading done') if cfg.motion_module_ckpt is not None: print(f"Loading the motion module checkpoint from {cfg.motion_module_ckpt}") mm_checkpoints = torch.load(cfg.motion_module_ckpt, map_location=unet.device) _, mm_u = unet.load_state_dict(mm_checkpoints, strict=False) assert len(mm_u) == 0 print("Loading done") if cfg.camera_adaptor_ckpt is not None: logger.info(f"Loading camera adaptor from {cfg.camera_adaptor_ckpt}") camera_adaptor_checkpoint = torch.load(cfg.camera_adaptor_ckpt, map_location=device) camera_encoder_state_dict = camera_adaptor_checkpoint['camera_encoder_state_dict'] attention_processor_state_dict = camera_adaptor_checkpoint['attention_processor_state_dict'] camera_enc_m, camera_enc_u = camera_adaptor.camera_encoder.load_state_dict(camera_encoder_state_dict, strict=False) assert len(camera_enc_m) == 0 and len(camera_enc_u) == 0 _, attention_processor_u = camera_adaptor.unet.load_state_dict(attention_processor_state_dict, strict=False) assert len(attention_processor_u) == 0 logger.info("Camera Adaptor loading done") else: logger.info("No Camera Adaptor checkpoint used") pipeline = GenPhotoPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=noise_scheduler, camera_encoder=camera_encoder ).to(device) pipeline.enable_vae_slicing() return pipeline, device def run_inference(pipeline, tokenizer, text_encoder, base_scene, focal_length_list, device, video_length=5, height=256, width=384): focal_length_values = json.loads(focal_length_list) focal_length_values = torch.tensor(focal_length_values).unsqueeze(1) # Ensure camera_embedding is on the correct device camera_embedding = Camera_Embedding(focal_length_values, tokenizer, text_encoder, device).load() camera_embedding = rearrange(camera_embedding.unsqueeze(0), "b f c h w -> b c f h w") with torch.no_grad(): sample = pipeline( prompt=base_scene, camera_embedding=camera_embedding, video_length=video_length, height=height, width=width, num_inference_steps=25, guidance_scale=8.0 ).videos[0].cpu() temporal_video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name save_videos_grid(sample[None], temporal_video_path, rescale=False) return temporal_video_path def main(config_path, base_scene, focal_length_list): torch.manual_seed(42) cfg = OmegaConf.load(config_path) logger.info("Loading models...") pipeline, device = load_models(cfg) logger.info("Starting inference...") video_path = run_inference(pipeline, pipeline.tokenizer, pipeline.text_encoder, base_scene, focal_length_list, device) logger.info(f"Video saved to {video_path}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file") parser.add_argument("--base_scene", type=str, required=True, help="invariant scene caption as JSON string") parser.add_argument("--focal_length_list", type=str, required=True, help="focal_length values as JSON string") args = parser.parse_args() main(args.config, args.base_scene, args.focal_length_list) # usage example # python inference_focal_length.py --config configs/inference_genphoto/adv3_256_384_genphoto_relora_focal_length.yaml --base_scene "A cozy living room with a large, comfy sofa and a coffee table." --focal_length_list "[25.0, 35.0, 45.0, 55.0, 65.0]"