import argparse import datetime import json import math import os import sys import time from glob import glob from pathlib import Path from typing import Optional import cv2 import numpy as np import torch import torchvision from einops import rearrange, repeat from fire import Fire from omegaconf import OmegaConf from PIL import Image from torchvision.transforms import CenterCrop, Compose, Resize, ToTensor import tempfile sys.path.insert(1, os.path.join(sys.path[0], '..')) from sgm.util import default, instantiate_from_config def to_relative_RT2(org_pose, keyframe_idx=0, keyframe_zero=False): org_pose = org_pose.reshape(-1, 3, 4) # [t, 3, 4] R_dst = org_pose[:, :, :3] T_dst = org_pose[:, :, 3:] R_src = R_dst[keyframe_idx: keyframe_idx+1].repeat(org_pose.shape[0], axis=0) # [t, 3, 3] T_src = T_dst[keyframe_idx: keyframe_idx+1].repeat(org_pose.shape[0], axis=0) R_src_inv = R_src.transpose(0, 2, 1) # [t, 3, 3] R_rel = R_dst @ R_src_inv # [t, 3, 3] T_rel = T_dst - R_rel@T_src RT_rel = np.concatenate([R_rel, T_rel], axis=-1) # [t, 3, 4] RT_rel = RT_rel.reshape(-1, 12) # [t, 12] if keyframe_zero: RT_rel[keyframe_idx] = np.zeros_like(RT_rel[keyframe_idx]) return RT_rel def build_model(config, ckpt, device, num_frames, num_steps): num_frames = default(num_frames, 14) num_steps = default(num_steps, 25) model_config = default(config, "configs/inference/config_motionctrl_cmcm.yaml") print(f"Loading model from {ckpt}") model, filter = load_model( model_config, ckpt, device, num_frames, num_steps, ) model.eval() return model def motionctrl_sample( model, image: Image = None, # Can either be image file or folder with image files RT: np.ndarray = None, num_frames: Optional[int] = None, fps_id: int = 6, motion_bucket_id: int = 127, cond_aug: float = 0.02, seed: int = 23, decoding_t: int = 1, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary. save_fps: int = 10, sample_num: int = 1, device: str = "cuda", ): """ Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`. """ torch.manual_seed(seed) w, h = image.size # RT: [t, 3, 4] # RT = RT.reshape(-1, 3, 4) # [t, 3, 4] # adaptive to different spatial ratio # base_len = min(w, h) * 0.5 # K = np.array([[w/base_len, 0, w/base_len], # [0, h/base_len, h/base_len], # [0, 0, 1]]) # for i in range(RT.shape[0]): # RT[i,:,:] = np.dot(K, RT[i,:,:]) RT = to_relative_RT2(RT) # [t, 12] RT = torch.tensor(RT).float().to(device) # [t, 12] RT = RT.unsqueeze(0).repeat(2,1,1) if h % 64 != 0 or w % 64 != 0: width, height = map(lambda x: x - x % 64, (w, h)) image = image.resize((width, height)) print( f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!" ) image = ToTensor()(image) image = image * 2.0 - 1.0 image = image.unsqueeze(0).to(device) H, W = image.shape[2:] assert image.shape[1] == 3 F = 8 C = 4 shape = (num_frames, C, H // F, W // F) if motion_bucket_id > 255: print( "WARNING: High motion bucket! This may lead to suboptimal performance." ) if fps_id < 5: print("WARNING: Small fps value! This may lead to suboptimal performance.") if fps_id > 30: print("WARNING: Large fps value! This may lead to suboptimal performance.") value_dict = {} value_dict["motion_bucket_id"] = motion_bucket_id value_dict["fps_id"] = fps_id value_dict["cond_aug"] = cond_aug value_dict["cond_frames_without_noise"] = image value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image) with torch.no_grad(): with torch.autocast(device): batch, batch_uc = get_batch( get_unique_embedder_keys_from_conditioner(model.conditioner), value_dict, [1, num_frames], T=num_frames, device=device, ) c, uc = model.conditioner.get_unconditional_conditioning( batch, batch_uc=batch_uc, force_uc_zero_embeddings=[ "cond_frames", "cond_frames_without_noise", ], ) for k in ["crossattn", "concat"]: uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames) uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames) c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames) c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames) additional_model_inputs = {} additional_model_inputs["image_only_indicator"] = torch.zeros( 2, num_frames ).to(device) #additional_model_inputs["image_only_indicator"][:,0] = 1 additional_model_inputs["num_video_frames"] = batch["num_video_frames"] additional_model_inputs["RT"] = RT.clone() def denoiser(input, sigma, c): return model.denoiser( model.model, input, sigma, c, **additional_model_inputs ) results = [] for j in range(sample_num): randn = torch.randn(shape, device=device) samples_z = model.sampler(denoiser, randn, cond=c, uc=uc) model.en_and_decode_n_samples_a_time = decoding_t samples_x = model.decode_first_stage(samples_z) samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0) # [1*t, c, h, w] samples = samples.data.cpu() results.append(samples) samples = torch.stack(results, dim=0) # [sample_num, t, c, h, w] # samples = samples.data.cpu() video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name save_results(samples, video_path, fps=save_fps) return video_path def save_results(resutls, filename, fps=10): video = resutls.permute(1, 0, 2, 3, 4) # [t, sample_num, c, h, w] frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(video.shape[1])) for framesheet in video] #[3, 1*h, n*w] grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] # already in [0,1] grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) torchvision.io.write_video(filename, grid, fps=fps, video_codec='h264', options={'crf': '10'}) def get_unique_embedder_keys_from_conditioner(conditioner): return list(set([x.input_key for x in conditioner.embedders])) def get_batch(keys, value_dict, N, T, device): batch = {} batch_uc = {} for key in keys: if key == "fps_id": batch[key] = ( torch.tensor([value_dict["fps_id"]]) .to(device) .repeat(int(math.prod(N))) ) elif key == "motion_bucket_id": batch[key] = ( torch.tensor([value_dict["motion_bucket_id"]]) .to(device) .repeat(int(math.prod(N))) ) elif key == "cond_aug": batch[key] = repeat( torch.tensor([value_dict["cond_aug"]]).to(device), "1 -> b", b=math.prod(N), ) elif key == "cond_frames": batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0]) elif key == "cond_frames_without_noise": batch[key] = repeat( value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0] ) else: batch[key] = value_dict[key] if T is not None: batch["num_video_frames"] = T for key in batch.keys(): if key not in batch_uc and isinstance(batch[key], torch.Tensor): batch_uc[key] = torch.clone(batch[key]) return batch, batch_uc def load_model( config: str, ckpt: str, device: str, num_frames: int, num_steps: int, ): config = OmegaConf.load(config) config.model.params.ckpt_path = ckpt if device == "cuda": config.model.params.conditioner_config.params.emb_models[ 0 ].params.open_clip_embedding_config.params.init_device = device config.model.params.sampler_config.params.num_steps = num_steps config.model.params.sampler_config.params.guider_config.params.num_frames = ( num_frames ) model = instantiate_from_config(config.model) model = model.to(device).eval() filter = None #DeepFloydDataFiltering(verbose=False, device=device) return model, filter