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import numpy as np
import imageio
import os
from omegaconf import OmegaConf
import torch
from tqdm import tqdm
from einops import rearrange
from torch_geometric.loader import DataLoader
from diffusers import AutoencoderKL, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from swomo.models.videoldm_unet import VideoLDMUNet3DConditionModel
from swomo.models.videoldm_controlnet import ControlNetModel
from swomo.pipelines.pipeline_conditional_animation import ConditionalAnimationPipeline
from swomo.pipelines.pipeline_controlnet_animation import ControlNetAnimationPipeline
from swomo.data.dataset import SurgicalDataset
from swomo.graph_encoder.graph_segclip_masked import GraphEncoder
def main(args, config):
savedir = f"{config.output_dir}"
if not os.path.exists(savedir):
os.makedirs(savedir)
class_embeddings_concat = (config.unet_graph_kwargs.get('class_embeddings_concat') if config.unet_graph_kwargs is not None else False)
class_embed_type = (config.unet_graph_kwargs.get('class_embed_type') if config.unet_graph_kwargs is not None else None)
time_embedding_dim = (config.unet_graph_kwargs.get('time_embedding_dim') if config.unet_graph_kwargs is not None else None)
ignore_mismatched_sizes = (config.unet_graph_kwargs.get('ignore_mismatched_sizes') if config.unet_graph_kwargs is not None else False)
### >>> create validation pipeline >>> ###
noise_scheduler = DDIMScheduler(**OmegaConf.to_container(config.noise_scheduler_kwargs))
tokenizer = CLIPTokenizer.from_pretrained(config.pretrained_model_path, subfolder="tokenizer", use_safetensors=True)
text_encoder = CLIPTextModel.from_pretrained(config.pretrained_model_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(config.finetuned_autoencoder_path if config.finetuned_autoencoder_path else config.pretrained_model_path, subfolder="vae", use_safetensors=True)
unet = VideoLDMUNet3DConditionModel.from_pretrained(
config.pretrained_model_path,
subfolder="unet",
variant=config.unet_additional_kwargs['variant'],
use_temporal=True,
temp_pos_embedding=config.unet_additional_kwargs['temp_pos_embedding'],
augment_temporal_attention=config.unet_additional_kwargs['augment_temporal_attention'],
n_frames=config.sampling_kwargs['n_frames'],
n_temp_heads=config.unet_additional_kwargs['n_temp_heads'],
first_frame_condition_mode=config.unet_additional_kwargs['first_frame_condition_mode'],
use_frame_stride_condition=config.unet_additional_kwargs['use_frame_stride_condition'],
class_embeddings_concat=class_embeddings_concat,
class_embed_type=class_embed_type,
time_embedding_dim=time_embedding_dim,
ignore_mismatched_sizes=ignore_mismatched_sizes,
use_safetensors=True
)
if config.controlnet_path is not None:
controlnet = ControlNetModel.from_unet(unet)
# 1. unet ckpt
if config.unet_path is not None:
if os.path.isdir(config.unet_path):
unet_dict = VideoLDMUNet3DConditionModel.from_pretrained(config.unet_path)
m, u = unet.load_state_dict(unet_dict.state_dict(), strict=False)
assert len(u) == 0
del unet_dict
else:
checkpoint_dict = torch.load(config.unet_path, map_location="cpu")
state_dict = checkpoint_dict["state_dict"] if "state_dict" in checkpoint_dict else checkpoint_dict
if config.unet_ckpt_prefix is not None:
state_dict = {k.replace(config.unet_ckpt_prefix, ''): v for k, v in state_dict.items()}
m, u = unet.load_state_dict(state_dict, strict=False)
assert len(u) == 0
if config.controlnet_path is not None:
if os.path.isdir(config.controlnet_path):
controlnet_dict = ControlNetModel.from_pretrained(config.controlnet_path)
m, u = controlnet.load_state_dict(controlnet_dict.state_dict(), strict=False)
assert len(u) == 0
del controlnet_dict
else:
raise NotImplementedError("ControlNet loading from non-directory path is not implemented yet. Please provide a directory path for ControlNet checkpoint.")
if is_xformers_available() and int(torch.__version__.split(".")[0]) < 2:
unet.enable_xformers_memory_efficient_attention()
if config.controlnet_path is not None:
controlnet.enable_xformers_memory_efficient_attention()
if config.controlnet_path is None:
pipeline = ConditionalAnimationPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=noise_scheduler)
else:
pipeline = ControlNetAnimationPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet, scheduler=noise_scheduler,
)
pipeline.to("cuda")
if config.test_data.return_graph_emb:
if 'model_masked' in config.test_data:
m_graph_emb_masked = GraphEncoder(config.test_data.graph_input_dim, config.test_data.graph_hidden_dim,
config.test_data.graph_embedding_dim, config.test_data.trainable,
graph_conv_type = config.test_data.graph_conv_type,
graph_norm_type = config.test_data.graph_norm_type,
graph_encoder_ckpt = config.test_data.model_masked)
m_graph_emb_masked.to("cuda")
m_graph_emb_masked.eval()
if 'model_segclip' in config.test_data:
m_graph_emb_segclip = GraphEncoder(config.test_data.graph_input_dim, config.test_data.graph_hidden_dim,
config.test_data.graph_embedding_dim, config.test_data.trainable,
graph_conv_type = config.test_data.graph_conv_type,
graph_norm_type = config.test_data.graph_norm_type,
graph_encoder_ckpt = config.test_data.model_segclip)
m_graph_emb_segclip.to("cuda")
m_graph_emb_segclip.eval()
def get_embedding(scene_graph, embedding_type):
if embedding_type == 'masked':
graph_embeddings = m_graph_emb_masked(scene_graph)
elif embedding_type == 'segclip':
graph_embeddings = m_graph_emb_segclip(scene_graph)
elif embedding_type == 'combined':
graph_embeddings_masked = m_graph_emb_masked(scene_graph)
graph_embeddings_segclip = m_graph_emb_segclip(scene_graph)
graph_embeddings = torch.cat((graph_embeddings_masked, graph_embeddings_segclip), dim=-1)
return graph_embeddings
# (frameinit) initialize frequency filter for noise reinitialization -------------
if config.frameinit_kwargs.enable:
pipeline.init_filter(
width = config.sampling_kwargs.width,
height = config.sampling_kwargs.height,
video_length = config.sampling_kwargs.n_frames,
filter_params = config.frameinit_kwargs.filter_params,
)
# -------------------------------------------------------------------------------
### <<< create validation pipeline <<< ###
test_dataset = SurgicalDataset(**config.test_data)
test_dataloader = DataLoader(
test_dataset,
shuffle=False,
batch_size=1,
num_workers=config.num_workers,
pin_memory=True,
)
resume_sampling_idx = getattr(config, "resume_sampling", 0) or 0
print(f"Length of test dataloader: {len(test_dataloader)}")
print(f"Resuming sampling from index {resume_sampling_idx}")
for idx, batch in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc="Sampling SWoMo:"):
if idx >= resume_sampling_idx:
prompt = batch["text"]
if config.test_data.return_graph_emb:
graph = batch.get('graph').to("cuda")
graph_emb = get_embedding(graph, config.test_data.embedding_type)
graph_emb = graph_emb.detach().squeeze()
else:
graph_emb = None
first_frame_paths = None
if config.unet_additional_kwargs['first_frame_condition_mode'] != "none":
first_frame_paths = batch["first_frame_path"]
cond_video_paths = [None] * len(prompt)
if config.controlnet_path is not None and config.test_data.return_cond_videos:
cond_video_paths = batch["cond_video_path"]
cond_video_paths = [[cond_video_paths[i][0] for i in range(len(cond_video_paths))]]
sample = pipeline(
prompt,
first_frame_paths = first_frame_paths,
cond_video_frames_paths = cond_video_paths,
num_inference_steps = config.sampling_kwargs.steps,
guidance_scale_txt = config.sampling_kwargs.guidance_scale_txt,
guidance_scale_img = config.sampling_kwargs.guidance_scale_img,
width = config.sampling_kwargs.width,
height = config.sampling_kwargs.height,
video_length = config.sampling_kwargs.n_frames,
noise_sampling_method = config.unet_additional_kwargs['noise_sampling_method'],
noise_alpha = float(config.unet_additional_kwargs['noise_alpha']),
class_labels = graph_emb,
eta = config.sampling_kwargs.ddim_eta,
frame_stride = config.sampling_kwargs.frame_stride,
guidance_rescale = config.sampling_kwargs.guidance_rescale,
num_videos_per_prompt = config.sampling_kwargs.num_videos_per_prompt,
use_frameinit = config.frameinit_kwargs.enable,
frameinit_noise_level = config.frameinit_kwargs.noise_level,
camera_motion = config.frameinit_kwargs.camera_motion,
).videos
current_batch = len(prompt)
sample = rearrange(sample, "b c t h w -> b t h w c")
for bc in range(current_batch):
save_folder = os.path.join(config.output_dir, str(idx + bc).zfill(5))
os.makedirs(save_folder, exist_ok=True)
seq = sample[bc]
for i, img in enumerate(seq):
img = (img * 255).numpy().astype(np.uint8)
imageio.imsave(os.path.join(save_folder, str(i).zfill(2)+".png"), img)
else:
continue
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--inference_config", type=str, default="configs/inference/inference.yaml")
parser.add_argument("optional_args", nargs='*', default=[])
args = parser.parse_args()
config = OmegaConf.load(args.inference_config)
if args.optional_args:
modified_config = OmegaConf.from_dotlist(args.optional_args)
config = OmegaConf.merge(config, modified_config)
main(args, config)
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