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