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# TODO | |
import numpy as np | |
import argparse | |
import torch | |
from torchvision.utils import make_grid | |
import tempfile | |
import gradio as gr | |
from omegaconf import OmegaConf | |
from einops import rearrange | |
from scripts.pub.V3D_512 import ( | |
sample_one, | |
get_batch, | |
get_unique_embedder_keys_from_conditioner, | |
load_model, | |
) | |
from sgm.util import default, instantiate_from_config | |
from safetensors.torch import load_file as load_safetensors | |
from PIL import Image | |
from kiui.op import recenter | |
from torchvision.transforms import ToTensor | |
from einops import rearrange, repeat | |
import rembg | |
import os | |
from glob import glob | |
from mediapy import write_video | |
from pathlib import Path | |
import spaces | |
from huggingface_hub import hf_hub_download | |
import imageio | |
import cv2 | |
def do_sample( | |
image, | |
num_frames, | |
num_steps, | |
decoding_t, | |
border_ratio, | |
ignore_alpha, | |
output_folder, | |
): | |
# if image.mode == "RGBA": | |
# image = image.convert("RGB") | |
image = Image.fromarray(image) | |
w, h = image.size | |
if border_ratio > 0: | |
if image.mode != "RGBA" or ignore_alpha: | |
image = image.convert("RGB") | |
image = np.asarray(image) | |
carved_image = rembg.remove(image, session=rembg_session) # [H, W, 4] | |
else: | |
image = np.asarray(image) | |
carved_image = image | |
mask = carved_image[..., -1] > 0 | |
image = recenter(carved_image, mask, border_ratio=border_ratio) | |
image = image.astype(np.float32) / 255.0 | |
if image.shape[-1] == 4: | |
image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4]) | |
image = Image.fromarray((image * 255).astype(np.uint8)) | |
else: | |
print("Ignore border ratio") | |
image = image.resize((512, 512)) | |
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) | |
value_dict = {} | |
value_dict["motion_bucket_id"] = 0 | |
value_dict["fps_id"] = 0 | |
value_dict["cond_aug"] = 0.05 | |
value_dict["cond_frames_without_noise"] = clip_model(image) | |
value_dict["cond_frames"] = ae_model.encode(image) | |
value_dict["cond_frames"] += 0.05 * torch.randn_like(value_dict["cond_frames"]) | |
value_dict["cond_aug"] = 0.05 | |
print(device) | |
with torch.no_grad(): | |
with torch.autocast(device_type="cuda"): | |
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) | |
randn = torch.randn(shape, device=device) | |
randn = randn.to(device) | |
additional_model_inputs = {} | |
additional_model_inputs["image_only_indicator"] = torch.zeros( | |
2, num_frames | |
).to(device) | |
additional_model_inputs["num_video_frames"] = batch["num_video_frames"] | |
def denoiser(input, sigma, c): | |
return model.denoiser( | |
model.model, input, sigma, c, **additional_model_inputs | |
) | |
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) | |
os.makedirs(output_folder, exist_ok=True) | |
base_count = len(glob(os.path.join(output_folder, "*.mp4"))) | |
video_path = os.path.join(output_folder, f"{base_count:06d}.mp4") | |
frames = ( | |
(rearrange(samples, "t c h w -> t h w c") * 255) | |
.cpu() | |
.numpy() | |
.astype(np.uint8) | |
) | |
# write_video(video_path, frames, fps=6) | |
# writer = cv2.VideoWriter( | |
# video_path, | |
# cv2.VideoWriter_fourcc(*"MP4V"), | |
# 6, | |
# (frames.shape[-1], frames.shape[-2]), | |
# ) | |
# for fr in frames: | |
# writer.write(cv2.cvtColor(fr, cv2.COLOR_RGB2BGR)) | |
# writer.release() | |
imageio.mimwrite(video_path, frames) | |
return video_path | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
# download | |
V3D_ckpt_path = hf_hub_download(repo_id="heheyas/V3D", filename="V3D.ckpt") | |
svd_xt_ckpt_path = hf_hub_download( | |
repo_id="stabilityai/stable-video-diffusion-img2vid-xt", | |
filename="svd_xt.safetensors", | |
) | |
model_config = "./scripts/pub/configs/V3D_512.yaml" | |
num_frames = OmegaConf.load( | |
model_config | |
).model.params.sampler_config.params.guider_config.params.num_frames | |
print("Detected num_frames:", num_frames) | |
# num_steps = default(num_steps, 25) | |
num_steps = 25 | |
output_folder = "outputs/V3D_512" | |
sd = load_safetensors(svd_xt_ckpt_path) | |
clip_model_config = OmegaConf.load("./configs/embedder/clip_image.yaml") | |
clip_model = instantiate_from_config(clip_model_config).eval() | |
clip_sd = dict() | |
for k, v in sd.items(): | |
if "conditioner.embedders.0" in k: | |
clip_sd[k.replace("conditioner.embedders.0.", "")] = v | |
clip_model.load_state_dict(clip_sd) | |
clip_model = clip_model.to(device) | |
ae_model_config = OmegaConf.load("./configs/ae/video.yaml") | |
ae_model = instantiate_from_config(ae_model_config).eval() | |
encoder_sd = dict() | |
for k, v in sd.items(): | |
if "first_stage_model" in k: | |
encoder_sd[k.replace("first_stage_model.", "")] = v | |
ae_model.load_state_dict(encoder_sd) | |
ae_model = ae_model.to(device) | |
rembg_session = rembg.new_session() | |
model, _ = load_model( | |
model_config, | |
device, | |
num_frames, | |
num_steps, | |
min_cfg=3.5, | |
max_cfg=3.5, | |
ckpt_path=V3D_ckpt_path, | |
) | |
model = model.to(device) | |
with gr.Blocks(title="V3D", theme=gr.themes.Monochrome()) as demo: | |
with gr.Row(equal_height=True): | |
with gr.Column(): | |
input_image = gr.Image(value=None, label="Input Image") | |
border_ratio_slider = gr.Slider( | |
value=0.3, | |
label="Border Ratio", | |
minimum=0.05, | |
maximum=0.5, | |
step=0.05, | |
) | |
decoding_t_slider = gr.Slider( | |
value=1, | |
label="Number of Decoding frames", | |
minimum=1, | |
maximum=num_frames, | |
step=1, | |
) | |
min_guidance_slider = gr.Slider( | |
value=3.5, | |
label="Min CFG Value", | |
minimum=0.05, | |
maximum=0.5, | |
step=0.05, | |
) | |
max_guidance_slider = gr.Slider( | |
value=3.5, | |
label="Max CFG Value", | |
minimum=0.05, | |
maximum=0.5, | |
step=0.05, | |
) | |
run_button = gr.Button(value="Run V3D") | |
with gr.Column(): | |
output_video = gr.Video(value=None, label="Output Orbit Video") | |
def _(image, border_ratio, min_guidance, max_guidance, decoding_t): | |
model.sampler.guider.max_scale = max_guidance | |
model.sampler.guider.min_scale = min_guidance | |
return do_sample( | |
image, | |
num_frames, | |
num_steps, | |
int(decoding_t), | |
border_ratio, | |
False, | |
output_folder, | |
) | |
demo.launch() | |