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import os
import PIL.Image
import numpy as np
import torchvision
from torchvision.transforms import Resize, InterpolationMode
import imageio
from einops import rearrange
import cv2
from PIL import Image
from annotator.util import resize_image, HWC3
from annotator.openpose import OpenposeDetector
import decord
import jax
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
download_from_original_stable_diffusion_ckpt,
)
from huggingface_hub import hf_hub_download
import flax.linen as nn
apply_openpose = OpenposeDetector()
def add_watermark(image, watermark_path, wm_rel_size=1 / 16, boundary=5):
"""
Creates a watermark on the saved inference image.
We request that you do not remove this to properly assign credit to
Shi-Lab's work.
"""
watermark = Image.open(watermark_path)
w_0, h_0 = watermark.size
H, W, _ = image.shape
wmsize = int(max(H, W) * wm_rel_size)
aspect = h_0 / w_0
if aspect > 1.0:
watermark = watermark.resize((wmsize, int(aspect * wmsize)), Image.LANCZOS)
else:
watermark = watermark.resize((int(wmsize / aspect), wmsize), Image.LANCZOS)
w, h = watermark.size
loc_h = H - h - boundary
loc_w = W - w - boundary
image = Image.fromarray(image)
mask = watermark if watermark.mode in ("RGBA", "LA") else None
image.paste(watermark, (loc_w, loc_h), mask)
return image
def load_safetensors_model(model_link):
ckpt_path = hf_hub_download(
repo_id=model_link, filename="ligne_claire_anime_diffusion_v1.safetensors"
)
print(f"Checkpoint path: {ckpt_path}")
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
pipe = download_from_original_stable_diffusion_ckpt(
checkpoint_path=ckpt_path,
original_config_file="v1-inference.yaml",
from_safetensors=True,
)
pipe.save_pretrained("./models/ligne_claire", safe_serialization=True)
return pipe
def pre_process_pose(input_video, apply_pose_detect: bool = True):
detected_maps = []
for frame in input_video:
img = rearrange(frame, "c h w -> h w c").astype(np.uint8)
img = HWC3(img)
if apply_pose_detect:
detected_map, _ = apply_openpose(img)
else:
detected_map = img
detected_map = HWC3(detected_map)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
detected_maps.append(detected_map[None])
detected_maps = np.concatenate(detected_maps)
control = (detected_maps.copy()) / 255.0
return rearrange(control, "f h w c -> f c h w")
def create_video(frames, fps, rescale=False, path=None, watermark=None):
if path is None:
dir = "temporal"
os.makedirs(dir, exist_ok=True)
path = os.path.join(dir, "movie.mp4")
outputs = []
for i, x in enumerate(frames):
x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
if watermark is not None:
x = add_watermark(x, watermark)
outputs.append(x)
# imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
imageio.mimsave(path, outputs, fps=fps)
return path
def create_gif(frames, fps, rescale=False, path=None, watermark=None):
if path is None:
dir = "temporal"
os.makedirs(dir, exist_ok=True)
path = os.path.join(dir, "canny_db.gif")
outputs = []
for i, x in enumerate(frames):
x = torchvision.utils.make_grid(torch.Tensor(x), nrow=4)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
if watermark is not None:
x = add_watermark(x, watermark)
outputs.append(x)
# imageio.imsave(os.path.join(dir, os.path.splitext(name)[0] + f'_{i}.jpg'), x)
imageio.mimsave(path, outputs, fps=fps)
return path
def prepare_video(
video_path: str,
resolution: int,
device,
dtype,
normalize=True,
start_t: float = 0,
end_t: float = -1,
output_fps: int = -1,
):
vr = decord.VideoReader(video_path)
initial_fps = vr.get_avg_fps()
if output_fps == -1:
output_fps = int(initial_fps)
if end_t == -1:
end_t = len(vr) / initial_fps
else:
end_t = min(len(vr) / initial_fps, end_t)
assert 0 <= start_t < end_t
assert output_fps > 0
start_f_ind = int(start_t * initial_fps)
end_f_ind = int(end_t * initial_fps)
num_f = int((end_t - start_t) * output_fps)
sample_idx = np.linspace(start_f_ind, end_f_ind, num_f, endpoint=False).astype(int)
video = vr.get_batch(sample_idx)
video = video.asnumpy()
_, h, w, _ = video.shape
video = rearrange(video, "f h w c -> f c h w")
video = torch.Tensor(video) # .to(device).to(dtype)
# Use max if you want the larger side to be equal to resolution (e.g. 512)
# k = float(resolution) / min(h, w)
k = float(resolution) / max(h, w)
h *= k
w *= k
h = int(np.round(h / 64.0)) * 64
w = int(np.round(w / 64.0)) * 64
video = Resize((h, w), interpolation=InterpolationMode.BILINEAR, antialias=True)(
video
)
if normalize:
video = video / 127.5 - 1.0
# video = rearrange(video, "f c h w -> f h w c").numpy() #channel first to channel last
video = video.numpy()
return video, output_fps
def post_process_gif(list_of_results, image_resolution):
output_file = "/tmp/ddxk.gif"
imageio.mimsave(output_file, list_of_results, fps=4)
return output_file
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