from __future__ import annotations
#import spaces
import math
import random
import sys
from argparse import ArgumentParser
from tqdm.auto import trange
import einops
import gradio as gr
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps, ImageFilter
from torch import autocast
import cv2
import imageio
import devicetorch
DEVICE = devicetorch.get(torch)
sys.path.append("./stable_diffusion")
from stable_diffusion.ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z_0, z_1, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z_0 = einops.repeat(z_0, "1 ... -> n ...", n=3)
cfg_z_1 = einops.repeat(z_1, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
output_0, output_1 = self.inner_model(cfg_z_0, cfg_z_1, cfg_sigma, cond=cfg_cond)
out_cond_0, out_img_cond_0, out_uncond_0 = output_0.chunk(3)
out_cond_1, _, _ = output_1.chunk(3)
return out_uncond_0 + text_cfg_scale * (out_cond_0 - out_img_cond_0) + image_cfg_scale * (out_img_cond_0 - out_uncond_0), \
out_cond_1
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=True)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
return x[(...,) + (None,) * dims_to_append]
class CompVisDenoiser(K.external.CompVisDenoiser):
def __init__(self, model, quantize=False, device='cpu'):
super().__init__(model, quantize, device)
def get_eps(self, *args, **kwargs):
return self.inner_model.apply_model(*args, **kwargs)
def forward(self, input_0, input_1, sigma, **kwargs):
c_out, c_in = [append_dims(x, input_0.ndim) for x in self.get_scalings(sigma)]
# eps_0, eps_1 = self.get_eps(input_0 * c_in, input_1 * c_in, self.sigma_to_t(sigma), **kwargs)
eps_0, eps_1 = self.get_eps(input_0 * c_in, self.sigma_to_t(sigma.cpu().float()).to(DEVICE), **kwargs)
return input_0 + eps_0 * c_out, eps_1
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
def default_noise_sampler(x):
return lambda sigma, sigma_next: torch.randn_like(x)
def get_ancestral_step(sigma_from, sigma_to, eta=1.):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
if not eta:
return sigma_to, 0.
sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
return sigma_down, sigma_up
def decode_mask(mask, height = 256, width = 256):
mask = nn.functional.interpolate(mask, size=(height, width), mode="bilinear", align_corners=False)
mask = torch.where(mask > 0, 1, -1) # Thresholding step
mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
mask = 255.0 * rearrange(mask, "1 c h w -> h w c")
mask = torch.cat([mask, mask, mask], dim=-1)
mask = mask.type(torch.uint8).cpu().numpy()
return mask
def sample_euler_ancestral(model, x_0, x_1, sigmas, height, width, extra_args=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x_0) if noise_sampler is None else noise_sampler
s_in = x_0.new_ones([x_0.shape[0]])
mask_list = []
image_list = []
for i in trange(len(sigmas) - 1, disable=disable):
denoised_0, denoised_1 = model(x_0, x_1, sigmas[i] * s_in, **extra_args)
image_list.append(denoised_0)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
d_0 = to_d(x_0, sigmas[i], denoised_0)
# Euler method
dt = sigma_down - sigmas[i]
x_0 = x_0 + d_0 * dt
if sigmas[i + 1] > 0:
x_0 = x_0 + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
x_1 = denoised_1
mask_list.append(decode_mask(x_1, height, width))
image_list = torch.cat(image_list, dim=0)
return x_0, x_1, image_list, mask_list
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--config", default="configs/generate_diffree.yaml", type=str)
parser.add_argument("--ckpt", default="checkpoints/epoch=000041-step=000010999.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = load_model_from_config(config, args.ckpt, args.vae_ckpt)
model.eval().to(DEVICE)
model_wrap = CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
#@spaces.GPU(duration=30)
def generate(
input_image: Image.Image,
instruction: str,
steps: int,
randomize_seed: bool,
seed: int,
randomize_cfg: bool,
text_cfg_scale: float,
image_cfg_scale: float,
weather_close_video: bool,
decode_image_batch: int
):
seed = random.randint(0, 100000) if randomize_seed else seed
text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
width, height = input_image.size
factor = args.resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
input_image_copy = input_image.convert("RGB")
if instruction == "":
return [input_image, seed]
model.to(DEVICE)
if DEVICE == "cuda":
with torch.no_grad(), autocast("cuda"), model.ema_scope():
return run(model, input_image, input_image_copy, width, height, instruction, steps, seed)
else:
return run(model, input_image, input_image_copy, width, height, instruction, steps, seed)
def run(model, input_image, input_image_copy, width, height, instruction, steps, seed):
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([instruction]).to(model.device)]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode().to(model.device)]
uncond = {}
uncond["c_crossattn"] = [null_token.to(model.device)]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(steps).to(model.device)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": text_cfg_scale,
"image_cfg_scale": image_cfg_scale,
}
torch.manual_seed(seed)
z_0 = torch.randn_like(cond["c_concat"][0]).to(model.device) * sigmas[0]
z_1 = torch.randn_like(cond["c_concat"][0]).to(model.device) * sigmas[0]
z_0, z_1, image_list, mask_list = sample_euler_ancestral(model_wrap_cfg, z_0, z_1, sigmas, height, width, extra_args=extra_args)
x_0 = model.decode_first_stage(z_0)
if model.first_stage_downsample:
x_1 = nn.functional.interpolate(z_1, size=(height, width), mode="bilinear", align_corners=False)
x_1 = torch.where(x_1 > 0, 1, -1) # Thresholding step
else:
x_1 = model.decode_first_stage(z_1)
x_0 = torch.clamp((x_0 + 1.0) / 2.0, min=0.0, max=1.0)
x_1 = torch.clamp((x_1 + 1.0) / 2.0, min=0.0, max=1.0)
x_0 = 255.0 * rearrange(x_0, "1 c h w -> h w c")
x_1 = 255.0 * rearrange(x_1, "1 c h w -> h w c")
x_1 = torch.cat([x_1, x_1, x_1], dim=-1)
edited_image = Image.fromarray(x_0.type(torch.uint8).cpu().numpy())
edited_mask = Image.fromarray(x_1.type(torch.uint8).cpu().numpy())
image_video_path = None
if not weather_close_video:
image_video = []
for i in range(0, len(image_list), decode_image_batch):
if i + decode_image_batch < len(image_list):
tmp_image_list = image_list[i:i+decode_image_batch]
else:
tmp_image_list = image_list[i:]
tmp_image_list = model.decode_first_stage(tmp_image_list)
tmp_image_list = torch.clamp((tmp_image_list + 1.0) / 2.0, min=0.0, max=1.0)
tmp_image_list = 255.0 * rearrange(tmp_image_list, "b c h w -> b h w c")
tmp_image_list = tmp_image_list.type(torch.uint8).cpu().numpy()
# image list to image
for image in tmp_image_list:
image_video.append(image)
image_video_path = "image.mp4"
fps = 30
with imageio.get_writer(image_video_path, fps=fps) as video:
for image in image_video:
video.append_data(image)
edited_mask_copy = edited_mask.copy()
kernel = np.ones((3, 3), np.uint8)
edited_mask = cv2.dilate(np.array(edited_mask), kernel, iterations=3)
edited_mask = Image.fromarray(edited_mask)
m_img = edited_mask.filter(ImageFilter.GaussianBlur(radius=3))
m_img = np.asarray(m_img).astype('float') / 255.0
img_np = np.asarray(input_image_copy).astype('float') / 255.0
ours_np = np.asarray(edited_image).astype('float') / 255.0
mix_image_np = m_img * ours_np + (1 - m_img) * img_np
mix_image = Image.fromarray((mix_image_np * 255).astype(np.uint8)).convert('RGB')
red = np.array(mix_image).astype('float') * 1
red[:, :, 0] = 180.0
red[:, :, 2] = 0
red[:, :, 1] = 0
mix_result_with_red_mask = np.array(mix_image)
mix_result_with_red_mask = Image.fromarray(
(mix_result_with_red_mask.astype('float') * (1 - m_img.astype('float') / 2.0) +
m_img.astype('float') / 2.0 * red).astype('uint8'))
mask_video_path = "mask.mp4"
fps = 30
with imageio.get_writer(mask_video_path, fps=fps) as video:
for image in mask_list:
video.append_data(image)
return [int(seed), text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask_copy, mask_video_path, image_video_path, input_image_copy, mix_result_with_red_mask]
def reset():
return [100, "Randomize Seed", 1372, "Fix CFG", 7.5, 1.5, None, None, None, None, None, None, None, "Close Image Video", 10]
def get_example():
return [
["example_images/dufu.png", "black and white suit", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "reflective sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/road_sign.png", "stop sign", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/dufu.png", "blue medical mask", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/people_standing.png", "dark green pleated skirt", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "shiny golden crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/dufu.png", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "diamond necklace", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/iron_man.jpg", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "the queen's crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "gorgeous yellow gown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
]
with gr.Blocks(css="footer {visibility: hidden}") as demo:
with gr.Row():
gr.Markdown(
"<div align='center'><font size='14'>Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model</font></div>" # noqa
)
with gr.Row():
gr.Markdown(
"""
<div align='center'>
<a href="https://opengvlab.github.io/Diffree/"><u>[🌐Project Page]</u></a>
<a href="https://drive.google.com/file/d/1AdIPA5TK5LB1tnqqZuZ9GsJ6Zzqo2ua6/view"><u>[🎥 Video]</u></a>
<a href="https://github.com/OpenGVLab/Diffree"><u>[🔍 Code]</u></a>
<a href="https://arxiv.org/pdf/2407.16982"><u>[📜 Arxiv]</u></a>
</div>
""" # noqa
)
with gr.Row():
with gr.Column(scale=1, min_width=100):
with gr.Row():
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
with gr.Row():
instruction = gr.Textbox(lines=1, label="Object description", interactive=True)
with gr.Row():
steps = gr.Number(value=100, precision=0, label="Steps", interactive=True)
randomize_seed = gr.Radio(
["Fix Seed", "Randomize Seed"],
value="Randomize Seed",
type="index",
label="Seed Selection",
show_label=False,
interactive=True,
)
seed = gr.Number(value=1372, precision=0, label="Seed", interactive=True)
randomize_cfg = gr.Radio(
["Fix CFG", "Randomize CFG"],
value="Fix CFG",
type="index",
label="CFG Selection",
show_label=False,
interactive=True,
)
text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
with gr.Row():
reset_button = gr.Button("Reset")
generate_button = gr.Button("Generate")
with gr.Column(scale=1, min_width=100):
with gr.Column():
mix_image = gr.Image(label=f"Mix Image", type="pil", interactive=False)
with gr.Column():
edited_mask = gr.Image(label=f"Output Mask", type="pil", interactive=False)
with gr.Accordion('More outputs', open=False):
with gr.Row():
weather_close_video = gr.Radio(
["Show Image Video", "Close Image Video"],
value="Close Image Video",
type="index",
label="Image Generation Process Selection (close for faster generation)",
interactive=True,
)
decode_image_batch = gr.Number(value=10, precision=0, label="Decode Image Batch (<steps)", interactive=True)
with gr.Row():
image_video = gr.Video(label="Image Video of Generation Process")
mask_video = gr.Video(label="Mask Video of Generation Process")
with gr.Row():
original_image = gr.Image(label=f"Original Image", type="pil", interactive=False)
edited_image = gr.Image(label=f"Output Image", type="pil", interactive=False)
mix_result_with_red_mask = gr.Image(label=f"Mix Image With Red Mask", type="pil", interactive=False)
with gr.Row():
gr.Examples(
examples=get_example(),
fn=generate,
inputs=[input_image, instruction, steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale],
outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask],
cache_examples=False,
)
generate_button.click(
fn=generate,
inputs=[
input_image,
instruction,
steps,
randomize_seed,
seed,
randomize_cfg,
text_cfg_scale,
image_cfg_scale,
weather_close_video,
decode_image_batch
],
outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask],
)
reset_button.click(
fn=reset,
inputs=[],
outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask, weather_close_video, decode_image_batch],
)
# demo.queue(concurrency_count=1)
# demo.launch(share=True)
demo.queue().launch()