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import sys
import cv2
import torch
import numpy as np
import streamlit as st
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat
from streamlit_drawable_canvas import st_canvas
from imwatermark import WatermarkEncoder
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
torch.set_grad_enabled(False)
def put_watermark(img, wm_encoder=None):
if wm_encoder is not None:
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
img = wm_encoder.encode(img, 'dwtDct')
img = Image.fromarray(img[:, :, ::-1])
return img
@st.cache(allow_output_mutation=True)
def initialize_model(config, ckpt):
config = OmegaConf.load(config)
model = instantiate_from_config(config.model)
model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
return sampler
def make_batch_sd(
image,
mask,
txt,
device,
num_samples=1):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
batch = {
"image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
"txt": num_samples * [txt],
"mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
"masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
}
return batch
def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512, eta=1.):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = sampler.model
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
wm = "SDV2"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
prng = np.random.RandomState(seed)
start_code = prng.randn(num_samples, 4, h // 8, w // 8)
start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32)
with torch.no_grad(), \
torch.autocast("cuda"):
batch = make_batch_sd(image, mask, txt=prompt, device=device, num_samples=num_samples)
c = model.cond_stage_model.encode(batch["txt"])
c_cat = list()
for ck in model.concat_keys:
cc = batch[ck].float()
if ck != model.masked_image_key:
bchw = [num_samples, 4, h // 8, w // 8]
cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
else:
cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
c_cat.append(cc)
c_cat = torch.cat(c_cat, dim=1)
# cond
cond = {"c_concat": [c_cat], "c_crossattn": [c]}
# uncond cond
uc_cross = model.get_unconditional_conditioning(num_samples, "")
uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
shape = [model.channels, h // 8, w // 8]
samples_cfg, intermediates = sampler.sample(
ddim_steps,
num_samples,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=uc_full,
x_T=start_code,
)
x_samples_ddim = model.decode_first_stage(samples_cfg)
result = torch.clamp((x_samples_ddim + 1.0) / 2.0,
min=0.0, max=1.0)
result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
def run():
st.title("Stable Diffusion Inpainting")
sampler = initialize_model(sys.argv[1], sys.argv[2])
image = st.file_uploader("Image", ["jpg", "png"])
if image:
image = Image.open(image)
w, h = image.size
print(f"loaded input image of size ({w}, {h})")
width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32
image = image.resize((width, height))
prompt = st.text_input("Prompt")
seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0)
num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1)
scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=10., step=0.1)
ddim_steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1)
eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
fill_color = "rgba(255, 255, 255, 0.0)"
stroke_width = st.number_input("Brush Size",
value=64,
min_value=1,
max_value=100)
stroke_color = "rgba(255, 255, 255, 1.0)"
bg_color = "rgba(0, 0, 0, 1.0)"
drawing_mode = "freedraw"
st.write("Canvas")
st.caption(
"Draw a mask to inpaint, then click the 'Send to Streamlit' button (bottom left, with an arrow on it).")
canvas_result = st_canvas(
fill_color=fill_color,
stroke_width=stroke_width,
stroke_color=stroke_color,
background_color=bg_color,
background_image=image,
update_streamlit=False,
height=height,
width=width,
drawing_mode=drawing_mode,
key="canvas",
)
if canvas_result:
mask = canvas_result.image_data
mask = mask[:, :, -1] > 0
if mask.sum() > 0:
mask = Image.fromarray(mask)
result = inpaint(
sampler=sampler,
image=image,
mask=mask,
prompt=prompt,
seed=seed,
scale=scale,
ddim_steps=ddim_steps,
num_samples=num_samples,
h=height, w=width, eta=eta
)
st.write("Inpainted")
for image in result:
st.image(image, output_format='PNG')
if __name__ == "__main__":
run() |