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import sys
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion
from ldm.util import exists, instantiate_from_config
torch.set_grad_enabled(False)
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,
txt,
device,
num_samples=1,
):
image = np.array(image.convert("RGB"))
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
batch = {
"lr": rearrange(image, 'h w c -> 1 c h w'),
"txt": num_samples * [txt],
}
batch["lr"] = repeat(batch["lr"].to(device=device),
"1 ... -> n ...", n=num_samples)
return batch
def make_noise_augmentation(model, batch, noise_level=None):
x_low = batch[model.low_scale_key]
x_low = x_low.to(memory_format=torch.contiguous_format).float()
x_aug, noise_level = model.low_scale_model(x_low, noise_level)
return x_aug, noise_level
def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=1, callback=None, eta=0., noise_level=None):
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
model = sampler.model
seed_everything(seed)
prng = np.random.RandomState(seed)
start_code = prng.randn(num_samples, model.channels, h, w)
start_code = torch.from_numpy(start_code).to(
device=device, dtype=torch.float32)
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'))
with torch.no_grad(),\
torch.autocast("cuda"):
batch = make_batch_sd(
image, txt=prompt, device=device, num_samples=num_samples)
c = model.cond_stage_model.encode(batch["txt"])
c_cat = list()
if isinstance(model, LatentUpscaleFinetuneDiffusion):
for ck in model.concat_keys:
cc = batch[ck]
if exists(model.reshuffle_patch_size):
assert isinstance(model.reshuffle_patch_size, int)
cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
p1=model.reshuffle_patch_size, p2=model.reshuffle_patch_size)
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]}
elif isinstance(model, LatentUpscaleDiffusion):
x_augment, noise_level = make_noise_augmentation(
model, batch, noise_level)
cond = {"c_concat": [x_augment],
"c_crossattn": [c], "c_adm": noise_level}
# uncond cond
uc_cross = model.get_unconditional_conditioning(num_samples, "")
uc_full = {"c_concat": [x_augment], "c_crossattn": [
uc_cross], "c_adm": noise_level}
else:
raise NotImplementedError()
shape = [model.channels, h, w]
samples, intermediates = sampler.sample(
steps,
num_samples,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=uc_full,
x_T=start_code,
callback=callback
)
with torch.no_grad():
x_samples_ddim = model.decode_first_stage(samples)
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 pad_image(input_image):
pad_w, pad_h = np.max(((2, 2), np.ceil(
np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
im_padded = Image.fromarray(
np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
return im_padded
def predict(input_image, prompt, steps, num_samples, scale, seed, eta, noise_level):
init_image = input_image.convert("RGB")
image = pad_image(init_image) # resize to integer multiple of 32
width, height = image.size
noise_level = torch.Tensor(
num_samples * [noise_level]).to(sampler.model.device).long()
sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
result = paint(
sampler=sampler,
image=image,
prompt=prompt,
seed=seed,
scale=scale,
h=height, w=width, steps=steps,
num_samples=num_samples,
callback=None,
noise_level=noise_level
)
return result
sampler = initialize_model(sys.argv[1], sys.argv[2])
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("## Stable Diffusion Upscaling")
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
gr.Markdown(
"Tip: Add a description of the object that should be upscaled, e.g.: 'a professional photograph of a cat")
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button(label="Run")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(
label="Number of Samples", minimum=1, maximum=4, value=1, step=1)
steps = gr.Slider(label="DDIM Steps", minimum=2,
maximum=200, value=75, step=1)
scale = gr.Slider(
label="Scale", minimum=0.1, maximum=30.0, value=10, step=0.1
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
randomize=True,
)
eta = gr.Number(label="eta (DDIM)",
value=0.0, min=0.0, max=1.0)
noise_level = None
if isinstance(sampler.model, LatentUpscaleDiffusion):
# TODO: make this work for all models
noise_level = gr.Number(
label="Noise Augmentation", min=0, max=350, value=20, step=1)
with gr.Column():
gallery = gr.Gallery(label="Generated images", show_label=False).style(
grid=[2], height="auto")
run_button.click(fn=predict, inputs=[
input_image, prompt, steps, num_samples, scale, seed, eta, noise_level], outputs=[gallery])
block.launch()
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