scripts/gradio/superresolution.py

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()