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import sys |
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import cv2 |
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import torch |
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import numpy as np |
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import gradio as gr |
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from PIL import Image |
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from omegaconf import OmegaConf |
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from einops import repeat |
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from imwatermark import WatermarkEncoder |
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from pathlib import Path |
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from ldm.models.diffusion.ddim import DDIMSampler |
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from ldm.util import instantiate_from_config |
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torch.set_grad_enabled(False) |
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def put_watermark(img, wm_encoder=None): |
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if wm_encoder is not None: |
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) |
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img = wm_encoder.encode(img, 'dwtDct') |
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img = Image.fromarray(img[:, :, ::-1]) |
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return img |
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def initialize_model(config, ckpt): |
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config = OmegaConf.load(config) |
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model = instantiate_from_config(config.model) |
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model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False) |
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device = torch.device( |
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"cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model = model.to(device) |
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sampler = DDIMSampler(model) |
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return sampler |
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def make_batch_sd( |
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image, |
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mask, |
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txt, |
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device, |
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num_samples=1): |
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image = np.array(image.convert("RGB")) |
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image = image[None].transpose(0, 3, 1, 2) |
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image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 |
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mask = np.array(mask.convert("L")) |
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mask = mask.astype(np.float32) / 255.0 |
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mask = mask[None, None] |
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mask[mask < 0.5] = 0 |
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mask[mask >= 0.5] = 1 |
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mask = torch.from_numpy(mask) |
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masked_image = image * (mask < 0.5) |
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batch = { |
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"image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples), |
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"txt": num_samples * [txt], |
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"mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples), |
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"masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples), |
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} |
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return batch |
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def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512): |
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device = torch.device( |
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"cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model = sampler.model |
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print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") |
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wm = "SDV2" |
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wm_encoder = WatermarkEncoder() |
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wm_encoder.set_watermark('bytes', wm.encode('utf-8')) |
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prng = np.random.RandomState(seed) |
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start_code = prng.randn(num_samples, 4, h // 8, w // 8) |
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start_code = torch.from_numpy(start_code).to( |
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device=device, dtype=torch.float32) |
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with torch.no_grad(), \ |
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torch.autocast("cuda"): |
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batch = make_batch_sd(image, mask, txt=prompt, |
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device=device, num_samples=num_samples) |
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c = model.cond_stage_model.encode(batch["txt"]) |
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c_cat = list() |
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for ck in model.concat_keys: |
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cc = batch[ck].float() |
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if ck != model.masked_image_key: |
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bchw = [num_samples, 4, h // 8, w // 8] |
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cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) |
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else: |
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cc = model.get_first_stage_encoding( |
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model.encode_first_stage(cc)) |
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c_cat.append(cc) |
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c_cat = torch.cat(c_cat, dim=1) |
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cond = {"c_concat": [c_cat], "c_crossattn": [c]} |
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uc_cross = model.get_unconditional_conditioning(num_samples, "") |
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uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]} |
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shape = [model.channels, h // 8, w // 8] |
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samples_cfg, intermediates = sampler.sample( |
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ddim_steps, |
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num_samples, |
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shape, |
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cond, |
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verbose=False, |
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eta=1.0, |
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unconditional_guidance_scale=scale, |
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unconditional_conditioning=uc_full, |
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x_T=start_code, |
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) |
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x_samples_ddim = model.decode_first_stage(samples_cfg) |
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result = torch.clamp((x_samples_ddim + 1.0) / 2.0, |
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min=0.0, max=1.0) |
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result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255 |
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return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result] |
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def pad_image(input_image): |
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pad_w, pad_h = np.max(((2, 2), np.ceil( |
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np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size |
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im_padded = Image.fromarray( |
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np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) |
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return im_padded |
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def predict(input_image, prompt, ddim_steps, num_samples, scale, seed): |
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init_image = input_image["image"].convert("RGB") |
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init_mask = input_image["mask"].convert("RGB") |
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image = pad_image(init_image) |
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mask = pad_image(init_mask) |
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width, height = image.size |
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print("Inpainting...", width, height) |
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result = inpaint( |
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sampler=sampler, |
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image=image, |
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mask=mask, |
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prompt=prompt, |
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seed=seed, |
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scale=scale, |
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ddim_steps=ddim_steps, |
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num_samples=num_samples, |
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h=height, w=width |
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) |
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return result |
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sampler = initialize_model(sys.argv[1], sys.argv[2]) |
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block = gr.Blocks().queue() |
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with block: |
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with gr.Row(): |
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gr.Markdown("## Stable Diffusion Inpainting") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', tool='sketch', type="pil") |
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prompt = gr.Textbox(label="Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Accordion("Advanced options", open=False): |
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num_samples = gr.Slider( |
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label="Images", minimum=1, maximum=4, value=4, step=1) |
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ddim_steps = gr.Slider(label="Steps", minimum=1, |
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maximum=50, value=45, step=1) |
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scale = gr.Slider( |
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label="Guidance Scale", minimum=0.1, maximum=30.0, value=10, step=0.1 |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=2147483647, |
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step=1, |
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randomize=True, |
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) |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False).style( |
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grid=[2], height="auto") |
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run_button.click(fn=predict, inputs=[ |
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input_image, prompt, ddim_steps, num_samples, scale, seed], outputs=[gallery]) |
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block.launch() |
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