190 lines
6.3 KiB
Python
190 lines
6.3 KiB
Python
import argparse, os, sys, glob
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from omegaconf import OmegaConf
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from PIL import Image
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from tqdm import tqdm
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import numpy as np
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import torch
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from main import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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def make_batch_ldm(image, mask, device):
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image = np.array(Image.open(image).convert("RGB"))
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image = image.astype(np.float32)/255.0
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image = image[None].transpose(0,3,1,2)
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image = torch.from_numpy(image)
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mask = np.array(Image.open(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 = (1-mask)*image
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batch = {"image": image, "mask": mask, "masked_image": masked_image}
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for k in batch:
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batch[k] = batch[k].to(device=device)
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batch[k] = batch[k]*2.0-1.0
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return batch
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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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# image hwc in -1 1
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image = np.array(Image.open(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(Image.open(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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"jpg": image.to(device=device),
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"txt": [txt],
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"mask": mask.to(device=device),
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"masked_image": masked_image.to(device=device),
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}
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return batch
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--indir",
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type=str,
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nargs="?",
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help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
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)
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parser.add_argument(
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"--outdir",
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type=str,
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nargs="?",
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help="dir to write results to",
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)
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parser.add_argument(
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"--steps",
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type=int,
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default=50,
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help="number of ddim sampling steps",
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)
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parser.add_argument(
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"--eta",
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type=float,
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default=0.0,
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help="eta of ddim",
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)
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parser.add_argument(
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"--scale",
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type=float,
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default=6.0,
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help="scale of unconditional guidance",
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)
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parser.add_argument(
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"--worldsize",
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type=int,
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default=1,
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help="scale of unconditional guidance",
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)
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parser.add_argument(
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"--rank",
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type=int,
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default=0,
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help="scale of unconditional guidance",
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)
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parser.add_argument(
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"--ckpt",
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type=str,
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default="/fsx/robin/stable-diffusion/stable-diffusion/logs/2022-08-01T08-52-14_v1-finetune-for-inpainting-laion-aesthetic-larger-masks-and-ucfg/checkpoints/last.ckpt",
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help="scale of unconditional guidance",
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)
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opt = parser.parse_args()
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assert opt.rank < opt.worldsize
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mstr = "mask000.png"
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masks = sorted(glob.glob(os.path.join(opt.indir, f"*_{mstr}")))
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images = [x.replace(f"_{mstr}", ".png") for x in masks]
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print(f"Found {len(masks)} inputs.")
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#config = "models/ldm/inpainting_big/config.yaml"
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config="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-iaesthe.yaml"
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config = OmegaConf.load(config)
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model = instantiate_from_config(config.model)
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#ckpt="models/ldm/inpainting_big/last.ckpt"
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ckpt=opt.ckpt
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model.load_state_dict(torch.load(ckpt)["state_dict"],
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strict=False)
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device = torch.device("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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indices = [i for i in range(len(images)) if i % opt.worldsize == opt.rank]
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images = [images[i] for i in indices]
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masks = [masks[i] for i in indices]
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os.makedirs(opt.outdir, exist_ok=True)
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with torch.no_grad():
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with model.ema_scope():
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for image, mask in tqdm(zip(images, masks), total=len(images)):
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outpath = os.path.join(opt.outdir, os.path.split(image)[1])
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#batch = make_batch_ldm(image, mask, device=device)
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##### unroll
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batch = make_batch_sd(image, mask, txt="photograph of a beautiful empty scene, highest quality settings",
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device=device)
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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 = (1, 4, 64, 64)
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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(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
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cond={"c_concat": [c_cat], "c_crossattn": [c]}
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# uncond cond
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uc_cross = model.get_unconditional_conditioning(1, "")
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uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
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shape = (model.channels, model.image_size, model.image_size)
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samples_cfg, intermediates = sampler.sample(
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opt.steps,
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1,
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shape,
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cond,
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verbose=False,
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eta=opt.eta,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc_full,
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)
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x_samples_ddim = model.decode_first_stage(samples_cfg)
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image = torch.clamp((batch["jpg"]+1.0)/2.0,
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min=0.0, max=1.0)
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mask = torch.clamp((batch["mask"]+1.0)/2.0,
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min=0.0, max=1.0)
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predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
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min=0.0, max=1.0)
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inpainted = (1-mask)*image+mask*predicted_image
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inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
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Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
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