from __future__ import annotations import math import random import sys from argparse import ArgumentParser import einops import k_diffusion as K import numpy as np import torch import torch.nn as nn from einops import rearrange from omegaconf import OmegaConf from PIL import Image, ImageOps from torch import autocast sys.path.append("./stable_diffusion") from stable_diffusion.ldm.util import instantiate_from_config class CFGDenoiser(nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale): cfg_z = einops.repeat(z, "1 ... -> n ...", n=3) cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3) cfg_cond = { "c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])], "c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])], } out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3) return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond) def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] if vae_ckpt is not None: print(f"Loading VAE from {vae_ckpt}") vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"] sd = { k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v for k, v in sd.items() } model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) return model def main(): parser = ArgumentParser() parser.add_argument("--resolution", default=512, type=int) parser.add_argument("--steps", default=100, type=int) parser.add_argument("--config", default="configs/generate.yaml", type=str) parser.add_argument("--ckpt", default="checkpoints/instruct-pix2pix-00-20000.ckpt", type=str) parser.add_argument("--vae-ckpt", default=None, type=str) parser.add_argument("--input", required=True, type=str) parser.add_argument("--output", required=True, type=str) parser.add_argument("--edit", required=True, type=str) parser.add_argument("--cfg-text", default=7.5, type=float) parser.add_argument("--cfg-image", default=1.2, type=float) parser.add_argument("--seed", type=int) args = parser.parse_args() config = OmegaConf.load(args.config) model = load_model_from_config(config, args.ckpt, args.vae_ckpt) model.eval().cuda() model_wrap = K.external.CompVisDenoiser(model) model_wrap_cfg = CFGDenoiser(model_wrap) null_token = model.get_learned_conditioning([""]) seed = random.randint(0, 100000) if args.seed is None else args.seed input_image = Image.open(args.input).convert("RGB") width, height = input_image.size factor = args.resolution / max(width, height) factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) width = int((width * factor) // 64) * 64 height = int((height * factor) // 64) * 64 input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) if args.edit == "": input_image.save(args.output) return with torch.no_grad(), autocast("cuda"), model.ema_scope(): cond = {} cond["c_crossattn"] = [model.get_learned_conditioning([args.edit])] input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1 input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device) cond["c_concat"] = [model.encode_first_stage(input_image).mode()] uncond = {} uncond["c_crossattn"] = [null_token] uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])] sigmas = model_wrap.get_sigmas(args.steps) extra_args = { "cond": cond, "uncond": uncond, "text_cfg_scale": args.cfg_text, "image_cfg_scale": args.cfg_image, } torch.manual_seed(seed) z = torch.randn_like(cond["c_concat"][0]) * sigmas[0] z = K.sampling.sample_euler_ancestral(model_wrap_cfg, z, sigmas, extra_args=extra_args) x = model.decode_first_stage(z) x = torch.clamp((x + 1.0) / 2.0, min=0.0, max=1.0) x = 255.0 * rearrange(x, "1 c h w -> h w c") edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy()) edited_image.save(args.output) if __name__ == "__main__": main()