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| # -------------------------------------------------------- | |
| # InstructDiffusion | |
| # Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix) | |
| # Modified by Zigang Geng (zigang@mail.ustc.edu.cn) | |
| # -------------------------------------------------------- | |
| from __future__ import annotations | |
| import os | |
| 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 | |
| import requests | |
| 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, "b ... -> (repeat b) ...", repeat=3) | |
| cfg_sigma = einops.repeat(sigma, "b ... -> (repeat b) ...", repeat=3) | |
| cfg_cond = { | |
| "c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], cond["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_txt_cond \ | |
| = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3) | |
| return 0.5 * (out_img_cond + out_txt_cond) + \ | |
| text_cfg_scale * (out_cond - out_img_cond) + \ | |
| image_cfg_scale * (out_cond - out_txt_cond) | |
| def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False): | |
| model = instantiate_from_config(config.model) | |
| print(f"Loading model from {ckpt}") | |
| pl_sd = torch.load(ckpt, map_location="cpu") | |
| if 'state_dict' in pl_sd: | |
| pl_sd = pl_sd['state_dict'] | |
| m, u = model.load_state_dict(pl_sd, strict=False) | |
| print(m, 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/instruct_diffusion.yaml", type=str) | |
| parser.add_argument("--ckpt", default="checkpoints/v1-5-pruned-emaonly-adaption-task.ckpt", type=str) | |
| parser.add_argument("--vae-ckpt", default=None, type=str) | |
| parser.add_argument("--input", required=True, type=str) | |
| parser.add_argument("--outdir", default="logs", type=str) | |
| parser.add_argument("--edit", required=True, type=str) | |
| parser.add_argument("--cfg-text", default=5.0, type=float) | |
| parser.add_argument("--cfg-image", default=1.25, 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 | |
| if args.input.startswith("http"): | |
| input_image = Image.open(requests.get(args.input, stream=True).raw).convert("RGB") | |
| else: | |
| 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_resize = int((width * factor) // 64) * 64 | |
| height_resize = int((height * factor) // 64) * 64 | |
| input_image = ImageOps.fit(input_image, (width_resize, height_resize), method=Image.Resampling.LANCZOS) | |
| output_dir = args.outdir | |
| os.makedirs(output_dir, exist_ok=True) | |
| with torch.no_grad(), autocast("cuda"): | |
| 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(next(model.parameters()).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") | |
| print(x.shape) | |
| edited_image = Image.fromarray(x.type(torch.uint8).cpu().numpy()) | |
| edited_image = ImageOps.fit(edited_image, (width, height), method=Image.Resampling.LANCZOS) | |
| edited_image.save(output_dir+'/output_'+args.input.split('/')[-1].split('.')[0]+'_seed'+str(seed)+'.jpg') | |
| if __name__ == "__main__": | |
| main() | |