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edit_cli_batch.py
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edit_cli_batch.py
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'''
* Copyright (c) 2023 Salesforce, Inc.
* All rights reserved.
* SPDX-License-Identifier: Apache License 2.0
* For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
* By Shu Zhang
* Modified from InstructPix2Pix repo: https://github.com/timothybrooks/instruct-pix2pix
* Copyright (c) 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros. All rights reserved.
'''
from __future__ import annotations
import math
import os
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
import glob
import re
import jsonlines
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/hive.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
parser.add_argument("--output_dir", required=True, type=str)
parser.add_argument("--jsonl_file", required=True, type=str)
parser.add_argument("--image_dir", required=True, default="data/evaluation/", type=str)
parser.add_argument("--cfg-text", default=7.5, type=float)
parser.add_argument("--cfg-image", default=1.5, type=float)
parser.add_argument("--seed", default=100, 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
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
image_dir = args.image_dir
instructions = []
image_paths = []
with jsonlines.open(args.jsonl_file) as reader:
for ll in reader:
instructions.append(ll["instruction"])
image_paths.append(os.path.join(image_dir, ll['source_img']))
for i, instruction in enumerate(instructions):
output_image = os.path.join(output_dir, f'instruct_{i}.png')
input_image = Image.open(image_paths[i]).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)
edit = instructions[i]
with torch.no_grad(), autocast("cuda"), model.ema_scope():
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([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(output_image)
if __name__ == "__main__":
main()