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run_editing_instructdiffusion.py
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# --------------------------------------------------------
# InstructDiffusion
# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
# Modified by Zigang Geng ([email protected])
# --------------------------------------------------------
from __future__ import annotations
import os
import math
import random
import sys
from argparse import ArgumentParser
import argparse
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 json
from utils.utils import txt_draw
import requests
sys.path.append("models/InstructDiffusion/stable_diffusion")
from 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 mask_decode(encoded_mask,image_shape=[512,512]):
length=image_shape[0]*image_shape[1]
mask_array=np.zeros((length,))
for i in range(0,len(encoded_mask),2):
splice_len=min(encoded_mask[i+1],length-encoded_mask[i])
for j in range(splice_len):
mask_array[encoded_mask[i]+j]=1
mask_array=mask_array.reshape(image_shape[0], image_shape[1])
# to avoid annotation errors in boundary
mask_array[0,:]=1
mask_array[-1,:]=1
mask_array[:,0]=1
mask_array[:,-1]=1
return mask_array
def setup_seed(seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def instruct_diffusion_edit(edit_method, image_path,model, edit_prompt, resolution=512,steps=50,cfg_text=5.0,cfg_image=1.25):
if edit_method=="instruct-diffusion":
input_image_ = Image.open(image_path).convert("RGB")
width, height = input_image_.size
factor = 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)
with torch.no_grad(), autocast("cuda"):
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([edit_prompt])]
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(steps)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": cfg_text,
"image_cfg_scale": cfg_image,
}
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)
image_instruct = txt_draw(f"edit prompt: {edit_prompt}")
return Image.fromarray(np.concatenate((image_instruct, np.array(input_image_), np.zeros_like(image_instruct), np.array(edited_image)),axis=1))
else:
raise NotImplementedError(f"No edit method named {edit_method}")
image_save_paths={
"instruct-diffusion":"instruct-diffusion",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--rerun_exist_images', action= "store_true") # rerun existing images
parser.add_argument('--data_path', type=str, default="data") # the editing category that needed to run
parser.add_argument('--output_path', type=str, default="output") # the editing category that needed to run
parser.add_argument('--edit_category_list', nargs = '+', type=str, default=["0","1","2","3","4","5","6","7","8","9"]) # the editing category that needed to run
parser.add_argument('--edit_method_list', nargs = '+', type=str, default=["instruct-diffusion"]) # the editing methods that needed to run
parser.add_argument('--checkpoint', type=str, require=True, default="data/checkpoints/v1-5-pruned-emaonly-adaption.ckpt") # the editing methods that needed to run
args = parser.parse_args()
rerun_exist_images=args.rerun_exist_images
data_path=args.data_path
output_path=args.output_path
edit_category_list=args.edit_category_list
edit_method_list=args.edit_method_list
checkpoint=args.checkpoint
with open(f"{data_path}/mapping_file.json", "r") as f:
editing_instruction = json.load(f)
config = OmegaConf.load("models/InstructDiffusion/configs/instruct_diffusion.yaml")
model = load_model_from_config(config, checkpoint, None)
model.eval().cuda()
model_wrap = K.external.CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
for key, item in editing_instruction.items():
if item["editing_type_id"] not in edit_category_list:
continue
original_prompt = item["original_prompt"].replace("[", "").replace("]", "")
editing_prompt = item["editing_prompt"].replace("[", "").replace("]", "")
image_path = os.path.join(f"{data_path}/annotation_images", item["image_path"])
editing_instruction = item["editing_instruction"]
blended_word = item["blended_word"].split(" ") if item["blended_word"] != "" else []
mask = Image.fromarray(np.uint8(mask_decode(item["mask"])[:,:,np.newaxis].repeat(3,2))).convert("L")
for edit_method in edit_method_list:
present_image_save_path=image_path.replace(data_path, os.path.join(output_path,image_save_paths[edit_method]))
if ((not os.path.exists(present_image_save_path)) or rerun_exist_images):
print(f"editing image [{image_path}] with [{edit_method}]")
setup_seed()
torch.cuda.empty_cache()
edited_image = instruct_diffusion_edit(edit_method,
image_path=image_path,
model=model,
edit_prompt=editing_instruction
)
if not os.path.exists(os.path.dirname(present_image_save_path)):
os.makedirs(os.path.dirname(present_image_save_path))
edited_image.save(present_image_save_path)
print(f"finish")
else:
print(f"skip image [{image_path}] with [{edit_method}]")