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# *************************************************************************
# Copyright (2023) Bytedance Inc.
#
# Copyright (2023) DragDiffusion Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# *************************************************************************
# run results of DragDiffusion
import argparse
import os
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
import PIL
from PIL import Image
from copy import deepcopy
from einops import rearrange
from types import SimpleNamespace
from diffusers import DDIMScheduler, AutoencoderKL
from torchvision.utils import save_image
from pytorch_lightning import seed_everything
import sys
sys.path.insert(0, '../')
from drag_pipeline import DragPipeline
from utils.drag_utils import drag_diffusion_update
from utils.attn_utils import register_attention_editor_diffusers, MutualSelfAttentionControl
def preprocess_image(image,
device):
image = torch.from_numpy(image).float() / 127.5 - 1 # [-1, 1]
image = rearrange(image, "h w c -> 1 c h w")
image = image.to(device)
return image
# copy the run_drag function to here
def run_drag(source_image,
# image_with_clicks,
mask,
prompt,
points,
inversion_strength,
lam,
latent_lr,
unet_feature_idx,
n_pix_step,
model_path,
vae_path,
lora_path,
start_step,
start_layer,
# save_dir="./results"
):
# initialize model
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012,
beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False, steps_offset=1)
model = DragPipeline.from_pretrained(model_path, scheduler=scheduler).to(device)
# call this function to override unet forward function,
# so that intermediate features are returned after forward
model.modify_unet_forward()
# set vae
if vae_path != "default":
model.vae = AutoencoderKL.from_pretrained(
vae_path
).to(model.vae.device, model.vae.dtype)
# initialize parameters
seed = 42 # random seed used by a lot of people for unknown reason
seed_everything(seed)
args = SimpleNamespace()
args.prompt = prompt
args.points = points
args.n_inference_step = 50
args.n_actual_inference_step = round(inversion_strength * args.n_inference_step)
args.guidance_scale = 1.0
args.unet_feature_idx = [unet_feature_idx]
args.r_m = 1
args.r_p = 3
args.lam = lam
args.lr = latent_lr
args.n_pix_step = n_pix_step
full_h, full_w = source_image.shape[:2]
args.sup_res_h = int(0.5*full_h)
args.sup_res_w = int(0.5*full_w)
print(args)
source_image = preprocess_image(source_image, device)
# image_with_clicks = preprocess_image(image_with_clicks, device)
# set lora
if lora_path == "":
print("applying default parameters")
model.unet.set_default_attn_processor()
else:
print("applying lora: " + lora_path)
model.unet.load_attn_procs(lora_path)
# invert the source image
# the latent code resolution is too small, only 64*64
invert_code = model.invert(source_image,
prompt,
guidance_scale=args.guidance_scale,
num_inference_steps=args.n_inference_step,
num_actual_inference_steps=args.n_actual_inference_step)
mask = torch.from_numpy(mask).float() / 255.
mask[mask > 0.0] = 1.0
mask = rearrange(mask, "h w -> 1 1 h w").cuda()
mask = F.interpolate(mask, (args.sup_res_h, args.sup_res_w), mode="nearest")
handle_points = []
target_points = []
# here, the point is in x,y coordinate
for idx, point in enumerate(points):
cur_point = torch.tensor([point[1]/full_h*args.sup_res_h, point[0]/full_w*args.sup_res_w])
cur_point = torch.round(cur_point)
if idx % 2 == 0:
handle_points.append(cur_point)
else:
target_points.append(cur_point)
print('handle points:', handle_points)
print('target points:', target_points)
init_code = invert_code
init_code_orig = deepcopy(init_code)
model.scheduler.set_timesteps(args.n_inference_step)
t = model.scheduler.timesteps[args.n_inference_step - args.n_actual_inference_step]
# feature shape: [1280,16,16], [1280,32,32], [640,64,64], [320,64,64]
# update according to the given supervision
updated_init_code = drag_diffusion_update(model, init_code,
None, t, handle_points, target_points, mask, args)
# hijack the attention module
# inject the reference branch to guide the generation
editor = MutualSelfAttentionControl(start_step=start_step,
start_layer=start_layer,
total_steps=args.n_inference_step,
guidance_scale=args.guidance_scale)
if lora_path == "":
register_attention_editor_diffusers(model, editor, attn_processor='attn_proc')
else:
register_attention_editor_diffusers(model, editor, attn_processor='lora_attn_proc')
# inference the synthesized image
gen_image = model(
prompt=args.prompt,
batch_size=2,
latents=torch.cat([init_code_orig, updated_init_code], dim=0),
guidance_scale=args.guidance_scale,
num_inference_steps=args.n_inference_step,
num_actual_inference_steps=args.n_actual_inference_step
)[1].unsqueeze(dim=0)
# resize gen_image into the size of source_image
# we do this because shape of gen_image will be rounded to multipliers of 8
gen_image = F.interpolate(gen_image, (full_h, full_w), mode='bilinear')
# save the original image, user editing instructions, synthesized image
# save_result = torch.cat([
# source_image * 0.5 + 0.5,
# torch.ones((1,3,full_h,25)).cuda(),
# image_with_clicks * 0.5 + 0.5,
# torch.ones((1,3,full_h,25)).cuda(),
# gen_image[0:1]
# ], dim=-1)
# if not os.path.isdir(save_dir):
# os.mkdir(save_dir)
# save_prefix = datetime.datetime.now().strftime("%Y-%m-%d-%H%M-%S")
# save_image(save_result, os.path.join(save_dir, save_prefix + '.png'))
out_image = gen_image.cpu().permute(0, 2, 3, 1).numpy()[0]
out_image = (out_image * 255).astype(np.uint8)
return out_image
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="setting arguments")
parser.add_argument('--lora_steps', type=int, help='number of lora fine-tuning steps')
parser.add_argument('--inv_strength', type=float, help='inversion strength')
parser.add_argument('--latent_lr', type=float, default=0.01, help='latent learning rate')
parser.add_argument('--unet_feature_idx', type=int, default=3, help='feature idx of unet features')
args = parser.parse_args()
all_category = [
'art_work',
'land_scape',
'building_city_view',
'building_countryside_view',
'animals',
'human_head',
'human_upper_body',
'human_full_body',
'interior_design',
'other_objects',
]
# assume root_dir and lora_dir are valid directory
root_dir = 'drag_bench_data'
lora_dir = 'drag_bench_lora'
result_dir = 'drag_diffusion_res' + \
'_' + str(args.lora_steps) + \
'_' + str(args.inv_strength) + \
'_' + str(args.latent_lr) + \
'_' + str(args.unet_feature_idx)
# mkdir if necessary
if not os.path.isdir(result_dir):
os.mkdir(result_dir)
for cat in all_category:
os.mkdir(os.path.join(result_dir,cat))
for cat in all_category:
file_dir = os.path.join(root_dir, cat)
for sample_name in os.listdir(file_dir):
if sample_name == '.DS_Store':
continue
sample_path = os.path.join(file_dir, sample_name)
# read image file
source_image = Image.open(os.path.join(sample_path, 'original_image.png'))
source_image = np.array(source_image)
# load meta data
with open(os.path.join(sample_path, 'meta_data.pkl'), 'rb') as f:
meta_data = pickle.load(f)
prompt = meta_data['prompt']
mask = meta_data['mask']
points = meta_data['points']
# load lora
lora_path = os.path.join(lora_dir, cat, sample_name, str(args.lora_steps))
print("applying lora: " + lora_path)
out_image = run_drag(
source_image,
mask,
prompt,
points,
inversion_strength=args.inv_strength,
lam=0.1,
latent_lr=args.latent_lr,
unet_feature_idx=args.unet_feature_idx,
n_pix_step=80,
model_path="runwayml/stable-diffusion-v1-5",
vae_path="default",
lora_path=lora_path,
start_step=0,
start_layer=10,
)
save_dir = os.path.join(result_dir, cat, sample_name)
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
Image.fromarray(out_image).save(os.path.join(save_dir, 'dragged_image.png'))
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