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run_dataset.py
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"""
Copyright (c) 2023 Samsung Electronics Co., Ltd.
Author(s):
Hongsuk Choi ([email protected])
Licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at https://creativecommons.org/licenses/by-nc/4.0/
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.
For conditions of distribution and use, see the accompanying LICENSE file.
Modified from ControlNet: https://github.com/lllyasviel/ControlNet
"""
from share import *
import config
import os
import os.path as osp
from pathlib import Path
import glob
import argparse
import copy
import json
from tqdm import tqdm
from pycocotools.coco import COCO
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import torch.nn.functional as F
import random
import matplotlib.pyplot as plt
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.openpose import OpenposeDetector, draw_pose
from annotator.openpose.util import get_bbox_from_joints
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
preprocessor = None
model_name = 'control_v11p_sd15_openpose'
model = create_model(f'./models/{model_name}.yaml').cpu()
model.load_state_dict(load_state_dict('./models/v1-5-pruned.ckpt', location='cuda'), strict=False)
model.load_state_dict(load_state_dict(f'./models/{model_name}.pth', location='cuda'), strict=False)
model = model.cuda()
ddim_sampler = DDIMSampler(model)
def process(det, persons_detections, fusion_type, harmony_level, mask_kernel_size, mask_blur, mask_softmax_temperature, input_image, prompt_list, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, eta):
input_image = HWC3(input_image)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
print("[Generation Image shape]: ", H, W)
multi_identity_text_prompt, person_setting, global_text_prompt = prompt_list
if global_text_prompt == '':
global_text_prompt = ' and '.join(multi_identity_text_prompt) + ' ' + person_setting
global_text_prompt = global_text_prompt.lower()
global_detection_map = np.zeros(shape=(H, W, 3), dtype=np.uint8)
with torch.no_grad():
if config.save_memory:
model.low_vram_shift(is_diffusing=False)
###################################################
# Obtain a (text,2D control) pair for each person #
###################################################
control_tensor_list = []
mask_tensor_list = []
text_tensor_list = []
n_text_tensor_list = []
for person_id, person_detection in enumerate(persons_detections):
""" Draw skeleton """
if 'Full' not in det:
person_detection['hands'] = []
person_detection['faces'] = []
person_detection = copy.deepcopy(person_detection)
input_mask = np.zeros_like(img[:, :, :1])
# draw each instance skeleton
detected_map = draw_pose(person_detection, H, W)
detected_map = HWC3(detected_map)
# draw global skeleton
local_detection_mask = detected_map.sum(2) > 0
global_detection_map[local_detection_mask] = detected_map[local_detection_mask]
""" Draw skeleton """
""" Sanitize joint pixel coordinates to get a bounding box """
joints_img = []
body_joints = person_detection['bodies']['candidate']
body_subset = person_detection['bodies']['subset'][0]
for num in body_subset[:18]:
if num != -1:
joints_img.append(body_joints[int(num)][:2])
if 'Full' in det:
hand_joints = person_detection['hands']
face_joints = person_detection['faces']
if len(hand_joints) > 0:
for hj in hand_joints:
for joint in hj:
if joint[0] > 0.01:
joints_img.append(joint)
if len(face_joints) > 0:
for joint in face_joints[0]:
if joint[0] > 0.01:
joints_img.append(joint)
joints_img = np.array(joints_img, dtype=np.float32)
joints_valid = np.ones_like(joints_img[:, 0])
joints_img[:, 0] *= W
joints_img[:, 1] *= H
bbox = get_bbox_from_joints(joints_img, joints_valid, (H, W), expand_ratio=1.2)
x,y,w,h = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
""" Sanitize joint pixel coordinates to get a bounding box """
""" Parse instance masks """
# bounding box mask
# input_mask[y:y+h, x:x+w] = 255
# skeleton mask
input_mask[detected_map.sum(2) > 0] = 255
# dilation
kernel = np.ones((mask_kernel_size, mask_kernel_size), np.uint8)
input_mask = cv2.dilate(input_mask, kernel)
# mask_pixel: 1. visible
mask_pixel = cv2.resize(input_mask, (W, H), interpolation=cv2.INTER_LINEAR).astype(np.float32) / 255.0
mask_pixel = cv2.GaussianBlur(mask_pixel, (0, 0), mask_blur)
mask_latent = cv2.resize(mask_pixel, (W // 8, H // 8), interpolation=cv2.INTER_AREA)
# mask: 1. visible. occupied. 0. not visible. not occupied
mask = torch.from_numpy(mask_latent.copy()).float().cuda()
mask = torch.stack([mask for _ in range(num_samples)], dim=0)
mask = einops.rearrange(mask, 'b h w -> b 1 h w').clone()
mask_tensor_list.append(mask)
""" Parse instance masks """
""" Parse 2D control """
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
control_tensor_list.append(control)
""" Parse 2D control """
""" Parse prompts """
positive_prompt = multi_identity_text_prompt[person_id] + ' ' + person_setting + ', ' + a_prompt
text_tensor_list.append(model.get_learned_conditioning([positive_prompt]))
n_text_tensor_list.append(model.get_learned_conditioning([n_prompt]))
""" Parse prompts """
###################################################
# Obtain a (text,2D control) pair for each person #
###################################################
# list of (text,2D control) pairs
cond = {"c_concat": [control_tensor_list], "c_crossattn": [text_tensor_list] * num_samples}
un_cond = {"c_concat": None if guess_mode else [control_tensor_list], "c_crossattn": [n_text_tensor_list] * num_samples}
# reshape conditions
if fusion_type != '':
c_concat_list = []
c_crossattn_list = []
uc_concat_list = []
uc_crossattn_list = []
num_humans = len(persons_detections)
for idx in list(range(num_humans)):
c_concat = cond["c_concat"][0][idx] # b,c,h,w.
c_concat_list.append(c_concat)
c_crossattn = torch.stack([x[idx][0] for x in cond["c_crossattn"]], dim=0) # b, c', w
c_crossattn_list.append(c_crossattn)
uc_concat = un_cond["c_concat"][0][idx] # b,c,h,w.
uc_concat_list.append(uc_concat)
uc_crossattn = torch.stack([x[idx][0] for x in un_cond["c_crossattn"]], dim=0) # b, c', w
uc_crossattn_list.append(uc_crossattn)
c_concat = torch.stack(c_concat_list).transpose(0,1) # b,n,c,h,w
c_crossattn = torch.stack(c_crossattn_list).transpose(0,1) # b,n,c',w
c_concat = c_concat.reshape(num_samples*num_humans, *c_concat.shape[2:]) # b*n,c,h,w
c_crossattn = c_crossattn.reshape(num_samples*num_humans, *c_crossattn.shape[2:]) # b*n,c',w
uc_concat = torch.stack(uc_concat_list).transpose(0,1) # b,n,c,h,w
uc_crossattn = torch.stack(uc_crossattn_list).transpose(0,1) # b,n,c',w
uc_concat = uc_concat.reshape(num_samples*num_humans, *uc_concat.shape[2:]) # b*n,c,h,w
uc_crossattn = uc_crossattn.reshape(num_samples*num_humans, *uc_crossattn.shape[2:]) # b*n,c',w
cond = {"c_concat": [c_concat], "c_crossattn": [c_crossattn]}
un_cond = {"c_concat": [uc_concat], "c_crossattn": [uc_crossattn]}
# Global condition for ControlNet and MultiControlnet
global_detection_map = cv2.resize(global_detection_map, (W, H), interpolation=cv2.INTER_LINEAR)
global_control = torch.from_numpy(global_detection_map.copy()).float().cuda() / 255.0
global_control = torch.stack([global_control for _ in range(num_samples)], dim=0)
global_control = einops.rearrange(global_control, 'b h w c -> b c h w').clone()
global_cond = {"c_concat": [global_control], "c_crossattn": [model.get_learned_conditioning([global_text_prompt + ', ' + a_prompt] * num_samples)]}
global_un_cond = {"c_concat": None if guess_mode else [global_control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
if fusion_type == '':
cond = global_cond
un_cond = global_un_cond
if config.save_memory:
model.low_vram_shift(is_diffusing=True)
shape = (4, H // 8, W // 8)
# From ControlNet: Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)
samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond,
mask_tensor_list=mask_tensor_list,
mask_softmax_temperature=mask_softmax_temperature,
global_cond=global_cond,
global_un_cond=global_un_cond,
harmony_level=harmony_level,
fusion_type=fusion_type,
)
if config.save_memory:
model.low_vram_shift(is_diffusing=False)
x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
results = [x_samples[i] for i in range(num_samples)]
return [global_detection_map] + results
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', type=str, default='./')
parser.add_argument('--dataset_path', type=str, default='./data_generation/coco_val_pose_with_prompt_data_finecontrolnet.json')
parser.add_argument('--fusion_type', type=str, default='h-control', help='pick from h-control, h-all, h-ediff-i, m. empty string for ControlNet.')
args = parser.parse_args()
return args
if __name__ == '__main__':
# argument parse and create log
args = parse_args()
dataset_path = args.dataset_path
split = 'val' if 'val' in dataset_path else 'train'
save_dir = args.save_dir
Path(save_dir).mkdir(parents=True, exist_ok=True)
fusion_type = args.fusion_type # '', 'h-ediff-i', 'h-all', 'h-control', 'm'
# ControlNet hyperparameters
a_prompt = 'best quality'
n_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality' # "Negative Prompt"
num_samples = 1 # 50
guess_mode = False
strength = 1.5
scale = 9.0 # guidance scale # max 30, min 0.1
eta = 0.0
# Main hyperparemeters for FineControlNet
image_resolution = 512
det = "Openpose" # ["Openpose_Full", "Openpose", "None"]
ddim_steps = 20
harmony_level = ddim_steps // 2 # higher more harmony. but trade-off with identity observance. used in attention level
mask_blur = 1
mask_kernel_size = image_resolution // 8
mask_kernel_size = mask_kernel_size if mask_kernel_size % 2 == 1 else mask_kernel_size + 1
mask_softmax_temperature = 0.001 # lower -> more distinct boundary between instances during denoising. higher -> more blurry boundary between instances
print(f"[Options]: fusion_type {fusion_type} hardmony level {harmony_level} det {det} ddim_steps {ddim_steps} mask_kernel_size {mask_kernel_size} mask_temperature {mask_softmax_temperature}")
# load 2d poses, instance & setting descriptions, global descriptions, and seed from the curated dataset
db = COCO(dataset_path)
count = 0
for iid in tqdm(db.imgs.keys()):
img = db.imgs[iid]
aid = db.getAnnIds([iid])[0] # one annotation per image.
ann = db.anns[aid]
imgname = osp.join(f'{split}2017', img['file_name'])
file_name = img['file_name']
img_width, img_height = img['width'], img['height']
# load data
poses = ann['people']['poses']
seed = ann['seed']
multi_identity_text_prompt = ann['instance_descs']
person_setting = ann['setting_desc']
global_text_prompt = ann['global_desc']
print("[Global Text Prompt]: ", global_text_prompt)
print("[Identity Text Prompt]: ", multi_identity_text_prompt)
print("[Setting Text Prompt]: ", person_setting)
print("[Source file of Poses in MSCOCO]: ", imgname)
print("[Seed]: ", seed)
# Run model
seed_everything(seed)
input_image = np.zeros((img_height, img_width, 3), dtype=np.uint8)
ips = [det, poses, fusion_type, harmony_level, mask_kernel_size, mask_blur, mask_softmax_temperature, input_image, [multi_identity_text_prompt, person_setting, global_text_prompt], a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, eta]
results = process(*ips)
global_detection_map, output = results[0], results[1]
count += 1
# Save images
detection_path = f'finecontrolnet_detection_{iid}.png'
detection_path = osp.join(save_dir, detection_path)
cv2.imwrite(detection_path, global_detection_map[:, :, ::-1])
output_path = f'finecontrolnet_output_{iid}.png'
output_path = osp.join(save_dir, output_path)
cv2.imwrite(output_path, output[:, :, ::-1])
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(poses) + 2)]
output = output.copy()
cv2.putText(output, person_setting, (15, output.shape[0]-20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (5, 5, 5 ))
for person_id, person_detection in enumerate(poses):
person_detection = copy.deepcopy(person_detection)
detected_map = draw_pose(person_detection, output.shape[0], output.shape[1], color=colors[person_id])
detected_map = HWC3(detected_map)
tmp = detected_map.sum(2) > 0
output[tmp] = detected_map[tmp] * 0.9
cv2.putText(output, multi_identity_text_prompt[person_id], (15, 15*(1+person_id)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (colors[person_id][0] * 255, colors[person_id][1] *255, colors[person_id][2] *255 ))
output_path = f'finecontrolnet_annotoverlaid_{iid}.png'
output_path = osp.join(save_dir, output_path)
cv2.imwrite(output_path, output[:, :, ::-1])
print("[File Saved To]: ", output_path)
print(f"Ran on {count} images")
print("END")