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cmd_pose2image.py
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cmd_pose2image.py
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from share import *
import config
import cv2
import einops
import numpy as np
import torch
import random
import argparse
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.openpose import OpenposeDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
apply_openpose = OpenposeDetector()
model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict('./models/control_sd15_openpose.pth', location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)
def process(input_image, prompt, a_prompt='best quality, extremely detailed', n_prompt='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality', num_samples=1, image_resolution=516, detect_resolution=516, ddim_steps=30, guess_mode=False, strength=1.0, scale=9.0, seed=-1, eta=0.0):
with torch.no_grad():
input_image = HWC3(input_image)
detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
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()
if seed == -1:
seed = random.randint(0, 65535)
seed_everything(seed)
if config.save_memory:
model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)
if config.save_memory:
model.low_vram_shift(is_diffusing=True)
model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
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 [detected_map] + results
def test():
img=cv2.imread('./test_imgs/man.png')
prompt='1girl,beautiful background,beautiful face,beazutiful clothes,normal face'
a_prompt='best quality, extremely detailed'
n_prompt='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
num_samples=1
image_resolution=512
detect_resolution=512
ddim_steps=60
guess_mode=False
strength=1.0
scale=9.0
seed=-1
eta=0.0
# ips=[img, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold]
out=process(img, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta)
cv2.imshow('out',out[1])
cv2.imwrite('out.png',out[1])
print('saved to out.png')
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('--run_test', type=bool, default=False, help='run a easy test')
parser.add_argument('--image_path', type=str, default='test.png', help='original image path')
parser.add_argument('--prompt', type=str, default='1people', help='prompt')
parser.add_argument('--a_prompt', type=str, default='best quality, extremely detailed', help='added prompt')
parser.add_argument('--n_prompt', type=str, default='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality', help='negative prompt')
parser.add_argument('--num_samples', type=int, default=1, help='number of samples')
parser.add_argument('--image_resolution', type=int, default=512, help='image resolution')
parser.add_argument('--detect_resolution', type=int, default=512, help='detect resolution')
parser.add_argument('--ddim_steps', type=int, default=30, help='ddim steps')
parser.add_argument('--is_saved', type=bool, default=True, help='is saved?')
parser.add_argument('--is_show', type=bool, default=False, help='is show?')
parser.add_argument('--guess_mode', type=bool, default=False, help='guess mode')
parser.add_argument('--strength', type=float, default=1.0, help='strength')
parser.add_argument('--scale', type=float, default=9.0, help='scale')
parser.add_argument('--seed', type=int, default=-1, help='seed')
parser.add_argument('--eta', type=float, default=0.0, help='eta')
opt = parser.parse_args()
img=cv2.imread(opt.image_path)
out=process(img, opt.prompt, opt.a_prompt, opt.n_prompt, opt.num_samples, opt.image_resolution, opt.detect_resolution, opt.ddim_steps, opt.guess_mode, opt.strength, opt.scale, opt.seed, opt.eta)
if(opt.is_show):
cv2.imshow('out',out[1])
if(opt.is_saved):
cv2.imwrite('out.png',out[1])
print('saved to out.png')