Spaces:
Runtime error
Runtime error
File size: 9,401 Bytes
7da7768 47163f5 7da7768 47163f5 28425ed 47163f5 928a499 758cfc5 47163f5 7e98f35 7093637 7e98f35 c6e2b5c 5914cbe 7beb833 5914cbe 7093637 5914cbe c6e2b5c 5914cbe 7beb833 47163f5 1eefa67 7093637 47163f5 c6e2b5c 47163f5 c6e2b5c 7093637 47163f5 03bc7e7 c6e2b5c 47163f5 acce61f a976ca4 47163f5 1eefa67 47163f5 aff3d3b 47163f5 1eefa67 47163f5 1eefa67 47163f5 5f8442e 47163f5 c6e2b5c c242bd5 c6e2b5c 2b80ac9 c6e2b5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import os
import os.path as osp
import cv2
import numpy as np
import torch
from basicsr.utils import img2tensor, tensor2img
from pytorch_lightning import seed_everything
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.encoders.adapter import Adapter
from ldm.util import instantiate_from_config
from model_edge import pidinet
import gradio as gr
from omegaconf import OmegaConf
import pathlib
import random
import shlex
import subprocess
import sys
sys.path.append('T2I-Adapter')
config_path = 'https://github.com/TencentARC/T2I-Adapter/raw/main/configs/stable-diffusion/'
model_path = 'https://github.com/TencentARC/T2I-Adapter/raw/main/models/'
def load_model_from_config(config, ckpt, 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']}")
if "state_dict" in pl_sd:
sd = pl_sd["state_dict"]
else:
sd = pl_sd
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)
model.cuda()
model.eval()
return model
class Model:
def __init__(self,
model_config_path: str = 'ControlNet/models/cldm_v15.yaml',
model_dir: str = 'models',
use_lightweight: bool = True):
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.model_dir = pathlib.Path(model_dir)
self.download_models()
def download_models(self) -> None:
self.model_dir.mkdir(exist_ok=True, parents=True)
device = 'cuda'
config = OmegaConf.load("configs/stable-diffusion/test_sketch.yaml")
config.model.params.cond_stage_config.params.device = device
base_model_file = "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt"
base_model_file_anything = "https://huggingface.co/andite/anything-v4.0/resolve/main/anything-v4.0-pruned.ckpt"
sketch_adapter_file = "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth"
pose_adapter_file = "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth"
pidinet_file = model_path+"table5_pidinet.pth"
clip_file = "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/*"
subprocess.run(shlex.split(f'wget {base_model_file} -O models/sd-v1-4.ckpt'))
subprocess.run(shlex.split(f'wget {base_model_file_anything} -O models/anything-v4.0-pruned.ckpt'))
subprocess.run(shlex.split(f'wget {sketch_adapter_file} -O models/t2iadapter_sketch_sd14v1.pth'))
subprocess.run(shlex.split(f'wget {pose_adapter_file} -O models/t2iadapter_keypose_sd14v1.pth'))
subprocess.run(shlex.split(f'wget {pidinet_file} -O models/table5_pidinet.pth'))
self.model = load_model_from_config(config, "models/sd-v1-4.ckpt").to(device)
self.model_anything = load_model_from_config(config, "models/anything-v4.0-pruned.ckpt").to(device)
current_base = 'sd-v1-4.ckpt'
self.model_ad_sketch = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device)
self.model_ad_sketch.load_state_dict(torch.load("models/t2iadapter_sketch_sd14v1.pth"))
net_G = pidinet()
ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict']
net_G.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()})
net_G.to(device)
self.sampler= PLMSSampler(self.model)
self.sampler_anything= PLMSSampler(self.model_anything)
save_memory=True
self.model_ad_pose = Adapter(cin=int(3*64),channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device)
self.model_ad_pose.load_state_dict(torch.load("models/t2iadapter_keypose_sd14v1.pth"))
@torch.inference_mode()
def process_sketch(self, input_img, type_in, color_back, prompt, neg_prompt, fix_sample, scale, con_strength, base_model):
global current_base
device = 'cuda'
# if current_base != base_model:
# ckpt = os.path.join("models", base_model)
# pl_sd = torch.load(ckpt, map_location="cpu")
# if "state_dict" in pl_sd:
# sd = pl_sd["state_dict"]
# else:
# sd = pl_sd
# model.load_state_dict(sd, strict=False) #load_model_from_config(config, os.path.join("models", base_model)).to(device)
# current_base = base_model
con_strength = int((1-con_strength)*50)
if fix_sample == 'True':
seed_everything(42)
im = cv2.resize(input_img,(512,512))
if type_in == 'Sketch':
# net_G = net_G.cpu()
if color_back == 'White':
im = 255-im
im_edge = im.copy()
im = img2tensor(im)[0].unsqueeze(0).unsqueeze(0)/255.
# edge = 1-edge # for white background
im = im>0.5
im = im.float()
elif type_in == 'Image':
im = img2tensor(im).unsqueeze(0)/255.
im = net_G(im.to(device))[-1]
im = im>0.5
im = im.float()
im_edge = tensor2img(im)
c = self.model.get_learned_conditioning([prompt])
nc = self.model.get_learned_conditioning([neg_prompt])
with torch.no_grad():
# extract condition features
features_adapter = self.model_ad_sketch(im.to(device))
shape = [4, 64, 64]
# sampling
samples_ddim, _ = self.sampler.sample(S=50,
conditioning=c,
batch_size=1,
shape=shape,
verbose=False,
unconditional_guidance_scale=scale,
unconditional_conditioning=nc,
eta=0.0,
x_T=None,
features_adapter1=features_adapter,
mode = 'sketch',
con_strength = con_strength)
x_samples_ddim = self.model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).cpu().numpy()[0]
x_samples_ddim = 255.*x_samples_ddim
x_samples_ddim = x_samples_ddim.astype(np.uint8)
return [im_edge, x_samples_ddim]
@torch.inference_mode()
def process_pose(self, input_img, prompt, neg_prompt, fix_sample, scale, con_strength, base_model):
global current_base
device = 'cuda'
# if current_base != base_model:
# ckpt = os.path.join("models", base_model)
# pl_sd = torch.load(ckpt, map_location="cpu")
# if "state_dict" in pl_sd:
# sd = pl_sd["state_dict"]
# else:
# sd = pl_sd
# model.load_state_dict(sd, strict=False) #load_model_from_config(config, os.path.join("models", base_model)).to(device)
# current_base = base_model
con_strength = int((1-con_strength)*50)
if fix_sample == 'True':
seed_everything(42)
im = cv2.resize(input_img,(512,512))
pose = img2tensor(im, bgr2rgb=True, float32=True)/255.
pose = pose.unsqueeze(0)
im_pose = tensor2img(pose)
c = self.model.get_learned_conditioning([prompt])
nc = self.model.get_learned_conditioning([neg_prompt])
with torch.no_grad():
# extract condition features
features_adapter = self.model_ad_pose(pose.to(device))
shape = [4, 64, 64]
# sampling
samples_ddim, _ = self.sampler.sample(S=50,
conditioning=c,
batch_size=1,
shape=shape,
verbose=False,
unconditional_guidance_scale=scale,
unconditional_conditioning=nc,
eta=0.0,
x_T=None,
features_adapter1=features_adapter,
mode = 'sketch',
con_strength = con_strength)
x_samples_ddim = self.model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_samples_ddim = x_samples_ddim.permute(0, 2, 3, 1).cpu().numpy()[0]
x_samples_ddim = 255.*x_samples_ddim
x_samples_ddim = x_samples_ddim.astype(np.uint8)
return [im_pose, x_samples_ddim]
|