File size: 9,474 Bytes
5d2a4a6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 |
from convert_diffusers_to_sdxl import convert_unet_state_dict
from huggingface_hub import hf_hub_download, hf_hub_url, HfApi, HfFileSystem
import gradio
import gguf
import os
import requests
from safetensors.torch import load_file
import shutil
import time
import urllib
from urllib.parse import urlparse, parse_qs, unquote
import urllib.request
def convert(intro, url, api_key, arch):
path = urllib.parse.urlparse(url).path
components = path.split('/')
filename = components[-1]
output_file = 'locked_model.safetensors'
print('Step 1/3')
lock = Filelock(output_file)
if not os.path.exists(output_file):
if len(url.split('/')) == 2:
if not check_hf_safety(url):
raise Exception('Unexpected error ;)')
if not lock.acquire():
raise Exception('Wait your time in the queue.')
print('Download safetensors from {}.'.format(url))
try:
# We won't download the file by hf_hub_download, urllib.request,
# but access it remotely.
fs = HfFileSystem()
with fs.open('{}/unet/diffusion_pytorch_model.safetensors'.format(url)), 'r') as f:
byte_data = f.read()
sd_fp16 = load_transformer_by_diffuser_checkpoint(sd=safetensors.torch.load(byte_data))
except:
lock.release()
raise
else:
if not check_model_safety(filename):
raise Exception('Unexpected error ;)')
if not lock.acquire():
raise Exception('Wait your time in the queue.')
print('Download model by id {}.'.format(filename))
try:
# Save a hf copy of the remote file, then access it remotely.
fs = HfFileSystem()
copy_path = 'twodgirl/wild-sdxl/civit/{}.safetensors'
with fs.open(copy_path, 'wb') as f:
download_file(url, f, api_key)
with fs.open(copy_path, 'r') as f:
byte_data = f.read()
sd_fp16 = load_transformer_by_original_checkpoint(sd=safetensors.torch.load(byte_data))
except:
lock.release()
raise
print('Step 2/3')
os.remove(output_file) # Free hugging space runs out of free space.
write('locked_model.gguf', output_file, arch, sd_fp16)
print('Step 3/3')
api = HfApi()
api.upload_file(path_or_fileobj='locked_model.gguf',
path_in_repo=filename + '.comfyui.Q8.gguf',
repo_id='twodgirl/wild-sdxl',
repo_type='model')
lock.release()
gradio.Info('Download the file from twodgirl/wild-sdxl/{}'.format(filename + '.comfyui.Q8.gguf'))
print(output_file)
def download_file(url: str, f, token: str):
###
# Code from ashleykleynhans/civitai-downloader.
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) Gecko/20100101 Firefox/119.0'
headers = {
'Authorization': f'Bearer {token}',
'User-Agent': USER_AGENT,
}
# Disable automatic redirect handling
class NoRedirection(urllib.request.HTTPErrorProcessor):
def http_response(self, request, response):
return response
https_response = http_response
request = urllib.request.Request(url, headers=headers)
opener = urllib.request.build_opener(NoRedirection)
response = opener.open(request)
if response.status in [301, 302, 303, 307, 308]:
redirect_url = response.getheader('Location')
# Extract filename from the redirect URL
parsed_url = urlparse(redirect_url)
query_params = parse_qs(parsed_url.query)
content_disposition = query_params.get('response-content-disposition', [None])[0]
if content_disposition:
filename = unquote(content_disposition.split('filename=')[1].strip('"'))
else:
raise Exception('Unable to determine filename')
response = urllib.request.urlopen(redirect_url)
elif response.status == 404:
raise Exception('File not found')
else:
raise Exception('No redirect found, something went wrong')
total_size = response.getheader('Content-Length')
if total_size is not None:
total_size = int(total_size)
# With file pointer.
downloaded = 0
start_time = time.time()
CHUNK_SIZE = 1638400
while True:
chunk_start_time = time.time()
buffer = response.read(CHUNK_SIZE)
chunk_end_time = time.time()
if not buffer:
break
downloaded += len(buffer)
f.write(buffer)
chunk_time = chunk_end_time - chunk_start_time
if chunk_time > 0:
speed = len(buffer) / chunk_time / (1024 ** 2) # Speed in MB/s
if total_size is not None:
progress = downloaded / total_size
# sys.stdout.write(f'\rDownloading: {filename} [{progress*100:.2f}%] - {speed:.2f} MB/s')
# sys.stdout.flush()
end_time = time.time()
time_taken = end_time - start_time
hours, remainder = divmod(time_taken, 3600)
minutes, seconds = divmod(remainder, 60)
if hours > 0:
time_str = f'{int(hours)}h {int(minutes)}m {int(seconds)}s'
elif minutes > 0:
time_str = f'{int(minutes)}m {int(seconds)}s'
else:
time_str = f'{int(seconds)}s'
# sys.stdout.write('\n')
print(f'Download completed. File saved as: {filename}')
print(f'Downloaded in {time_str}')
###
# huggingface/twodgirl.
# License: apache-2.0
class Filelock:
def __init__(self, file_path):
self.file_path = file_path
self.lock_path = "{}.lock".format(file_path)
self.lock_file = None
def acquire(self):
if os.path.exists(self.lock_path):
lock_stat = os.stat(self.lock_path)
if time.time() - lock_stat.st_mtime > 900: # 15 minutes
os.remove(self.lock_path)
if not os.path.exists(self.lock_path):
try:
self.lock_file = open(self.lock_path, 'w')
self.lock_file.write(str(os.getpid()))
self.lock_file.flush()
return True
except IOError:
return False
return False
def release(self):
if self.lock_file:
self.lock_file.close()
os.remove(self.lock_path)
self.lock_file = None
def check_hf_safety(repo_id):
return 'porn' not in repo_id
def check_model_safety(model_id):
url = f"https://civitai.com/api/v1/model-versions/{model_id}"
response = requests.get(url)
data = response.json()
model_id = data.get('model_id')
if model_id:
url = f"https://civitai.com/api/v1/models/{model_id}"
response = requests.get(url)
data = response.json()
tags = data.get('tags', [])
if 'porn' in tags:
return False
else:
return True
else:
return True
def load_transformer_by_diffuser_checkpoint(filepath=None, sd=None):
if sd is None:
sd = load_file(filepath)
unet_state_dict = convert_unet_state_dict(sd)
sd_copy = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
return sd_copy
def load_transformer_by_original_checkpoint(ckpt_path=None, sd=None):
if sd is None:
sd = load_file(ckpt_path)
sd_copy = {}
for key in sd.keys():
if key.startswith('model.diffusion_model.'):
sd_copy[key] = sd3[key]
return sd_copy
def write(target_path, checkpoint_path, arch, sd_fp16):
writer = gguf.GGUFWriter(target_path, arch=arch)
target_quant = gguf.GGMLQuantizationType.Q8_0
writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
writer.add_file_type(target_quant)
sd = {}
for key in sd_fp16.keys():
tensor = sd_fp16[key]
if len(tensor.shape) == 1 or len(tensor.shape) == 4:
q = gguf.GGMLQuantizationType.F16
else:
q = target_quant
sd[key] = gguf.quants.quantize(tensor.numpy(), q)
writer.add_tensor(key, sd[key], raw_dtype=q)
writer.write_header_to_file(target_path)
writer.write_kv_data_to_file()
writer.write_tensors_to_file()
writer.close()
intro = gradio.Markdown("""
## Convert a SDXL model to GGUF
Convert a Pony/SDXL model's UNet to GGUF (Q8).
The question is whether I can automate tasks to the extent that would allow me to spend more time with my cat at home.
This space takes a diffusers file from 🤗, then converts it to [name your UI] compatible* format. The result should be avail in 10 minutes in the twodgirl/wild-sdxl model directory.
*That's an overstatement, as I only test it with my own comfy-gguf node.
The url format must follow:
*[hf-username]/[sdxl-repo-name]* which must lead to the /unet/diffusion_pytorch_model.safetensors.
### Disclaimer
Use of this code requires citation and attribution to the author via a link to their Hugging Face profile in all resulting work.
""")
url = gradio.Textbox(label='Download url')
api_key = gradio.Textbox(label='API key')
arch = gradio.Textbox(label='Architecture', value='sdxl')
if __name__ == '__main__':
demo = gradio.Interface(convert,
[intro, url, api_key, arch],
outputs=None)
demo.queue().launch()
|