DiffuseCraft / utils.py
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import os
import re
import gradio as gr
from constants import (
DIFFUSERS_FORMAT_LORAS,
CIVITAI_API_KEY,
HF_TOKEN,
MODEL_TYPE_CLASS,
DIRECTORY_LORAS,
)
from huggingface_hub import HfApi
from diffusers import DiffusionPipeline
from huggingface_hub import model_info as model_info_data
from diffusers.pipelines.pipeline_loading_utils import variant_compatible_siblings
from pathlib import PosixPath
def download_things(directory, url, hf_token="", civitai_api_key=""):
url = url.strip()
if "drive.google.com" in url:
original_dir = os.getcwd()
os.chdir(directory)
os.system(f"gdown --fuzzy {url}")
os.chdir(original_dir)
elif "huggingface.co" in url:
url = url.replace("?download=true", "")
# url = urllib.parse.quote(url, safe=':/') # fix encoding
if "/blob/" in url:
url = url.replace("/blob/", "/resolve/")
user_header = f'"Authorization: Bearer {hf_token}"'
if hf_token:
os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
else:
os.system(f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
elif "civitai.com" in url:
if "?" in url:
url = url.split("?")[0]
if civitai_api_key:
url = url + f"?token={civitai_api_key}"
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
else:
print("\033[91mYou need an API key to download Civitai models.\033[0m")
else:
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
def get_model_list(directory_path):
model_list = []
valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
for filename in os.listdir(directory_path):
if os.path.splitext(filename)[1] in valid_extensions:
# name_without_extension = os.path.splitext(filename)[0]
file_path = os.path.join(directory_path, filename)
# model_list.append((name_without_extension, file_path))
model_list.append(file_path)
print('\033[34mFILE: ' + file_path + '\033[0m')
return model_list
def extract_parameters(input_string):
parameters = {}
input_string = input_string.replace("\n", "")
if "Negative prompt:" not in input_string:
if "Steps:" in input_string:
input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
else:
print("Invalid metadata")
parameters["prompt"] = input_string
return parameters
parm = input_string.split("Negative prompt:")
parameters["prompt"] = parm[0].strip()
if "Steps:" not in parm[1]:
print("Steps not detected")
parameters["neg_prompt"] = parm[1].strip()
return parameters
parm = parm[1].split("Steps:")
parameters["neg_prompt"] = parm[0].strip()
input_string = "Steps:" + parm[1]
# Extracting Steps
steps_match = re.search(r'Steps: (\d+)', input_string)
if steps_match:
parameters['Steps'] = int(steps_match.group(1))
# Extracting Size
size_match = re.search(r'Size: (\d+x\d+)', input_string)
if size_match:
parameters['Size'] = size_match.group(1)
width, height = map(int, parameters['Size'].split('x'))
parameters['width'] = width
parameters['height'] = height
# Extracting other parameters
other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
for param in other_parameters:
parameters[param[0]] = param[1].strip('"')
return parameters
def get_my_lora(link_url):
for url in [url.strip() for url in link_url.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
new_lora_model_list = get_model_list(DIRECTORY_LORAS)
new_lora_model_list.insert(0, "None")
new_lora_model_list = new_lora_model_list + DIFFUSERS_FORMAT_LORAS
return gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
),
def info_html(json_data, title, subtitle):
return f"""
<div style='padding: 0; border-radius: 10px;'>
<p style='margin: 0; font-weight: bold;'>{title}</p>
<details>
<summary>Details</summary>
<p style='margin: 0; font-weight: bold;'>{subtitle}</p>
</details>
</div>
"""
def get_model_type(repo_id: str):
api = HfApi(token=os.environ.get("HF_TOKEN")) # if use private or gated model
default = "SD 1.5"
try:
model = api.model_info(repo_id=repo_id, timeout=5.0)
tags = model.tags
for tag in tags:
if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
except Exception:
return default
return default
def restart_space(repo_id: str, factory_reboot: bool, token: str):
api = HfApi(token=token)
api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot)
def extract_exif_data(image):
if image is None: return ""
try:
metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
for key in metadata_keys:
if key in image.info:
return image.info[key]
return str(image.info)
except Exception as e:
return f"Error extracting metadata: {str(e)}"
def create_mask_now(img, invert):
import numpy as np
import time
time.sleep(0.5)
transparent_image = img["layers"][0]
# Extract the alpha channel
alpha_channel = np.array(transparent_image)[:, :, 3]
# Create a binary mask by thresholding the alpha channel
binary_mask = alpha_channel > 1
if invert:
print("Invert")
# Invert the binary mask so that the drawn shape is white and the rest is black
binary_mask = np.invert(binary_mask)
# Convert the binary mask to a 3-channel RGB mask
rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
# Convert the mask to uint8
rgb_mask = rgb_mask.astype(np.uint8) * 255
return img["background"], rgb_mask
def download_diffuser_repo(repo_name: str, model_type: str, revision: str = "main", token=True):
variant = None
if token is True and not os.environ.get("HF_TOKEN"):
token = None
if model_type == "SDXL":
info = model_info_data(
repo_name,
token=token,
revision=revision,
timeout=5.0,
)
filenames = {sibling.rfilename for sibling in info.siblings}
model_filenames, variant_filenames = variant_compatible_siblings(
filenames, variant="fp16"
)
if len(variant_filenames):
variant = "fp16"
cached_folder = DiffusionPipeline.download(
pretrained_model_name=repo_name,
force_download=False,
token=token,
revision=revision,
# mirror="https://hf-mirror.com",
variant=variant,
use_safetensors=True,
trust_remote_code=False,
timeout=5.0,
)
if isinstance(cached_folder, PosixPath):
cached_folder = cached_folder.as_posix()
# Task model
# from huggingface_hub import hf_hub_download
# hf_hub_download(
# task_model,
# filename="diffusion_pytorch_model.safetensors", # fix fp16 variant
# )
return cached_folder
def progress_step_bar(step, total):
# Calculate the percentage for the progress bar width
percentage = min(100, ((step / total) * 100))
return f"""
<div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
<div style="width: {percentage}%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
<div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 13px;">
{int(percentage)}%
</div>
</div>
"""
def html_template_message(msg):
return f"""
<div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
<div style="width: 0%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
<div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 14px; font-weight: bold; text-shadow: 1px 1px 2px black;">
{msg}
</div>
</div>
"""