import requests
import os
import gradio as gr
from huggingface_hub import update_repo_visibility, whoami, upload_folder, create_repo, upload_file, update_repo_visibility
from slugify import slugify
import gradio as gr
import re
import uuid
from typing import Optional
import json
from bs4 import BeautifulSoup
TRUSTED_UPLOADERS = ["KappaNeuro", "CiroN2022", "multimodalart", "Norod78", "joachimsallstrom", "blink7630", "e-n-v-y", "DoctorDiffusion", "RalFinger", "artificialguybr"]
def get_json_data(url):
url_split = url.split('/')
api_url = f"https://civitai.com/api/v1/models/{url_split[4]}"
try:
response = requests.get(api_url)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f"Error fetching JSON data: {e}")
return None
def check_nsfw(json_data, profile):
if json_data["nsfw"]:
return False
print(profile)
if(profile.username in TRUSTED_UPLOADERS):
return True
for model_version in json_data["modelVersions"]:
for image in model_version["images"]:
if image["nsfwLevel"] > 5:
return False
return True
def get_prompts_from_image(image_id):
url = f'https://civitai.com/images/{image_id}'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
content = soup.find_all(class_='mantine-Code-root mantine-Code-block mantine-2v44jn')
if(content):
if(len(content) > 1):
return content[0].text, content[1].text
else:
return content[0].text, ""
else:
return "", ""
def extract_info(json_data):
if json_data["type"] == "LORA":
for model_version in json_data["modelVersions"]:
if model_version["baseModel"] in ["SDXL 1.0", "SDXL 0.9", "SD 1.5", "SD 1.4", "SD 2.1", "SD 2.0", "SD 2.0 768", "SD 2.1 768"]:
for file in model_version["files"]:
print(file)
if "primary" in file:
# Start by adding the primary file to the list
urls_to_download = [{"url": file["downloadUrl"], "filename": file["name"], "type": "weightName"}]
# Then append all image URLs to the list
for image in model_version["images"]:
image_id = image["url"].split("/")[-1].split(".")[0]
prompt, negative_prompt = get_prompts_from_image(image_id)
if image["nsfwLevel"] > 5:
pass #ugly before checking the actual logic
else:
urls_to_download.append({
"url": image["url"],
"filename": os.path.basename(image["url"]),
"type": "imageName",
"prompt": prompt, #if "meta" in image and "prompt" in image["meta"] else ""
"negative_prompt": negative_prompt
})
model_mapping = {
"SDXL 1.0": "stabilityai/stable-diffusion-xl-base-1.0",
"SDXL 0.9": "stabilityai/stable-diffusion-xl-base-1.0",
"SD 1.5": "runwayml/stable-diffusion-v1-5",
"SD 1.4": "CompVis/stable-diffusion-v1-4",
"SD 2.1": "stabilityai/stable-diffusion-2-1-base",
"SD 2.0": "stabilityai/stable-diffusion-2-base",
"SD 2.1 768": "stabilityai/stable-diffusion-2-1",
"SD 2.0 768": "stabilityai/stable-diffusion-2"
}
base_model = model_mapping[model_version["baseModel"]]
info = {
"urls_to_download": urls_to_download,
"id": model_version["id"],
"baseModel": base_model,
"modelId": model_version.get("modelId", ""),
"name": json_data["name"],
"description": json_data["description"],
"trainedWords": model_version["trainedWords"] if "trainedWords" in model_version else [],
"creator": json_data["creator"]["username"],
"tags": json_data["tags"],
"allowNoCredit": json_data["allowNoCredit"],
"allowCommercialUse": json_data["allowCommercialUse"],
"allowDerivatives": json_data["allowDerivatives"],
"allowDifferentLicense": json_data["allowDifferentLicense"]
}
return info
return None
def download_files(info, folder="."):
downloaded_files = {
"imageName": [],
"imagePrompt": [],
"imageNegativePrompt": [],
"weightName": []
}
for item in info["urls_to_download"]:
download_file(item["url"], item["filename"], folder)
downloaded_files[item["type"]].append(item["filename"])
if(item["type"] == "imageName"):
prompt_clean = re.sub(r'<.*?>', '', item["prompt"])
negative_prompt_clean = re.sub(r'<.*?>', '', item["negative_prompt"])
downloaded_files["imagePrompt"].append(prompt_clean)
downloaded_files["imageNegativePrompt"].append(negative_prompt_clean)
return downloaded_files
def download_file(url, filename, folder="."):
headers = {}
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
except requests.exceptions.HTTPError as e:
print(e)
if response.status_code == 401:
headers['Authorization'] = f'Bearer {os.environ["CIVITAI_API"]}'
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
except requests.exceptions.RequestException as e:
raise gr.Error(f"Error downloading file: {e}")
else:
raise gr.Error(f"Error downloading file: {e}")
except requests.exceptions.RequestException as e:
raise gr.Error(f"Error downloading file: {e}")
with open(f"{folder}/{filename}", 'wb') as f:
f.write(response.content)
def process_url(url, profile, do_download=True, folder="."):
json_data = get_json_data(url)
if json_data:
if check_nsfw(json_data, profile):
info = extract_info(json_data)
if info:
if(do_download):
downloaded_files = download_files(info, folder)
else:
downloaded_files = []
return info, downloaded_files
else:
raise gr.Error("Only SDXL LoRAs are supported for now")
else:
raise gr.Error("This model has content tagged as unsafe by CivitAI")
else:
raise gr.Error("Something went wrong in fetching CivitAI API")
def create_readme(info, downloaded_files, user_repo_id, link_civit=False, is_author=True, folder="."):
readme_content = ""
original_url = f"https://civitai.com/models/{info['modelId']}"
link_civit_disclaimer = f'([CivitAI]({original_url}))'
non_author_disclaimer = f'This model was originally uploaded on [CivitAI]({original_url}), by [{info["creator"]}](https://civitai.com/user/{info["creator"]}/models). The information below was provided by the author on CivitAI:'
default_tags = ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora", "migrated"]
civit_tags = [t.replace(":", "") for t in info["tags"] if t not in default_tags]
tags = default_tags + civit_tags
unpacked_tags = "\n- ".join(tags)
trained_words = info['trainedWords'] if 'trainedWords' in info and info['trainedWords'] else []
formatted_words = ', '.join(f'`{word}`' for word in trained_words)
if formatted_words:
trigger_words_section = f"""## Trigger words
You should use {formatted_words} to trigger the image generation.
"""
else:
trigger_words_section = ""
widget_content = ""
for index, (prompt, negative_prompt, image) in enumerate(zip(downloaded_files["imagePrompt"], downloaded_files["imageNegativePrompt"], downloaded_files["imageName"])):
escaped_prompt = prompt.replace("'", "''")
negative_prompt_content = f"""parameters:
negative_prompt: {negative_prompt}
""" if negative_prompt else ""
widget_content += f"""- text: '{escaped_prompt if escaped_prompt else ' ' }'
{negative_prompt_content}
output:
url: >-
{image}
"""
content = f"""---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit={info["allowNoCredit"]}&allowCommercialUse={info["allowCommercialUse"][0] if info["allowCommercialUse"] else 1}&allowDerivatives={info["allowDerivatives"]}&allowDifferentLicense={info["allowDifferentLicense"]}
tags:
- {unpacked_tags}
base_model: {info["baseModel"]}
instance_prompt: {info['trainedWords'][0] if 'trainedWords' in info and len(info['trainedWords']) > 0 else ''}
widget:
{widget_content}
---
# {info["name"]}
{non_author_disclaimer if not is_author else ''}
{link_civit_disclaimer if link_civit else ''}
## Model description
{info["description"]}
{trigger_words_section}
## Download model
Weights for this model are available in Safetensors format.
[Download](/{user_repo_id}/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('{info["baseModel"]}', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('{user_repo_id}', weight_name='{downloaded_files["weightName"][0]}')
image = pipeline('{prompt if prompt else (formatted_words if formatted_words else 'Your custom prompt')}').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
"""
#for index, (image, prompt) in enumerate(zip(downloaded_files["imageName"], downloaded_files["imagePrompt"])):
# if index == 1:
# content += f"## Image examples for the model:\n\n> {prompt}\n"
# elif index > 1:
# content += f"\n\n> {prompt}\n"
readme_content += content + "\n"
with open(f"{folder}/README.md", "w") as file:
file.write(readme_content)
def get_creator(username):
url = f"https://civitai.com/api/trpc/user.getCreator?input=%7B%22json%22%3A%7B%22username%22%3A%22{username}%22%2C%22authed%22%3Atrue%7D%7D"
headers = {
"authority": "civitai.com",
"accept": "*/*",
"accept-language": "en-BR,en;q=0.9,pt-BR;q=0.8,pt;q=0.7,es-ES;q=0.6,es;q=0.5,de-LI;q=0.4,de;q=0.3,en-GB;q=0.2,en-US;q=0.1,sk;q=0.1",
"content-type": "application/json",
"cookie": f'{os.environ["COOKIE_INFO"]}',
"if-modified-since": "Tue, 22 Aug 2023 07:18:52 GMT",
"referer": f"https://civitai.com/user/{username}/models",
"sec-ch-ua": "\"Not.A/Brand\";v=\"8\", \"Chromium\";v=\"114\", \"Google Chrome\";v=\"114\"",
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": "macOS",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-origin",
"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36"
}
response = requests.get(url, headers=headers)
return response.json()
def extract_huggingface_username(username):
data = get_creator(username)
links = data.get('result', {}).get('data', {}).get('json', {}).get('links', [])
for link in links:
url = link.get('url', '')
if url.startswith('https://huggingface.co/') or url.startswith('https://www.huggingface.co/'):
username = url.split('/')[-1]
return username
return None
def check_civit_link(profile: Optional[gr.OAuthProfile], url):
info, _ = process_url(url, profile, do_download=False)
hf_username = extract_huggingface_username(info['creator'])
attributes_methods = dir(profile)
if(profile.username == "multimodalart"):
return '', gr.update(interactive=True), gr.update(visible=False), gr.update(visible=True)
if(not hf_username):
no_username_text = f'If you are {info["creator"]} on CivitAI, hi! Your CivitAI profile seems to not have information about your Hugging Face account. Please visit https://civitai.com/user/account and include your ๐ค username there, here\'s mine:
(if you are not {info["creator"]}, you cannot submit their model at this time)'
return no_username_text, gr.update(interactive=False), gr.update(visible=True), gr.update(visible=False)
if(profile.username != hf_username):
unmatched_username_text = 'Oops, the Hugging Face account in your CivitAI profile seems to be different than the one your are using here. Please visit https://civitai.com/user/account and update it there to match your Hugging Face account
'
return unmatched_username_text, gr.update(interactive=False), gr.update(visible=True), gr.update(visible=False)
else:
return '', gr.update(interactive=True), gr.update(visible=False), gr.update(visible=True)
def swap_fill(profile: Optional[gr.OAuthProfile]):
if profile is None:
return gr.update(visible=True), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True)
def show_output():
return gr.update(visible=True)
def list_civit_models(username):
url = f"https://civitai.com/api/v1/models?username={username}&limit=100"
json_models_list = []
while url:
response = requests.get(url)
data = response.json()
# Add current page items to the list
json_models_list.extend(data.get('items', []))
# Check if there is a nextPage URL in the metadata
metadata = data.get('metadata', {})
url = metadata.get('nextPage', None)
urls = ""
for model in json_models_list:
urls += f'https://civitai.com/models/{model["id"]}/{slugify(model["name"])}\n'
return urls
def upload_civit_to_hf(profile: Optional[gr.OAuthProfile], oauth_token: gr.OAuthToken, url, link_civit=False):
if not profile.name:
return gr.Error("Are you sure you are logged in?")
folder = str(uuid.uuid4())
os.makedirs(folder, exist_ok=False)
gr.Info(f"Starting download of model {url}")
info, downloaded_files = process_url(url, profile, folder=folder)
username = {profile.username}
slug_name = slugify(info["name"])
user_repo_id = f"{profile.username}/{slug_name}"
create_readme(info, downloaded_files, user_repo_id, link_civit, folder=folder)
try:
create_repo(repo_id=user_repo_id, private=True, exist_ok=True, token=oauth_token.token)
gr.Info(f"Starting to upload repo {user_repo_id} to Hugging Face...")
upload_folder(
folder_path=folder,
repo_id=user_repo_id,
repo_type="model",
token=oauth_token.token
)
update_repo_visibility(repo_id=user_repo_id, private=False, token=oauth_token.token)
gr.Info(f"Model uploaded!")
except Exception as e:
print(e)
raise gr.Error("Your Hugging Face Token expired. Log out and in again to upload your models.")
return f'''# Model uploaded to ๐ค!
## Access it here [{user_repo_id}](https://huggingface.co/{user_repo_id}) '''
def bulk_upload(profile: Optional[gr.OAuthProfile], oauth_token: gr.OAuthToken, urls, link_civit=False):
urls = urls.split("\n")
print(urls)
upload_results = ""
for url in urls:
if(url):
try:
upload_result = upload_civit_to_hf(profile, oauth_token, url, link_civit)
upload_results += upload_result+"\n"
except Exception as e:
gr.Warning(f"Error uploading the model {url}")
return upload_results
css = '''
#login {
width: 100% !important;
margin: 0 auto;
}
#disabled_upload{
opacity: 0.5;
pointer-events:none;
}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown('''# Upload your CivitAI LoRA to Hugging Face ๐ค
By uploading your LoRAs to Hugging Face you get diffusers compatibility, a free GPU-based Inference Widget, and possibility to submit your model to the [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer) โจ
''')
gr.LoginButton(elem_id="login")
with gr.Column(elem_id="disabled_upload") as disabled_area:
with gr.Row():
submit_source_civit = gr.Textbox(
placeholder="https://civitai.com/models/144684/pixelartredmond-pixel-art-loras-for-sd-xl",
label="CivitAI model URL",
info="URL of the CivitAI LoRA",
)
submit_button_civit = gr.Button("Upload model to Hugging Face and submit", interactive=False)
with gr.Column(visible=False) as enabled_area:
with gr.Column():
submit_source_civit = gr.Textbox(
placeholder="https://civitai.com/models/144684/pixelartredmond-pixel-art-loras-for-sd-xl",
label="CivitAI model URL",
info="URL of the CivitAI LoRA",
)
with gr.Accordion("Bulk upload (bring in multiple LoRAs)", open=False):
civit_username_to_bulk = gr.Textbox(label="CivitAI username (optional)", info="Type your CivitAI username here to automagically fill the bulk models URLs list below (optional, you can paste links down here directly)")
submit_bulk_civit = gr.Textbox(
label="CivitAI bulk models URLs",
info="Add one URL per line",
lines=6,
)
link_civit = gr.Checkbox(label="Link back to CivitAI?", value=False)
bulk_button = gr.Button("Bulk upload")
instructions = gr.HTML("")
try_again_button = gr.Button("I have added my HF profile to my account (it may take 1 minute to refresh)", visible=False)
submit_button_civit = gr.Button("Upload model to Hugging Face", interactive=False)
output = gr.Markdown(label="Output progress", visible=False)
demo.load(fn=swap_fill, outputs=[disabled_area, enabled_area], queue=False)
submit_source_civit.change(fn=check_civit_link, inputs=[submit_source_civit], outputs=[instructions, submit_button_civit, try_again_button, submit_button_civit])
civit_username_to_bulk.change(fn=list_civit_models, inputs=[civit_username_to_bulk], outputs=[submit_bulk_civit])
try_again_button.click(fn=check_civit_link, inputs=[submit_source_civit], outputs=[instructions, submit_button_civit, try_again_button, submit_button_civit])
submit_button_civit.click(fn=show_output, inputs=[], outputs=[output]).then(fn=upload_civit_to_hf, inputs=[submit_source_civit, link_civit], outputs=[output])
bulk_button.click(fn=show_output, inputs=[], outputs=[output]).then(fn=bulk_upload, inputs=[submit_bulk_civit, link_civit], outputs=[output])
#gr.LogoutButton(elem_id="logout")
demo.queue(default_concurrency_limit=50)
demo.launch()