gpt4free/g4f/Provider/nexra/NexraSDLora.py

69 lines
2.4 KiB
Python

from __future__ import annotations
from aiohttp import ClientSession
import json
from ...typing import AsyncResult, Messages
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ...image import ImageResponse
class NexraSDLora(AsyncGeneratorProvider, ProviderModelMixin):
label = "Nexra Stable Diffusion Lora"
url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = 'sdxl-lora'
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
guidance: str = 0.3, # Min: 0, Max: 5
steps: str = 2, # Min: 2, Max: 10
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json"
}
async with ClientSession(headers=headers) as session:
prompt = messages[0]['content']
data = {
"prompt": prompt,
"model": model,
"response": response,
"data": {
"guidance": guidance,
"steps": steps
}
}
async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
text_data = await response.text()
if response.status == 200:
try:
json_start = text_data.find('{')
json_data = text_data[json_start:]
data = json.loads(json_data)
if 'images' in data and len(data['images']) > 0:
image_url = data['images'][-1]
yield ImageResponse(image_url, prompt)
else:
yield ImageResponse("No images found in the response.", prompt)
except json.JSONDecodeError:
yield ImageResponse("Failed to parse JSON. Response might not be in JSON format.", prompt)
else:
yield ImageResponse(f"Request failed with status: {response.status}", prompt)