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Update (g4f/models.py g4f/Provider/ docs/providers-and-models.md)

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14
docs/providers-and-models.md
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@ -49,20 +49,6 @@ This document provides an overview of various AI providers and models, including
|[liaobots.work](https://liaobots.work)|`g4f.Provider.Liaobots`|`gpt-3.5-turbo, gpt-4o-mini, gpt-4o, gpt-4-turbo, grok-2, grok-2-mini, claude-3-opus, claude-3-sonnet, claude-3-5-sonnet, claude-3-haiku, claude-2.1, gemini-flash, gemini-pro`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌| |[liaobots.work](https://liaobots.work)|`g4f.Provider.Liaobots`|`gpt-3.5-turbo, gpt-4o-mini, gpt-4o, gpt-4-turbo, grok-2, grok-2-mini, claude-3-opus, claude-3-sonnet, claude-3-5-sonnet, claude-3-haiku, claude-2.1, gemini-flash, gemini-pro`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[magickpen.com](https://magickpen.com)|`g4f.Provider.MagickPen`|`gpt-4o-mini`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌| |[magickpen.com](https://magickpen.com)|`g4f.Provider.MagickPen`|`gpt-4o-mini`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[meta.ai](https://www.meta.ai)|`g4f.Provider.MetaAI`|✔|✔|?|?|![Active](https://img.shields.io/badge/Active-brightgreen)|✔| |[meta.ai](https://www.meta.ai)|`g4f.Provider.MetaAI`|✔|✔|?|?|![Active](https://img.shields.io/badge/Active-brightgreen)|✔|
|[nexra.aryahcr.cc/bing](https://nexra.aryahcr.cc/documentation/bing/en)|`g4f.Provider.NexraBing`|✔|❌|❌|✔|![Disabled](https://img.shields.io/badge/Disabled-red)|❌|
|[nexra.aryahcr.cc/blackbox](https://nexra.aryahcr.cc/documentation/blackbox/en)|`g4f.Provider.NexraBlackbox`|`blackboxai` |❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/chatgpt](https://nexra.aryahcr.cc/documentation/chatgpt/en)|`g4f.Provider.NexraChatGPT`|`gpt-4, gpt-3.5-turbo, gpt-3, gpt-4o` |❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/dall-e](https://nexra.aryahcr.cc/documentation/dall-e/en)|`g4f.Provider.NexraDallE`|❌|`dalle`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/dall-e](https://nexra.aryahcr.cc/documentation/dall-e/en)|`g4f.Provider.NexraDallE2`|❌|`dalle-2`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/emi](https://nexra.aryahcr.cc/documentation/emi/en)|`g4f.Provider.NexraEmi`|❌|`emi`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/flux-pro](https://nexra.aryahcr.cc/documentation/flux-pro/en)|`g4f.Provider.NexraFluxPro`|❌|`flux-pro`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/gemini-pro](https://nexra.aryahcr.cc/documentation/gemini-pro/en)|`g4f.Provider.NexraGeminiPro`|`gemini-pro`|❌|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/midjourney](https://nexra.aryahcr.cc/documentation/midjourney/en)|`g4f.Provider.NexraMidjourney`|❌|`midjourney`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/prodia](https://nexra.aryahcr.cc/documentation/prodia/en)|`g4f.Provider.NexraProdiaAI`|❌|✔|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/qwen](https://nexra.aryahcr.cc/documentation/qwen/en)|`g4f.Provider.NexraQwen`|`qwen`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
|[nexra.aryahcr.cc/stable-diffusion](https://nexra.aryahcr.cc/documentation/stable-diffusion/en)|`g4f.Provider.NexraSD15`|❌|`sd-1.5`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌
|[nexra.aryahcr.cc/stable-diffusion](https://nexra.aryahcr.cc/documentation/stable-diffusion/en)|`g4f.Provider.NexraSDLora`|❌|`sdxl-lora`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌
|[nexra.aryahcr.cc/stable-diffusion](https://nexra.aryahcr.cc/documentation/stable-diffusion/en)|`g4f.Provider.NexraSDTurbo`|❌|`sdxl-turbo`|❌|❌|![Active](https://img.shields.io/badge/Active-brightgreen)|❌
|[platform.openai.com](https://platform.openai.com/)|`g4f.Provider.Openai`|✔|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔| |[platform.openai.com](https://platform.openai.com/)|`g4f.Provider.Openai`|✔|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔|
|[chatgpt.com](https://chatgpt.com/)|`g4f.Provider.OpenaiChat`|`gpt-4o, gpt-4o-mini, gpt-4`|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔| |[chatgpt.com](https://chatgpt.com/)|`g4f.Provider.OpenaiChat`|`gpt-4o, gpt-4o-mini, gpt-4`|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔|
|[www.perplexity.ai)](https://www.perplexity.ai)|`g4f.Provider.PerplexityAi`|✔|❌|❌|?|![Disabled](https://img.shields.io/badge/Disabled-red)|❌| |[www.perplexity.ai)](https://www.perplexity.ai)|`g4f.Provider.PerplexityAi`|✔|❌|❌|?|![Disabled](https://img.shields.io/badge/Disabled-red)|❌|

2
g4f/Provider/__init__.py
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@ -11,8 +11,6 @@ from .needs_auth import *
from .not_working import * from .not_working import *
from .local import * from .local import *
from .nexra import *
from .AI365VIP import AI365VIP from .AI365VIP import AI365VIP
from .AIChatFree import AIChatFree from .AIChatFree import AIChatFree
from .AIUncensored import AIUncensored from .AIUncensored import AIUncensored

93
g4f/Provider/nexra/NexraBing.py
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@ -1,93 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ..helper import format_prompt
class NexraBing(AbstractProvider, ProviderModelMixin):
label = "Nexra Bing"
url = "https://nexra.aryahcr.cc/documentation/bing/en"
api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
working = True
supports_stream = True
default_model = 'Balanced'
models = [default_model, 'Creative', 'Precise']
model_aliases = {
"gpt-4": "Balanced",
"gpt-4": "Creative",
"gpt-4": "Precise",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"messages": [
{
"role": "user",
"content": format_prompt(messages)
}
],
"conversation_style": model,
"markdown": markdown,
"stream": stream,
"model": "Bing"
}
response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=True)
return cls.process_response(response)
@classmethod
def process_response(cls, response):
if response.status_code != 200:
yield f"Error: {response.status_code}"
return
full_message = ""
for chunk in response.iter_content(chunk_size=None):
if chunk:
messages = chunk.decode('utf-8').split('\x1e')
for message in messages:
try:
json_data = json.loads(message)
if json_data.get('finish', False):
return
current_message = json_data.get('message', '')
if current_message:
new_content = current_message[len(full_message):]
if new_content:
yield new_content
full_message = current_message
except json.JSONDecodeError:
continue
if not full_message:
yield "No message received"

100
g4f/Provider/nexra/NexraBlackbox.py
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@ -1,100 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ..helper import format_prompt
class NexraBlackbox(AbstractProvider, ProviderModelMixin):
label = "Nexra Blackbox"
url = "https://nexra.aryahcr.cc/documentation/blackbox/en"
api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
working = True
supports_stream = True
default_model = "blackbox"
models = [default_model]
model_aliases = {"blackboxai": "blackbox",}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
stream: bool,
proxy: str = None,
markdown: bool = False,
websearch: bool = False,
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"messages": [
{
"role": "user",
"content": format_prompt(messages)
}
],
"websearch": websearch,
"stream": stream,
"markdown": markdown,
"model": model
}
response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
if stream:
return cls.process_streaming_response(response)
else:
return cls.process_non_streaming_response(response)
@classmethod
def process_non_streaming_response(cls, response):
if response.status_code == 200:
try:
full_response = ""
for line in response.iter_lines(decode_unicode=True):
if line:
data = json.loads(line)
if data.get('finish'):
break
message = data.get('message', '')
if message:
full_response = message
return full_response
except json.JSONDecodeError:
return "Error: Unable to decode JSON response"
else:
return f"Error: {response.status_code}"
@classmethod
def process_streaming_response(cls, response):
previous_message = ""
for line in response.iter_lines(decode_unicode=True):
if line:
try:
data = json.loads(line)
if data.get('finish'):
break
message = data.get('message', '')
if message and message != previous_message:
yield message[len(previous_message):]
previous_message = message
except json.JSONDecodeError:
pass

285
g4f/Provider/nexra/NexraChatGPT.py
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@ -1,285 +0,0 @@
from __future__ import annotations
import asyncio
import json
import requests
from typing import Any, Dict
from ...typing import AsyncResult, Messages
from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..helper import format_prompt
class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin):
label = "Nexra ChatGPT"
url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
api_endpoint_nexra_chatgpt = "https://nexra.aryahcr.cc/api/chat/gpt"
api_endpoint_nexra_chatgpt4o = "https://nexra.aryahcr.cc/api/chat/complements"
api_endpoint_nexra_chatgpt_v2 = "https://nexra.aryahcr.cc/api/chat/complements"
api_endpoint_nexra_gptweb = "https://nexra.aryahcr.cc/api/chat/gptweb"
working = True
supports_system_message = True
supports_message_history = True
supports_stream = True
default_model = 'gpt-3.5-turbo'
nexra_chatgpt = [
'gpt-4', 'gpt-4-0613', 'gpt-4-0314', 'gpt-4-32k-0314',
default_model, 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0301',
'text-davinci-003', 'text-davinci-002', 'code-davinci-002', 'gpt-3', 'text-curie-001', 'text-babbage-001', 'text-ada-001', 'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002'
]
nexra_chatgpt4o = ['gpt-4o']
nexra_chatgptv2 = ['chatgpt']
nexra_gptweb = ['gptweb']
models = nexra_chatgpt + nexra_chatgpt4o + nexra_chatgptv2 + nexra_gptweb
model_aliases = {
"gpt-4": "gpt-4-0613",
"gpt-4-32k": "gpt-4-32k-0314",
"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
"gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
"gpt-3": "text-davinci-003",
"text-davinci-002": "code-davinci-002",
"text-curie-001": "text-babbage-001",
"text-ada-001": "davinci",
"curie": "babbage",
"ada": "babbage-002",
"davinci-002": "davinci-002",
"chatgpt": "chatgpt",
"gptweb": "gptweb"
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> AsyncResult:
if model in cls.nexra_chatgpt:
async for chunk in cls._create_async_generator_nexra_chatgpt(model, messages, proxy, **kwargs):
yield chunk
elif model in cls.nexra_chatgpt4o:
async for chunk in cls._create_async_generator_nexra_chatgpt4o(model, messages, stream, proxy, markdown, **kwargs):
yield chunk
elif model in cls.nexra_chatgptv2:
async for chunk in cls._create_async_generator_nexra_chatgpt_v2(model, messages, stream, proxy, markdown, **kwargs):
yield chunk
elif model in cls.nexra_gptweb:
async for chunk in cls._create_async_generator_nexra_gptweb(model, messages, proxy, **kwargs):
yield chunk
@classmethod
async def _create_async_generator_nexra_chatgpt(
cls,
model: str,
messages: Messages,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json"
}
prompt = format_prompt(messages)
data = {
"messages": messages,
"prompt": prompt,
"model": model,
"markdown": markdown
}
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt, data, headers, proxy)
filtered_response = cls._filter_response(response)
for chunk in filtered_response:
yield chunk
except Exception as e:
print(f"Error during API request (nexra_chatgpt): {e}")
@classmethod
async def _create_async_generator_nexra_chatgpt4o(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json"
}
prompt = format_prompt(messages)
data = {
"messages": [
{
"role": "user",
"content": prompt
}
],
"stream": stream,
"markdown": markdown,
"model": model
}
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt4o, data, headers, proxy, stream)
if stream:
async for chunk in cls._process_streaming_response(response):
yield chunk
else:
for chunk in cls._process_non_streaming_response(response):
yield chunk
except Exception as e:
print(f"Error during API request (nexra_chatgpt4o): {e}")
@classmethod
async def _create_async_generator_nexra_chatgpt_v2(
cls,
model: str,
messages: Messages,
stream: bool = False,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json"
}
prompt = format_prompt(messages)
data = {
"messages": [
{
"role": "user",
"content": prompt
}
],
"stream": stream,
"markdown": markdown,
"model": model
}
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt_v2, data, headers, proxy, stream)
if stream:
async for chunk in cls._process_streaming_response(response):
yield chunk
else:
for chunk in cls._process_non_streaming_response(response):
yield chunk
except Exception as e:
print(f"Error during API request (nexra_chatgpt_v2): {e}")
@classmethod
async def _create_async_generator_nexra_gptweb(
cls,
model: str,
messages: Messages,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json"
}
prompt = format_prompt(messages)
data = {
"prompt": prompt,
"markdown": markdown,
}
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_gptweb, data, headers, proxy)
for chunk in response.iter_content(1024):
if chunk:
decoded_chunk = chunk.decode().lstrip('_')
try:
response_json = json.loads(decoded_chunk)
if response_json.get("status"):
yield response_json.get("gpt", "")
except json.JSONDecodeError:
continue
except Exception as e:
print(f"Error during API request (nexra_gptweb): {e}")
@staticmethod
def _sync_post_request(url: str, data: Dict[str, Any], headers: Dict[str, str], proxy: str = None, stream: bool = False) -> requests.Response:
proxies = {
"http": proxy,
"https": proxy,
} if proxy else None
try:
response = requests.post(url, json=data, headers=headers, proxies=proxies, stream=stream)
response.raise_for_status()
return response
except requests.RequestException as e:
print(f"Request failed: {e}")
raise
@staticmethod
def _process_non_streaming_response(response: requests.Response) -> str:
if response.status_code == 200:
try:
content = response.text.lstrip('')
data = json.loads(content)
return data.get('message', '')
except json.JSONDecodeError:
return "Error: Unable to decode JSON response"
else:
return f"Error: {response.status_code}"
@staticmethod
async def _process_streaming_response(response: requests.Response):
full_message = ""
for line in response.iter_lines(decode_unicode=True):
if line:
try:
line = line.lstrip('')
data = json.loads(line)
if data.get('finish'):
break
message = data.get('message', '')
if message:
yield message[len(full_message):]
full_message = message
except json.JSONDecodeError:
pass
@staticmethod
def _filter_response(response: requests.Response) -> str:
response_json = response.json()
return response_json.get("gpt", "")

63
g4f/Provider/nexra/NexraDallE.py
View file

@ -1,63 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraDallE(AbstractProvider, ProviderModelMixin):
label = "Nexra DALL-E"
url = "https://nexra.aryahcr.cc/documentation/dall-e/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = "dalle"
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

63
g4f/Provider/nexra/NexraDallE2.py
View file

@ -1,63 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraDallE2(AbstractProvider, ProviderModelMixin):
label = "Nexra DALL-E 2"
url = "https://nexra.aryahcr.cc/documentation/dall-e/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = "dalle2"
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

63
g4f/Provider/nexra/NexraEmi.py
View file

@ -1,63 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraEmi(AbstractProvider, ProviderModelMixin):
label = "Nexra Emi"
url = "https://nexra.aryahcr.cc/documentation/emi/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = "emi"
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

70
g4f/Provider/nexra/NexraFluxPro.py
View file

@ -1,70 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraFluxPro(AbstractProvider, ProviderModelMixin):
url = "https://nexra.aryahcr.cc/documentation/flux-pro/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = 'flux'
models = [default_model]
model_aliases = {
"flux-pro": "flux",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

86
g4f/Provider/nexra/NexraGeminiPro.py
View file

@ -1,86 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ..helper import format_prompt
class NexraGeminiPro(AbstractProvider, ProviderModelMixin):
label = "Nexra Gemini PRO"
url = "https://nexra.aryahcr.cc/documentation/gemini-pro/en"
api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
working = True
supports_stream = True
default_model = 'gemini-pro'
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
stream: bool,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"messages": [
{
"role": "user",
"content": format_prompt(messages)
}
],
"stream": stream,
"markdown": markdown,
"model": model
}
response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
if stream:
return cls.process_streaming_response(response)
else:
return cls.process_non_streaming_response(response)
@classmethod
def process_non_streaming_response(cls, response):
if response.status_code == 200:
try:
content = response.text.lstrip('')
data = json.loads(content)
return data.get('message', '')
except json.JSONDecodeError:
return "Error: Unable to decode JSON response"
else:
return f"Error: {response.status_code}"
@classmethod
def process_streaming_response(cls, response):
full_message = ""
for line in response.iter_lines(decode_unicode=True):
if line:
try:
line = line.lstrip('')
data = json.loads(line)
if data.get('finish'):
break
message = data.get('message', '')
if message:
yield message[len(full_message):]
full_message = message
except json.JSONDecodeError:
pass

63
g4f/Provider/nexra/NexraMidjourney.py
View file

@ -1,63 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraMidjourney(AbstractProvider, ProviderModelMixin):
label = "Nexra Midjourney"
url = "https://nexra.aryahcr.cc/documentation/midjourney/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = "midjourney"
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

151
g4f/Provider/nexra/NexraProdiaAI.py
View file

@ -1,151 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraProdiaAI(AbstractProvider, ProviderModelMixin):
label = "Nexra Prodia AI"
url = "https://nexra.aryahcr.cc/documentation/prodia/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = 'absolutereality_v181.safetensors [3d9d4d2b]'
models = [
'3Guofeng3_v34.safetensors [50f420de]',
'absolutereality_V16.safetensors [37db0fc3]',
default_model,
'amIReal_V41.safetensors [0a8a2e61]',
'analog-diffusion-1.0.ckpt [9ca13f02]',
'aniverse_v30.safetensors [579e6f85]',
'anythingv3_0-pruned.ckpt [2700c435]',
'anything-v4.5-pruned.ckpt [65745d25]',
'anythingV5_PrtRE.safetensors [893e49b9]',
'AOM3A3_orangemixs.safetensors [9600da17]',
'blazing_drive_v10g.safetensors [ca1c1eab]',
'breakdomain_I2428.safetensors [43cc7d2f]',
'breakdomain_M2150.safetensors [15f7afca]',
'cetusMix_Version35.safetensors [de2f2560]',
'childrensStories_v13D.safetensors [9dfaabcb]',
'childrensStories_v1SemiReal.safetensors [a1c56dbb]',
'childrensStories_v1ToonAnime.safetensors [2ec7b88b]',
'Counterfeit_v30.safetensors [9e2a8f19]',
'cuteyukimixAdorable_midchapter3.safetensors [04bdffe6]',
'cyberrealistic_v33.safetensors [82b0d085]',
'dalcefo_v4.safetensors [425952fe]',
'deliberate_v2.safetensors [10ec4b29]',
'deliberate_v3.safetensors [afd9d2d4]',
'dreamlike-anime-1.0.safetensors [4520e090]',
'dreamlike-diffusion-1.0.safetensors [5c9fd6e0]',
'dreamlike-photoreal-2.0.safetensors [fdcf65e7]',
'dreamshaper_6BakedVae.safetensors [114c8abb]',
'dreamshaper_7.safetensors [5cf5ae06]',
'dreamshaper_8.safetensors [9d40847d]',
'edgeOfRealism_eorV20.safetensors [3ed5de15]',
'EimisAnimeDiffusion_V1.ckpt [4f828a15]',
'elldreths-vivid-mix.safetensors [342d9d26]',
'epicphotogasm_xPlusPlus.safetensors [1a8f6d35]',
'epicrealism_naturalSinRC1VAE.safetensors [90a4c676]',
'epicrealism_pureEvolutionV3.safetensors [42c8440c]',
'ICantBelieveItsNotPhotography_seco.safetensors [4e7a3dfd]',
'indigoFurryMix_v75Hybrid.safetensors [91208cbb]',
'juggernaut_aftermath.safetensors [5e20c455]',
'lofi_v4.safetensors [ccc204d6]',
'lyriel_v16.safetensors [68fceea2]',
'majicmixRealistic_v4.safetensors [29d0de58]',
'mechamix_v10.safetensors [ee685731]',
'meinamix_meinaV9.safetensors [2ec66ab0]',
'meinamix_meinaV11.safetensors [b56ce717]',
'neverendingDream_v122.safetensors [f964ceeb]',
'openjourney_V4.ckpt [ca2f377f]',
'pastelMixStylizedAnime_pruned_fp16.safetensors [793a26e8]',
'portraitplus_V1.0.safetensors [1400e684]',
'protogenx34.safetensors [5896f8d5]',
'Realistic_Vision_V1.4-pruned-fp16.safetensors [8d21810b]',
'Realistic_Vision_V2.0.safetensors [79587710]',
'Realistic_Vision_V4.0.safetensors [29a7afaa]',
'Realistic_Vision_V5.0.safetensors [614d1063]',
'Realistic_Vision_V5.1.safetensors [a0f13c83]',
'redshift_diffusion-V10.safetensors [1400e684]',
'revAnimated_v122.safetensors [3f4fefd9]',
'rundiffusionFX25D_v10.safetensors [cd12b0ee]',
'rundiffusionFX_v10.safetensors [cd4e694d]',
'sdv1_4.ckpt [7460a6fa]',
'v1-5-pruned-emaonly.safetensors [d7049739]',
'v1-5-inpainting.safetensors [21c7ab71]',
'shoninsBeautiful_v10.safetensors [25d8c546]',
'theallys-mix-ii-churned.safetensors [5d9225a4]',
'timeless-1.0.ckpt [7c4971d4]',
'toonyou_beta6.safetensors [980f6b15]',
]
model_aliases = {}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
steps: str = 25, # Min: 1, Max: 30
cfg_scale: str = 7, # Min: 0, Max: 20
sampler: str = "DPM++ 2M Karras", # Select from these: "Euler","Euler a","Heun","DPM++ 2M Karras","DPM++ SDE Karras","DDIM"
negative_prompt: str = "", # Indicates what the AI should not do
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": "prodia",
"response": response,
"data": {
"model": model,
"steps": steps,
"cfg_scale": cfg_scale,
"sampler": sampler,
"negative_prompt": negative_prompt
}
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_') # Remove leading underscores
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

86
g4f/Provider/nexra/NexraQwen.py
View file

@ -1,86 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ..helper import format_prompt
class NexraQwen(AbstractProvider, ProviderModelMixin):
label = "Nexra Qwen"
url = "https://nexra.aryahcr.cc/documentation/qwen/en"
api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
working = True
supports_stream = True
default_model = 'qwen'
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
stream: bool,
proxy: str = None,
markdown: bool = False,
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"messages": [
{
"role": "user",
"content": format_prompt(messages)
}
],
"stream": stream,
"markdown": markdown,
"model": model
}
response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
if stream:
return cls.process_streaming_response(response)
else:
return cls.process_non_streaming_response(response)
@classmethod
def process_non_streaming_response(cls, response):
if response.status_code == 200:
try:
content = response.text.lstrip('')
data = json.loads(content)
return data.get('message', '')
except json.JSONDecodeError:
return "Error: Unable to decode JSON response"
else:
return f"Error: {response.status_code}"
@classmethod
def process_streaming_response(cls, response):
full_message = ""
for line in response.iter_lines(decode_unicode=True):
if line:
try:
line = line.lstrip('')
data = json.loads(line)
if data.get('finish'):
break
message = data.get('message', '')
if message is not None and message != full_message:
yield message[len(full_message):]
full_message = message
except json.JSONDecodeError:
pass

72
g4f/Provider/nexra/NexraSD15.py
View file

@ -1,72 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraSD15(AbstractProvider, ProviderModelMixin):
label = "Nexra Stable Diffusion 1.5"
url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = 'stablediffusion-1.5'
models = [default_model]
model_aliases = {
"sd-1.5": "stablediffusion-1.5",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

69
g4f/Provider/nexra/NexraSDLora.py
View file

@ -1,69 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraSDLora(AbstractProvider, 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
def create_completion(
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
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response,
"data": {
"guidance": guidance,
"steps": steps
}
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_')
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

69
g4f/Provider/nexra/NexraSDTurbo.py
View file

@ -1,69 +0,0 @@
from __future__ import annotations
import json
import requests
from ...typing import CreateResult, Messages
from ..base_provider import ProviderModelMixin, AbstractProvider
from ...image import ImageResponse
class NexraSDTurbo(AbstractProvider, ProviderModelMixin):
label = "Nexra Stable Diffusion Turbo"
url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
default_model = "sdxl-turbo"
models = [default_model]
@classmethod
def get_model(cls, model: str) -> str:
return cls.default_model
@classmethod
def create_completion(
cls,
model: str,
messages: Messages,
proxy: str = None,
response: str = "url", # base64 or url
strength: str = 0.7, # Min: 0, Max: 1
steps: str = 2, # Min: 1, Max: 10
**kwargs
) -> CreateResult:
model = cls.get_model(model)
headers = {
'Content-Type': 'application/json'
}
data = {
"prompt": messages[-1]["content"],
"model": model,
"response": response,
"data": {
"strength": strength,
"steps": steps
}
}
response = requests.post(cls.api_endpoint, headers=headers, json=data)
result = cls.process_response(response)
yield result
@classmethod
def process_response(cls, response):
if response.status_code == 200:
try:
content = response.text.strip()
content = content.lstrip('_') # Remove the leading underscore
data = json.loads(content)
if data.get('status') and data.get('images'):
image_url = data['images'][0]
return ImageResponse(images=[image_url], alt="Generated Image")
else:
return "Error: No image URL found in the response"
except json.JSONDecodeError as e:
return f"Error: Unable to decode JSON response. Details: {str(e)}"
else:
return f"Error: {response.status_code}, Response: {response.text}"

14
g4f/Provider/nexra/__init__.py
View file

@ -1,14 +0,0 @@
from .NexraBing import NexraBing
from .NexraBlackbox import NexraBlackbox
from .NexraChatGPT import NexraChatGPT
from .NexraDallE import NexraDallE
from .NexraDallE2 import NexraDallE2
from .NexraEmi import NexraEmi
from .NexraFluxPro import NexraFluxPro
from .NexraGeminiPro import NexraGeminiPro
from .NexraMidjourney import NexraMidjourney
from .NexraProdiaAI import NexraProdiaAI
from .NexraQwen import NexraQwen
from .NexraSD15 import NexraSD15
from .NexraSDLora import NexraSDLora
from .NexraSDTurbo import NexraSDTurbo

112
g4f/models.py
View file

@ -32,19 +32,6 @@ from .Provider import (
Liaobots, Liaobots,
MagickPen, MagickPen,
MetaAI, MetaAI,
NexraBing,
NexraBlackbox,
NexraChatGPT,
NexraDallE,
NexraDallE2,
NexraEmi,
NexraFluxPro,
NexraGeminiPro,
NexraMidjourney,
NexraQwen,
NexraSD15,
NexraSDLora,
NexraSDTurbo,
OpenaiChat, OpenaiChat,
PerplexityLabs, PerplexityLabs,
Pi, Pi,
@ -107,25 +94,18 @@ default = Model(
############ ############
### OpenAI ### ### OpenAI ###
# gpt-3
gpt_3 = Model(
name = 'gpt-3',
base_provider = 'OpenAI',
best_provider = NexraChatGPT
)
# gpt-3.5 # gpt-3.5
gpt_35_turbo = Model( gpt_35_turbo = Model(
name = 'gpt-3.5-turbo', name = 'gpt-3.5-turbo',
base_provider = 'OpenAI', base_provider = 'OpenAI',
best_provider = IterListProvider([DarkAI, NexraChatGPT, Airforce, Liaobots, Allyfy]) best_provider = IterListProvider([DarkAI, Airforce, Liaobots, Allyfy])
) )
# gpt-4 # gpt-4
gpt_4o = Model( gpt_4o = Model(
name = 'gpt-4o', name = 'gpt-4o',
base_provider = 'OpenAI', base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, ChatGptEs, DarkAI, NexraChatGPT, Airforce, ChatGpt, Liaobots, OpenaiChat]) best_provider = IterListProvider([Blackbox, ChatGptEs, DarkAI, Airforce, ChatGpt, Liaobots, OpenaiChat])
) )
gpt_4o_mini = Model( gpt_4o_mini = Model(
@ -143,7 +123,7 @@ gpt_4_turbo = Model(
gpt_4 = Model( gpt_4 = Model(
name = 'gpt-4', name = 'gpt-4',
base_provider = 'OpenAI', base_provider = 'OpenAI',
best_provider = IterListProvider([Chatgpt4Online, Ai4Chat, NexraBing, NexraChatGPT, ChatGpt, Airforce, Bing, OpenaiChat, gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider]) best_provider = IterListProvider([Chatgpt4Online, Ai4Chat, ChatGpt, Airforce, Bing, OpenaiChat, gpt_4_turbo.best_provider, gpt_4o.best_provider, gpt_4o_mini.best_provider])
) )
# o1 # o1
@ -342,7 +322,7 @@ phi_3_5_mini = Model(
gemini_pro = Model( gemini_pro = Model(
name = 'gemini-pro', name = 'gemini-pro',
base_provider = 'Google DeepMind', base_provider = 'Google DeepMind',
best_provider = IterListProvider([GeminiPro, Blackbox, AIChatFree, FreeGpt, NexraGeminiPro, Airforce, Liaobots]) best_provider = IterListProvider([GeminiPro, Blackbox, AIChatFree, FreeGpt, Airforce, Liaobots])
) )
gemini_flash = Model( gemini_flash = Model(
@ -430,7 +410,7 @@ reka_core = Model(
blackboxai = Model( blackboxai = Model(
name = 'blackboxai', name = 'blackboxai',
base_provider = 'Blackbox AI', base_provider = 'Blackbox AI',
best_provider = IterListProvider([Blackbox, NexraBlackbox]) best_provider = Blackbox
) )
blackboxai_pro = Model( blackboxai_pro = Model(
@ -501,12 +481,6 @@ qwen_2_5_72b = Model(
best_provider = Airforce best_provider = Airforce
) )
qwen = Model(
name = 'qwen',
base_provider = 'Qwen',
best_provider = NexraQwen
)
### Upstage ### ### Upstage ###
solar_10_7b = Model( solar_10_7b = Model(
name = 'solar-10-7b', name = 'solar-10-7b',
@ -683,20 +657,6 @@ zephyr_7b = Model(
############# #############
### Stability AI ### ### Stability AI ###
sdxl_turbo = Model(
name = 'sdxl-turbo',
base_provider = 'Stability AI',
best_provider = NexraSDTurbo
)
sdxl_lora = Model(
name = 'sdxl-lora',
base_provider = 'Stability AI',
best_provider = NexraSDLora
)
sdxl = Model( sdxl = Model(
name = 'sdxl', name = 'sdxl',
base_provider = 'Stability AI', base_provider = 'Stability AI',
@ -704,13 +664,6 @@ sdxl = Model(
) )
sd_1_5 = Model(
name = 'sd-1.5',
base_provider = 'Stability AI',
best_provider = IterListProvider([NexraSD15])
)
sd_3 = Model( sd_3 = Model(
name = 'sd-3', name = 'sd-3',
base_provider = 'Stability AI', base_provider = 'Stability AI',
@ -735,13 +688,6 @@ flux = Model(
) )
flux_pro = Model(
name = 'flux-pro',
base_provider = 'Flux AI',
best_provider = IterListProvider([NexraFluxPro])
)
flux_realism = Model( flux_realism = Model(
name = 'flux-realism', name = 'flux-realism',
base_provider = 'Flux AI', base_provider = 'Flux AI',
@ -792,37 +738,7 @@ flux_schnell = Model(
) )
### OpenAI ###
dalle_2 = Model(
name = 'dalle-2',
base_provider = 'OpenAI',
best_provider = NexraDallE2
)
dalle = Model(
name = 'dalle',
base_provider = 'OpenAI',
best_provider = NexraDallE
)
### Midjourney ###
midjourney = Model(
name = 'midjourney',
base_provider = 'Midjourney',
best_provider = NexraMidjourney
)
### Other ### ### Other ###
emi = Model(
name = 'emi',
base_provider = '',
best_provider = NexraEmi
)
any_dark = Model( any_dark = Model(
name = 'any-dark', name = 'any-dark',
base_provider = '', base_provider = '',
@ -844,9 +760,6 @@ class ModelUtils:
############ ############
### OpenAI ### ### OpenAI ###
# gpt-3
'gpt-3': gpt_3,
# gpt-3.5 # gpt-3.5
'gpt-3.5-turbo': gpt_35_turbo, 'gpt-3.5-turbo': gpt_35_turbo,
@ -959,8 +872,6 @@ class ModelUtils:
### Qwen ### ### Qwen ###
'qwen': qwen,
# qwen 1.5 # qwen 1.5
'qwen-1.5-5b': qwen_1_5_5b, 'qwen-1.5-5b': qwen_1_5_5b,
'qwen-1.5-7b': qwen_1_5_7b, 'qwen-1.5-7b': qwen_1_5_7b,
@ -1063,9 +974,6 @@ class ModelUtils:
### Stability AI ### ### Stability AI ###
'sdxl': sdxl, 'sdxl': sdxl,
'sdxl-lora': sdxl_lora,
'sdxl-turbo': sdxl_turbo,
'sd-1.5': sd_1_5,
'sd-3': sd_3, 'sd-3': sd_3,
@ -1075,7 +983,6 @@ class ModelUtils:
### Flux AI ### ### Flux AI ###
'flux': flux, 'flux': flux,
'flux-pro': flux_pro,
'flux-realism': flux_realism, 'flux-realism': flux_realism,
'flux-anime': flux_anime, 'flux-anime': flux_anime,
'flux-3d': flux_3d, 'flux-3d': flux_3d,
@ -1085,16 +992,7 @@ class ModelUtils:
'flux-schnell': flux_schnell, 'flux-schnell': flux_schnell,
### OpenAI ###
'dalle': dalle,
'dalle-2': dalle_2,
### Midjourney ###
'midjourney': midjourney,
### Other ### ### Other ###
'emi': emi,
'any-dark': any_dark, 'any-dark': any_dark,
} }