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import os
import random
import numpy as np
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
from gradio_client import Client, handle_file
from PIL import Image
# Constants
MAX_SEED = np.iinfo(np.int32).max
HF_TOKEN = os.getenv('HF_TOKEN') # Set the environment variable for HF_TOKEN
HF_TOKEN_UPSCALER = os.getenv('HF_TOKEN') # Set the environment variable for HF_TOKEN_UPSCALER
class Lorify:
def __init__(self, hf_token=None, hf_token_upscaler=None):
# Optionally load tokens from environment if not passed
self.hf_token = hf_token or HF_TOKEN
self.hf_token_upscaler = hf_token_upscaler or HF_TOKEN_UPSCALER
# Initialize clients
self.qwen_client = Client("K00B404/HugChatWrap", hf_token=self.hf_token)
self.client = AsyncInferenceClient()
# List of available LoRAs (replace with your LoRA repo names or paths)
self.loaded_loras = []
self.loras = [
"Shakker-Labs/FLUX.1-dev-LoRA-add-details",
"XLabs-AI/flux-RealismLora",
"enhanceaiteam/Flux-uncensored"
]
self.loaded_loras.extend(self.loras)
# Enable or disable LoRA
def enable_lora(self, lora_add, basemodel):
return basemodel if not lora_add else lora_add
# Generate image function
async def generate_image(self, prompt, model, lora_word, width, height, scales, steps, seed):
try:
if seed == -1:
seed = random.randint(0, MAX_SEED)
seed = int(seed)
# Translate prompt
text = str(Translator().translate(prompt, 'English')) + "," + lora_word
# Generate image
image = await self.client.text_to_image(
prompt=text,
height=height,
width=width,
guidance_scale=scales,
num_inference_steps=steps,
model=model
)
return image, seed
except Exception as e:
print(f"Error generating image: {e}")
return None, None
# Upscale image function
def upscale_image(self, prompt, img_path, upscale_factor):
try:
# Initialize the upscale client
upscale_client = Client("finegrain/finegrain-image-enhancer", hf_token=self.hf_token_upscaler)
result = upscale_client.predict(
input_image=handle_file(img_path),
prompt=prompt,
negative_prompt="worst quality, low quality, normal quality",
upscale_factor=upscale_factor,
controlnet_scale=0.6,
controlnet_decay=1,
condition_scale=6,
denoise_strength=0.35,
num_inference_steps=18,
solver="DDIM",
api_name="/process"
)
return result[1] # Return upscale image path
except Exception as e:
print(f"Error scaling image: {e}")
return None
# Main method to generate and optionally upscale image
async def gen_image(self, prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
model = self.enable_lora(lora_model, basemodel) if process_lora else basemodel
image, seed = await self.generate_image(prompt, model, "", width, height, scales, steps, seed)
if image is None:
print("Image generation failed.")
return []
image_path = "temp_image.jpg"
image.save(image_path, format="JPEG")
upscale_image_path = None
if process_upscale:
upscale_image_path = self.upscale_image(prompt, image_path, upscale_factor)
if upscale_image_path and os.path.exists(upscale_image_path):
return [image_path, upscale_image_path]
return [image_path] |