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import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
import requests
import re
import asyncio
from PIL import Image
translator = Translator()
HF_TOKEN = os.environ.get("HF_TOKEN", None)
basemodel = "XLabs-AI/flux-RealismLora"
MAX_SEED = np.iinfo(np.int32).max
CSS = """
footer {
visibility: hidden;
}
"""
JS = """function () {
gradioURL = window.location.href
if (!gradioURL.endsWith('?__theme=dark')) {
window.location.replace(gradioURL + '?__theme=dark');
}
}"""
def enable_lora(lora_add):
if not lora_add:
return basemodel
else:
return lora_add
async def generate_image(
prompt:str,
model:str,
lora_word:str,
width:int=768,
height:int=1024,
scales:float=3.5,
steps:int=24,
seed:int=-1
):
if seed == -1:
seed = random.randint(0, MAX_SEED)
seed = int(seed)
print(f'prompt:{prompt}')
text = str(translator.translate(prompt, 'English')) + "," + lora_word
client = AsyncInferenceClient()
try:
image = await client.text_to_image(
prompt=text,
height=height,
width=width,
guidance_scale=scales,
num_inference_steps=steps,
model=model,
)
except Exception as e:
raise gr.Error(f"Error in {e}")
return image, seed
async def upscale_image(
prompt:str,
img_path:str,
upscale_factor:int=2,
controlnet_scale:float=0.6,
controlnet_decay:float=1,
condition_scale:int=6,
tile_width:int=112,
tile_height:int=144,
denoise_strength:float=0.35,
num_inference_steps:int=18,
solver:str="DDIM"
):
client = AsyncInferenceClient()
try:
result = await client.image_to_image(
prompt=prompt,
input_image=img_path,
negative_prompt="",
seed=42,
upscale_factor=upscale_factor,
controlnet_scale=controlnet_scale,
controlnet_decay=controlnet_decay,
condition_scale=condition_scale,
tile_width=tile_width,
tile_height=tile_height,
denoise_strength=denoise_strength,
num_inference_steps=num_inference_steps,
solver=solver,
model="finegrain/finegrain-image-enhancer",
)
except Exception as e:
raise gr.Error(f"Error in {e}")
return result[0]
async def gen(
prompt:str,
lora_add:str="",
lora_word:str="",
width:int=768,
height:int=1024,
scales:float=3.5,
steps:int=24,
seed:int=-1,
upscale_factor:int=2,
controlnet_scale:float=0.6,
controlnet_decay:float=1,
condition_scale:int=6,
tile_width:int=112,
tile_height:int=144,
denoise_strength:float=0.35,
num_inference_steps:int=18,
solver:str="DDIM",
progress=gr.Progress(track_tqdm=True)
):
model = enable_lora(lora_add)
print(model)
image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
upscale_img = await upscale_image(prompt, image, upscale_factor, controlnet_scale, controlnet_decay, condition_scale, tile_width, tile_height, denoise_strength, num_inference_steps, solver)
return image, upscale_img, seed
with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
gr.HTML("<h1><center>Flux Lab Light</center></h1>")
gr.HTML("<p><center>Powered By HF Inference API</center></p>")
with gr.Row():
with gr.Column(scale=4):
with gr.Row():
img = gr.Image(type="filepath", label='Flux Image', height=600)
upscale_img = gr.Image(type="filepath", label='Upscale Image', height=600)
with gr.Row():
prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
sendBtn = gr.Button(scale=1, variant='primary')
with gr.Accordion("Advanced Options", open=True):
with gr.Column(scale=1):
width = gr.Slider(
label="Width",
minimum=512,
maximum=1280,
step=8,
value=768,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=1280,
step=8,
value=1024,
)
scales = gr.Slider(
label="Guidance",
minimum=3.5,
maximum=7,
step=0.1,
value=3.5,
)
steps = gr.Slider(
label="Steps",
minimum=1,
maximum=100,
step=1,
value=24,
)
seed = gr.Slider(
label="Seeds",
minimum=-1,
maximum=MAX_SEED,
step=1,
value=-1,
)
lora_add = gr.Textbox(
label="Add Flux LoRA",
info="Copy the HF LoRA model name here",
lines=1,
value="XLabs-AI/flux-RealismLora"
)
lora_word = gr.Textbox(
label="Add Flux LoRA Trigger Word",
info="Add the Trigger Word",
lines=1,
value="",
)
upscale_factor = gr.Radio(
label="UpScale Factor",
choices=[
2, 3, 4
],
value=2,
scale=2
)
controlnet_scale = gr.Slider(
label="ControlNet Scale",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.6
)
controlnet_decay = gr.Slider(
label="ControlNet Decay",
minimum=0.1,
maximum=1.0,
step=0.1,
value=1
)
condition_scale = gr.Slider(
label="Condition Scale",
minimum=1,
maximum=10,
step=1,
value=6
)
tile_width = gr.Slider(
label="Tile Width",
minimum=64,
maximum=256,
step=16,
value=112
)
tile_height = gr.Slider(
label="Tile Height",
minimum=64,
maximum=256,
step=16,
value=144
)
denoise_strength = gr.Slider(
label="Denoise Strength",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.35
)
num_inference_steps = gr.Slider(
label="Num Inference Steps",
minimum=1,
maximum=50,
step=1,
value=18
)
solver = gr.Radio(
label="Solver",
choices=[
"DDIM", "DPM"
],
value="DDIM",
scale=2
)
gr.on(
triggers=[
prompt.submit,
sendBtn.click,
],
fn=gen,
inputs=[
prompt,
lora_add,
lora_word,
width,
height,
scales,
steps,
seed,
upscale_factor,
controlnet_scale,
controlnet_decay,
condition_scale,
tile_width,
tile_height,
denoise_strength,
num_inference_steps,
solver
],
outputs=[img, upscale_img, seed]
)
if __name__ == "__main__":
demo.queue(api_open=False).launch(show_api=False, share=False) |