File size: 4,341 Bytes
0cfb4a5 d4fba6d 0dec378 6c6a21d 0dec378 0a67e9a a484b84 d4fba6d 6c6a21d 0dec378 1a52ee5 0dec378 d4fba6d 20ffdd2 0dec378 1a52ee5 908e25f 0dec378 1a52ee5 3c2650c 1a52ee5 3d2ee8a ccd05c2 1a52ee5 ccd05c2 1a52ee5 908e25f 1a52ee5 0cfb4a5 1a52ee5 0cfb4a5 1a52ee5 908e25f 79024bb 908e25f ccd05c2 908e25f 1a52ee5 ccd05c2 289d5f1 b5806de 0dec378 1a52ee5 d4fba6d 0dec378 908e25f 1a52ee5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient, login
from translatepy import Translator
import requests
import re
import asyncio
from PIL import Image
from gradio_client import Client, handle_file
translator = Translator()
HF_TOKEN = os.environ.get("HF_TOKEN", None)
basemodel = "black-forest-labs/FLUX.1-schnell"
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
def get_upscale_finegrain(prompt, img_path, upscale_factor):
client = Client("finegrain/finegrain-image-enhancer")
result = client.predict(
input_image=handle_file(img_path),
prompt=prompt,
negative_prompt="",
seed=42,
upscale_factor=upscale_factor,
controlnet_scale=0.6,
controlnet_decay=1,
condition_scale=6,
tile_width=112,
tile_height=144,
denoise_strength=0.35,
num_inference_steps=18,
solver="DDIM",
api_name="/process"
)
return result[1]
async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
if seed == -1:
seed = random.randint(0, MAX_SEED)
seed = int(seed)
text = str(translator.translate(prompt, 'English')) + "," + lora_word
async with AsyncInferenceClient() as client:
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 gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor):
model = enable_lora(lora_add)
image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
if upscale_factor != 0:
upscaled_image = get_upscale_finegrain(prompt, image, upscale_factor)
combined_image = Image.new('RGB', (image.width + upscaled_image.width, image.height))
combined_image.paste(image, (0, 0))
combined_image.paste(upscaled_image, (image.width, 0))
return combined_image, seed
else:
return image, seed
with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
gr.HTML("<h1><center>Flux Lab Light</center></h1>")
with gr.Row():
with gr.Column(scale=4):
with gr.Row():
img = gr.Image(type="filepath", label='Comparison 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, placeholder="Please use Warm status model")
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=[0, 2, 3, 4], value=0, scale=2)
gr.on(
triggers=[prompt.submit, sendBtn.click],
fn=gen,
inputs=[
prompt,
lora_add,
lora_word,
width,
height,
scales,
steps,
seed,
upscale_factor
],
outputs=[img, seed]
) |