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import gradio as gr
import io
import random
import os
import time
import numpy as np
import subprocess
import torch
import json
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
from deep_translator import GoogleTranslator
from datetime import datetime
from model import models
from theme import theme
from fastapi import FastAPI
app = FastAPI()
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100
max_images = 6
def flip_image(x):
return np.fliplr(x)
def clear():
return None
def query(lora_id, prompt, is_negative=False, steps=28, cfg_scale=3.5, sampler="DPM++ 2M Karras", seed=-1, strength=100, width=896, height=1152):
if prompt == "" or prompt == None:
return None
if lora_id.strip() == "" or lora_id == None:
lora_id = "black-forest-labs/FLUX.1-dev"
key = random.randint(0, 999)
API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
headers = {"Authorization": f"Bearer {API_TOKEN}"}
# prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
# print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
print(f'\033[1mGeneration {key}:\033[0m {prompt}')
# If seed is -1, generate a random seed and use it
if seed == -1:
seed = random.randint(1, 1000000000)
# Prepare the payload for the API call, including width and height
payload = {
"inputs": prompt,
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed != -1 else random.randint(1, 1000000000),
"strength": strength,
"parameters": {
"width": width, # Pass the width to the API
"height": height # Pass the height to the API
}
}
response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
if response.status_code != 200:
print(f"Error: Failed to get image. Response status: {response.status_code}")
print(f"Response content: {response.text}")
if response.status_code == 503:
raise gr.Error(f"{response.status_code} : The model is being loaded")
raise gr.Error(f"{response.status_code}")
try:
image_bytes = response.content
image = Image.open(io.BytesIO(image_bytes))
print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
return image, seed
except Exception as e:
print(f"Error when trying to open the image: {e}")
return None
with gr.Group():
examples = [
"a beautiful woman with blonde hair and blue eyes",
"a beautiful woman with brown hair and grey eyes",
"a beautiful woman with black hair and brown eyes",
]
css = """
.title { font-size: 3em; align-items: center; text-align: center; }
.info { align-items: center; text-align: center; }
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""
with gr.Blocks(theme=theme, fill_width=True, css=css) as app:
with gr.Tab("Image Generator"):
with gr.Row():
with gr.Column(scale=10, elem_id="prompt-container"):
with gr.Group():
with gr.Row(equal_height=True):
text_prompt = gr.Textbox(label="Image Prompt ✍️", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
with gr.Row():
with gr.Accordion("🎨 Lora trigger words", open=False):
gr.Markdown("""
- **Canopus-Pencil-Art-LoRA**: Pencil Art
- **Flux-Realism-FineDetailed**: Fine Detailed
- **Fashion-Hut-Modeling-LoRA**: Modeling
- **SD3.5-Large-Turbo-HyperRealistic-LoRA**: hyper realistic
- **Flux-Fine-Detail-LoRA**: Super Detail
- **SD3.5-Turbo-Realism-2.0-LoRA**: Turbo Realism
- **Canopus-LoRA-Flux-UltraRealism-2.0**: Ultra realistic
- **Canopus-Pencil-Art-LoRA**: Pencil Art
- **SD3.5-Large-Photorealistic-LoRA**: photorealistic
- **Flux.1-Dev-LoRA-HDR-Realism**: HDR
- **prithivMLmods/Ton618-Epic-Realism-Flux-LoRA**: Epic Realism
- **john-singer-sargent-style**: John Singer Sargent Style
- **alphonse-mucha-style**: Alphonse Mucha Style
- **ultra-realistic-illustration**: ultra realistic illustration
- **eye-catching**: eye-catching
- **john-constable-style**: John Constable Style
- **film-noir**: in the style of FLMNR
- **flux-lora-pro-headshot**: PROHEADSHOT
""")
with gr.Row():
custom_lora =
FLUX.Dev-LORA
/ app.py
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#!/usr/bin/env python
import gradio as gr
import requests
import io
import random
import os
import time
import numpy as np
import subprocess
import torch
import json
import uuid
import spaces
from typing import Tuple
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
from deep_translator import GoogleTranslator
from datetime import datetime
from theme import theme
from typing import Tuple
from fastapi import FastAPI
app = FastAPI()
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100
def flip_image(x):
return np.fliplr(x)
def clear():
return None
def query(lora_id, prompt, is_negative=False, steps=28, cfg_scale=3.5, sampler="DPM++ 2M Karras", seed=-1, strength=100, width=896, height=1152):
if prompt == "" or prompt == None:
return None
if lora_id.strip() == "" or lora_id == None:
lora_id = "black-forest-labs/FLUX.1-dev"
key = random.randint(0, 999)
API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
headers = {"Authorization": f"Bearer {API_TOKEN}"}
# prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
# print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
print(f'\033[1mGeneration {key}:\033[0m {prompt}')
# If seed is -1, generate a random seed and use it
if seed == -1:
seed = random.randint(1, 1000000000)
# Prepare the payload for the API call, including width and height
payload = {
"inputs": prompt,
"is_negative": is_negative,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed != -1 else random.randint(1, 1000000000),
"strength": strength,
"parameters": {
"width": width, # Pass the width to the API
"height": height # Pass the height to the API
}
}
response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
if response.status_code != 200:
print(f"Error: Failed to get image. Response status: {response.status_code}")
print(f"Response content: {response.text}")
if response.status_code == 503:
raise gr.Error(f"{response.status_code} : The model is being loaded")
raise gr.Error(f"{response.status_code}")
try:
image_bytes = response.content
image = Image.open(io.BytesIO(image_bytes))
print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
return image, seed
except Exception as e:
print(f"Error when trying to open the image: {e}")
return None
examples = [
"a beautiful woman with blonde hair and blue eyes",
"a beautiful woman with brown hair and grey eyes",
"a beautiful woman with black hair and brown eyes",
]
css = """
#app-container {
max-width: 930px;
margin-left: auto;
margin-right: auto;
}
".gradio-container {background: url('file=abstract.jpg')}
"""
with gr.Blocks(theme=theme, css=css, elem_id="app-container") as app:
gr.HTML("<center><h6>🎨 FLUX.1-Dev with LoRA 🇬🇧</h6></center>")
with gr.Tab("Text to Image"):
with gr.Column(elem_id="app-container"):
with gr.Row():
with gr.Column(elem_id="prompt-container"):
with gr.Group():
with gr.Row():
text_prompt = gr.Textbox(label="Image Prompt ✍️", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
with gr.Row():
with gr.Accordion("🎨 Lora trigger words", open=False):
gr.Markdown("""
- **Canopus-Pencil-Art-LoRA**: Pencil Art
- **Flux-Realism-FineDetailed**: Fine Detailed
- **Fashion-Hut-Modeling-LoRA**: Modeling
- **SD3.5-Large-Turbo-HyperRealistic-LoRA**: hyper realistic
- **Flux-Fine-Detail-LoRA**: Super Detail
- **SD3.5-Turbo-Realism-2.0-LoRA**: Turbo Realism
- **Canopus-LoRA-Flux-UltraRealism-2.0**: Ultra realistic
- **Canopus-Pencil-Art-LoRA**: Pencil Art
- **SD3.5-Large-Photorealistic-LoRA**: photorealistic
- **Flux.1-Dev-LoRA-HDR-Realism**: HDR
- **prithivMLmods/Ton618-Epic-Realism-Flux-LoRA**: Epic Realism
- **john-singer-sargent-style**: John Singer Sargent Style
- **alphonse-mucha-style**: Alphonse Mucha Style
- **ultra-realistic-illustration**: ultra realistic illustration
- **eye-catching**: eye-catching
- **john-constable-style**: John Constable Style
- **film-noir**: in the style of FLMNR
- **flux-lora-pro-headshot**: PROHEADSHOT
""")
with gr.Row():
custom_lora = gr.Dropdown([" ", "prithivMLmods/Canopus-Pencil-Art-LoRA", "prithivMLmods/Flux-Realism-FineDetailed", "prithivMLmods/Fashion-Hut-Modeling-LoRA", "prithivMLmods/SD3.5-Large-Turbo-HyperRealistic-LoRA", "prithivMLmods/Flux-Fine-Detail-LoRA", "prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", "hugovntr/flux-schnell-realism", "fofr/sdxl-deep-down", "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0", "prithivMLmods/Canopus-Realism-LoRA", "prithivMLmods/Canopus-LoRA-Flux-FaceRealism", "prithivMLmods/SD3.5-Large-Photorealistic-LoRA", "prithivMLmods/Flux.1-Dev-LoRA-HDR-Realism", "prithivMLmods/Ton618-Epic-Realism-Flux-LoRA", "KappaNeuro/john-singer-sargent-style", "KappaNeuro/alphonse-mucha-style", "ntc-ai/SDXL-LoRA-slider.ultra-realistic-illustration", "ntc-ai/SDXL-LoRA-slider.eye-catching", "KappaNeuro/john-constable-style", "dvyio/flux-lora-film-noir", "dvyio/flux-lora-pro-headshot"], label="Custom LoRA",)
with gr.Accordion("Advanced options", open=False):
negative_prompt = gr.Textbox(label="Negative Prompt", lines=5, placeholder="What should not be in the image", value="(((hands:-1.25))), physical-defects:2, unhealthy-deformed-joints:2, unhealthy-hands:2, out of frame, (((bad face))), (bad-image-v2-39000:1.3), (((out of frame))), deformed body features, (((poor facial details))), (poorly drawn face:1.3), jpeg artifacts, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), [asymmetrical features], warped expressions, distorted eyes")
with gr.Row(equal_height=True):
width = gr.Slider(label="Image Width", value=896, minimum=64, maximum=1216, step=32)
height = gr.Slider(label="Image Height", value=1152, minimum=64, maximum=1216, step=32)
strength = gr.Slider(label="Prompt Strength", value=100, minimum=0, maximum=100, step=1)
steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 Karras", "DPM2 a Karras", "DPM++ SDE Karras", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "DEIS", "DDIM", "Euler CFG PP", "Euler", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "LMS", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
with gr.Row(equal_height=True):
with gr.Accordion("🫘Seed", open=False):
seed_output = gr.Textbox(label="Seed Used", elem_id="seed-output")
with gr.Row(equal_height=True):
image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
# Add a button to trigger the image generation
with gr.Row(equal_height=True):
text_button = gr.Button("Generate Image 🎨", variant='primary', elem_id="gen-button")
clear_prompt =gr.Button("Clear Prompt 🗑️",variant="primary", elem_id="clear_button")
clear_prompt.click(lambda: (None), None, [text_prompt], queue=False, show_api=False)
with gr.Column(scale=10):
with gr.Group():
with gr.Row():
image_output = gr.Image(type="pil", label="Image Output", format="png", show_share_button=False, elem_id="gallery")
with gr.Group():
with gr.Row():
gr.Examples(
examples = examples,
inputs = [text_prompt],
)
with gr.Group():
with gr.Row():
clear_results = gr.Button(value="Clear Image 🗑️", variant="primary", elem_id="clear_button")
clear_results.click(lambda: (None), None, [image_output], queue=False, show_api=False)
text_button.click(query, inputs=[custom_lora, text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=[image_output, seed_output])
pp.queue(default_concurrency_limit=200, max_size=200) # <-- Sets up a queue with default parameters
if __name__ == "__main__":
timeout = 100
app.launch(show_api=False, share=False)
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