import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
from diffusers import StableDiffusion3Pipeline # pip install diffusers>=0.31.0
import copy
import random
import time
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
# Initialize the base model
base_model = "ariG23498/sd-3.5-merged"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
MAX_SEED = 2**32-1
class calculateDuration:
def __init__(self, activity_name=""):
self.activity_name = activity_name
def __enter__(self):
self.start_time = time.time()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end_time = time.time()
self.elapsed_time = self.end_time - self.start_time
if self.activity_name:
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
else:
print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
def update_selection(evt: gr.SelectData, width, height):
selected_lora = loras[evt.index]
new_placeholder = f"Type a prompt for {selected_lora['title']}"
lora_repo = selected_lora["repo"]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
if "aspect" in selected_lora:
if selected_lora["aspect"] == "portrait":
width = 768
height = 1024
elif selected_lora["aspect"] == "landscape":
width = 1024
height = 768
return (
gr.update(placeholder=new_placeholder),
updated_text,
evt.index,
width,
height,
)
@spaces.GPU(duration=70)
def infer(prompt, negative_prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(seed)
with calculateDuration("Generating image"):
# Generate image
image = pipe(
prompt=f"{prompt} {trigger_word}",
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
lora_scale=lora_scale,
).images[0]
return image
def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
if selected_index is None:
raise gr.Error("You must select a LoRA before proceeding.")
selected_lora = loras[selected_index]
lora_path = selected_lora["repo"]
trigger_word = selected_lora["trigger_word"]
# Load LoRA weights
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
if "weights" in selected_lora:
pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
pipe.fuse_lora(lora_scale=1.0)
else:
pipe.load_lora_weights(lora_path)
pipe.fuse_lora(lora_scale=1.0)
# Set random seed for reproducibility
with calculateDuration("Randomizing seed"):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
image = infer(prompt, negative_prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
pipe.to("cpu")
pipe.unload_lora_weights()
return image, seed
run_lora.zerogpu = True
css = '''
#gen_btn{height: 100%}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
title = gr.HTML(
"""
OpenFlux LoRAsoon®
""",
elem_id="title",
)
# Info blob stating what the app is running
info_blob = gr.HTML(
""" SOON®'s curated LoRa Gallery & Art Manufactory Space.|Runs on Stable Diffusion 3.5 Turbo. Now testing HST-triggerable historic photo-trained LoRAs.
"""
)
# Info blob stating what the app is running
info_blob = gr.HTML(
"""Prephrase prompts w/: "HST style autochrome photo"
"""
)
selected_index = gr.State(None)
with gr.Row():
with gr.Column(scale=2):
prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!")
with gr.Column(scale=2):
negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="What to exclude!")
with gr.Column(scale=1, elem_id="gen_column"):
generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
with gr.Row():
with gr.Column(scale=3):
selected_info = gr.Markdown("")
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Inventory",
allow_preview=False,
columns=3,
elem_id="gallery"
)
with gr.Column(scale=4):
result = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion("Advanced Settings", open=True):
with gr.Column():
with gr.Row():
cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.5, value=1.5)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=8)
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
with gr.Row():
randomize_seed = gr.Checkbox(True, label="Randomize seed")
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3.0, step=0.01, value=1.0)
gallery.select(
update_selection,
inputs=[width, height],
outputs=[prompt, selected_info, selected_index, width, height]
)
gr.on(
triggers=[generate_button.click, prompt.submit],
fn=run_lora,
inputs=[prompt, negative_prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed]
)
app.queue(default_concurrency_limit=2).launch(show_error=True)
app.launch()