SD35soonrLoRAs / app.py
multimodalart's picture
Update app.py
428b3d2 verified
raw
history blame
4.45 kB
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
import copy
import random
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
# Initialize the base model
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
pipe.to("cuda")
MAX_SEED = 2**32-1
def update_selection(evt: gr.SelectData):
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}) ✨"
return (
gr.update(placeholder=new_placeholder),
updated_text,
evt.index
)
@spaces.GPU(duration=80)
def run_lora(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
if "weights" in selected_lora:
pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
else:
pipe.load_lora_weights(lora_path)
# Set random seed for reproducibility
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device="cuda").manual_seed(seed)
# Generate image
image = pipe(
prompt=f"{prompt} {trigger_word}",
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
).images[0]
yield image, seed
pipe.unload_lora_weights()
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}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
title = gr.HTML(
"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""",
elem_id="title",
)
selected_index = gr.State(None)
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
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 Gallery",
allow_preview=False,
columns=3
)
with gr.Column(scale=4):
result = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion("Advanced Settings", open=False):
with gr.Column():
with gr.Row():
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
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=1, step=0.01, value=0.85)
gallery.select(update_selection, outputs=[prompt, selected_info, selected_index])
gr.on(
triggers=[generate_button.click, prompt.submit],
fn=run_lora,
inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed]
)
app.queue()
app.launch()