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import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
# 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")
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=90)
def run_lora(prompt, cfg_scale, steps, selected_index, 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
generator = torch.Generator(device="cuda").manual_seed(seed)
# 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,
#cross_attention_kwargs={"scale": lora_scale},
).images[0]
# Unload LoRA weights
pipe.unload_lora_weights()
return image
with gr.Blocks(theme=gr.themes.Soft()) as app:
gr.Markdown("# FLUX.1 LoRA the Explorer")
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):
generate_button = gr.Button("Generate", variant="primary")
with gr.Row():
with gr.Column(scale=1):
selected_info = gr.Markdown("")
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Gallery",
allow_preview=False,
columns=2
)
with gr.Column(scale=2):
result = gr.Image(label="Generated Image")
with gr.Row():
#with gr.Column():
#prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it")
#negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry")
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():
seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True)
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=1)
gallery.select(update_selection, outputs=[prompt, selected_info, selected_index])
generate_button.click(
fn=run_lora,
inputs=[prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale],
outputs=[result]
)
app.queue()
app.launch() |