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Running
on
Zero
import gradio as gr | |
import json | |
import logging | |
import torch | |
from PIL import Image | |
from diffusers import DiffusionPipeline | |
import spaces | |
# 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 | |
) | |
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 | |
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=2): | |
result = gr.Image(label="Generated Image", height=768) | |
generate_button = gr.Button("Generate", variant="primary") | |
with gr.Column(scale=1): | |
gallery = gr.Gallery( | |
[(item["image"], item["title"]) for item in loras], | |
label="LoRA Gallery", | |
allow_preview=False, | |
columns=2 | |
) | |
with gr.Row(): | |
with gr.Column(): | |
prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it") | |
selected_info = gr.Markdown("") | |
prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Type a prompt after selecting a LoRA") | |
#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() | |