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import gradio as gr
import torch
from diffusers import DiffusionPipeline
import random
from huggingface_hub import login
import os
# Authenticate using the token stored in Hugging Face Spaces secrets
if 'HF_TOKEN' in os.environ:
login(token=os.environ['HF_TOKEN'])
else:
raise ValueError("HF_TOKEN not found in environment variables. Please add it to your Space's secrets.")
# Initialize the base model and specific LoRA
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
# Check if CUDA is available and move the model to GPU if possible
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipe.to(device)
lora_repo = "sagar007/sagar_flux"
trigger_word = "sagar"
pipe.load_lora_weights(lora_repo)
MAX_SEED = 2**32-1
def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
progress(0, f"Starting image generation (using {device})...")
image = pipe(
prompt=f"{prompt} {trigger_word}",
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
cross_attention_kwargs={"scale": lora_scale},
).images[0]
progress(100, "Completed!")
return image, seed
# Gradio interface setup
with gr.Blocks() as app:
gr.Markdown("# Text-to-Image Generation with LoRA")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button("Generate")
with gr.Column():
result = gr.Image(label="Result")
with gr.Row():
cfg_scale = gr.Slider(minimum=1, maximum=20, value=7, step=0.1, label="CFG Scale")
steps = gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Steps")
with gr.Row():
width = gr.Slider(minimum=128, maximum=1024, value=512, step=64, label="Width")
height = gr.Slider(minimum=128, maximum=1024, value=512, step=64, label="Height")
with gr.Row():
seed = gr.Number(label="Seed", precision=0)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
lora_scale = gr.Slider(minimum=0, maximum=1, value=0.75, step=0.01, label="LoRA Scale")
run_button.click(
run_lora,
inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed]
)
# Launch the app
app.launch() |