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from huggingface_hub import login
import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
huggingface-cli login
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "black-forest-labs/FLUX.1-dev"
lora_repo_id = "abmSS/Amer" # Replace with your LoRA model
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe.to(device)
pipe.load_lora_weights(lora_repo_id) # Load LoRA weights
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
pipe.fuse_lora() # Enable LoRA
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown(" # Text-to-Image with LoRA Support")
prompt = gr.Text(label="Prompt", placeholder="Enter your prompt")
run_button = gr.Button("Run")
result = gr.Image(label="Result")
gr.Examples(
examples=["Astronaut in a jungle", "A futuristic city"],
inputs=[prompt],
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, "", 0, True, 1024, 1024, 7.5, 25],
outputs=[result, None],
)
if __name__ == "__main__":
demo.launch()
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