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import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
def generate_image(prompt, guidance_scale=7.5, num_steps=30, lora_scale=0.8):
try:
# Initialize the pipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)
# Load LoRA weights
pipe.load_lora_weights("Scalino84/my-flux-face-v2")
if torch.cuda.is_available():
pipe = pipe.to("cuda")
# Generate image
image = pipe(
prompt=prompt,
num_inference_steps=int(num_steps),
guidance_scale=float(guidance_scale),
).images[0]
return image
except Exception as e:
print(f"Error: {str(e)}")
raise gr.Error(str(e))
# Create Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt", value="a photo of xyz person, professional headshot"),
gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale"),
gr.Slider(minimum=20, maximum=100, value=30, label="Number of Steps"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.8, label="LoRA Scale")
],
outputs=gr.Image(label="Generated Image"),
title="Flux Face Generator",
description="Generate images using your custom LoRA model"
)
# Launch the interface
interface.launch()
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