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import gradio as gr
import torch
from diffusers import AuraFlowPipeline
# Initialize the AuraFlow pipeline
pipe = AuraFlowPipeline.from_pretrained("purplesmartai/pony-v7-base", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
def generate_image(prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed):
"""
Function to generate an image using the AuraFlow pipeline.
"""
generator = torch.Generator("cuda").manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=int(height),
width=int(width),
num_inference_steps=int(num_inference_steps),
guidance_scale=guidance_scale,
generator=generator
).images[0]
return image
# Create the Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world"),
gr.Textbox(label="Negative Prompt", placeholder="Enter prompts to exclude"),
gr.Slider(label="Height", minimum=256, maximum=2048, step=64, value=1024),
gr.Slider(label="Width", minimum=256, maximum=2048, step=64, value=1024),
gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=50),
gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.1, value=5.0),
gr.Number(label="Seed", value=42)
],
outputs=gr.Image(label="Generated Image"),
title="AuraFlow Text-to-Image Generation",
description="Generate images from text prompts using the AuraFlow model."
)
# Launch the Gradio app
iface.launch() |