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import gradio as gr
from diffusers import AutoPipelineForText2Image
import torch
def load_model():
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16 if device == "cuda" else torch.float32 # Adjusted for device compatibility
).to(device)
# Ensure this line points to the correct path of your weights
pipeline.load_lora_weights("./", weight_name="EnvyFloorplansXL01.safetensors")
return pipeline
pipeline = load_model()
def generate_image(prompt, negative_prompt):
# Generate an image using the model pipeline
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
scheduler="DPM++ 2M Karras Sharp v1",
generator=torch.manual_seed(3145577831),
num_inference_steps=60,
guidance_scale=9.5
).images[0]
return image
# Define your Gradio interface
# Define your Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here...", value="(poor quality:1.2) (worst quality, low quality:1.4),word,cropped,username,watermark,signature,blurry,soft,soft line,curved line,sketch,ugly,logo,pixelated,lowres,ceiling light, monochrome, negativeXL_D, color pencil, soft line,worst quality, blurry,sketch,, text,, sketch, cartoon, drawing") # Corrected here
],
outputs="image",
title="Text-to-Image Generation with Stable Diffusion",
description="Generate images based on your text inputs."
)
if __name__ == "__main__":
interface.launch(debug=True)
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