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import gradio as gr
import config
from inference import DiffusionInference
from PIL import Image
import io
# Initialize the inference class
inference = DiffusionInference()
def text_to_image_fn(prompt, model, negative_prompt=None, guidance_scale=7.5, num_inference_steps=50):
"""
Handle text to image generation request
"""
try:
if not model:
model = config.DEFAULT_TEXT2IMG_MODEL
# Call the inference module
image = inference.text_to_image(
prompt=prompt,
model_name=model,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps
)
return image, None
except Exception as e:
return None, str(e)
def image_to_image_fn(image, prompt, model, negative_prompt=None, guidance_scale=7.5, num_inference_steps=50):
"""
Handle image to image transformation request
"""
try:
if not model:
model = config.DEFAULT_IMG2IMG_MODEL
# Call the inference module
result = inference.image_to_image(
image=image,
prompt=prompt,
model_name=model,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps
)
return result, None
except Exception as e:
return None, str(e)
# Create Gradio UI
with gr.Blocks(title="Diffusion Models") as app:
gr.Markdown("# Hugging Face Diffusion Models")
with gr.Tab("Text to Image"):
with gr.Row():
with gr.Column():
txt2img_prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
txt2img_negative = gr.Textbox(label="Negative Prompt (Optional)", placeholder="What to exclude from the image")
txt2img_model = gr.Textbox(label="Model", placeholder=f"Enter model name (default: {config.DEFAULT_TEXT2IMG_MODEL})")
txt2img_guidance = gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.5, label="Guidance Scale")
txt2img_steps = gr.Slider(minimum=10, maximum=100, value=50, step=1, label="Inference Steps")
txt2img_button = gr.Button("Generate Image")
with gr.Column():
txt2img_output = gr.Image(type="pil", label="Generated Image")
txt2img_error = gr.Textbox(label="Error", visible=True)
txt2img_button.click(
fn=text_to_image_fn,
inputs=[txt2img_prompt, txt2img_model, txt2img_negative, txt2img_guidance, txt2img_steps],
outputs=[txt2img_output, txt2img_error]
)
with gr.Tab("Image to Image"):
with gr.Row():
with gr.Column():
img2img_input = gr.Image(type="pil", label="Input Image")
img2img_prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
img2img_negative = gr.Textbox(label="Negative Prompt (Optional)", placeholder="What to exclude from the image")
img2img_model = gr.Textbox(label="Model", placeholder=f"Enter model name (default: {config.DEFAULT_IMG2IMG_MODEL})")
img2img_guidance = gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.5, label="Guidance Scale")
img2img_steps = gr.Slider(minimum=10, maximum=100, value=50, step=1, label="Inference Steps")
img2img_button = gr.Button("Transform Image")
with gr.Column():
img2img_output = gr.Image(type="pil", label="Generated Image")
img2img_error = gr.Textbox(label="Error", visible=True)
img2img_button.click(
fn=image_to_image_fn,
inputs=[img2img_input, img2img_prompt, img2img_model, img2img_negative, img2img_guidance, img2img_steps],
outputs=[img2img_output, img2img_error]
)
# Launch the Gradio app
if __name__ == "__main__":
app.launch(server_name=config.GRADIO_HOST, server_port=config.GRADIO_PORT)
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