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import gradio as gr
from diffusers import AutoPipelineForText2Image
import torch
# Load Dreambooth model
pipeline = AutoPipelineForText2Image.from_pretrained("sd-dreambooth-library/herge-style", torch_dtype=torch.float16).to("cuda")
def generate_image(prompt):
# Generate image based on prompt
pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
image = pipeline(prompt).images[0]
return image
def image_to_base64(image):
# Convert image to base64
buffered = BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode()
def base64_to_image(base64_str):
# Convert base64 to image
image_data = base64.b64decode(base64_str)
return Image.open(BytesIO(image_data))
def handle_prompt_image(prompt):
# Generate image based on prompt and convert to base64
image = generate_image(prompt)
base64_str = image_to_base64(image)
return base64_str
def main():
# Interface setup
image_input = gr.Textbox(label="Prompt", lines=3, placeholder="Enter your prompt here...")
prompt_output = gr.Textbox(label="Base64 Encoded Image", readonly=True)
iface = gr.Interface(
fn=handle_prompt_image,
inputs=image_input,
outputs=prompt_output,
title="Dreambooth Image Generator",
description="Enter a prompt to generate an image using the Dreambooth model.",
theme="compact"
)
iface.launch(share=True)
if __name__ == "__main__":
main()
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