app.py

import torch
from safetensors.torch import save_file
import gradio as gr
import os

def convert_embedding(sd15_embedding):
    # Temporary file paths
    input_path = "temp_input.pt"
    output_path = "temp_output.safetensors"
    
    # Save uploaded file to disk to be processed
    with open(input_path, "wb") as f:
        f.write(sd15_embedding.read())

    # Your existing conversion logic
    sd15_embedding = torch.load(input_path, map_location=torch.device('cpu'))
    sd15_tensor = sd15_embedding['string_to_param']['*']
    num_vectors = sd15_tensor.shape[0]
    clip_g_shape = (num_vectors, 1280)
    clip_l_shape = (num_vectors, 768)
    clip_g = torch.zeros(clip_g_shape, dtype=torch.float16)
    clip_l = torch.zeros(clip_l_shape, dtype=torch.float16)
    clip_l[:sd15_tensor.shape[0], :sd15_tensor.shape[1]] = sd15_tensor.to(dtype=torch.float16)
    save_file({"clip_g": clip_g, "clip_l": clip_l}, output_path)

    # Remove the temporary input file
    os.remove(input_path)

    # Return the path to the converted file for download
    return output_path

iface = gr.Interface(
    fn=convert_embedding,
    inputs=gr.inputs.File(label="Upload SD1.5 Embedding"),
    outputs=gr.outputs.File(label="Download Converted SDXL Embedding"),
    title="SD1.5 to SDXL Embedding Converter",
    description="Upload an SD1.5 embedding file to convert it to SDXL format."
)

if __name__ == "__main__":
    iface.launch()