| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| pipeline = DiffusionPipeline.from_pretrained("uripper/GIANNIS", revision="ONNX") | |
| pipeline.to("cuda") | |
| im = pipeline(height=1,width=1,num_channels=1, num_inference_steps=1).images[0] | |
| im.show() | |
| # When a user clicks button, pipeline will generate an image | |
| # def pipeline_interface(): | |
| # pipeline.to("cuda") | |
| # return pipeline("height=100,width=100").images[0] | |
| # iface = gr.Interface(fn=pipeline_interface, inputs=[], outputs="image") | |
| # iface.launch() |