-4", torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.load_textual_inversion("path-to-save-model", weight_name="<new1>.bin")

image = pipeline(
    "<new1> cat sitting in a bucket",
    num_inference_steps=100,
    guidance_scale=6.0,
    eta=1.0,
).images[0]
image.save("cat.png")   Next steps Congratulations on training a model with Custom D