= StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet.load_attn_procs(model_path)
pipe.to("cuda")

prompt = "A pokemon with blue eyes."
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("pokemon.png")
Here are some example images you can expect:

sayakpaul/sd-model-finetuned-lora-t4 contains LoRA fine-tuned update matrices
which is only 3 MBs in size. 