Running model prompt on stable diffusion model from hugging face on mps MacOS

Question

import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
import os

os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"

device = 'cuda' if torch.cuda.is_available() else 'mps'

if torch.cuda.is_available():
    PYTORCH_CUDA_ALLOC_CONF = {'max_split_size_mb': 8000}
    torch.cuda.max_memory_allocated(device=device)
    torch.cuda.empty_cache()

    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16,
                                             variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
    torch.cuda.empty_cache()

    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True,
                                                torch_dtype=torch.float16, variant="fp16")
    refiner.enable_xformers_memory_efficient_attention()
    refiner = refiner.to(device)
    torch.cuda.empty_cache()

    upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16,
                                                 use_safetensors=True)
    upscaler.enable_xformers_memory_efficient_attention()
    upscaler = upscaler.to(device)
    torch.cuda.empty_cache()
else:
    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
    pipe = pipe.to(device)
    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
    refiner = refiner.to(device)
    refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)

n_steps = 40
high_noise_frac = 0.8

pipe.enable_attention_slicing()
PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0

def genie(prompt, negative_prompt, height, width, scale, steps, seed, upscaling, prompt_2, negative_prompt_2):
    generator = torch.Generator(device=device).manual_seed(seed)
    int_image = pipe(prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2,
                     num_inference_steps=steps, height=height, width=width, guidance_scale=scale,
                     num_images_per_prompt=1, generator=generator, output_type="latent").images
    if upscaling == 'Yes':
        image = \
            refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt,
                    negative_prompt_2=negative_prompt_2,
                    image=int_image).images[0]
        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5,
                            guidance_scale=0).images[0]
        torch.cuda.empty_cache()
        return (image, upscaled)
    else:
        image = \
            refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt,
                    negative_prompt_2=negative_prompt_2,
                    image=int_image).images[0]
        torch.cuda.empty_cache()
    return (image, image)


gr.Interface(fn=genie, inputs=[gr.Textbox(
    label='What you want the AI to generate. 77 Token Limit. A Token is Any Word, Number, Symbol, or Punctuation. Everything Over 77 Will Be Truncated!'),
    gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
    gr.Slider(512, 1024, 768, step=128, label='Height'),
    gr.Slider(512, 1024, 768, step=128, label='Width'),
    gr.Slider(1, 15, 10, step=.25,
              label='Guidance Scale: How Closely the AI follows the Prompt'),
    gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'),
    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Seed'),
    gr.Radio(['Yes', 'No'], value='No', label='Upscale?'),
    gr.Textbox(label='Embedded Prompt'),
    gr.Textbox(label='Embedded Negative Prompt')],
             outputs=['image', 'image'],
             title="Stable Diffusion XL 1.0 GPU",
             description="SDXL 1.0 GPU. <br><br><b>WARNING: Capable of producing NSFW (Softcore) images.</b>",
             article="If You Enjoyed this Demo and would like to Donate, you can send to any of these Wallets. <br>BTC: bc1qzdm9j73mj8ucwwtsjx4x4ylyfvr6kp7svzjn84 <br>3LWRoKYx6bCLnUrKEdnPo3FCSPQUSFDjFP <br>DOGE: DK6LRc4gfefdCTRk9xPD239N31jh9GjKez <br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(
    debug=True, max_threads=80)

This is an example of one usage, but all models that are related to graphics from huggingface returns the same error which is

Error

step_index = (self.timesteps == timestep).nonzero().item()

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

RuntimeError: Expected dst.dim() >= src.dim() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

Any suggestion please?

Thanks in advance

I was trying to generate image from prompt, and expected a PIL Image