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import os

import torch

import torch_npu

import PIL



from PIL import Image

from diffusers import StableDiffusionPipeline

from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

from torch_npu.contrib import transfer_to_npu



torch.npu.set_compile_mode(jit=False)



device = "npu"



def image_grid(imgs, rows, cols):

    assert len(imgs) == rows * cols

    w, h = imgs[0].size

    image_grid_size = Image.new("RGB",size=(cols * w, rows * h))

    image_grid_size_w, image_grid_size_h = image_grid_size.size





    for i, img in enumerate(imgs):

        image_grid_size.paste(img,box=(i % cols * w, i // cols * h))

    return image_grid_size



pretrained_model_name = "runwayml/stable-diffusion-v1-5"

repo_id = "sd-concepts-library/cat-toy"



prompt = "a grafitti in a favela wall with a <cat-toy> on it"



pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name).to(device)



pipeline.load_textual_inversion(repo_id)



num_samples = 2

num_rows = 2



all_images = []



for _ in range(num_rows):

    images = pipeline(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, gudiance_scale=7.5).images

    all_images.extend(images)



grid = image_grid(all_images,num_samples,num_rows)

grid.save("./grad.png")