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#!/usr/bin/env python3
import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
from PIL import Image
import numpy as np
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
DDIMScheduler,
)
import sys
checkpoint = sys.argv[1]
# pre-process image and mask
image = load_image("https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png").convert('RGB')
mask_image = load_image("https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png").convert("L")
# convert to float32
image = np.asarray(image, dtype=np.float32)
mask_image = np.asarray(mask_image, dtype=np.float32)
image[mask_image > 127] = -255.0
image = torch.from_numpy(image)[None].permute(0, 3, 1, 2) / 255.0
prompt = "A blue cat sitting on a park bench"
controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
generator = torch.manual_seed(0)
out_image = pipe(prompt, num_inference_steps=20, generator=generator, image=image, guidance_scale=9.0).images[0]
path = os.path.join(Path.home(), "images", "aa.png")
out_image.save(path)
api = HfApi()
api.upload_file(
path_or_fileobj=path,
path_in_repo=path.split("/")[-1],
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png")
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