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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
import cv2
from PIL import Image
import numpy as np
from diffusers import (
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionControlNetPipeline,
StableDiffusionXLControlNetPipeline,
UniPCMultistepScheduler,
)
import sys
checkpoint = sys.argv[1]
prompts = [
"beautiful room",
"a photo-realistic image of two paradise birds",
"a snowy house behind a forest",
"a couple watching a romantic sunset",
"boats in the Amazonas",
"a photo of a beautiful face of a woman",
"a skater in Brooklyn",
"a tornado in Iowa"
]
sd_xl = "control_v11p" not in checkpoint
if sd_xl:
base_ckpt = "stabilityai/stable-diffusion-xl-base-0.9"
controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_ckpt, controlnet=controlnet, torch_dtype=torch.float16
)
size = 1024
else:
base_ckpt = "runwayml/stable-diffusion-v1-5"
controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_ckpt, controlnet=controlnet, torch_dtype=torch.float16
)
size = 512
import ipdb; ipdb.set_trace()
# pipe.enable_model_cpu_offload()
pipe.to("cuda")
for i in range(8):
for seed in range(4):
image = load_image(
f"https://huggingface.co/datasets/patrickvonplaten/webdatasets_images/resolve/main/image_{i}.png"
)
image = image.resize((size, size))
prompt = prompts[i]
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
generator = torch.manual_seed(seed)
out_image = pipe(prompt, generator=generator, num_inference_steps=20, image=canny_image, controlnet_conditioning_scale=1.0).images[0]
path = os.path.join(Path.home(), "images", "control_sdxl", f"{i}_{seed}.png")
path_in_repo = "/".join(path.split("/")[-2:])
out_image.save(path)
api = HfApi()
api.upload_file(
path_or_fileobj=path,
path_in_repo=path_in_repo,
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/control_sdxl/{i}_{seed}.png")
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