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#!/usr/bin/env python3
from huggingface_hub import HfApi
import torch
# https://github.com/pix2pixzero/pix2pix-zero/blob/main/src/edit_synthetic.py
import requests
from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
api = HfApi()
def download(embedding_url, local_filepath):
r = requests.get(embedding_url)
with open(local_filepath, "wb") as f:
f.write(r.content)
model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
model_ckpt, conditions_input_image=False, torch_dtype=torch.float16
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.to("cuda")
prompt = "a high resolution painting of a cat in the style of van gough"
source_embedding_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt"
target_embedding_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt"
for url in [source_embedding_url, target_embedding_url]:
download(url, url.split("/")[-1])
source_embeds = torch.load(source_embedding_url.split("/")[-1])
target_embeds = torch.load(target_embedding_url.split("/")[-1])
image = pipeline(
prompt,
source_embeds=source_embeds,
target_embeds=target_embeds,
num_inference_steps=50,
cross_attention_guidance_amount=0.15,
).images[0]
path = "/home/patrick_huggingface_co/images/aa.png"
image.save(path)
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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