metadata
license: mit
tags:
- image-to-image
datasets:
- yulu2/InstructCV-Demo-Data
INSTRUCTCV: YOUR TEXT-TO-IMAGE MODEL IS SECRETLY A VISION GENERALIST
GitHub: https://github.com
Example
To use InstructCV
, install diffusers
using main
for now. The pipeline will be available in the next release
pip install diffusers accelerate safetensors transformers
import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
model_id = "yulu2/InstructCV"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None, variant="ema")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
url = "put your url here"
def download_image(url):
image = PIL.Image.open(requests.get(url, stream=True).raw)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
image = download_image(URL)
seed = random.randint(0, 100000)
generator = torch.manual_seed(seed)
width, height = image.size
factor = 512 / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
image = ImageOps.fit(image, (width, height), method=Image.Resampling.LANCZOS)
prompt = "Detect the person."
images = pipe(prompt, image=image, num_inference_steps=100, generator=generator).images
images[0]