Instructcv

🚀 InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists

InstructCV is an instruction - tuned text - to - image diffusion model that serves as a vision generalist, enabling various image - related tasks. It utilizes the InstructCV - Demo - Data dataset and is released under the MIT license.

🚀 Quick Start

To use InstructCV, you need to install diffusers using the main branch for now. The pipeline will be available in the next release.

📦 Installation

pip install diffusers accelerate safetensors transformers

💻 Usage Examples

Basic Usage

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[0]
images[0]

📄 License

This project is licensed under the MIT license.

GitHub: https://github.com/AlaaLab/InstructCV