Magicbrush Jul7

🚀 MagicBrush Diffuser Port

This project is a diffuser port of the InstructPix2Pix - MagicBrush model, offering a diffuser version of MagicBrush - epoch - 52 - step - 4999.ckpt.

🚀 Quick Start

Prerequisites

• Ensure you have the necessary Python libraries installed, including PIL, requests, torch, and diffusers.

Installation

The installation steps are mainly about installing the required Python libraries. You can use pip to install them:

pip install pillow requests torch diffusers

Usage

Here is a Python code example demonstrating how to use the MagicBrush model:

from PIL import Image, ImageOps
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
from PIL import Image

url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"

def download_image(url):
    image = Image.open(requests.get(url, stream=True).raw)
    image = ImageOps.exif_transpose(image)
    image = image.convert("RGB")
    return image

image = download_image(url)
prompt = "make the mountains snowy"

class MagicBrush():
    def __init__(self, weight="vinesmsuic/magicbrush-jul7"):
        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
                        weight, 
                        torch_dtype=torch.float16
                    ).to("cuda")
        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
        
    def infer_one_image(self, src_image, instruct_prompt, seed):
        generator = torch.manual_seed(seed)
        image = self.pipe(instruct_prompt, image=src_image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7, generator=generator).images[0]
        return image

model = MagicBrush()
image_output = model.infer_one_image(image, prompt, 42)
image_output

Example Output

📄 License

This model is open access and available to all, with a CreativeML OpenRAIL - M license further specifying rights and usage.

The CreativeML OpenRAIL License specifies:

1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content.
2. The authors claim no rights on the outputs you generate. You are free to use them and are accountable for their use, which must not go against the provisions set in the license.
3. You may re - distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL - M to all your users (please read the license entirely and carefully).

Please read the full license here