Edit anything in images powered by segment-anything, ControlNet, StableDiffusion, etc.

Edit Anything by Segment-Anything

This is an ongoing project aims to Edit and Generate Anything in an image, powered by Segment Anything, ControlNet, BLIP2, Stable Diffusion, etc.

A project for fun. Any forms of contribution and suggestion are very welcomed!

News

2023/04/12 - An initial version of text-guided edit-anything is in sam2groundingdino_edit.py(object-level) and sam2vlpart_edit.py(part-level).

2023/04/10 - An initial version of edit-anything is in sam2edit.py.

2023/04/10 - We transfer the pretrained model into diffusers style, the pretrained model is auto loaded when using sam2image_diffuser.py. Now you can combine our pretrained model with different base models easily!

2023/04/09 - We released a pretrained model of StableDiffusion based ControlNet that generate images conditioned by SAM segmentation.

Features

Highlight features:

• Pretrained ControlNet with SAM mask as condition enables the image generation with fine-grained control.
• category-unrelated SAM mask enables more forms of editing and generation.
• BLIP2 text generation enables text guidance-free control.

Edit Specific Thing by Text-Grounding and Segment-Anything

Editing by Text-guided Part Mask

Text Grounding: "dog head"

Human Prompt: "cute dog"

Text Grounding: "cat eye"

Human Prompt: "A cute small humanoid cat"

Editing by Text-guided Object Mask

Text Grounding: "bench"

Human Prompt: "bench"

Edit Anything by Segment-Anything

Human Prompt: "esplendent sunset sky, red brick wall"

Human Prompt: "chairs by the lake, sunny day, spring" An initial version of edit-anything. (We will add more controls on masks very soon.)

Generate Anything by Segment-Anything

BLIP2 Prompt: "a large white and red ferry" (1:input image; 2: segmentation mask; 3-8: generated images.)

BLIP2 Prompt: "a cloudy sky"

BLIP2 Prompt: "a black drone flying in the blue sky"

1. The human prompt and BLIP2 generated prompt build the text instruction.
2. The SAM model segment the input image to generate segmentation mask without category.
3. The segmentation mask and text instruction guide the image generation.

Note: Due to the privacy protection in the SAM dataset, faces in generated images are also blurred. We are training new models with unblurred images to solve this.

Ongoing

• Conditional Generation trained with 85k samples in SAM dataset.

• Training with more images from LAION and SAM.

• Interactive control on different masks for image editing.

• Using Grounding DINO for category-related auto editing.

• ChatGPT guided image editing.

Setup

Create a environment

    conda env create -f environment.yaml
    conda activate control

Install BLIP2 and SAM

Put these models in models folder.

pip install git+https://github.com/huggingface/transformers.git

pip install git+https://github.com/facebookresearch/segment-anything.git

# For text-guided editing
pip install git+https://github.com/openai/CLIP.git

pip install git+https://github.com/facebookresearch/detectron2.git

pip install git+https://github.com/IDEA-Research/GroundingDINO.git

Download pretrained model

# Segment-anything ViT-H SAM model. 
cd models/
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

# BLIP2 model will be auto downloaded.

# Part Grounding Swin-Base Model.
wget https://github.com/Cheems-Seminar/segment-anything-and-name-it/releases/download/v1.0/swinbase_part_0a0000.pth

# Grounding DINO Model.
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth

# Get edit-anything-ckpt-v0-1.ckpt pretrained model from huggingface. 
# No need to download this if your are using sam2image_diffuser.py!!! But please install safetensors for reading the ckpt.
https://huggingface.co/shgao/edit-anything-v0-1

Run Demo

python sam2image_diffuser.py
# or 
python sam2image.py
# or 
python sam2edit.py
# or
python sam2vlpart_edit.py
# or
python sam2groundingdino_edit.py

Set 'use_gradio = True' in these files if you have GUI to run the gradio demo.

Training

1. Generate training dataset with dataset_build.py.
2. Transfer stable-diffusion model with tool_add_control_sd21.py.
3. Train model with sam_train_sd21.py.

Acknowledgement

This project is based on:

Segment Anything, ControlNet, BLIP2, MDT, Stable Diffusion, Large-scale Unsupervised Semantic Segmentation, Grounded Segment Anything: From Objects to Parts, Grounded-Segment-Anything

Thanks for these amazing projects!