Ziang/add ip adapter

.gitignore

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 */*/*/*/*/__pycache__/
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+
+outputs
+models

.pre-commit-config.yaml

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+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+#files: ^SCM/
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+        exclude: "mkdocs.yml"
+      - id: check-added-large-files
+        args: ["--maxkb=1000"]
+      - id: check-ast
+      - id: check-json
+      - id: check-merge-conflict
+  - repo: https://github.com/psf/black
+    rev: 23.11.0
+    hooks:
+      - id: black
+        args: ["--line-length=150"]
+        files: .
+  - repo: https://github.com/pycqa/flake8
+    rev: 6.1.0
+    hooks:
+      - id: flake8
+        args: ["--ignore=E203, W503, E501, E722"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+        files: .

README.md

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-# ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
+# **ELITE with Control**
 
+ELITE has the ability to create images of a specific object without the need for training. To further enhance control over the generated images, I integrated the use of Ip-Adapter.
 
-<a href="https://arxiv.org/pdf/2302.13848.pdf"><img src="https://img.shields.io/badge/arXiv-2302.13848-b31b1b.svg" height=22.5></a>
-<a href="https://huggingface.co/spaces/ELITE-library/ELITE"><img src="https://img.shields.io/static/v1?label=HuggingFace&message=gradio demo&color=darkgreen" height=22.5></a>
+### Ip-Adapter
 
-![method](assets/results.png)
-
-
-## Method Details
-
-![method](assets/method.png)
-
-Given an image indicates the target concept (usually an object), we propose a learning-based encoder ELITE to encode the visual concept into the textual embeddings, which can be further flexibly composed into new scenes.  It consists of two modules: (a) a global mapping network is first trained to encode a concept image into multiple textual word embeddings, where one primary word (w0) for well-editable concept and other auxiliary words (w1···N) to exclude irrelevant disturbances. (b) A local mapping network is further trained, which projects the foreground object into textual feature space to provide local details.
+![method](assets/ip-adapter-results.png)
 
+Ip-Adapter was originally impelemented with huggingface diffusors >=0.19.3. And the diffusors used in ELITE is 0.11.1 which is lack of many indispensibale features compared to 0.19.3 for implementing Ip-Adapter. I implemented Ip-Adapter here without using diffusors because it is eaiser than upgrade the diffusors.
 
 ## Getting Started
 
 ### Environment Setup
 
 ```shell
-git clone https://github.com/csyxwei/ELITE.git
-cd ELITE
+git clone https://github.com/Stevada/ELITE_plus_Control.git
+cd ELITE_pluc_Control
 conda create -n elite python=3.9
 conda activate elite
 pip install -r requirements.txt
 ```
 
-### Pretrained Models
-
-We provide the pretrained checkpoints in [Google Drive](https://drive.google.com/drive/folders/1VkiVZzA_i9gbfuzvHaLH2VYh7kOTzE0x?usp=sharing). One can download them and save to the directory `checkpoints`.
-
-### Setting up HuggingFace
-
-Our code is built on the [diffusers](https://github.com/huggingface/diffusers/) version of Stable Diffusion, you need to accept the [model license](https://huggingface.co/CompVis/stable-diffusion-v1-4) before downloading or using the weights. In our experiments, we use model version v1-4.
-
-You have to be a registered user in Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
-
-Run the following command to authenticate your token
-```shell
-huggingface-cli login
-```
-If you have already cloned the repo, then you won't need to go through these steps.
-
-### Customized Generation
-
-We provide some testing images in [test_datasets](./test_datasets), which contains both images and object masks. For testing, you can run, 
-```
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATA_DIR='./test_datasets/'
-CUDA_VISIBLE_DEVICES=0 python inference_local.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --test_data_dir=$DATA_DIR \
-  --output_dir="./outputs/local_mapping"  \
-  --suffix="object" \
-  --template="a photo of a S" \
-  --llambda="0.8" \
-  --global_mapper_path="./checkpoints/global_mapper.pt" \
-  --local_mapper_path="./checkpoints/local_mapper.pt"
-```
-or you can use the shell script,
-```
-bash inference_local.sh
-```
-If you want to test your customized dataset, you should align the image to ensure the object is at the center of image, and also provide the corresponding object mask. The object mask can be obtained by [image-matting-app](https://huggingface.co/spaces/SankarSrin/image-matting-app), or other image matting methods.
-
-
-### Gradio Demo
-
-We also provide a UI for testing our method that is built with gradio. This demo also supports generating new directions on the fly! Running the following command in a terminal will launch the demo:
-
-```python
-python app_gradio.py
-```
-
-Or you can try the online demo [here](https://huggingface.co/spaces/ELITE-library/ELITE).
-
-## Training
-
-### Preparing Dataset
-
-We use the **test** dataset of Open-Images V6 to train our ELITE. You can prepare the dataset as follows:
-
-- Download Open-Images test dataset from [CVDF's site](https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations) and unzip it to the directory `datasets/Open_Images/images/test`.
-- Download class names file `oidv6-class-descriptions.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
-- Download bbox annotations file `test-annotations-bbox.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
-- Download segmentation annotations of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and unzip them to the directory `datasets/Open_Images/segs/test`. And put the `test-annotations-object-segmentation.csv` into `datasets/Open_Images/annotations/`. 
-- Obtain the mask bbox by running the following command:
-    ```shell
-    python data_scripts/cal_bbox_by_seg.py
-    ```
-
-The final data structure is like this:
-
-```
-datasets
-├── Open_Images
-│  ├── annotations
-│  │  ├── oidv6-class-descriptions.csv
-│  │  ├── test-annotations-object-segmentation.csv
-│  │  ├── test-annotations-bbox.csv
-│  ├── images
-│  │  ├── test
-│  │  │  ├── xxx.jpg
-│  │  │  ├── ...
-│  ├── segs
-│  │  ├── test
-│  │  │  ├── xxx.png
-│  │  │  ├── ...
-│  │  ├── test_bbox_dict.npy
-```
-
-### Training Global Mapping Network
-
-To train the global mapping network, you can run,
-
-```Shell
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATA_DIR='./datasets/Open_Images/'
-CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25656 train_global.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --placeholder_token="S" \
-  --resolution=512 \
-  --train_batch_size=4 \
-  --gradient_accumulation_steps=4 \
-  --max_train_steps=200000 \
-  --learning_rate=1e-06 --scale_lr \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --output_dir="./elite_experiments/global_mapping" \
-  --save_steps 200
-```
-or you can use the shell script,
-```shell
-bash train_global.sh
-```
-
-### Training Local Mapping Network
-
-After the global mapping network is trained, you can train the local mapping network by running,
-
-```Shell
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATA_DIR='/home/weiyuxiang/datasets/Open_Images/'
-CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25657 train_local.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --placeholder_token="S" \
-  --resolution=512 \
-  --train_batch_size=2 \
-  --gradient_accumulation_steps=4 \
-  --max_train_steps=200000 \
-  --learning_rate=1e-5 --scale_lr \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --global_mapper_path "./elite_experiments/global_mapping/mapper_070000.pt" \
-  --output_dir="./elite_experiments/local_mapping" \
-  --save_steps 200
-```
-or you can use the shell script,
+## Inference
+Play around with your own image by the inference script.
 ```shell
-bash train_local.sh
+sh inferece_local_control.sh
 ```
 
 
-## Citation
-
-```
-@article{wei2023elite,
-  title={ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation},
-  author={Wei, Yuxiang and Zhang, Yabo and Ji, Zhilong and Bai, Jinfeng and Zhang, Lei and Zuo, Wangmeng},
-  journal={arXiv preprint arXiv:2302.13848},
-  year={2023}
-}
-```
+## TODO
+ControlNet is on the way!!
 
 ## Acknowledgements
 
-This code is built on [diffusers](https://github.com/huggingface/diffusers/) version of [Stable Diffusion](https://github.com/CompVis/stable-diffusion). We thank the authors for sharing the codes.
+This repo is a fork of [ELITE](https://github.com/csyxwei/ELITE) which is built on [diffusers](https://github.com/huggingface/diffusers/) version of [Stable Diffusion](https://github.com/CompVis/stable-diffusion). And reference to [Ip-Adpater](https://github.com/tencent-ailab/IP-Adapter). I sincerely appreciate the authors for sharing the codes.