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.gitignore

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+.DS_Store
+**/__pycache__
+examples/
+.idea/
+.vscode/
+build
+!lama_cleaner/app/build
+dist/
+lama_cleaner.egg-info/
+venv/

LICENSE

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README.md

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----
-license: other
----
-
-# OpenAssistant LLaMa 30B SFT 6
-
-Due to the license attached to LLaMa models by Meta AI it is not possible to directly distribute LLaMa-based models. Instead we provide XOR weights for the OA models.
-
-Thanks to Mick for writing the `xor_codec.py` script which enables this process
-
-## The Process
-
-Note: This process applies to `oasst-sft-6-llama-30b` model. The same process can be applied to other models in future, but the checksums will be different..
-
-**This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.**
-
-To use OpenAssistant LLaMa-Based Models, you need to have a copy of the original LLaMa model weights and add them to a `llama` subdirectory here.
-
-Ensure your LLaMa 30B checkpoint matches the correct md5sums:
-
-```
-f856e9d99c30855d6ead4d00cc3a5573  consolidated.00.pth
-d9dbfbea61309dc1e087f5081e98331a  consolidated.01.pth
-2b2bed47912ceb828c0a37aac4b99073  consolidated.02.pth
-ea0405cdb5bc638fee12de614f729ebc  consolidated.03.pth
-4babdbd05b8923226a9e9622492054b6  params.json
-```
-
-**Important: Follow these exact steps to convert your original LLaMa checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.**
-
-1. Create a clean Python **3.10** virtual environment & activate it:
-
-```
-python3.10 -m venv xor_venv
-source xor_venv/bin/activate
-```
-
-2. Clone transformers repo and switch to tested version:
-
-```
-git clone https://github.com/huggingface/transformers.git
-cd transformers
-git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c
-pip install .
-```
-
-3. Install **exactly** these dependency versions:
-
-```
-pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1
-```
-
-4. Check `pip freeze` output:
-
-```
-accelerate==0.18.0
-certifi==2022.12.7
-charset-normalizer==3.1.0
-filelock==3.12.0
-huggingface-hub==0.13.4
-idna==3.4
-numpy==1.24.2
-nvidia-cublas-cu11==11.10.3.66
-nvidia-cuda-nvrtc-cu11==11.7.99
-nvidia-cuda-runtime-cu11==11.7.99
-nvidia-cudnn-cu11==8.5.0.96
-packaging==23.1
-protobuf==3.20.1
-psutil==5.9.5
-PyYAML==6.0
-regex==2023.3.23
-requests==2.28.2
-sentencepiece==0.1.98
-tokenizers==0.13.3
-torch==1.13.1
-tqdm==4.65.0
-transformers @ file:///mnt/data/koepf/transformers
-typing_extensions==4.5.0
-urllib3==1.26.15
-```
-
-5. While in `transformers` repo root, run HF LLaMA conversion script:
-
-```
-python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir <input_path_llama_base>  --output_dir <output_path_llama30b_hf> --model_size 30B
-```
-
-6. Run `find . -type f -exec md5sum "{}" +` in the conversion target directory (`output_dir`). This should produce exactly the following checksums if your files are correct:
-
-```
-462a2d07f65776f27c0facfa2affb9f9  ./pytorch_model-00007-of-00007.bin
-e1dc8c48a65279fb1fbccff14562e6a3  ./pytorch_model-00003-of-00007.bin
-9cffb1aeba11b16da84b56abb773d099  ./pytorch_model-00001-of-00007.bin
-aee09e21813368c49baaece120125ae3  ./generation_config.json
-92754d6c6f291819ffc3dfcaf470f541  ./pytorch_model-00005-of-00007.bin
-3eddc6fc02c0172d38727e5826181adb  ./pytorch_model-00004-of-00007.bin
-eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
-99762d59efa6b96599e863893cf2da02  ./pytorch_model-00006-of-00007.bin
-598538f18fed1877b41f77de034c0c8a  ./config.json
-fdb311c39b8659a5d5c1991339bafc09  ./tokenizer.json
-fecfda4fba7bfd911e187a85db5fa2ef  ./pytorch_model.bin.index.json
-edd1a5897748864768b1fab645b31491  ./tokenizer_config.json
-6b2e0a735969660e720c27061ef3f3d3  ./special_tokens_map.json
-5cfcb78b908ffa02e681cce69dbe4303  ./pytorch_model-00002-of-00007.bin
-```
-
-**Important: You should now have the correct LLaMa weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.**
-
-7. Once you have LLaMa weights in the correct format, you can apply the XOR decoding:
-
-```
-python xor_codec.py oasst-sft-6-llama-30b/ oasst-sft-6-llama-30b-xor/oasst-sft-6-llama-30b-xor/ llama30b_hf/
-```
-
-You should **expect to see one warning message** during execution:
-
-`Exception when processing 'added_tokens.json'`
-
-This is normal. **If similar messages appear for other files, something has gone wrong**.
-
-8. Now run `find . -type f -exec md5sum "{}" +` in the output directory (here `oasst-sft-6-llama-30b`). You should get a file with exactly these checksums:
-
-```
-970e99665d66ba3fad6fdf9b4910acc5  ./pytorch_model-00007-of-00007.bin
-659fcb7598dcd22e7d008189ecb2bb42  ./pytorch_model-00003-of-00007.bin
-ff6e4cf43ddf02fb5d3960f850af1220  ./pytorch_model-00001-of-00007.bin
-27b0dc092f99aa2efaf467b2d8026c3f  ./added_tokens.json
-2917a1cafb895cf57e746cfd7696bfe5  ./generation_config.json
-740c324ae65b1ec25976643cda79e479  ./pytorch_model-00005-of-00007.bin
-f7aefb4c63be2ac512fd905b45295235  ./pytorch_model-00004-of-00007.bin
-eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
-369df2f0e38bda0d9629a12a77c10dfc  ./pytorch_model-00006-of-00007.bin
-cc9dbf56b68b68a585cc7367696e06a7  ./config.json
-76d47e4f51a8df1d703c6f594981fcab  ./pytorch_model.bin.index.json
-fd9452959d711be29ccf04a97598e8d1  ./tokenizer_config.json
-785905630a0fe583122a8446a5abe287  ./special_tokens_map.json
-ae48c4c68e4e171d502dd0896aa19a84  ./pytorch_model-00002-of-00007.bin
-```
-
-If so you have successfully decoded the weights and should be able to use the model with HuggingFace Transformers. **If your checksums do not match those above, there is a problem.**
-
-### Configuration
-
-```
-llama-30b-sft-6:
-  dtype: fp16
-  log_dir: "llama_log_30b"
-  learning_rate: 1e-5
-  model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500
-  output_dir: llama_model_30b
-  deepspeed_config: configs/zero3_config_sft.json
-  weight_decay: 0.0
-  residual_dropout: 0.0
-  max_length: 2048
-  use_flash_attention: true
-  warmup_steps: 20
-  gradient_checkpointing: true
-  gradient_accumulation_steps: 16
-  per_device_train_batch_size: 2
-  per_device_eval_batch_size: 3
-  eval_steps: 101
-  save_steps: 292
-  num_train_epochs: 8
-  save_total_limit: 3
-  use_custom_sampler: true
-  sort_by_length: false
-  save_strategy: steps
-  datasets:
-    - oasst_export:
-        lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
-        input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
-        val_split: 0.05
-    - vicuna:
-        val_split: 0.05
-        max_val_set: 800
-        fraction: 0.8
-    - dolly15k:
-        val_split: 0.05
-        max_val_set: 300
-    - grade_school_math_instructions:
-        val_split: 0.05
-    - code_alpaca:
-        val_split: 0.05
-        max_val_set: 250
-```
-
-- **OASST dataset paper:** https://arxiv.org/abs/2304.07327
+<h1 align="center">Lama Cleaner</h1>
+<p align="center">A free and open-source inpainting tool powered by SOTA AI model.</p>
+
+<p align="center">
+  <a href="https://github.com/Sanster/lama-cleaner">
+    <img alt="total download" src="https://pepy.tech/badge/lama-cleaner" />
+  </a>
+  <a href="https://pypi.org/project/lama-cleaner/">
+    <img alt="version" src="https://img.shields.io/pypi/v/lama-cleaner" />
+  </a>
+  <a href="https://colab.research.google.com/drive/1e3ZkAJxvkK3uzaTGu91N9TvI_Mahs0Wb?usp=sharing">
+    <img alt="Open in Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />
+  </a>
+
+  <a href="https://huggingface.co/spaces/Sanster/Lama-Cleaner-lama">
+    <img alt="Hugging Face Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" />
+  </a>
+
+  <a href="">
+    <img alt="python version" src="https://img.shields.io/pypi/pyversions/lama-cleaner" />
+  </a>
+  <a href="https://hub.docker.com/r/cwq1913/lama-cleaner">
+    <img alt="version" src="https://img.shields.io/docker/pulls/cwq1913/lama-cleaner" />
+  </a>
+</p>
+
+https://user-images.githubusercontent.com/3998421/196976498-ba1ad3ab-fa18-4c55-965f-5c6683141375.mp4
+
+## Sponsor
+
+<table>
+   <tr>
+    <td >
+        <img src="./assets/GitHub_Copilot_logo.svg" style="background: white;padding: 8px;"/>
+    </td>
+    <td >
+      <a href="https://ko-fi.com/Z8Z1CZJGY/tiers" target="_blank" >
+        ❤️ Your logo
+      </a>
+    </td>
+  </tr>
+</table>
+
+## Features
+
+- Completely free and open-source, fully self-hosted, support CPU & GPU & M1/2
+- [Windows 1-Click Installer](https://lama-cleaner-docs.vercel.app/install/windows_1click_installer)
+- Multiple SOTA AI [models](https://lama-cleaner-docs.vercel.app/models)
+  - Erase model: LaMa/LDM/ZITS/MAT/FcF/Manga
+  - Erase and Replace model: Stable Diffusion/Paint by Example
+- [Plugins](https://lama-cleaner-docs.vercel.app/plugins) for post-processing:
+  - [RemoveBG](https://github.com/danielgatis/rembg): Remove images background 
+  - [RealESRGAN](https://github.com/xinntao/Real-ESRGAN): Super Resolution
+  - [GFPGAN](https://github.com/TencentARC/GFPGAN): Face Restoration
+  - [RestoreFormer](https://github.com/wzhouxiff/RestoreFormer): Face Restoration
+- More features at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/)
+
+## Quick Start
+
+Lama Cleaner make it easy to use SOTA AI model in just two commands:
+
+```bash
+# In order to use the GPU, install cuda version of pytorch first.
+# pip install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
+pip install lama-cleaner
+lama-cleaner --model=lama --device=cpu --port=8080
+```
+
+That's it, Lama Cleaner is now running at http://localhost:8080
+
+See all command line arguments at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/install/pip)
+
+## Development
+
+Only needed if you plan to modify the frontend and recompile yourself.
+
+### Frontend
+
+Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
+great online services [here](https://cleanup.pictures/).
+
+- Install dependencies:`cd lama_cleaner/app/ && pnpm install`
+- Start development server: `pnpm start`
+- Build: `pnpm build`

build_docker.sh

@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+set -e
+
+GIT_TAG=$1
+IMAGE_DESC="Image inpainting tool powered by SOTA AI Model" 
+GIT_REPO="https://github.com/Sanster/lama-cleaner"
+
+echo "Building cpu docker image..."
+
+docker buildx build \
+--file ./docker/CPUDockerfile \
+--label org.opencontainers.image.title=lama-cleaner \
+--label org.opencontainers.image.description="$IMAGE_DESC" \
+--label org.opencontainers.image.url=$GIT_REPO \
+--label org.opencontainers.image.source=$GIT_REPO \
+--label org.opencontainers.image.version=$GIT_TAG \
+--build-arg version=$GIT_TAG \
+--tag cwq1913/lama-cleaner:cpu-$GIT_TAG .
+
+
+echo "Building NVIDIA GPU docker image..."
+
+docker buildx build \
+--file ./docker/GPUDockerfile \
+--label org.opencontainers.image.title=lama-cleaner \
+--label org.opencontainers.image.description="$IMAGE_DESC" \
+--label org.opencontainers.image.url=$GIT_REPO \
+--label org.opencontainers.image.source=$GIT_REPO \
+--label org.opencontainers.image.version=$GIT_TAG \
+--build-arg version=$GIT_TAG \
+--tag cwq1913/lama-cleaner:gpu-$GIT_TAG .

main.py

@@ -0,0 +1,4 @@
+from lama_cleaner import entry_point
+
+if __name__ == "__main__":
+    entry_point()

publish.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+set -e
+
+pushd ./lama_cleaner/app
+yarn run build
+popd
+
+rm -r -f dist
+python3 setup.py sdist bdist_wheel
+twine upload dist/*

requirements-dev.txt

@@ -0,0 +1,2 @@
+wheel
+twine

requirements.txt

@@ -0,0 +1,20 @@
+torch>=1.9.0
+opencv-python
+flask_cors
+Jinja2==2.11.3
+flask==1.1.4
+flaskwebgui==0.3.5
+tqdm
+pydantic
+rich
+loguru
+pytest
+yacs
+markupsafe==2.0.1
+scikit-image==0.19.3
+diffusers[torch]==0.14.0
+transformers==4.27.4
+gradio
+piexif==1.1.3
+safetensors
+omegaconf

setup.py

@@ -0,0 +1,46 @@
+import setuptools
+from pathlib import Path
+
+web_files = Path("lama_cleaner/app/build/").glob("**/*")
+web_files = [str(it).replace("lama_cleaner/", "") for it in web_files]
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+
+def load_requirements():
+    requirements_file_name = "requirements.txt"
+    requires = []
+    with open(requirements_file_name) as f:
+        for line in f:
+            if line:
+                requires.append(line.strip())
+    return requires
+
+
+# https://setuptools.readthedocs.io/en/latest/setuptools.html#including-data-files
+setuptools.setup(
+    name="lama-cleaner",
+    version="1.1.1",
+    author="PanicByte",
+    author_email="cwq1913@gmail.com",
+    description="Image inpainting tool powered by SOTA AI Model",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/Sanster/lama-cleaner",
+    packages=setuptools.find_packages("./"),
+    package_data={"lama_cleaner": web_files},
+    install_requires=load_requirements(),
+    python_requires=">=3.7",
+    entry_points={"console_scripts": ["lama-cleaner=lama_cleaner:entry_point"]},
+    classifiers=[
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+)