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sharpenb
- opened
- README.md +83 -0
- config.json +1 -0
- model/optimized_model.pkl +3 -0
- model/smash_config.json +1 -0
- plots.png +0 -0
README.md
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---
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license: apache-2.0
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library_name: pruna-engine
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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metrics:
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- memory_disk
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- memory_inference
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- inference_latency
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- inference_throughput
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- inference_CO2_emissions
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- inference_energy_consumption
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
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<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</a>
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</div>
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<!-- header end -->
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# Simply make AI models cheaper, smaller, faster, and greener!
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[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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- Give a thumbs up if you like this model!
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
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- Share feedback and suggestions on the Slack of Pruna AI (Coming soon!).
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## Results
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![image info](./plots.png)
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These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json. Results may vary in other settings (e.g. other hardware, image size, batch size, ...).
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## Setup
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You can run the smashed model with these steps:
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0. Check cuda, torch, packaging requirements are installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. For packaging and torch, run `pip install packaging torch`.
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1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
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```bash
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pip install pruna-engine[gpu] --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
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```
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3. Download the model files using one of these three options.
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- Option 1 - Use command line interface (CLI):
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```bash
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mkdir nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed
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huggingface-cli download PrunaAI/nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed --local-dir nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed --local-dir-use-symlinks False
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```
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- Option 2 - Use Python:
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```python
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import subprocess
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repo_name = "nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed"
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subprocess.run(["mkdir", repo_name])
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subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
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```
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- Option 3 - Download them manually on the HuggingFace model page.
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3. Load & run the model.
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```python
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from pruna_engine.PrunaModel import PrunaModel
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model_path = "nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
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smashed_model = PrunaModel.load_model(model_path) # Load the model.
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smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
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```
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## Configurations
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The configuration info are in `config.json`.
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## License
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We follow the same license as the original model. Please check the license of the original model ORIGINAL_nitrosocke-Arcane-Diffusion-turbo-tiny-green-smashed before using this model.
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## Want to compress other models?
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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config.json
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{"pruners": "None", "pruning_ratio": 0.0, "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.005, "compilers": "['diffusers2']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "save_dir": "/ceph/hdd/staff/charpent/models/.models/optimized_model", "batch_size": 1, "max_batch_size": 1, "image_height": 512, "image_width": 512, "version": "1.5", "task": "txt2img", "model_name": "nitrosocke/Arcane-Diffusion", "weight_name": "None", "save_load_fn": "stable_fast"}
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model/optimized_model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:bde853ca0bf8908fbff664cb45bb286c4645f2dfbccc9c6e862cfe32bdae8fdd
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size 2743383882
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model/smash_config.json
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{"api_key": "pruna_c4c77860c62a2965f6bc281841ee1d7bd3", "verify_url": "http://johnrachwan.pythonanywhere.com", "smash_config": {"pruners": "None", "pruning_ratio": 0.0, "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.005, "compilers": "['diffusers2']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "cache_dir": "/ceph/hdd/staff/charpent/.cache/models", "save_dir": "/ceph/hdd/staff/charpent/models/.models/optimized_model", "batch_size": 1, "max_batch_size": 1, "image_height": 512, "image_width": 512, "version": "1.5", "task": "txt2img", "model_name": "nitrosocke/Arcane-Diffusion", "weight_name": "None", "save_load_fn": "stable_fast"}}
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plots.png
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