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- aedd4b869b32b7ffee1c85907cc8ec6450d888457a83a42ad0bc821f1a843846 (a0ef2d2d0f73e10070562867ba0df17793d92e68)
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Files changed (5) hide show
  1. README.md +11 -6
  2. config.json +1 -1
  3. model/optimized_model.pkl +2 -2
  4. model/smash_config.json +2 -2
  5. plots.png +0 -0
README.md CHANGED
@@ -19,27 +19,32 @@ metrics:
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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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-
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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
@@ -72,9 +77,9 @@ You can run the smashed model with these steps:
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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 CompVis-stable-diffusion-v1-4 before using this model.
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  ## Want to compress other models?
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  </div>
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  <!-- header end -->
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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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+ [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck)
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+
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+ # Simply make AI models cheaper, smaller, faster, and greener!
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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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+ - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
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  ## Results
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  ![image info](./plots.png)
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+ **Important remarks:**
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+ - The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
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+ - These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json and are obtained after a hardware warmup. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...).
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+ - You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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  ## Setup
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  You can run the smashed model with these steps:
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+
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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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  The configuration info are in `config.json`.
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+ ## Credits & License
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+ We follow the same license as the original model. Please check the license of the original model CompVis/stable-diffusion-v1-4 before using this model which provided the base model.
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  ## Want to compress other models?
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config.json CHANGED
@@ -1 +1 @@
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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": "1.4", "task": "txt2img", "model_name": "CompVis/stable-diffusion-v1-4", "weight_name": "None", "save_load_fn": "stable_fast"}
 
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+ {"pruners": "None", "pruning_ratio": "None", "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.0, "compilers": "['step_caching', 'tiling', 'diffusers2']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "batch_size": 1, "max_batch_size": 1, "image_height": 512, "image_width": 512, "version": "1.4", "task": "txt2img", "weight_name": "None", "model_name": "CompVis/stable-diffusion-v1-4", "save_load_fn": "stable_fast"}
model/optimized_model.pkl CHANGED
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plots.png CHANGED