Upload folder using huggingface_hub (#4)

- 872995b413b53a6ce2891f491054b2b2c80627f72f24343e9744da519d584f88 (51d6c2b686b73e3479fbe46c4640cfbdd84cca84)
- a53eecaa316f75d30ff366f5d820294a9d503905c949f7b342de3f4b178b4a5c (3920125c5988ca2ebc09da14abd5d1d3000020d1)

README.md

@@ -19,40 +19,67 @@ metrics:
 </div>
 <!-- header end -->
 
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck)
+
 # Simply make AI models cheaper, smaller, faster, and greener!
 
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
 ## Results
 
 ![image info](./plots.png)
 
+**Important remarks:**
+- The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
+- 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, ...).
+- 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).
+
 ## Setup
 
-You can run the smashed model by:
-1. Installing and importing the `pruna-engine` (version 0.2.9) package. Use `pip install pruna-engine==0.2.9 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com` for installation. See [Pypi](https://pypi.org/project/pruna-engine/) for details on the package.
-2. Downloading the model files. This can be done using the Hugging Face CLI with the following commands:
+You can run the smashed model with these steps:
+
+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`.
+1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
     ```bash
-    mkdir CompVis-stable-diffusion-v1-4-smashed
-    huggingface-cli download PrunaAI/segmind-SSD-1B-turbo-tiny-green-smashed --local-dir segmind-SSD-1B-turbo-tiny-green-smashed --local-dir-use-symlinks False
+   pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
+    ```
+3. Download the model files using one of these three options. 
+   - Option 1 - Use command line interface (CLI):
+       ```bash
+       mkdir segmind-SSD-1B-turbo-tiny-green-smashed
+       huggingface-cli download PrunaAI/segmind-SSD-1B-turbo-tiny-green-smashed --local-dir segmind-SSD-1B-turbo-tiny-green-smashed --local-dir-use-symlinks False
+       ```
+   - Option 2 - Use Python:
+       ```python
+       import subprocess
+       repo_name = "segmind-SSD-1B-turbo-tiny-green-smashed"
+       subprocess.run(["mkdir", repo_name])
+       subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
+       ```
+   - Option 3 - Download them manually on the HuggingFace model page.
+3. Load & run the model.
+    ```python
+    from pruna_engine.PrunaModel import PrunaModel
+   
+    model_path = "segmind-SSD-1B-turbo-tiny-green-smashed/model"  # Specify the downloaded model path.
+    smashed_model = PrunaModel.load_model(model_path)  # Load the model.
+    smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0]  # Run the model where x is the expected input of.
     ```
-   Alternatively, you can download them manually.
-3. Loading the model.
-4. Running the model.
-You can achieve this by running the following code:
-
-```python
-from pruna_engine.PrunaModel import PrunaModel  # Step (1): install and import `pruna-engine` package.
-model_path = "segmind-SSD-1B-turbo-tiny-green-smashed/model" # Step (2): specify the downloaded model path.
-smashed_model = PrunaModel.load_model(model_path)  # Step (3): load the model.
-y = smashed_model(prompt="an astronaut riding a horse on mars", image_height=1024, image_width=1024)[0]  # Step (4): run the model.
-```
 
 ## Configurations
 
 The configuration info are in `config.json`.
 
-## License
+## Credits & License
 
-We follow the same license as the original model. Please check the license of the original model before using this model.
+We follow the same license as the original model. Please check the license of the original model segmind/SSD-1B before using this model which provided the base model.
 
 ## Want to compress other models?
 

config.json

@@ -1 +1 @@
-{"pruner": "None", "pruning_ratio": 0.0, "factorizer": "None", "quantizer": "None", "n_quantization_bits": 32, "output_deviation": 0.005, "compiler": "diffusers", "static_batch": true, "static_shape": true, "controlnet": "None", "unet_dim": 4, "max_batch_size": 1, "image_height": 1024, "image_width": 1024, "version": "xl-1.0"}
+{"pruners": "None", "pruning_ratio": 0.0, "factorizers": "None", "quantizers": "None", "n_quantization_bits": 32, "output_deviation": 0.005, "compilers": "['diffusers2', 'tiling', 'step_caching']", "static_batch": true, "static_shape": false, "controlnet": "None", "unet_dim": 4, "device": "cuda", "batch_size": 1, "max_batch_size": 1, "image_height": 1024, "image_width": 1024, "version": "xl-1.0", "scheduler": "DDIM", "task": "txt2imgxl", "model_name": "segmind/SSD-1B", "weight_name": "None", "save_load_fn": "stable_fast"}

model → model/optimized_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d3d5fb056d0762f56ed4a437be6100d13ecf25a7cd47996f4c8c73eddb811de7
-size 5588367240
+oid sha256:fb9c7cca88d136c4220a2e7c39f898d117eeb439c7263a725c93b4f91fcafba7
+size 4470556353

model/smash_config.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b204d22b90e6eed84e4762b0f1e04a52cded9a8b7a9539facdde272a60a4859
+size 695