Instructions to use pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Pruna AI
How to use pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro with Pruna AI:
from pruna_pro import PrunaProModel pip install -U diffusers transformers accelerate
from pruna_pro import PrunaProModel import torch # switch to "mps" for apple devices pipe = PrunaProModel.from_pretrained("pruna_pro-test/test-save-tiny-stable-diffusion-pipe-smashed-pro", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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library_name: diffusers
tags:
- safetensors
- pruna_pro-ai
- pruna-ai
---
# Model Card for pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro
This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.
## Usage
First things first, you need to install the pruna library:
```bash
pip install pruna_pro
```
You can [use the diffusers library to load the model](https://huggingface.co/pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro?library=diffusers) but this might not include all optimizations by default.
To ensure that all optimizations are applied, use the pruna library to load the model using the following code:
```python
from pruna_pro import PrunaProModel
loaded_model = PrunaProModel.from_pretrained(
"pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro"
)
# we can then run inference using the methods supported by the base model
```
For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe?library=diffusers).
Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more information.
## Smash Configuration
The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.
```bash
{
"adaptive": false,
"auto": false,
"awq": false,
"bottleneck": false,
"c_generate": false,
"c_translate": false,
"c_whisper": false,
"deepcache": false,
"diffusers_higgs": false,
"diffusers_int8": false,
"fastercache": false,
"flash_attn3": false,
"flux_caching": false,
"fora": false,
"fp4": false,
"fp8": false,
"gptq": false,
"half": false,
"higgs": false,
"hqq": false,
"hqq_diffusers": false,
"hyper": false,
"ifw": false,
"img2img_denoise": false,
"ipex_llm": false,
"kvpress": false,
"llm_int8": false,
"moe_kernel_tuner": false,
"pab": false,
"padding_pruning": false,
"periodic": false,
"prores": false,
"qkv_diffusers": false,
"quanto": false,
"radial_attn": false,
"realesrgan_upscale": false,
"reduce_noe": false,
"ring_attn": false,
"sage_attn": false,
"stable_fast": false,
"taylor": false,
"taylor_auto": false,
"text_to_image_distillation_inplace_perp": false,
"text_to_image_distillation_lora": false,
"text_to_image_distillation_perp": false,
"text_to_image_inplace_perp": false,
"text_to_image_lora": false,
"text_to_image_perp": false,
"text_to_text_inplace_perp": false,
"text_to_text_lora": false,
"text_to_text_perp": false,
"token_merging": false,
"torch_compile": false,
"torch_dynamic": false,
"torch_structured": false,
"torch_unstructured": false,
"torchao": false,
"torchao_autoquant": false,
"whisper_s2t": false,
"x_fast": false,
"zipar": false,
"batch_size": 1,
"device": "cpu",
"device_map": null,
"save_fns": [],
"save_artifacts_fns": [],
"load_fns": [
"diffusers"
],
"load_artifacts_fns": [],
"reapply_after_load": {}
}
```
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