Instructions to use TensorVizion/StableDiffusion-1.4-Pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TensorVizion/StableDiffusion-1.4-Pruned with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TensorVizion/StableDiffusion-1.4-Pruned", dtype=torch.bfloat16, device_map="cuda") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Pruna AI
How to use TensorVizion/StableDiffusion-1.4-Pruned with Pruna AI:
from pruna import PrunaModel pip install -U diffusers transformers accelerate
from pruna import PrunaModel import torch # switch to "mps" for apple devices pipe = PrunaModel.from_pretrained("TensorVizion/StableDiffusion-1.4-Pruned", dtype=torch.bfloat16, device_map="cuda") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 5,003 Bytes
edbdf0c f002c92 edbdf0c f002c92 edbdf0c f002c92 edbdf0c f002c92 edbdf0c f002c92 edbdf0c f002c92 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | ---
license: creativeml-openrail-m
tags:
- safetensors
- pruna-ai
widget:
- text: A high tech solarpunk utopia in the Amazon rainforest
example_title: Amazon rainforest
- text: A pikachu fine dining with a view to the Eiffel Tower
example_title: Pikachu in Paris
- text: A mecha robot in a favela in expressionist style
example_title: Expressionist robot
- text: an insect robot preparing a delicious meal
example_title: Insect robot
- text: A small cabin on top of a snowy mountain in the style of Disney, artstation
example_title: Snowy disney cabin
extra_gated_prompt: "This model is open access and available to all, with a CreativeML\
\ OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL\
\ License specifies: \n\n1. You can't use the model to deliberately produce nor\
\ share illegal or harmful outputs or content \n2. The authors claim no rights on\
\ the outputs you generate, you are free to use them and are accountable for their\
\ use which must not go against the provisions set in the license\n3. You may re-distribute\
\ the weights and use the model commercially and/or as a service. If you do, please\
\ be aware you have to include the same use restrictions as the ones in the license\
\ and share a copy of the CreativeML OpenRAIL-M to all your users (please read the\
\ license entirely and carefully)\nPlease read the full license carefully here:\
\ https://huggingface.co/spaces/CompVis/stable-diffusion-license\n "
extra_gated_heading: Please read the LICENSE to access this model
---
# Model Card for TensorVizion/StableDiffusion-1.4-Pruned
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
```
You can [use the library_name library to load the model](https://huggingface.co/TensorVizion/StableDiffusion-1.4-Pruned?library=library_name) 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 import PrunaModel
loaded_model = PrunaModel.from_pretrained(
"TensorVizion/StableDiffusion-1.4-Pruned"
)
# we can then run inference using the methods supported by the base model
```
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
{
"awq": false,
"c_generate": false,
"c_translate": false,
"c_whisper": false,
"deepcache": true,
"diffusers_int8": false,
"fastercache": false,
"flash_attn3": false,
"fora": false,
"gptq": false,
"half": false,
"hqq": false,
"hqq_diffusers": false,
"hyper": false,
"ifw": false,
"img2img_denoise": false,
"kvpress": false,
"llama_cpp": false,
"llm_int8": false,
"moe_kernel_tuner": false,
"pab": false,
"padding_pruning": false,
"qkv_diffusers": false,
"quanto": false,
"realesrgan_upscale": false,
"reduce_noe": false,
"ring_attn": false,
"sage_attn": false,
"stable_fast": 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,
"x_fast": false,
"zipar": false,
"deepcache_interval": 2,
"batch_size": 1,
"device": "cuda",
"device_map": null,
"save_fns": [],
"save_artifacts_fns": [],
"load_fns": [
"diffusers"
],
"load_artifacts_fns": [],
"reapply_after_load": {
"deepcache": true
}
}
```
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