metadata
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.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or
harmful outputs or content
2. 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
3. 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)
Please read the full license carefully here:
https://huggingface.co/spaces/CompVis/stable-diffusion-license
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 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:
pip install pruna
You can use the library_name library to load the model 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:
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 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.
{
"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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