_io32: Model input is fp32, model will convert the input to fp16, perform ops in fp16 and write the final result in fp32
_io16: Model input is fp16, perform ops in fp16 and write the final result in fp16
Running
Use Amuse GUI application to run it: https://www.amuse-ai.com/
use *_io32 version to run with the Amuse application
Inference Result
Intro Image
Prompt="a majestic Royal Bengal Tiger on the mountain top overlooking beautiful Lake Tahoe snowy mountains and deep blue lake, deep blue sky, ultra hd, 8k, photorealistic"
License
Community License: Free for research, non-commercial, and commercial use for organizations or individuals with less than $1M in total annual revenue. More details can be found in the Community License Agreement. Read more at https://stability.ai/license.
For individuals and organizations with annual revenue above $1M: please contact us to get an Enterprise License.
Model Sources
For research purposes, we recommend our SD3.5 Github repository (https://github.com/Stability-AI/sd3.5), which provides a reference implementation for SD3.5 inference.
The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
Safety
As part of our safety-by-design and responsible AI deployment approach, we take deliberate measures to ensure Integrity starts at the early stages of development. We implement safety measures throughout the development of our models. We have implemented safety mitigations that are intended to reduce the risk of certain harms, however we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases.
For more about our approach to Safety, please visit our Safety page.
Integrity Evaluation
Our integrity evaluation methods include structured evaluations and red-teaming testing for certain harms. Testing was conducted primarily in English and may not cover all possible harms.
Risks identified and mitigations:
Harmful content: We have used filtered data sets when training our models and implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. TAll developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases.
Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our Acceptable Use Policy, including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products.
Privacy violations: Developers and deployers are encouraged to adhere to privacy regulations with techniques that respect data privacy.
Contact
Please report any issues with the model or contact us:
