license: apache-2.0
language:
- en
OpenChaT v1
This model card focuses on the model associated with the OpenChaT model, codebase available here.
[TODO: description] This OpenChaT v1 model is fine-tuned from...
Model Details
- Developed by: [TODO] Together Computer, LAION, ...
- Model type: [TODO]
- Language(s): English
- License: Apache 2.0
- Model Description: [TODO]
- Resources for more information: GitHub Repository.
Cite as
[TODO] @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} }
Examples
[TODO]
Uses
[TODO]
Direct Use
[TODO]
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
Misuse, Malicious Use, and Out-of-Scope Use
[TODO] Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Out-of-Scope Use
[TODO] The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
Misuse and Malicious Use
[TODO] Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
Limitations and Bias
Limitations
[TODO]
Bias
[TODO]
Training
Training Data [TODO]
Training Procedure [TODO]
[TODO]
- Hardware: 32 x 8 x A100 GPUs
- Optimizer: AdamW
- Gradient Accumulations: 1
- Batch: 32 x 8 x 2 x 4 = 2048
- Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant
Evaluation Results
[TODO]
Environmental Impact
[TODO] Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- Hardware Type: A100 PCIe 40GB
- Hours used: 200000
- Cloud Provider: AWS
- Compute Region: US-east
- Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 15000 kg CO2 eq.
Citation
[TODO] @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} }
*This model card was written by: [TODO]