diffusers safetensors license:openrail++ autotrain_compatible endpoints_compatible diffusers:StableDiffusionXLPipeline region:us
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license: openrail++
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# Terminus XL Gamma (v2 preview, EMA weights)

## Model Details

This is a variant of the Terminus XL Gamma v2 repository, with the EMA weights as the default.

### Model Description

Terminus XL Gamma is a new state-of-the-art latent diffusion model that uses zero-terminal SNR noise schedule and velocity prediction objective at training and inference time.

Terminus is based on a similar architecture to SDXL, and has the same layout. It has been trained on fewer steps with very high quality data captions via COCO and Midjourney.

This model will not be capable of as many concepts as SDXL, and some subjects will simply look very bad.

The objective of this model was to use v-prediction and min-SNR gamma loss to efficiently train a full zero-terminal SNR model on a single A100-80G.


- **Fine-tuned from:** ptx0/terminus-xl-gamma-v1
- **Developed by:** pseudoterminal X (@bghira)
- **Funded by:** pseudoterminal X (@bghira)
- **Model type:** Latent Diffusion
- **License:** openrail++
- **Architecture:** SDXL

### Model Sources

- **Repository:** https://github.com/bghira/SimpleTuner

## Uses

### Direct Use

Terminus XL Gamma can be used for generating high-quality images given text prompts. It should particularly excel at inpainting tasks, where a zero-terminal SNR noise schedule allows it to more effectively retain contrast.

The model can be utilized in creative industries such as art, advertising, and entertainment to create visually appealing content.

### Downstream Use

Terminus XL Gamma can be fine-tuned for specific tasks such as image super-resolution, style transfer, and more.

### Out-of-Scope Use

The model is not designed for tasks outside of image generation. It should not be used to produce harmful content, or deceive others. Please use common sense.

## Bias, Risks, and Limitations

The model might exhibit biases present in the training data. The generated images should be carefully reviewed to ensure they meet ethical and societal standards.

### Recommendations

Users should be cautious of potential biases in the generated images and thoroughly review them before use.

## Training Details

### Training Data

This model's success largely depended on a somewhat small collection of very high quality data samples.

* LAION-HD, filtered down to EXIF samples without watermarks. Luminance value of samples capped to 100 (.5).
* Midjourney 5.2 dataset `ptx0/mj-general` with zero filtration.

### Training Procedure

#### Preprocessing

Most of the existing process for terminus-xl-gamma-v1 was followed, with the exception of training extensively on cropped images using SDXL's crop coordinates to improve fine details.

No images were upsampled during this training session. Images were downsampled using LANCZOS instead of BICUBIC filters to attain higher image fidelity and maintain more image context for the model to learn from.

Only high-quality photos were used in this training session, greatly improving the realism qualities.

~770,000 images were used for this training run.

#### Training Hyperparameters

- **Training regime:** bf16 mixed precision
- **Learning rate:** \(4 \times 10^{-7}\) to \(8 \times 10^{-7}\), cosine schedule
- **Epochs:** 60
- **Batch size:** 24 * 15 = 360

#### Speeds, Sizes, Times

[More Information Needed]

## Evaluation

### Testing Data, Factors & Metrics

[More Information Needed]

### Results

[More Information Needed]

#### Summary

[More Information Needed]

## Environmental Impact

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications

### Model Architecture and Objective

The model uses an SDXL-compatible latent diffusion architecture with a unique min-SNR augmented velocity objective.

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation

**BibTeX:**

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**APA:**

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## Glossary

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## More Information

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## Model Card Authors

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## Model Card Contact

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