Stable Diffusion 3.5 Large
stableDiffusion35_large.safetensors Checkpoint / SD 3.5 Large
- Model Name
- Stable Diffusion 3.5 Large
- Version
- Large
- Creator
- theally
- Size
- 15.33 GB
- Downloads
- 20,402
- BTIH
- E15441E4D24D676DDF9222EB143EB1141D133515
- BTMH
- 5A418D4830A171B6DCDE26552B40D229DD40CBFF25788E93528A51A3352C51B9
- SHA256
- FFEF7A279D9134626E6CE0D494FBA84FC1C7E720B3C7DF2D19A09DC3796D8F93
- Upload Date
- 5 months ago
- Uploader
- CivitasBay.org
- Status
- 2 Seeders0 Peers
Please see our Quickstart Guide to Stable Diffusion 3.5 for all the latest info!
Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
Please note: This model is released under the Stability Community License. Visit Stability AI to learn or contact us for commercial licensing details.
Model Description
Developed by: Stability AI
Model type: MMDiT text-to-image generative model
Model Description: This model generates images based on text prompts. It is a Multimodal Diffusion Transformer that use three fixed, pretrained text encoders, and with QK-normalization to improve training stability.
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.
Implementation Details
QK Normalization: Implements the QK normalization technique to improve training Stability.
Text Encoders:
CLIPs: OpenCLIP-ViT/G, CLIP-ViT/L, context length 77 tokens
T5: T5-xxl, context length 77/256 tokens at different stages of training
Training Data and Strategy:
This model was trained on a wide variety of data, including synthetic data and filtered publicly available data.
For more technical details of the original MMDiT architecture, please refer to the Research paper.
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