Stable Diffusion 3.5 is a popular text-to-image generation model
Stable Diffusion 3.5 is an 8B parameter base model that produces high-quality images, with Depth and Canny ControlNets offering controllability over image outputs. Choose this model when you want high quality, artistic style, and more fine-tuning flexibility.
This model is ready for non-commercial use. Contact Stability AI at https://stability.ai/enterprise for commercial use of Stable Diffusion 3.5 Large model.
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Creators and professionals can use this model to generate high-quality images from text prompts, simplifying visual communication.
Architecture Type: Transformer and Convolutional Neural Network (CNN)
Network Architecture: Diffusion Transformer
LiheYoung/Depth-anything-large-hf leverages the DPT architecture with a DINOv2 backbone.
Input Type: Text, Image (optional)
Input Parameters: Text: One-Dimensional (1D); Image: Two-Dimensional (2D)
Input Format: Text: String. Image: Red, Green, Blue (RGB)
Other Properties Related to Input: Steps, Classifier-Free Guidance Scale, Output Image Aspect Ratio, and Seed
Output Type: Image
Output Parameters: Two-Dimensional (2D)
Output Format: Red, Green, Blue (RGB)
Other Properties Related to Output: Supported resolutions 1024x1024, 768x1344, 1344x768, 1344x768, 1344x768, 1344x768, 1216x832
Runtime Engines:
Supported Hardware Platforms:
Supported Operating Systems: Linux, Windows Subsystem for Linux
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Properties (Quantity, Dataset Descriptions, Sensor(s)): Undisclosed
Properties (Quantity, Dataset Descriptions, Sensor(s)): Undisclosed
Properties (Quantity, Dataset Descriptions, Sensor(s)): Undisclosed
Engine: TensorRT
Test Hardware: H100
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