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stable-diffusion-2-1

stable-diffusion-2

stable-diffusion-v1.5

stable-diffusion-2-base

tiny-random-stable-diffusion-3

This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

hunyuan3d-2.1xet

We would like to thank the contributors to the TripoSG, DINOv2, Stable Diffusion, FLUX, diffusers and HuggingFace repositories, for their open research and exploration.

vit-base-nsfw-detector

This model is a fine-tuned version of vit-base-patch16-384 on around 25_000 images (drawings, photos...). It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9654 ** New [07/30] **: I created a new ViT model specifically to detect NSFW/SFW images for stable diffusion usage (read the disclaimer below for the reason): **AdamCodd/vit-nsfw-stable-diffusion**. **Disclaimer**: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea: - Loss: 0.3682 (↑ 292.95%) - Accuracy: 0.8600 (↓ 10.91%) - F1: 0.8654 - AUC: 0.9376 (↓ 5.75%) - Precision: 0.8350 - Recall: 0.8980

Gustavosta-MagicPrompt-Stable-Diffusion-GGUF-smashed

stable-diffusion-xl-refiner-1.0

Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. - **Developed by:** Stability AI - **Model type:** Diffusion-based text-to-image generative model - **License:** CreativeML Open RAIL++-M License - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L). - **Resources for more information:** Check out our GitHub Repository and the SDXL report on arXiv. For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference.

stable-diffusion-3.5-large-turbo-ungated

Stable Diffusion 3.5 Large Turbo is a Multimodal Diffusion Transformer (MMDiT) text-to-image model with Adversarial Diffusion Distillation (ADD) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps. Please note: This model is released under the Stability Community License. Visit Stability AI to learn or contact us for commercial licensing details. For local or self-hosted use, we recommend ComfyUI for node-based UI inference, or diffusers or GitHub for programmatic use.