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Pony-Diffusion-V6-XL

stable-diffusion-3-medium_amdgpu

CyberRealistic

**CyberRealistic** is a photorealistic image generation model built upon the **Stable Diffusion 1.5 (SD 1.5)** architecture. Developed by Cyberdelia, this model excels at producing lifelike portraits and scenes with minimal prompt engineering, making it ideal for creators seeking high-quality outputs without complex configurations. - **Model Type**: Text-to-Image Generation - **Base Model**: Stable Diffusion 1.5 (SD 1.5) - **Format**: `safetensors` - **Version**: v8.0 - **Creator**: Cyberdelia - **File Size**: ~6.3 GB - **License**: CreativeML Open RAIL++-M License - **Photorealism**: Generates highly detailed and realistic images, particularly effective for human subjects. - **Ease of Use**: Designed for simplicity; achieves impressive results with straightforward prompts. - **Integrated VAE**: Comes with a baked-in Variational Autoencoder for enhanced image quality. - **Versatility**: Suitable for various applications, including portraits, fashion, and cinematic scenes.

openjourney

This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion.

stable-audio-3-optimized

> **Note:** This repository contains experimental checkpoints optimised for acceleration on specific hardware. For standard checkpoints, please use Stable Audio 3 Medium instead. Please note: For commercial use, please refer to https://stability.ai/license `Stable Audio 3` is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline.

dreamshaper-8-inpainting

`lykon-models/dreamshaper-8-inpainting` is a Stable Diffusion Inpainting model that has been fine-tuned on runwayml/stable-diffusion-inpainting.

Z-Image-Turbo-GGUF

The license of the quantized files follows the license of the original model: - Z-Image-Turbo: apache-2.0 https://github.com/leejet/stable-diffusion.cpp/wiki/How-to-Use-Z%E2%80%90Image-on-a-GPU-with-Only-4GB-VRAM

stable-video-diffusion-img2vid

Please note: For commercial use of this model, please refer to https://stability.ai/license. (SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. This model was trained to generate 14 frames at resolution 576x1024 given a context frame of the same size. We also finetune the widely used f8-decoder for temporal consistency. For convenience, we additionally provide the model with the standard frame-wise decoder here. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative image-to-video model

stable-diffusion-2-1

This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available here. This `stable-diffusion-2-1` model is fine-tuned from stable-diffusion-2 (`768-v-ema.ckpt`) with an additional 55k steps on the same dataset (with `punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`. @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} }

stable-diffusion-xl-base-1.0-gguf