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Stable Diffusion 2-1 - a Hugging Face Space by stabilityai. 10.5k. Loading... Discover amazing ML apps made by the community.
Stable Diffusion is a Latent Diffusion model developed by researchers from the Machine Vision and Learning group at LMU Munich, a.k.a CompVis. Model checkpoints were publicly released at the end of August 2022 by a collaboration of Stability AI, CompVis, and Runway with support from EleutherAI and LAION.
Jun 12, 2024 · Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
This stable-diffusion-2 model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on 768x768 images.
Nov 1, 2023 · HuggingFace Stable Diffusion XL is a multi-expert pipeline for latent diffusion. Initially, a base model produces preliminary latents, which are then refined by a specialized model (found here) that focuses on the final denoising. The base model is also functional independently.
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under Model Access.
The Stable Diffusion model is a good starting point, and since its official launch, several improved versions have also been released. However, using a newer version doesn’t automatically mean you’ll get better results.
Aug 23, 2023 · Models. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
New stable diffusion finetune ( Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO.
To load an ONNX model and run inference with ONNX Runtime, you need to replace StableDiffusionXLPipeline with Optimum ORTStableDiffusionXLPipeline. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can set export=True.
