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This model card focuses on the model associated with the Stable Diffusion Upscaler, available here. This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided latent upscaling diffusion model. In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule. @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} } **Notes**: - Despite not being a dependency, we highly recommend you to install xformers for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)

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SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. 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. For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference.

Image generationOpen weights
Input Text→ Output Image
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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.

Image editing
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stable-fast-3d

Stability AI

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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

Video generation
Input Image→ Output Video
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(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 25 frames at resolution 576x1024 given a context frame of the same size, finetuned from [SVD Image-to-Video [14 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid). 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 - **Finetuned from model:** SVD Image-to-Video [14 frames] For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).

Video generation
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stable-zero123

Stability AI

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StableBeluga2

Stability AI

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Text generation
Input Text→ Output Text
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