Huggingface stable diffusion

Getting the DiffusionPipeline to generate images in a certain style or include what you want can be tricky. This tutorial walks you through how to generate faster and better with the DiffusionPipeline. One of the simplest ways to speed up inference huggingface stable diffusion to place the pipeline on a GPU the same way you would with any PyTorch module:, huggingface stable diffusion.

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here. Model Description: This is a model that can be used to generate and modify images based on text prompts. Resources for more information: GitHub Repository , Paper. You can do so by telling diffusers to expect the weights to be in float16 precision:. Note : If you are limited by TPU memory, please make sure to load the FlaxStableDiffusionPipeline in bfloat16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.

Huggingface stable diffusion

Stable Video Diffusion SVD is a powerful image-to-video generation model that can generate second high resolution x videos conditioned on an input image. This guide will show you how to use SVD to generate short videos from images. Before you begin, make sure you have the following libraries installed:. To reduce the memory requirement, there are multiple options that trade-off inference speed for lower memory requirement:. Stable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over the generated video:. Diffusers documentation Stable Video Diffusion. Get started. Overview Understanding pipelines, models and schedulers AutoPipeline Train a diffusion model Load LoRAs for inference Accelerate inference of text-to-image diffusion models. Using Diffusers. Overview Load pipelines, models, and schedulers Load and compare different schedulers Load community pipelines and components Load safetensors Load different Stable Diffusion formats Load adapters Push files to the Hub. Overview Unconditional image generation Text-to-image Image-to-image Inpainting Text or image-to-video Depth-to-image. Specific pipeline examples.

Training Procedure Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. Model Description: This is a model that can be used to generate and modify images based on text prompts, huggingface stable diffusion.

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. For the first version 4 model checkpoints are released. Higher versions have been trained for longer and are thus usually better in terms of image generation quality then lower versions.

This model card focuses on the model associated with the Stable Diffusion v2 model, available here. This stable-diffusion-2 model is resumed from stable-diffusionbase base-ema. Resumed for another k steps on x images. Model Description: This is a model that can be used to generate and modify images based on text prompts. Resources for more information: GitHub Repository.

Huggingface stable diffusion

Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. For additional details and context about diffusion models like how they work, check out the notebook!

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The autoencoding part of the model is lossy The model was trained on a subset of the large-scale dataset LAION-5B , which contains adult, violent and sexual content. Downloads last month , Training Training Data The model developers used the following dataset for training the model: LAION-2B en and subsets thereof see next section Training Procedure Stable Diffusion v is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. Model Description: This is a model that can be used to generate and modify images based on text prompts. The autoencoding part of the model is lossy The model was trained on a subset of the large-scale dataset LAION-5B , which contains adult, violent and sexual content. For more in-detail model cards, please have a look at the model repositories listed under Model Access. No additional measures were used to deduplicate the dataset. Training Data The model developers used the following dataset for training the model:. This custom inference handler can be used to implement simple inference pipelines for ML Frameworks like Keras, Tensorflow, and sci-kit learn or to add custom business logic to your existing transformers pipeline. This includes, but is not limited to: Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. Sexual content without consent of the people who might see it.

Why is this important?

See for example this nice blog post. Philosophy Controlled generation How to contribute? Other Modalities. Not optimized for FID scores. This affects the overall output of the model, as white and western cultures are often set as the default. We aim at generating a beautiful photograph of an old warrior chief and will later try to find the best prompt to generate such a photograph. Collaborate on models, datasets and Spaces. Internal classes. I believe the original idea came from this GitHub thread. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.