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#stable-diffusion

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15 items

modelsOct 22

Diffusers welcomes Stable Diffusion 3.5 Large

Hugging Face's Diffusers library now supports Stable Diffusion 3.5 Large, the latest text-to-image model from Stability AI. This integration enables developers to leverage the model's capabilities within their applications. Stable Diffusion 3.5 Large offers improved performance and features. You can access the model through the Diffusers library for your projects.

Key takeaways
  • Stable Diffusion 3.5 Large is now supported in Diffusers.
  • The model offers improved performance and features.
  • Developers can integrate it into their applications using Diffusers.
HHugging Face Blog#text-to-image#stable-diffusion#open-source
modelsOct 3

🧨 Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e

Hugging Face and Google collaborated to optimize Stable Diffusion XL inference using JAX on Cloud TPU v5e. The work resulted in a 30% increase in inference speed and 16% reduction in memory usage. You can deploy optimized models on Hugging Face's Inference API or run them locally with Transformers. This optimization enables faster and more efficient image generation.

Key takeaways
  • 30% faster inference speed on Cloud TPU v5e.
  • 16% reduction in memory usage.
  • Optimized models deployable via Hugging Face's Inference API or local Transformers.
HHugging Face Blog#stable-diffusion#jax#cloud-tpu#inference-optimization
modelsSep 29

Finetune Stable Diffusion Models with DDPO via TRL

The TRL library from Hugging Face now supports DDPO, enabling finetuning of Stable Diffusion models. DDPO is a method for optimizing diffusion models like Stable Diffusion using preferences. This update allows builders to adapt Stable Diffusion models to specific tasks or datasets via finetuning.

Key takeaways
  • TRL library supports DDPO for Stable Diffusion finetuning.
  • DDPO optimizes diffusion models using preference data.
  • Finetuning enables adapting models to specific tasks or datasets.
HHugging Face Blog#stable-diffusion#finetuning#diffusion-models
modelsJul 14

Fine-tuning Stable Diffusion models on Intel CPUs

Intel and Hugging Face collaborated on optimized fine-tuning of Stable Diffusion models on Intel CPUs. The approach uses Intel's OpenVINO toolkit to accelerate model training. This enables developers to fine-tune models locally on commodity hardware, reducing reliance on specialized GPU clusters. You can now deploy and fine-tune Stable Diffusion models on a wider range of hardware.

Key takeaways
  • Stable Diffusion models can be fine-tuned on Intel CPUs with OpenVINO.
  • Fine-tuning on commodity hardware reduces costs and infrastructure needs.
  • Developers can deploy models on a broader range of devices.
HHugging Face Blog#fine-tuning#stable-diffusion#intel-cpu
modelsJun 15

Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac

Hugging Face has optimized Stable Diffusion for Apple's Core ML, enabling faster inference on iPhone, iPad, and Mac devices. This optimization allows for local deployment of text-to-image models with improved performance. You can now run Stable Diffusion on Apple devices with reduced latency. The optimized models are available on the Hugging Face Hub.

Key takeaways
  • Stable Diffusion optimized for Core ML on Apple devices.
  • Faster inference on iPhone, iPad, and Mac.
  • Optimized models available on Hugging Face Hub.
HHugging Face Blog#core-ml#stable-diffusion#apple-devices#text-to-image
modelsMay 25

Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum

Intel and Hugging Face collaborated to optimize Stable Diffusion for Intel CPUs using Neural Network Compression Framework (NNCF) and Hugging Face Optimum. This optimization enables faster inference on Intel hardware. You can deploy optimized models on Intel CPUs for efficient image generation.

Key takeaways
  • Stable Diffusion optimized for Intel CPUs using NNCF and Optimum.
  • Faster inference on Intel hardware for efficient image generation.
  • Optimized models deployable on Intel CPUs.
HHugging Face Blog#intel-cpus#stable-diffusion#model-optimization
researchMay 23

Instruction-tuning Stable Diffusion with InstructPix2Pix

Researchers from Stability AI and Hugging Face collaborated on InstructPix2Pix, an instruction-tuning method for text-to-image models like Stable Diffusion. This approach enables models to follow specific editing instructions, improving their ability to generate images based on detailed text prompts. You can explore the project's code and models on the Hugging Face platform.

Key takeaways
  • InstructPix2Pix improves text-to-image models' ability to follow editing instructions.
  • Method tested on Stable Diffusion models.
  • Code and models available on Hugging Face platform.
HHugging Face Blog#text-to-image#instruction-tuning#stable-diffusion
modelsMar 28

Accelerating Stable Diffusion Inference on Intel CPUs

Intel and Hugging Face collaborated to optimize Stable Diffusion inference on Intel CPUs, achieving up to 2x faster performance. The optimization leverages Intel's AVX-512 and VNNI instructions. This work enables faster and more efficient image generation on widely available hardware, benefiting developers who deploy Stable Diffusion models in production.

Key takeaways
  • Up to 2x faster Stable Diffusion inference on Intel CPUs.
  • Optimization uses Intel's AVX-512 and VNNI instructions.
  • Faster inference on widely available hardware reduces deployment costs.
HHugging Face Blog#stable-diffusion#intel#optimization#inference
modelsFeb 24

Swift 🧨Diffusers - Fast Stable Diffusion for Mac

Hugging Face released Swift Diffusers, a fast implementation of Stable Diffusion optimized for Mac hardware. This enables fast local inference on Apple Silicon devices. You can integrate it into your apps for efficient image generation. The optimized implementation targets performance on M-series chips.

Key takeaways
  • Optimized for Apple Silicon, enabling fast local inference.
  • Enables efficient image generation on M-series chips.
  • Integratable into apps for fast Stable Diffusion.
HHugging Face Blog#stable-diffusion#mac-optimization#apple-silicon
researchJan 26

Using LoRA for Efficient Stable Diffusion Fine-Tuning

The LoRA method allows for efficient fine-tuning of large models like Stable Diffusion by updating only a small subset of model weights. This approach reduces the memory and computational requirements for fine-tuning, making it more accessible for builders with limited resources. By applying LoRA, you can adapt Stable Diffusion to specific tasks or datasets without requiring significant computational resources. The method has been shown to be effective in various applications.

Key takeaways
  • LoRA updates only a small subset of model weights for efficient fine-tuning.
  • Reduces memory and computational requirements for fine-tuning large models.
  • Enables adaptation of Stable Diffusion to specific tasks or datasets.
HHugging Face Blog#fine-tuning#stable-diffusion#efficient-training
modelsDec 1

Using Stable Diffusion with Core ML on Apple Silicon

Apple Silicon Macs can now run Stable Diffusion models locally using Core ML, thanks to Hugging Face's optimized implementation. This allows for fast and private image generation on-device. The optimized models work on M1 and M2-based Macs. You can access pre-trained models and code through the Hugging Face Hub.

Key takeaways
  • Stable Diffusion models optimized for Core ML on Apple Silicon.
  • Local image generation possible on M1 and M2 Macs.
  • Pre-trained models available on Hugging Face Hub.
HHugging Face Blog#apple-silicon#coreml#stable-diffusion#local-inference
toolsNov 7

Training Stable Diffusion with Dreambooth using Diffusers

The Diffusers library now supports training Stable Diffusion models with Dreambooth. This update allows users to fine-tune text-to-image models for specific objects or concepts. Builders can use this feature to create customized models for their applications.

Key takeaways
  • Diffusers library supports Dreambooth for Stable Diffusion training.
  • Enables fine-tuning for specific objects or concepts.
  • Allows creation of customized text-to-image models.
HHugging Face Blog#text-to-image#stable-diffusion#fine-tuning
modelsOct 13

🧨 Stable Diffusion in JAX / Flax !

Hugging Face has released a JAX/Flax implementation of Stable Diffusion. This allows for faster and more efficient deployment on TPUs and GPUs. The new implementation enables builders to leverage JAX's performance optimizations and Flax's ease of use.

Key takeaways
  • Stable Diffusion now available in JAX/Flax.
  • Enables faster deployment on TPUs and GPUs.
  • Leverages JAX performance optimizations and Flax ease of use.
HHugging Face Blog#jax#flax#stable-diffusion#deployment
modelsOct 5

Japanese Stable Diffusion

Hugging Face released a Japanese Stable Diffusion model, optimized for generating high-quality images in the Japanese aesthetic. The model is open-source and available for download. You can use it for various applications, including art and design. The release aims to promote diversity in AI-generated content.

Key takeaways
  • Open-source Japanese Stable Diffusion model available.
  • Optimized for high-quality Japanese-style image generation.
  • Promotes diversity in AI-generated content.
HHugging Face Blog#open-source#stable-diffusion#japanese
modelsAug 22

Stable Diffusion with 🧨 Diffusers

The Stable Diffusion model is now available with Diffusers on the Hugging Face platform. This integration allows for easier model usage and customization. You can access the model through the Hugging Face interface. The Diffusers library provides a simple way to work with the model.

Key takeaways
  • Stable Diffusion integrated with Diffusers on Hugging Face.
  • Easier model usage and customization available.
  • Diffusers library simplifies model interaction.
HHugging Face Blog#stable-diffusion#diffusers#hugging-face