# diffusers

Published articles for diffusers.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

DevFeed: [Bringing Nunchaku 4-bit Diffusion Inference to Diffusers](<https://devfeed.tech/articles/bringing-nunchaku-4-bit-diffusion-inference-to-diffusers-7368.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nunchaku-diffusers>)

Author: Pham Hong Vinh; Sayak Paul

Published: 2026-07-23T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>)

Tags: [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [diffusion-transformers](<https://devfeed.tech/tags/diffusion-transformers.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>)

### AI overview

This article explains how Nunchaku brings 4-bit diffusion inference to Hugging Face Diffusers. It describes SVDQuant's W4A4 approach, integration of pre-quantized checkpoints without local CUDA compilation, and hardware requirements for NVFP4 and INT4 variants.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Beyond LoRA: Can you beat the most popular fine-tuning technique?

DevFeed: [Beyond LoRA: Can you beat the most popular fine-tuning technique?](<https://devfeed.tech/articles/beyond-lora-can-you-beat-the-most-popular-fine-tuning-technique-7439.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/peft-beyond-lora>)

Author: Benjamin Bossan; Sayak Paul; Marian Tietz; Kashif Rasul

Published: 2026-06-18T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [community](<https://devfeed.tech/tags/community.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [guide](<https://devfeed.tech/tags/guide.md>), [lora](<https://devfeed.tech/tags/lora.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [peft](<https://devfeed.tech/tags/peft.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article examines parameter-efficient fine-tuning (PEFT) for open models, focusing on whether LoRA is always the best technique. It explains how PEFT reduces memory requirements, can enable fine-tuning of quantized models, and offers a unified API for multiple techniques.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines

DevFeed: [Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines](<https://devfeed.tech/articles/introducing-modular-diffusers-composable-building-blocks-for-diffusion-pipelines-7357.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/modular-diffusers>)

Author: YiYi Xu; Alvaro Somoza; Dhruv Nair; Sayak Paul

Published: 2026-03-05T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [modular-diffusers](<https://devfeed.tech/topics/modular-diffusers.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [building](<https://devfeed.tech/tags/building.md>), [data](<https://devfeed.tech/tags/data.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [documentation](<https://devfeed.tech/tags/documentation.md>), [generative](<https://devfeed.tech/tags/generative.md>), [guide](<https://devfeed.tech/tags/guide.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [hub](<https://devfeed.tech/tags/hub.md>), [images](<https://devfeed.tech/tags/images.md>), [inference](<https://devfeed.tech/tags/inference.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [modular-diffusers](<https://devfeed.tech/tags/modular-diffusers.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>), [text-to-video](<https://devfeed.tech/tags/text-to-video.md>), [work](<https://devfeed.tech/tags/work.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

Modular Diffusers presents diffusion pipelines as composable blocks for text encoding, image encoding, denoising, and decoding. The article explains how to run inference, build and publish custom Python blocks, compose workflows, integrate with the node-based Mellon interface, and manage model loading and memory.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Custom Kernels for All from Codex and Claude

DevFeed: [Custom Kernels for All from Codex and Claude](<https://devfeed.tech/articles/custom-kernels-for-all-from-codex-and-claude-7153.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/custom-cuda-kernels-agent-skills>)

Author: ben burtenshaw; Sayak Paul; Aritra Roy Gosthipaty; shaun smith

Published: 2026-02-13T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [CUDA](<https://devfeed.tech/topics/cuda.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [kernel-builder](<https://devfeed.tech/topics/kernel-builder.md>)

Tags: [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [agents](<https://devfeed.tech/tags/agents.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [community](<https://devfeed.tech/tags/community.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cursor](<https://devfeed.tech/tags/cursor.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [memory](<https://devfeed.tech/tags/memory.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article describes an agent skill that teaches coding agents to write production CUDA kernels. Claude and Codex were used to create working kernels for a diffusers pipeline and a transformers model, including PyTorch bindings and benchmarks. The skill packages GPU architecture, memory optimization, project structure, and integration expertise for on-demand use.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Diffusers welcomes FLUX-2

DevFeed: [Diffusers welcomes FLUX-2](<https://devfeed.tech/articles/diffusers-welcomes-flux-2-7202.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/flux-2>)

Author: YiYi Xu; Daniel Gu; Sayak Paul; Alvaro Somoza; Dhruv Nair; Aritra Roy Gosthipaty; Linoy Tsaban; Apolinário from multimodal AI art

Published: 2025-11-25T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [black-forest-labs](<https://devfeed.tech/tags/black-forest-labs.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [flux](<https://devfeed.tech/tags/flux.md>), [generation](<https://devfeed.tech/tags/generation.md>), [images](<https://devfeed.tech/tags/images.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [streams](<https://devfeed.tech/tags/streams.md>)

### AI overview

The article introduces FLUX.2, an image-generation and editing model that supports text-guided and image-guided generation with multiple reference images. It outlines changes to its text encoder and diffusion-transformer architecture relative to FLUX.1.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Fast LoRA inference for Flux with Diffusers and PEFT

DevFeed: [Fast LoRA inference for Flux with Diffusers and PEFT](<https://devfeed.tech/articles/fast-lora-inference-for-flux-with-diffusers-and-peft-7344.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/lora-fast>)

Author: Sayak Paul; Benjamin Bossan

Published: 2025-07-23T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [flux](<https://devfeed.tech/topics/flux.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Hacktoberfest](<https://devfeed.tech/topics/hacktoberfest.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [peft](<https://devfeed.tech/topics/peft.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [amd](<https://devfeed.tech/tags/amd.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [peft](<https://devfeed.tech/tags/peft.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

This article presents an optimization recipe for faster LoRA inference with the Flux.1-Dev text-to-image model using Diffusers and PEFT. The approach addresses LoRA hotswapping and recompilation issues with Flash Attention 3, FP8 quantization from TorchAO, and hotswapping-ready compilation, achieving about a 2.3x speedup while balancing inference speed and memory use.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

DevFeed: [(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware](<https://devfeed.tech/articles/lora-fine-tuning-flux-1-dev-on-consumer-hardware-7203.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/flux-qlora>)

Author: Derek Liu; Marc Sun; Sayak Paul; merve; Linoy Tsaban

Published: 2025-06-19T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [flux](<https://devfeed.tech/topics/flux.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dev](<https://devfeed.tech/tags/dev.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This tutorial explains how to fine-tune the FLUX.1-dev diffusion model efficiently with QLoRA on a single consumer GPU using less than about 10 GB of VRAM. It describes the model components, focuses training on the transformer while keeping the text encoders and VAE frozen, and discusses LoRA, quantization, and FP8 training for memory and speed improvements.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Exploring Quantization Backends in Diffusers

DevFeed: [Exploring Quantization Backends in Diffusers](<https://devfeed.tech/articles/exploring-quantization-backends-in-diffusers-7170.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/diffusers-quantization>)

Author: Derek Liu; Marc Sun; Sayak Paul

Published: 2025-05-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [backends](<https://devfeed.tech/tags/backends.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [black-forest-labs](<https://devfeed.tech/tags/black-forest-labs.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [diffusion-transformers](<https://devfeed.tech/tags/diffusion-transformers.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [images](<https://devfeed.tech/tags/images.md>), [memory](<https://devfeed.tech/tags/memory.md>), [quantization](<https://devfeed.tech/tags/quantization.md>)

### AI overview

The article examines quantization backends in Hugging Face Diffusers using the FLUX.1-dev image-generation pipeline. It compares high-precision and 4-bit or 8-bit variants, explains pipeline components and memory use, and discusses backends including bitsandbytes, GGUF, torchao, Quanto, and FP8.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Remote VAEs for decoding with Inference Endpoints 🤗

DevFeed: [Remote VAEs for decoding with Inference Endpoints 🤗](<https://devfeed.tech/articles/remote-vaes-for-decoding-with-inference-endpoints-7456.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/remote_vae>)

Author: hlky; Sayak Paul

Published: 2025-02-24T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [VAE](<https://devfeed.tech/topics/vae.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Deadlock](<https://devfeed.tech/topics/deadlock.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data](<https://devfeed.tech/tags/data.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generate](<https://devfeed.tech/tags/generate.md>), [generation](<https://devfeed.tech/tags/generation.md>), [getting-started](<https://devfeed.tech/tags/getting-started.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hdd](<https://devfeed.tech/tags/hdd.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [nvme](<https://devfeed.tech/tags/nvme.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [queue](<https://devfeed.tech/tags/queue.md>), [remote](<https://devfeed.tech/tags/remote.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [time](<https://devfeed.tech/tags/time.md>), [vae](<https://devfeed.tech/tags/vae.md>)

### AI overview

This article presents an experimental approach for decoding latent-space diffusion outputs with remote VAEs hosted on Inference Endpoints. It explains how remote decoding can reduce consumer GPU memory pressure, avoid the quality loss associated with tiled decoding, and improve concurrency by queueing generation requests. The article includes setup and usage examples for random tensors and pipelines involving SD v1.5, Flux, and HunyuanVideo.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## The AI tools for Art Newsletter - Issue 1

DevFeed: [The AI tools for Art Newsletter - Issue 1](<https://devfeed.tech/articles/the-ai-tools-for-art-newsletter-issue-1-7076.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ai-art-newsletter-jan-25>)

Author: Linoy Tsaban; Apolinário from multimodal AI art

Published: 2025-01-31T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [releases](<https://devfeed.tech/topics/releases.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-art](<https://devfeed.tech/tags/ai-art.md>), [aiart](<https://devfeed.tech/tags/aiart.md>), [community](<https://devfeed.tech/tags/community.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [releases](<https://devfeed.tech/tags/releases.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

A newsletter issue reviewing 2024 developments in open-source creative AI, especially image and video generation. It discusses diffusion and flow-matching approaches, image-model releases including Stable Diffusion 3, HunyuanDiT, AuraFlow, Flux.1, and Stable Diffusion 3.5, plus personalization techniques for text-to-image models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## State of open video generation models in Diffusers

DevFeed: [State of open video generation models in Diffusers](<https://devfeed.tech/articles/state-of-open-video-generation-models-in-diffusers-7555.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/video_gen>)

Author: Sayak Paul; Aryan V S; Dhruv Nair

Published: 2025-01-27T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [video](<https://devfeed.tech/tags/video.md>), [video-gen](<https://devfeed.tech/tags/video-gen.md>)

### AI overview

An overview of open video generation models, their capabilities and limitations, and the Diffusers team's plans to support their adoption at scale. It covers inference optimization, quantization, latency, and fine-tuning.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Diffusers welcomes Stable Diffusion 3.5 Large

DevFeed: [Diffusers welcomes Stable Diffusion 3.5 Large](<https://devfeed.tech/articles/diffusers-welcomes-stable-diffusion-3-5-large-7470.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/sd3-5>)

Author: YiYi Xu; Aryan V S; Dhruv Nair; Sayak Paul; Linoy Tsaban; Apolinário from multimodal AI art; Alvaro Somoza; Aritra Roy Gosthipaty

Published: 2024-10-22T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [memory-optimization](<https://devfeed.tech/tags/memory-optimization.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [sd3-5](<https://devfeed.tech/tags/sd3-5.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

Hugging Face introduces Stable Diffusion 3.5 Large, providing an 8B base model and a timestep-distilled variant for few-step image generation. The article explains how to use the models with Diffusers for inference and training, including gated access, memory optimization, and quantized execution for lower-memory systems.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Memory-efficient Diffusion Transformers with Quanto and Diffusers

DevFeed: [Memory-efficient Diffusion Transformers with Quanto and Diffusers](<https://devfeed.tech/articles/memory-efficient-diffusion-transformers-with-quanto-and-diffusers-7450.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/quanto-diffusers>)

Author: Sayak Paul; David Corvoysier

Published: 2024-07-30T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>)

Tags: [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/tags/diffusion-transformers.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [quality](<https://devfeed.tech/tags/quality.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [sd3](<https://devfeed.tech/tags/sd3.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

This tutorial explains how to reduce the memory requirements of Transformer-based diffusion pipelines using Quanto quantization utilities from the Diffusers library. It benchmarks FP8 quantization on PixArt-Sigma, Stable Diffusion 3, and Aura Flow, reporting memory savings with slightly higher latency and little quality degradation.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Diffusers welcomes Stable Diffusion 3

DevFeed: [Diffusers welcomes Stable Diffusion 3](<https://devfeed.tech/articles/diffusers-welcomes-stable-diffusion-3-7469.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/sd3>)

Author: Dhruv Nair; YiYi Xu; Sayak Paul; Alvaro Somoza; Kashif Rasul; Apolinário from multimodal AI art

Published: 2024-06-12T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [guide](<https://devfeed.tech/tags/guide.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [sd3](<https://devfeed.tech/tags/sd3.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

The article announces Stable Diffusion 3 Medium, a 2B-parameter latent diffusion model. It describes the MMDiT architecture, multimodal text and image processing, rectified flow matching, inference support through a new scheduler, and accompanying Diffusers, DreamBooth, and LoRA resources.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Faster assisted generation support for Intel Gaudi

DevFeed: [Faster assisted generation support for Intel Gaudi](<https://devfeed.tech/articles/faster-assisted-generation-support-for-intel-gaudi-7108.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/assisted-generation-support-gaudi>)

Author: Haim Barad; Neha Raste; Tien Pei Chou

Published: 2024-06-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [intel](<https://devfeed.tech/topics/intel.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [gaudi](<https://devfeed.tech/tags/gaudi.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [intel](<https://devfeed.tech/tags/intel.md>), [latency](<https://devfeed.tech/tags/latency.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article explains how assisted decoding and speculative sampling were adapted and optimized for Intel Gaudi processors. Integrated into Optimum Habana, these techniques use draft and target models, KV caching, and quantized models to accelerate text generation while preserving the target model's sampling quality.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.