Entity · model
FLUX
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flux-9f0fbe1b·5 events·first seen Aliases: FLUX, FLUX.1
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Recent events (5)
OrbitQuant: Data-agnostic quantization for image and video diffusion transformers
OrbitQuant is a new post-training quantization method for diffusion transformers that avoids the need for calibration data by quantizing activations in a normalized, rotated basis using a randomized permuted block-Hadamard rotation. A single Lloyd-Max codebook covers all timesteps, prompts, and layers for a given input dimension, and the same recipe transfers from image to video models without per-modality tuning. The method is evaluated on FLUX.1, Z-Image-Turbo, Wan 2.1, and CogVideoX, claiming state-of-the-art PTQ results at several low-bit settings including W2A4 for image DiTs.
GeM-NR: Training-free multi-view editing for nonrigid 3D scene changes
GeM-NR is a training-free method for multi-view consistent image editing that handles nonrigid edits — changes that substantially alter scene geometry and appearance — a capability that existing methods largely lack. Given an anchor image edited by a backbone model (FLUX, Qwen, or BrushNet) and an unedited query image, the method propagates the edit consistently across viewpoints via depth estimation, point-cloud alignment, projection, and conditioned refinement. The authors report state-of-the-art performance on edit quality and geometric/photometric consistency across multiple views, including generation of 3D representations of edited scenes.
Memory-efficient Diffusion Transformers with Quanto and Diffusers
This Hugging Face blog post describes integrating the Quanto quantization library with the Diffusers framework to reduce memory requirements for diffusion transformer models. The approach enables running large image/video generation models on consumer-grade hardware by applying int8 and int4 quantization to model weights. The post covers practical implementation details and benchmarks showing memory savings for models like Flux and others in the diffusion transformer family.
Fast LoRA inference for Flux with Diffusers and PEFT
Hugging Face published a technical blog post detailing optimizations for LoRA inference speed with the Flux image generation model using the Diffusers and PEFT libraries. The post covers techniques to accelerate adapter loading and inference throughput for diffusion models. This is relevant to practitioners deploying fine-tuned image generation models in production or research settings.
Diffusers welcomes FLUX-2
Hugging Face's Diffusers library has added support for FLUX-2, the successor to Black Forest Labs' FLUX image generation model. The blog post announces integration of the new model into the Diffusers ecosystem, enabling developers to use FLUX-2 through the standard Diffusers API. This represents a tooling and ecosystem update for one of the leading open-weights image generation model families.