Injects reference image features into Anima's DiT via decoupled cross-attention, enabling character-consistent image generation.

Use with LTX-2.3 IC workflows to add color to black and white footage. It is not perfect by any means but for the best results I recommend to:

Model: https://huggingface.co/baseten/distilled_8step_FLUX.2-dev

Abstract

Diffusion models achieve state-of-the-art image synthesis, with their generative trajectories fundamentally exhibiting a spectral bias, resolving low-frequency global structures early and high-frequency fine details later. Conventional stochastic differential equation (SDE) solvers fail to account for this dynamic, naively injecting uniform white noise throughout the entire process and misusing the finite energy budget. In this work, we establish a mathematical framework that reconsiders SDE inference as a targeted, frequency-decoupled energy transfer. Leveraging this framework, we introduce Colored Noise Sampling (CNS), a novel, training-free stochastic solver. Rather than injecting uniform white noise, CNS utilizes a dynamic, timestep- and frequency-dependent schedule that more efficiently allocates injected energy toward structurally unresolved frequency bands. By actively exploiting the model's inherent spectral bias, CNS systematically steers the generated distribution toward the true data manifold. Extensive experiments demonstrate that CNS significantly outperforms standard ODE and SDE baselines as a strictly plug-and-play, inference-time sampler substitution across diverse architectures (SiT, JiT, FLUX). Compared to standard sampling on ImageNet-256, CNS achieves substantial unguided FID reductions, improving from 8.26 to 6.27 on SiT-XL/2, 32.39 to 26.69 on JiT-B/16, and 11.88 to 8.31 on JiT-H/16, while yielding consistent relative FID improvements with Classifier-Free Guidance. Project page is available at this https URL.

Paper: https://arxiv.org/abs/2605.30332

Code: https://github.com/hadardavidson/colored-noise-sampling

Project Page: https://hadardavidson.github.io/CNS/

Basic Comfy UI Implementation for Flux: https://github.com/IIs-fanta/ComfyUI-CNS-Sampler-CHENGOU

Abstract

Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual-acoustic content. However, existing open-source methods mainly rely on either dual-tower designs with posterior alignment or fully unified tri-modal designs that mix textual context, audio and video in one shared space. The former weakens fine-grained audio-video co-evolution, while the latter couples semantic conditioning with low-level synchronization. To address these limitations, we propose NAVA, a Native Audio-Visual Alignment framework for joint audio-video generation. NAVA is built upon context-conditioned native audio-visual alignment: it first establishes audio-video correspondence in a dedicated interaction space, and then uses external context to condition the joint denoising process. Specifically, NAVA is instantiated with an Align-then-Fuse MMDiT architecture, which transitions from modality-aware audio-video alignment to modality-shared joint denoising. Furthermore, we introduce Timbre-in-Context Conditioning to associate reference timbre cues with corresponding speech spans to achieve controllable speech timbre. Experiments on Verse-Bench and Seed-TTS, together with a user study, demonstrate that NAVA achieves superior video quality, precise audio-visual synchronization, competitive audio quality, and stronger reference-timbre controllability using only 6.3B parameters.

Paper: https://arxiv.org/abs/2605.30073

Code: https://github.com/ernie-research/NAVA

Model: https://huggingface.co/ernie-research/NAVA

Project Page: https://ernie-research.github.io/NAVA/

Release: https://github.com/invoke-ai/InvokeAI/releases/tag/v6.13.0

Site: https://invoke.ai/

Tutorial: https://youtu.be/FBv1b5gmjcE

Repo: https://github.com/daydreamlive/DEMON

LoRA: https://civitai.com/models/2416425/acestep-loras

DreamVAE: https://huggingface.co/daydreamlive/DreamVAE

Discord Server: https://discord.gg/g7F2HCa9VB

Demo: https://music.daydream.live/

Abstract

Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Rotary Position Embeddings (RoPE) extrapolation combined with attention scaling. However, these strategies apply a uniform and content-agnostic scaling across RoPE components with distinct frequency characteristics, inducing a trade-off between preserving global structure and recovering fine detail. We introduce SEGA, a training-free method that dynamically scales attention across RoPE components according to the latent's spatial-frequency structure at each denoising step. This adaptive scaling improves both structural coherence and fine-detail fidelity. Experiments show that SEGA consistently improves high-resolution synthesis across multiple target resolutions, outperforming state-of-the-art training-free baselines.

Paper: https://arxiv.org/abs/2605.22668

Hugging Face: https://huggingface.co/papers/2605.22668

Blog:

Project Page: https://rajabi2001.github.io/sega/

https://www.youtube.com/watch?v=ARlzfo3MU6U