“FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching” FlowAR employs a simplest scale design and is compatible with any VAE.

A lightweight ComfyUI custom scheduler(s) & sigma generator for Z-Image-Turbo/Flux2Klein. Delivers a stable linear sigma schedule (1.0 → 0.0) for rectified-flow/flow-matching, boosting few-step generation (8-9 steps) with superior consistency, reduced noise, and full compatibility with the model's distilled pipeline.

Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching, ICML2025 (Spotlight)

Utilized attention incorporated UNet model for conditional image generation using Flow Matching with Conditional Optimal Transport Objective

Official Repository for "FlawMatch: Conditional Defect Image Generation via Flow Matching for Improved Surface Defect Classification"

Conditional Flow Matching for image generation using PyTorch Lightning

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation

Autoregressive image generation with flow matching

Auto-Regressive Flow Matching: image generation from center to edge via spatially heterogeneous time fields

Image Generation using Conditional Flow Matching

Flow matching with transformer from scratch for image generation (mnis)

Repository for "Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields"

[ICLR 2026] Purrception: Variational Flow Matching for Vector-Quantized Image Generation

Repo for combining flow matching models with autoregressive transformers for image generation

Code zum Paper "LeDiFLow: Learned Distribution-guided Flow Matching to Accelerate Image Generation", welches ein neues Verfahren für die Bildgenerierung vorstellt.

Conditional Flow Matching (CFM) for image generation on CIFAR-10, benchmarked against DDPM and DDIM. Comparing generation quality (FID) vs. sampling speed (NFE) using shared U-Net backbone.

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Flow Matching for Controlled Image Generation

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Flow Matching Designs for Conditional Image Generation

Flow Matching Synthetic Image Generation for Data Augmentation

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Image generation on Fashion-MNIST with Diffusion and Flow Matching models

Flow Matching for image generation. Optimal transport, rectified flows, classifier-free guidance, ODE sampling.

Official repository of FlowInOne: Unifying Multimodal Generation as Image-In Image-Out Flow Matching

Reward Weighted Fine-tuning of Flow Matching Networks for Preferential Image Generation

Advanced Flow Matching suite: multi-resolution image generation, conditional synthesis, and video generation in PyTorch