Temporally stable generative illumination with a one-step diffusion model AMD researchers presented a generative method for global illumination at ECCV 2026 that produces temporally stable indirect lighting using a single-step latent diffusion model, outperforming prior methods on PSNR, SSIM, LPIPS, and temporal stability metrics. The model conditions on scene intrinsics and sparse lighting hints, with a Temporal VAE decoder for long-range coherence, and adapts to real-world images via a compact variant. AMD Capsaicin Framework Capsaicin is a Direct3D12 framework for real-time graphics research which implements the GI-1.0 technique and a reference path-tracer. Large diffusion models can generate images with convincing illumination directly from strong learned priors. At the The 19th European Conference on Computer Vision — ECCV 2026 https://eccv.ecva.net/virtual/2026/poster/4617 we are presenting a generative method for global illumination GI that produces geometrically aligned, temporally stable indirect lighting using a single-step latent diffusion model. Unlike iterative or video diffusion methods that are costly and hard to control, our model conditions a one-step image generator on scene-intrinsic signals and sparse lighting hints, and adds a Temporal VAE TVAE decoder for long-range temporal coherence — suitable for real-time rendering use cases with frame-by-frame generation. Figure 1. Left Direct lighting, Middle Our result, and Right Reference GI. We begin by reframing the problem itself. We formulate the screen-space approximation of GI as a conditional image generation task Figure 2 . Building on a distilled single-step model, we fine-tune the diffusion UNet while keeping the VAE frozen. Direct lighting serves as the initial lighting hint, encoded through the VAE encoder, while geometry normals , materials albedo, roughness, metallic , and lighting cues noisy GI, radiance cues are injected at multiple UNet levels through T2I-Adapters. Routing conditions through adapters rather than concatenating them onto the noisy input preserves quality and enables efficient multi-scale conditioning. We also demodulate direct lighting by albedo before encoding, so the model focuses on illumination, then re-modulate on output to restore texture and color. To keep long sequences stable, the TVAE decoder takes the current latent along with the previous output, reprojected using motion vectors. A small history encoder injects these signals via zero convolutions, so the decoder reuses pixels from the previous frame where the current latent is close to it — suppressing flicker while preserving detail. We train only the decoder and simply swap it in at inference time. Figure 2. Overview of our method. Multiple conditioning inputs are encoded via adapter modules and injected into a one-step diffusion model. The Temporal VAE decoder ensures temporal consistency over long sequences and replaces the frozen VAE decoder at inference time. We now turn to training and evaluation. We train on a synthetic dataset of interior scenes rendered with Blender® Cycles and evaluate on unseen scenes. Against a few prior methods, our results are visually closer to the reference and preserve geometric structure from the inputs, while the diffusion baselines show hallucinations and overly bright output Figure 3 . Quantitatively, we lead on PSNR, SSIM, and LPIPS by a large margin and achieve better temporal stability FovVideoVDP across all scenes. Figure 3. Quality comparison on the test dataset. Direct lighting a is the initial hint and the reference g is path-traced GI. We compare against SSGI b , BCNN