PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining

arXiv:2609.22789 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Code: https://github.com/black-forest-labs/flux

Terminology

• alpha — 45× in this paper · explained in 1 episode
• Transformer — 25× in this paper · explained in 2 episodes
• Ablation — 20× in this paper · explained in 1 episode
• Fidelity — 13× in this paper · explained in 2 episodes
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
• OmniPSD: Layered PSD Generation with Diffusion Transformer
• OmniAlpha: Aligning Transparency-Aware Generation via Multi-Task Unified Reinforcement Learning
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
• Qwen-Image Technical Report
• Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion
• PixelFlow: Pixel-Space Generative Models with Flow
• PixNerd: Pixel Neural Field Diffusion
• Back to Basics: Let Denoising Generative Models Denoise
• One-step Latent-free Image Generation with Pixel Mean Flows
• PixelGen: Improving Pixel Diffusion with Perceptual Supervision
• Text2Layer: Layered Image Generation using Latent Diffusion Model
• PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models
• COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design
• Controllable Layer Decomposition for Reversible Multi-Layer Image Generation
• SAM 2: Segment Anything in Images and Videos
• AlphaVAE: Unified End-to-End RGBA Image Reconstruction and Generation with Alpha-Aware Representation Learning
• Video Generation with Stable Transparency via Shiftable RGB-A Distribution Learner
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

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