Unconditional Image Generation
30 papers with code • 4 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Unconditional Image Generation
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CelebA-HQ
ArtBench-10 (32x32)
Large Labelled Logo Dataset (L3D)
Latest papers with no code
Generative Modelling with High-Order Langevin Dynamics
In this paper, we propose a novel fast high-quality generative modelling method based on high-order Langevin dynamics (HOLD) with score matching.
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources.
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers
As these parameters are independent, a single diffusion model with these task-specific parameters can be used to perform multiple tasks simultaneously.
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise
Thus, we were motivated to utilize the isotropy of the additive noise as a constraint on the objective function to enhance the fidelity of DDPMs.
EraseDiff: Erasing Data Influence in Diffusion Models
In this work, we introduce an unlearning algorithm for diffusion models.
Improving Diffusion-Based Image Synthesis with Context Prediction
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
We find that the experimental results are in good agreement with our theoretical predictions on the iteration complexity, and the models with our newly proposed forward processes can outperform existing models.
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics
Diffusion models have advanced generative AI significantly in terms of editing and creating naturalistic images.
Unified High-binding Watermark for Unconditional Image Generation Models
In the first stage, we use an encoder to invisibly write the watermark image into the output images of the original AIGC tool, and reversely extract the watermark image through the corresponding decoder.
Gradpaint: Gradient-Guided Inpainting with Diffusion Models
For the specific task of image inpainting, the current guiding mechanism relies on copying-and-pasting the known regions from the input image at each denoising step.
