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Mark Chen 0003

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5 papers

1 author row

ICML Conference 2023 Conference Paper

Consistency Models

Yang Song
Prafulla Dhariwal
Mark Chen 0003
Ilya Sutskever

Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3. 55 on CIFAR-10 and 6. 20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.

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ICML Conference 2022 Conference Paper

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Alexander Quinn Nichol
Prafulla Dhariwal
Aditya Ramesh
Pranav Shyam
Pamela Mishkin
Bob McGrew
Ilya Sutskever
Mark Chen 0003

Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3. 5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https: //github. com/openai/glide-text2im.

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ICML Conference 2021 Conference Paper

Zero-Shot Text-to-Image Generation

Aditya Ramesh
Mikhail Pavlov
Gabriel Goh
Scott Gray
Chelsea Voss
Alec Radford
Mark Chen 0003
Ilya Sutskever

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

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ICML Conference 2020 Conference Paper

Distribution Augmentation for Generative Modeling

Heewoo Jun
Rewon Child
Mark Chen 0003
John Schulman
Aditya Ramesh
Alec Radford
Ilya Sutskever

We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the specific function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2. 56 bits per dim (relative to the state-of-the-art 2. 80). Samples from this model attain FID 12. 75 and IS 8. 40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains.

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ICML Conference 2020 Conference Paper

Generative Pretraining From Pixels

Mark Chen 0003
Alec Radford
Rewon Child
Jeffrey Wu 0003
Heewoo Jun
David Luan
Ilya Sutskever

Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96. 3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99. 0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69. 0% top-1 accuracy on a linear probe of our features.

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