Prompt-Aware Scheduling for Efficient Text-to-Image Inferencing System

• URL: http://arxiv.org/abs/2502.06798v1
• Date: Wed, 29 Jan 2025 03:17:48 GMT
• Title: Prompt-Aware Scheduling for Efficient Text-to-Image Inferencing System
• Authors: Shubham Agarwal, Saud Iqbal, Subrata Mitra,
• Abstract summary: This work introduces a novel text-to-image inference system that optimally matches prompts across multiple instances of the same model operating at various approximation levels to deliver high-quality images under high loads and fixed budgets.
• Score: 6.305230222189566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional ML models utilize controlled approximations during high loads, employing faster, but less accurate models in a process called accuracy scaling. However, this method is less effective for generative text-to-image models due to their sensitivity to input prompts and performance degradation caused by large model loading overheads. This work introduces a novel text-to-image inference system that optimally matches prompts across multiple instances of the same model operating at various approximation levels to deliver high-quality images under high loads and fixed budgets.

Related papers

• Cost-Aware Routing for Efficient Text-To-Image Generation [19.848723289971208]
  We propose a framework to allow the amount of computation to vary for each prompt, depending on its complexity.<n>We empirically demonstrate on COCO and DiffusionDB that by learning to route to nine already-trained text-to-image models, our approach is able to deliver an average quality that is higher than that achievable by any of these models alone.
  arXiv  Detail & Related papers  (2025-06-17T17:48:50Z)
• Performance Plateaus in Inference-Time Scaling for Text-to-Image Diffusion Without External Models [31.873727540047156]
  We apply Best-of-N inference-time scaling to algorithms that optimize the initial noise of a text-to-image diffusion model.<n>We demonstrate that inference-time scaling for text-to-image diffusion models in this setting quickly reaches a performance plateau.
  arXiv  Detail & Related papers  (2025-06-14T21:25:08Z)
• TIPO: Text to Image with Text Presampling for Prompt Optimization [16.001151202788304]
  TIPO is an innovative framework designed to enhance text-to-image (T2I) generation by language model (LM) Unlike previous approaches that rely on Large Language Models (LLMs) or reinforcement learning (RL), TIPO adjusts user input prompts with the distribution of a trained prompt dataset.
  arXiv  Detail & Related papers  (2024-11-12T19:09:45Z)
• Fast constrained sampling in pre-trained diffusion models [77.21486516041391]
  Diffusion models have dominated the field of large, generative image models. We propose an algorithm for fast-constrained sampling in large pre-trained diffusion models.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [62.06970466554273]
  We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL.<n>We leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers.<n>Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images.
  arXiv  Detail & Related papers  (2024-10-10T17:59:17Z)
• Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images [0.0]
  This study proposes a method to mitigate such issues by fine-tuning the Stable Diffusion 3 model using the DreamBooth technique. Experimental results targeting the prompt "lying on the grass/street" demonstrate that the fine-tuned model shows improved performance in visual evaluation and metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Frechet Inception Distance (FID)
  arXiv  Detail & Related papers  (2024-09-23T00:51:47Z)
• Batch-Instructed Gradient for Prompt Evolution:Systematic Prompt Optimization for Enhanced Text-to-Image Synthesis [3.783530340696776]
  This study proposes a Multi-Agent framework to optimize input prompts for text-to-image generation models. A professional prompts database serves as a benchmark to guide the instruction modifier towards generating high-caliber prompts. Preliminary ablation studies highlight the effectiveness of various system components and suggest areas for future improvements.
  arXiv  Detail & Related papers  (2024-06-13T00:33:29Z)
• Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models [23.786473791344395]
  Cross-attention layers in diffusion models tend to disproportionately focus on certain tokens during the generation process. We introduce attention regulation, an on-the-fly optimization approach at inference time to align attention maps with the input text prompt. Experiment results show that our method consistently outperforms other baselines.
  arXiv  Detail & Related papers  (2024-03-11T02:18:27Z)
• FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis [25.356376402671536]
  FrameNeRF is designed to apply off-the-shelf fast high-fidelity NeRF models with fast training speed and high rendering quality for few-shot novel view synthesis tasks.
  arXiv  Detail & Related papers  (2024-02-22T14:41:02Z)
• AdaDiff: Adaptive Step Selection for Fast Diffusion Models [82.78899138400435]
  We introduce AdaDiff, a lightweight framework designed to learn instance-specific step usage policies. AdaDiff is optimized using a policy method to maximize a carefully designed reward function. We conduct experiments on three image generation and two video generation benchmarks and demonstrate that our approach achieves similar visual quality compared to the baseline.
  arXiv  Detail & Related papers  (2023-11-24T11:20:38Z)
• CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation [49.3016007471979]
  Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks. However, their widespread adoption is hindered by the high computational cost, which limits their real-time application. We introduce a novel method dubbed CoDi, that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs.
  arXiv  Detail & Related papers  (2023-10-02T17:59:18Z)
• Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning [36.4086473737433]
  We propose a lightweight image captioning network in combination with continuous diffusion, called Prefix-diffusion. To achieve diversity, we design an efficient method that injects prefix image embeddings into the denoising process of the diffusion model. In order to reduce trainable parameters, we employ a pre-trained model to extract image features and further design an extra mapping network.
  arXiv  Detail & Related papers  (2023-09-10T08:55:24Z)
• SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models [56.88192537044364]
  We propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. Our approach can make text-to-image diffusion models easier to use with better user experience.
  arXiv  Detail & Related papers  (2023-05-09T05:48:38Z)
• StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis [54.39789900854696]
  StyleGAN-T addresses the specific requirements of large-scale text-to-image synthesis. It significantly improves over previous GANs and outperforms distilled diffusion models in terms of sample quality and speed.
  arXiv  Detail & Related papers  (2023-01-23T16:05:45Z)
• A Generic Approach for Enhancing GANs by Regularized Latent Optimization [79.00740660219256]
  We introduce a generic framework called em generative-model inference that is capable of enhancing pre-trained GANs effectively and seamlessly. Our basic idea is to efficiently infer the optimal latent distribution for the given requirements using Wasserstein gradient flow techniques.
  arXiv  Detail & Related papers  (2021-12-07T05:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.