AI Foundation Models
HiDream-I1: A High-Efficient Image Generative Foundation Model
HiDream-I1 is a 17B-parameter open-source image generative foundation model leveraging a sparse Diffusion Transformer (DiT) structure for state-of-the-art image quality and high computational efficiency. It achieves remarkable speed with variants like HiDream-I1-Fast (14 steps) and extends to instruction-based editing (HiDream-E1) and interactive image agency (HiDream-A1). This report details its architecture, training, and superior performance across key benchmarks.
Executive Impact: Key Performance Metrics
HiDream-I1 delivers industry-leading performance across critical benchmarks, showcasing its efficiency and quality.
17B
Parameters
14
Fastest Steps
93.74%
Relation Accuracy
33.82
HPSv2.1 Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Architecture Overview
Explores the novel sparse Diffusion Transformer (DiT) structure, hybrid text encoding, and Mixture-of-Experts (MoE) design. Key innovations include dual-stream processing for image and text tokens, transitioning to single-stream interaction, and dynamic MoE routing for efficiency.
Training Strategy Overview
Details the multi-stage training process, encompassing latent space operation, progressive resolution training (256x256, 512x512, 1024x1024), AdamW optimization, FSDP, mixed-precision, and gradient checkpointing. Also covers post-training alignment tuning.
Inference Acceleration Overview
Describes the GAN-powered Diffusion Model Distillation, reducing sampling steps from 50+ to 28 (Dev) and 14 (Fast) while maintaining perceptual quality through adversarial training alongside DMD loss. This ensures high-quality image generation in seconds.
Sparse DiT for Cost-Effectiveness
17B Parameters, Optimal Cost-EffectivenessHiDream-I1 Development Flow
Data Pre-processing
→
Hybrid Text Encoding
→
Sparse DiT Backbone
→
Multi-stage Training
→
GAN-powered Distillation
→
Image Editing Extension (E1)
→
Image Agent Evolution (A1)
| Metric | HiDream-I1 (85.89% Overall) | FLUX.1-dev (83.79% Overall) |
|---|---|---|
| Key Features |
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Case Study: HiDream-A1: Interactive Image Agent
Introduction: HiDream-A1 integrates generation, editing, and understanding into a conversational AI interface.
Challenge: Traditional AIGC tools require switching platforms and complex parameter adjustments.
Solution: A unified multimodal agent system with Coordinator and Planner modules.
Result: Seamless visual content creation and manipulation via natural language dialogue, lowering user barrier.
Advanced ROI Calculator
Estimate the potential savings and reclaimed hours by integrating HiDream-I1 into your enterprise workflows.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Enterprise AI Implementation Roadmap
A phased approach to integrate HiDream-I1 seamlessly into your operations and unlock its full potential.
Phase 1: Foundation Model Pre-training
Establish core generative capabilities on a vast dataset, focusing on multi-resolution latent flow matching.
Phase 2: Distillation & Acceleration
Implement GAN-powered distillation to create faster variants (Dev, Fast) without sacrificing quality.
Phase 3: Instruction-based Editing (HiDream-E1)
Extend functionality with precise image editing through additional image conditions and spatially weighted loss.
Phase 4: Comprehensive Image Agent (HiDream-A1)
Integrate generation and editing into an interactive, multimodal conversational AI for full creative control.
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