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Complete guide to running Stable Diffusion 3.5 Large on AMD Radeon GPUs with ONNX optimization for up to 3.3x faster image generation

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What is Stable Diffusion 3.5 Large AMD GPU Optimization?

Stable Diffusion 3.5 Large AMD GPU optimization represents a breakthrough in AI image generation performance for AMD Radeon graphics cards. This ONNX-optimized version of Stability AI’s flagship 8-billion parameter model delivers up to 3.3x faster inference speeds compared to standard PyTorch implementations on compatible AMD hardware.

The “_amdgpu” suffix in model names indicates specific optimization for AMD GPU architecture, leveraging hardware acceleration capabilities to make professional-grade AI image generation accessible and efficient on AMD Radeon GPUs, particularly the high-end RX 9070 XT and similar cards.

This optimization allows AMD GPUs to compete directly with NVIDIA in generative AI workloads, providing creators, developers, and businesses with a cost-effective alternative for local and cloud-based image generation workflows.

Company Behind stabilityai/stable-diffusion-3.5-large_amdgpu

Discover more about Stability AI, the organization responsible for building and maintaining stabilityai/stable-diffusion-3.5-large_amdgpu.

Stability AI is a UK-based artificial intelligence company founded in 2019 by Emad Mostaque and Cyrus Hodes. The company is best known for developing Stable Diffusion, a widely adopted open-source text-to-image model that has significantly influenced the generative AI landscape. Stability AI’s mission centers on democratizing access to advanced AI by making its models and tools openly available, empowering creators and developers globally. The company has expanded its portfolio to include generative models for video, audio, 3D, and text, and offers commercial APIs such as DreamStudio. After rapid growth and major funding rounds, Stability AI has attracted high-profile investors and board members, including Sean Parker and James Cameron. In 2024, Emad Mostaque stepped down as CEO, with Prem Akkaraju appointed as his successor. Stability AI remains a foundational force in generative AI, holding a dominant share of AI-generated imagery online and continuing to drive innovation in open-access AI technologies.

How to Use Stable Diffusion 3.5 Large on AMD GPUs

System Requirements

Before getting started, ensure your system meets these essential requirements:

AMD GPU: High-end Radeon GPU (RX 9070 XT recommended for optimal performance)
Driver Version: AMD GPU drivers version 24.30.31.05 (preview) or newer, or official release 25.4.1
VRAM: Minimum 12GB recommended for Large variant; 8GB for Medium variant
Operating System: Windows 10/11 or Linux with compatible AMD drivers

Step-by-Step Setup Process

Update AMD Drivers: Download and install the latest AMD Radeon drivers (version 24.30.31.05 or 25.4.1+) from the official AMD website to ensure compatibility with ONNX Runtime optimizations
Download the Model: Access Stable Diffusion 3.5 Large “_amdgpu” optimized models from Stability AI’s official repository or Hugging Face. Look specifically for models with the “_amdgpu” suffix
Install ONNX Runtime: Set up ONNX Runtime with AMD GPU support, which provides the optimization framework for accelerated inference
Configure Your Workflow: Integrate the model into your preferred tool (Amuse, ComfyUI, or custom Python scripts) with ONNX Runtime backend enabled
Test Performance: Run initial test generations to verify the 3.3x performance improvement and adjust settings for optimal quality-speed balance
Optimize Settings: Fine-tune batch sizes, resolution settings, and sampling steps based on your specific AMD GPU’s VRAM and compute capabilities

Recommended Workflow Tools

Amuse: User-friendly interface with built-in AMD optimization support
ComfyUI: Node-based workflow for advanced users with ONNX backend integration
Cloud Platforms: DigitalOcean GPU Droplets and similar services offering AMD GPU instances
Custom Scripts: Python-based implementations using ONNX Runtime DirectML provider

Latest Performance Insights & Research

Breakthrough Performance Gains

Recent testing reveals that AMD-optimized Stable Diffusion models achieve up to 3.3x performance boost on supported Radeon GPUs, particularly the RX 9070 XT. This represents a significant leap in AMD’s competitiveness in the AI image generation market.

Key Technical Achievements

8 Billion Parameters: SD 3.5 Large is the most powerful variant in the Stable Diffusion 3.5 family, delivering superior image quality, exceptional prompt adherence, and diverse artistic output styles
ONNX Runtime Integration: The optimization leverages ONNX Runtime’s DirectML backend, making deployment seamless across existing workflows and compatible with multiple platforms
Hardware Acceleration: AMD’s GPU architecture optimizations are not bound to specific chip generations but require high-end GPUs for optimal performance, especially for Large and Turbo variants
Cross-Platform Compatibility: Models work across Windows and Linux environments with proper driver support, enabling flexible deployment options

Recent Developments (2024-2025)

The AI image generation landscape has evolved rapidly with several key milestones:

October 2024: Release of Stable Diffusion 3.5 Medium, providing a balanced option between quality and performance
Q4 2024: Introduction of AMD-specific ONNX optimizations, dramatically improving inference speeds on Radeon hardware
January 2025: Official driver release (25.4.1) with enhanced AI workload support and stability improvements
Ongoing: Continuous performance improvements and expanded compatibility with cloud platforms like DigitalOcean GPU Droplets

Competitive Positioning

These optimizations enable AMD GPUs to compete effectively with NVIDIA in generative AI workloads, offering creators and businesses a cost-effective alternative. The performance gains make AMD Radeon cards viable for professional AI image generation, previously dominated by NVIDIA’s CUDA ecosystem.

Technical Specifications & Performance Details

Model Variants Comparison

Variant	Parameters	VRAM Required	Best Use Case
SD 3.5 Large	8 Billion	12GB+	Maximum quality, professional work
SD 3.5 Medium	2.5 Billion	8GB+	Balanced quality and speed
SD 3.5 Turbo	Optimized	8GB+	Fast iteration, real-time generation

Understanding the “_amdgpu” Suffix

Models labeled with “_amdgpu” are specifically compiled and optimized for AMD Radeon GPU architecture. These versions include:

ONNX Format: Converted from PyTorch to ONNX for DirectML acceleration
Operator Optimization: GPU-specific kernel optimizations for AMD RDNA architecture
Memory Management: Efficient VRAM utilization patterns tailored to AMD memory controllers
Precision Tuning: Mixed-precision inference optimized for AMD compute units

Performance Benchmarks

Real-world testing on AMD RX 9070 XT demonstrates:

Standard PyTorch: ~15-20 seconds per image (512×512, 20 steps)
ONNX Optimized: ~5-7 seconds per image (same settings) – 3.3x improvement
Batch Processing: Up to 4x improvement with optimized batch sizes
Higher Resolutions: 2-2.5x improvement at 1024×1024 resolution

Driver Requirements Explained

The specific driver version requirements (24.30.31.05 preview or 25.4.1 official) are critical because they include:

Enhanced DirectML support for ONNX Runtime
Improved memory allocation for large AI models
Optimized compute shader compilation for inference workloads
Bug fixes specific to AI/ML operations on AMD hardware

Integration with Existing Workflows

The ONNX Runtime integration makes AMD-optimized models compatible with:

Desktop Applications: Amuse, Automatic1111 WebUI (with ONNX backend), ComfyUI
Cloud Services: DigitalOcean GPU Droplets, AWS with AMD instances, Azure ML
Custom Development: Python scripts using onnxruntime-directml package
Enterprise Solutions: Scalable deployment across AMD GPU clusters

Quality vs. Speed Trade-offs

Users can optimize for different priorities:

Maximum Quality: Use SD 3.5 Large with 50+ sampling steps, higher CFG scale
Balanced: SD 3.5 Medium with 20-30 steps for production workflows
Speed Priority: SD 3.5 Turbo with 4-8 steps for rapid iteration and previews

Cost-Effectiveness Analysis

AMD GPU optimization provides significant value:

Hardware Cost: AMD Radeon GPUs typically 20-30% less expensive than equivalent NVIDIA cards
Performance Parity: With ONNX optimization, performance gap with NVIDIA narrows significantly
Cloud Costs: AMD GPU instances on cloud platforms often priced lower than NVIDIA alternatives
Total Cost of Ownership: Lower initial investment with competitive ongoing operational costs

Frequently Asked Questions

What AMD GPUs are compatible with Stable Diffusion 3.5 Large optimization?

The ONNX optimizations work on most modern AMD Radeon GPUs, but high-end cards like the RX 9070 XT, RX 7900 XTX, and RX 7900 XT deliver the best performance. For SD 3.5 Large, you need at least 12GB VRAM. Mid-range cards with 8GB can run the Medium variant effectively. The optimizations are not architecture-specific but require sufficient compute power and VRAM for optimal results.

How do I download the AMD-optimized Stable Diffusion 3.5 models?

AMD-optimized models with the “_amdgpu” suffix can be downloaded from two primary sources: Stability AI’s official repository and Hugging Face model hub. Look specifically for models labeled “Stable-Diffusion-3.5-Large_amdgpu” or similar naming conventions. You’ll need to create an account on these platforms and may need to accept the model’s license agreement before downloading. Ensure you download the ONNX format versions specifically compiled for AMD GPUs.

Why is driver version 24.30.31.05 or 25.4.1 required?

These specific driver versions include critical updates for AI/ML workloads on AMD GPUs. They provide enhanced DirectML support required by ONNX Runtime, improved memory management for large models like SD 3.5 Large, optimized compute shader compilation for inference operations, and bug fixes specific to generative AI tasks. Using older drivers may result in compatibility issues, reduced performance, or stability problems during image generation.

What is the actual performance improvement compared to NVIDIA GPUs?

With ONNX optimization, AMD GPUs achieve competitive performance with NVIDIA cards in similar price ranges. The 3.3x improvement is measured against unoptimized PyTorch on AMD hardware. When compared to NVIDIA GPUs running optimized CUDA implementations, AMD cards with ONNX optimization typically perform within 10-20% of equivalent NVIDIA models, making them a viable alternative, especially considering the lower hardware costs. The exact performance depends on specific models, resolution, and sampling settings.

Can I use these optimized models on cloud platforms?

Yes, AMD-optimized Stable Diffusion models are compatible with several cloud platforms offering AMD GPU instances. DigitalOcean GPU Droplets explicitly support SD 3.5 Large with AMD optimization. Other platforms like AWS (with AMD instances) and Azure ML also support ONNX Runtime with DirectML. Cloud deployment offers advantages like scalability, no upfront hardware costs, and access to high-end AMD GPUs without purchasing them. Setup typically involves installing the correct drivers, ONNX Runtime, and downloading the “_amdgpu” model variants.

What’s the difference between SD 3.5 Large, Medium, and Turbo variants?

SD 3.5 Large (8 billion parameters) offers the highest quality with superior prompt adherence and artistic detail but requires 12GB+ VRAM and more processing time. SD 3.5 Medium (2.5 billion parameters) provides balanced quality and speed, suitable for production workflows with 8GB VRAM. SD 3.5 Turbo is optimized for speed with fewer sampling steps (4-8 steps), ideal for rapid iteration and real-time generation. Choose based on your priorities: Large for professional quality, Medium for balanced workflows, Turbo for speed and experimentation.

How do I integrate AMD-optimized models into existing workflows like ComfyUI or Automatic1111?

Integration requires using ONNX Runtime as the backend. For ComfyUI, install ONNX Runtime nodes and configure them to use DirectML provider for AMD GPUs. For Automatic1111 WebUI, you may need extensions that support ONNX models or use alternative interfaces like Amuse that have built-in AMD optimization support. The process typically involves: installing onnxruntime-directml Python package, placing the “_amdgpu” model files in the appropriate directory, and configuring the interface to use ONNX backend instead of PyTorch. Detailed setup guides are available from AMD and Stability AI.