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wangkanai/
sdxl-fp8

Name: sdxl-fp8
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Stable Diffusion XL (SDXL) - FP8 Quantized Models

High-quality FP8 quantized SDXL checkpoint models for efficient text-to-image generation at 1024x1024 resolution. This repository contains FP8-optimized versions of SDXL base and SDXL-Turbo models, providing reduced memory footprint while maintaining image quality.

Model Description

Stable Diffusion XL (SDXL) is Stability AI's flagship text-to-image model featuring a larger UNet backbone (2.6B parameters) and dual text encoders (OpenCLIP ViT-bigG and CLIP ViT-L) for superior prompt understanding and image quality at native 1024x1024 resolution.

SDXL-Turbo is a distilled variant enabling high-quality generation in 1-4 steps through Adversarial Diffusion Distillation (ADD), achieving up to 10x faster inference while maintaining image quality.

FP8 Quantization: These models use 8-bit floating point precision, reducing memory requirements by ~50% compared to FP16 versions while maintaining comparable image quality. FP8 models are ideal for systems with limited VRAM or when running multiple models simultaneously.

Key Capabilities

Native 1024x1024 resolution generation
Superior prompt adherence via dual text encoders
Enhanced composition and fine detail rendering
Accelerated generation with Turbo variant
50% reduced VRAM usage through FP8 quantization
Faster loading times with smaller model files
Commercial-friendly open license

Repository Contents

Model Files

E:\huggingface\sdxl-fp8\
├── checkpoints\sdxl\
│   ├── sdxl-base.safetensors       # 6.5 GB - SDXL Base 1.0
│   └── sdxl-turbo.safetensors      # 13 GB - SDXL-Turbo (distilled)
└── diffusion_models\sdxl\          # (Empty - reserved for future components)

Total Repository Size: ~20 GB

Model Specifications

Model	Size	Format	Precision	Architecture	Parameters	Native Resolution
SDXL Base FP8	6.5 GB	safetensors	FP8	UNet + Dual CLIP	2.6B UNet	1024x1024
SDXL-Turbo FP8	13 GB	safetensors	FP8	Distilled UNet	2.6B UNet	512x512 (optimized)

Precision: FP8 (8-bit floating point quantization) Format: SafeTensors (secure tensor serialization) Text Encoders: OpenCLIP ViT-bigG-14 + CLIP ViT-L/14 Memory Advantage: ~50% reduction vs FP16 versions (13GB → 6.5GB for base, 26GB → 13GB for turbo)

Hardware Requirements

SDXL Base FP8 Model

VRAM: 6 GB minimum, 8 GB+ recommended (50% less than FP16)
Disk Space: 7 GB
System RAM: 12 GB+ recommended
Inference Time: ~10-50 steps (3-15 seconds on RTX 3090)

SDXL-Turbo FP8 Model

VRAM: 6 GB minimum, 8 GB+ recommended (50% less than FP16)
Disk Space: 13 GB
System RAM: 12 GB+ recommended
Inference Time: 1-4 steps (0.5-2 seconds on RTX 3090)

FP8 Performance Notes

Memory Efficiency: FP8 uses ~50% less VRAM than FP16 models
Quality: Minimal quality loss compared to FP16 (typically <5% perceptual difference)
Speed: Slightly faster inference on GPUs with FP8 tensor cores (Ada Lovelace/Hopper)
Compatibility: Requires PyTorch 2.1+ and appropriate GPU drivers for optimal FP8 support
Use xformers or torch.compile() for additional 20-30% speedup
Batch size >1 requires additional VRAM (~3GB per image with FP8 vs ~4GB with FP16)

Usage Examples

SDXL Base Model - Standard Generation

python

from diffusers import DiffusionPipeline
import torch

# Load SDXL base model from local checkpoint
pipe = DiffusionPipeline.from_single_file(
    r"E:\huggingface\sdxl-fp8\checkpoints\sdxl\sdxl-base.safetensors",
    torch_dtype=torch.float16,
    use_safetensors=True
)

pipe.to("cuda")

# Enable memory-efficient attention (optional)
pipe.enable_xformers_memory_efficient_attention()

# Generate image
prompt = "a serene mountain landscape at sunset, photorealistic, 8k, detailed"
negative_prompt = "blurry, distorted, low quality, artifacts"

image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=40,
    guidance_scale=7.5,
    width=1024,
    height=1024
).images[0]

image.save("output.png")

SDXL-Turbo - Fast Generation

python

from diffusers import AutoPipelineForText2Image
import torch

# Load SDXL-Turbo for accelerated inference
pipe = AutoPipelineForText2Image.from_single_file(
    r"E:\huggingface\sdxl-fp8\checkpoints\sdxl\sdxl-turbo.safetensors",
    torch_dtype=torch.float16
)

pipe.to("cuda")

# Turbo models work best with 1-4 steps and LOW guidance
prompt = "a cute cat wearing sunglasses, digital art"

image = pipe(
    prompt=prompt,
    num_inference_steps=4,          # 1-4 steps optimal for Turbo
    guidance_scale=0.0,             # Turbo is trained for guidance_scale=0
    width=512,
    height=512
).images[0]

image.save("turbo_output.png")

Advanced - Using with ComfyUI

python

# Place models in ComfyUI checkpoint directory:
# ComfyUI\models\checkpoints\
# Then load via ComfyUI interface as "sdxl-base" or "sdxl-turbo"

# Recommended ComfyUI settings for SDXL Base:
# - Sampler: DPM++ 2M Karras / Euler a
# - Steps: 30-50
# - CFG Scale: 7-9
# - Resolution: 1024x1024 or aspect ratio variations

# Recommended settings for SDXL-Turbo:
# - Sampler: Euler a
# - Steps: 1-4
# - CFG Scale: 1.0-2.0
# - Resolution: 512x512 (fastest) or 768x768

Advanced - Custom Pipeline with Refiner

python

from diffusers import DiffusionPipeline, StableDiffusionXLImg2ImgPipeline
import torch

# Load base model
base = DiffusionPipeline.from_single_file(
    r"E:\huggingface\sdxl-fp8\checkpoints\sdxl\sdxl-base.safetensors",
    torch_dtype=torch.float16
).to("cuda")

# Generate base image
prompt = "majestic castle on a cliff, fantasy art, detailed"
image = base(
    prompt=prompt,
    num_inference_steps=40,
    denoising_end=0.8,              # End early for refiner
    output_type="latent"
).images[0]

# Note: Refiner model not included in this repository
# Download separately from Hugging Face if needed:
# refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
#     "stabilityai/stable-diffusion-xl-refiner-1.0",
#     torch_dtype=torch.float16
# ).to("cuda")
#
# refined_image = refiner(
#     prompt=prompt,
#     image=image,
#     denoising_start=0.8
# ).images[0]

Model Specifications

Architecture Details

SDXL Base 1.0 (FP8):

UNet: 2.6B parameters with cross-attention layers (FP8 quantized)
Text Encoder 1: OpenCLIP ViT-bigG-14 (695M params)
Text Encoder 2: CLIP ViT-L/14 (123M params)
VAE: AutoencoderKL for latent encoding/decoding
Training Resolution: 1024x1024 (multi-aspect ratio training)
Latent Channels: 4
Conditioning: Dual text embeddings + time embeddings + resolution conditioning

SDXL-Turbo (FP8):

Base Architecture: SDXL UNet (distilled, FP8 quantized)
Distillation Method: Adversarial Diffusion Distillation (ADD)
Optimization: 1-4 step inference with quality preservation
Guidance: Trained for classifier-free guidance scale 0.0
Speed Improvement: Up to 10x faster than base SDXL

Precision and Format

Tensor Format: SafeTensors (secure, memory-mapped loading)
Primary Precision: FP8 (8-bit floating point) for UNet weights
Text Encoders: Typically FP16/FP32 for numerical stability
Quantization Method: Post-training quantization from FP16 to FP8
Quality Retention: ~95-98% of original FP16 quality with 50% memory reduction

Performance Tips and Optimization

Memory Optimization

python

# FP8 models already use 50% less VRAM, but you can optimize further:

# Enable memory-efficient attention
pipe.enable_xformers_memory_efficient_attention()

# Or use scaled dot product attention (PyTorch 2.0+)
pipe.enable_attention_slicing()

# Enable VAE tiling for large images
pipe.enable_vae_tiling()

# CPU offloading for limited VRAM (even with FP8, useful for <6GB VRAM)
pipe.enable_model_cpu_offload()

# Sequential CPU offload for extreme memory constraints
pipe.enable_sequential_cpu_offload()

Speed Optimization

python

# FP8 provides inherent speed advantages on modern GPUs (RTX 40-series, H100)

# Compile UNet with torch.compile (PyTorch 2.0+)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

# Use faster samplers
# DPM++ 2M Karras: Good quality, ~25-35 steps
# Euler a: Fast, ~30-40 steps
# LCM: Ultra-fast with LCM-LoRA, ~4-8 steps

# Reduce resolution for faster inference
# 768x768: ~40% faster than 1024x1024
# 512x512: ~70% faster than 1024x1024

# Note: FP8 tensor cores on Ada/Hopper GPUs provide additional 10-20% speedup

Quality Optimization

python

# Use higher step counts for complex prompts
num_inference_steps = 50  # Default: 40

# Adjust CFG scale based on desired creativity
guidance_scale = 7.5      # Lower (5-7): more creative
                          # Higher (8-12): stronger prompt adherence

# Use negative prompts to avoid unwanted elements
negative_prompt = "blurry, bad anatomy, deformed, ugly, low quality"

# For SDXL-Turbo, use 1-4 steps and low guidance
num_inference_steps = 2
guidance_scale = 0.0

License

License: CreativeML Open RAIL++-M License

Stable Diffusion XL models are released under the CreativeML Open RAIL++-M license, which permits:

✅ Commercial use (with attribution)
✅ Modification and redistribution
✅ Private and public usage

Restrictions:

❌ Illegal activities or harmful content generation
❌ Misrepresentation of outputs as human-created
⚠️ Responsibility for generated content lies with the user

Attribution: When using these models commercially, please credit Stability AI and link to the original model repository.

Citation

If you use SDXL models in your research or projects, please cite:

bibtex

@misc{podell2023sdxl,
  title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
  author={Dustin Podell and Zion English and Kyle Lacey and Andreas Blattmann and Tim Dockhorn and Jonas Müller and Joe Penna and Robin Rombach},
  year={2023},
  eprint={2307.01952},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

For SDXL-Turbo:

bibtex

@misc{sauer2023adversarial,
  title={Adversarial Diffusion Distillation},
  author={Axel Sauer and Dominik Lorenz and Andreas Blattmann and Robin Rombach},
  year={2023},
  eprint={2311.17042},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Official Resources

Model Cards

SDXL Base 1.0 - Official Hugging Face repository
SDXL-Turbo - Distilled fast inference model
SDXL Refiner 1.0 - Optional refinement model

Documentation

Diffusers SDXL Guide - Comprehensive usage documentation
Stability AI Blog - Official announcements and research
SDXL Paper - Technical architecture details

Community Resources

Civitai SDXL Models - Community fine-tunes and LoRAs
r/StableDiffusion - Community support and tips
ComfyUI SDXL Workflows - Advanced generation workflows

Troubleshooting

Common Issues

Out of Memory (OOM) Errors:

You're already using FP8 models (50% less VRAM than FP16)
Reduce resolution (768x768 or 512x512)
Enable enable_model_cpu_offload() or enable_sequential_cpu_offload()
Enable attention slicing: pipe.enable_attention_slicing()
Enable VAE tiling: pipe.enable_vae_tiling()

Slow Generation:

FP8 models are already faster than FP16 on modern GPUs
Install xformers: pip install xformers
Use torch.compile() on PyTorch 2.0+
Switch to faster samplers (DPM++ 2M Karras)
Reduce step count (30-40 often sufficient)
Ensure you have updated GPU drivers for FP8 support

Poor Image Quality:

FP8 quantization typically has <5% quality loss vs FP16
Increase step count (40-50 for complex prompts)
Use negative prompts effectively
Adjust guidance_scale (7-9 typical range)
Ensure prompt is detailed and descriptive
If quality issues persist, consider comparing with FP16 version to isolate FP8 effects

SDXL-Turbo Specific:

Use guidance_scale=0.0 (Turbo is trained for this)
Keep steps at 1-4 (higher steps degrade quality)
Use 512x512 resolution for optimal results
Don't use negative prompts (not needed for Turbo)

Contact and Support

Issues: For bugs or problems, open an issue on the Stability AI GitHub
Discussions: Join the Hugging Face Diffusers Discord
Commercial Licensing: Contact Stability AI for enterprise support

Repository Maintained: October 2025 Models Updated: SDXL Base 1.0 FP8 (August 2024), SDXL-Turbo FP8 (August 2024) README Version: v1.3 Quantization: FP8 post-training quantization for memory efficiency

wangkanai/sdxl-fp8

Stable Diffusion XL (SDXL) - FP8 Quantized Models

Model Description

Key Capabilities

Repository Contents

Model Files

Model Specifications

Hardware Requirements

SDXL Base FP8 Model

SDXL-Turbo FP8 Model

FP8 Performance Notes

Usage Examples

SDXL Base Model - Standard Generation

SDXL-Turbo - Fast Generation

Advanced - Using with ComfyUI

Advanced - Custom Pipeline with Refiner

Model Specifications

Architecture Details

Precision and Format

Performance Tips and Optimization

Memory Optimization

Speed Optimization

Quality Optimization

License

Citation

Official Resources

Model Cards

Documentation

Community Resources

Troubleshooting

Common Issues

Contact and Support

wangkanai/
sdxl-fp8