Will It Run AI

DreamShaper XL

Stable

by Lykon

Versatile SDXL fine-tune known for handling diverse styles — from photorealism to digital art, fantasy, and anime. One of the most downloaded community models.

VRAM requirements, GPU fit, and setup notes for DreamShaper XL, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI, Automatic1111, and Diffusers support. Best download size: ~6.9 GB at FP16.

  • Versatile — handles photorealism, fantasy, anime, and digital art
  • Turbo variant needs only 8 steps
  • Full SDXL ControlNet and LoRA compatibility
  • One of the most popular community models
HuggingFaceCivitAIDocumentation
18K downloads73 likes
ComfyUI, Automatic1111, DiffusersFP16 safetensors

Your hardware

Detecting...

Parameters2.6B
Max Resolution1024×1024
Default Steps8
ArchitectureUNET
Licenseopenrail++

Image Quality Benchmarks

Measured quality metrics for DreamShaper XL outputs.

Human Preference Score76%

How often humans prefer this model's output (0-100%)

Aesthetic Score7.2

Visual quality and composition rating (5-9 scale)

VRAM Requirements by Resolution and Precision

Compare which GPUs can run DreamShaper XL at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.

FP16 (full precision)

ResolutionVRAM RequiredRTX 4090 24GBRTX 3060 12GBRTX 4060 8GBMacBook Pro M4 Pro 24GB
512×5127.6 GBS●S●B●S●
768×7687.8 GBS●S●B●S●
1024×10248.0 GBS●S●B●S●

Optimization Tips

ControlNets available

Add guided generation with 3 adapters (+1.2 GB VRAM each)

Rich LoRA ecosystem

Customize style, characters, and quality with community LoRAs

Run with Python

Run with Python (diffusers)
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "Lykon/dreamshaper-xl-v2-turbo",
    torch_dtype=torch.float16
)
pipe.to("cuda")

image = pipe(
    prompt="your prompt here",
    num_inference_steps=8,
    guidance_scale=7.5,
    height=1024,
    width=1024,
).images[0]
image.save("output.png")

Get started

Setup instructions for running DreamShaper XL locally

1. Download the model

Get the checkpoint from HuggingFace

2. Place in:

ComfyUI/models/checkpoints/

3. Launch ComfyUI

python main.py
Tip: For SDXL fine-tunes, you can optionally add the SDXL refiner for improved detail. Place the refiner checkpoint in the same folder and add a second KSampler with denoise ~0.3.

ComfyUI Workflow

Basic txt2img workflow for DreamShaper XL

7 nodes

Drag & drop into ComfyUI or use File → Import

Memory Breakdown

VRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)

Required: 8.0 GBAvailable: 24.0 GB
Weights5.2 GB
VAE0.2 GB
Text Encoder1.6 GB
Activations0.5 GB
Overhead0.5 GB

Estimated Generation Time

Time per image at 1024×1024, 28 steps, FP16.

RTX 4090 24GB~4.5s
RTX 3060 12GB~17s
RTX 4060 8GB~1m 8s
MacBook Pro M4 Pro 24GB~36.4s

Sample Outputs

Available Formats, Downloads & Setup

Download DreamShaper XL in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.

格式精度大小提供商
safetensors推荐FP166.9 GBLykon下载

ControlNet Support

3 ControlNets available for DreamShaper XL. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.

Canny Edge (SDXL)

+1.2 GB VRAM

Inherits SDXL base model ControlNet compatibility. Edge-based structural guidance.

comfyuiautomatic1111diffusers
View on HF

Depth Map (SDXL)

+1.2 GB VRAM

Inherits SDXL base model ControlNet compatibility. Depth-based spatial control.

comfyuiautomatic1111diffusers
View on HF

OpenPose (SDXL)

+1.2 GB VRAM

Inherits SDXL base model ControlNet compatibility. Human pose control.

comfyuiautomatic1111diffusers
View on HF

LoRA Ecosystem

Large Ecosystem

Full SDXL LoRA compatibility.

Browse all LoRAs on CivitAI
Fine-tune of sdxl-1-0 · Source: civitai

Related Workflows

Photorealism Workflow →

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Playground v2.53.5B · Playground AIAnimagine XL 4.02.6B · Cagliostro LabJuggernaut XL v92.6B · RunDiffusion

Frequently asked questions

FAQ — DreamShaper XL VRAM, Runtimes & Fit

How much VRAM does DreamShaper XL need?

DreamShaper XL (2.6B parameters) requires approximately 8.0 GB of VRAM at FP16 precision for standard 1024×1024 image generation. If you want a lighter setup, lower precisions like FP8 can reduce memory use when available.

Can I run DreamShaper XL on an 8GB GPU?

DreamShaper XL usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.

Does DreamShaper XL work in ComfyUI and Automatic1111?

DreamShaper XL is marked for ComfyUI, Automatic1111, and Diffusers support in our catalog, so those are the runtimes we recommend first for local setup. If your workflow uses another front end, check the model's available formats and workflow notes above before downloading.

Can I run DreamShaper XL on RTX 4090?

Yes, the RTX 4090 (24 GB VRAM) can run DreamShaper XL comfortably at FP16. Expected generation time is around ~4.5s per image at 1024×1024.

Does DreamShaper XL support ControlNet?

Yes, DreamShaper XL has 3 ControlNet adapters available: Canny Edge (SDXL), Depth Map (SDXL), OpenPose (SDXL). Each ControlNet adds roughly 1.2 GB of extra VRAM.

Does DreamShaper XL have LoRA support?

Full SDXL LoRA compatibility. The LoRA ecosystem for DreamShaper XL is rated as "large". Each LoRA adds roughly 0.2 GB of extra VRAM.

How fast is DreamShaper XL?

On a reference GPU (RTX 4090 24GB), DreamShaper XL generates a 1024×1024 image in approximately ~4.5s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.

About DreamShaper XL

Use cases
artphotorealisticfantasyversatile
Recommended runtimes
comfyuiautomatic1111diffusers

See also

Quantization GuideScoring MethodologyVRAM Calculator