Juggernaut XL v9
Stableby RunDiffusion
Premium photorealistic SDXL fine-tune focused on cinematic quality. Known for exceptional skin textures, lighting, and composition. Popular for portrait and fashion photography.
VRAM requirements, GPU fit, and setup notes for Juggernaut XL v9, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Automatic1111 support. Best download size: ~6.9 GB at FP16.
- Exceptional skin textures and lighting
- Cinematic composition and color grading
- Full SDXL ecosystem compatibility
- Preferred for portrait photography
Your hardware
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Image Quality Benchmarks
Measured quality metrics for Juggernaut XL v9 outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
VRAM Requirements by Resolution and Precision
Compare which GPUs can run Juggernaut XL v9 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)
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 7.6 GB | S● | S● | B● | S● |
| 768×768 | 7.8 GB | S● | S● | B● | S● |
| 1024×1024 | 8.0 GB | S● | S● | B● | S● |
Optimization Tips
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
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
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"RunDiffusion/Juggernaut-XL-v9",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=30,
guidance_scale=7.5,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Juggernaut XL v9 locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyComfyUI Workflow
Basic txt2img workflow for Juggernaut XL v9
Drag & drop into ComfyUI or use File → Import
Memory Breakdown
VRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Estimated Generation Time
Time per image at 1024×1024, 28 steps, FP16.
Sample Outputs
Available Formats, Downloads & Setup
Download Juggernaut XL v9 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 | FP16 | 6.9 GB | RunDiffusion | 下载 |
ControlNet Support
3 ControlNets available for Juggernaut XL v9. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge (SDXL)
+1.2 GB VRAMInherits SDXL base model ControlNet compatibility. Edge-based structural guidance.
Depth Map (SDXL)
+1.2 GB VRAMInherits SDXL base model ControlNet compatibility. Depth-based spatial control.
OpenPose (SDXL)
+1.2 GB VRAMInherits SDXL base model ControlNet compatibility. Human pose control.
LoRA Ecosystem
Large EcosystemFull SDXL LoRA compatibility.
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Frequently asked questions
FAQ — Juggernaut XL v9 VRAM, Runtimes & Fit
How much VRAM does Juggernaut XL v9 need?
Juggernaut XL v9 (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 Juggernaut XL v9 on an 8GB GPU?
Juggernaut XL v9 usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Does Juggernaut XL v9 work in ComfyUI and Automatic1111?
Juggernaut XL v9 is marked for ComfyUI and Automatic1111 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 Juggernaut XL v9 on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run Juggernaut XL v9 comfortably at FP16. Expected generation time is around ~4.5s per image at 1024×1024.
Does Juggernaut XL v9 support ControlNet?
Yes, Juggernaut XL v9 has 3 ControlNet adapters available: Canny Edge (SDXL), Depth Map (SDXL), OpenPose (SDXL). Each ControlNet adds roughly 1.2 GB of extra VRAM.
Does Juggernaut XL v9 have LoRA support?
Full SDXL LoRA compatibility. The LoRA ecosystem for Juggernaut XL v9 is rated as "large". Each LoRA adds roughly 0.2 GB of extra VRAM.
How fast is Juggernaut XL v9?
On a reference GPU (RTX 4090 24GB), Juggernaut XL v9 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 Juggernaut XL v9
See also