RealVisXL v5.0
Stableby SG161222
The most popular photorealistic SDXL fine-tune on CivitAI. Excels at lifelike portraits, landscapes, and product photography. Compatible with all SDXL ControlNets and LoRAs.
VRAM requirements, GPU fit, and setup notes for RealVisXL v5.0, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI, Automatic1111, and Diffusers support. Best download size: ~6.9 GB at FP16.
- Top photorealistic SDXL fine-tune on CivitAI
- Excellent portraits and landscapes
- Full SDXL ControlNet and LoRA compatibility
- Drop-in replacement for SDXL base
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for RealVisXL v5.0 outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Text-image alignment accuracy (higher is better)
VRAM Requirements by Resolution and Precision
Compare which GPUs can run RealVisXL v5.0 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(
"SG161222/RealVisXL_V5.0",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=25,
guidance_scale=7.5,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running RealVisXL v5.0 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 RealVisXL v5.0
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 RealVisXL v5.0 in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
ControlNet Support
3 ControlNets available for RealVisXL v5.0. 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 EcosystemInherits the full SDXL LoRA ecosystem. All SDXL LoRAs work with RealVisXL.
Approximately 5,000 LoRAs available on CivitAI. Each LoRA adds ~0.2 GB VRAM.
Browse all LoRAs on CivitAIRelated Workflows
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Frequently asked questions
FAQ — RealVisXL v5.0 VRAM, Runtimes & Fit
How much VRAM does RealVisXL v5.0 need?
RealVisXL v5.0 (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 RealVisXL v5.0 on an 8GB GPU?
RealVisXL v5.0 usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Does RealVisXL v5.0 work in ComfyUI and Automatic1111?
RealVisXL v5.0 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 RealVisXL v5.0 on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run RealVisXL v5.0 comfortably at FP16. Expected generation time is around ~4.5s per image at 1024×1024.
Does RealVisXL v5.0 support ControlNet?
Yes, RealVisXL v5.0 has 3 ControlNet adapters available: Canny Edge (SDXL), Depth Map (SDXL), OpenPose (SDXL). Each ControlNet adds roughly 1.2 GB of extra VRAM.
Does RealVisXL v5.0 have LoRA support?
Inherits the full SDXL LoRA ecosystem. All SDXL LoRAs work with RealVisXL. The LoRA ecosystem for RealVisXL v5.0 is rated as "large". There are approximately 5,000 LoRAs available on Civitai. Each LoRA adds roughly 0.2 GB of extra VRAM.
How fast is RealVisXL v5.0?
On a reference GPU (RTX 4090 24GB), RealVisXL v5.0 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 RealVisXL v5.0
See also