Stable Diffusion 1.5
Legacyby Stability AI
The original widely-adopted image generation model. Extremely lightweight — runs on 4GB VRAM. Massive legacy ecosystem of checkpoints, LoRAs, and tools. Still preferred for speed and low VRAM scenarios.
VRAM requirements, GPU fit, and setup notes for Stable Diffusion 1.5, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI, Automatic1111, and Diffusers support. Best download size: ~2.0 GB at FP16.
- Runs on 4GB+ VRAM — most accessible model
- Massive ecosystem of fine-tuned checkpoints
- Fastest generation of any quality model
- Legacy but still widely used for speed
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for Stable Diffusion 1.5 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 Stable Diffusion 1.5 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 | 4.0 GB | S● | S● | S● | S● |
| 768×768 | 6.5 GB | S● | S● | A● | S● |
| 1024×1024 | 5.9 GB | S● | S● | S● | S● |
Optimization Tips
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
ControlNets available
Add guided generation with 8 adapters (+0.7 GB VRAM each)
Rich LoRA ecosystem
Customize style, characters, and quality with community LoRAs
Run with Python
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=20,
guidance_scale=7.5,
height=512,
width=512,
).images[0]
image.save("output.png")Get started
Setup instructions for running Stable Diffusion 1.5 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 Stable Diffusion 1.5
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 Stable Diffusion 1.5 in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| Format | Präzision | Größe | Anbieter | |
|---|---|---|---|---|
| safetensorsEmpfohlen | FP16 | 2.0 GB | official | Herunterladen |
| safetensors | FP32 | 4.0 GB | official | Herunterladen |
| ckptEmpfohlen | FP16 | 2.1 GB | official | Herunterladen |
ControlNet Support
8 ControlNets available for Stable Diffusion 1.5. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge
+0.7 GB VRAMThe original and most popular ControlNet. Edge-based structural guidance.
OpenPose
+0.7 GB VRAMHuman pose estimation for character control.
Scribble
+0.7 GB VRAMRough sketch to image. Great for quick conceptual exploration.
Lineart
+0.7 GB VRAMClean lineart extraction for illustration and coloring workflows.
Normal Map
+0.7 GB VRAMSurface normal estimation for material and lighting control.
Tile/Upscale
+0.7 GB VRAMTile-based generation for upscaling and detail enhancement.
Inpaint
+0.7 GB VRAMMask-based inpainting for editing specific regions of images.
LoRA Ecosystem
Massive EcosystemThe largest LoRA ecosystem in AI image generation. Thousands of LoRAs on CivitAI covering every imaginable style, character, concept, and quality modifier. SD 1.5 remains the most customizable image model.
Approximately 50,000 LoRAs available on CivitAI. Each LoRA adds ~0.1 GB VRAM.
Popular LoRAs for Stable Diffusion 1.5
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Frequently asked questions
FAQ — Stable Diffusion 1.5 VRAM, Runtimes & Fit
How much VRAM does Stable Diffusion 1.5 need?
Stable Diffusion 1.5 (0.86B parameters) requires approximately 5.9 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 Stable Diffusion 1.5 on an 8GB GPU?
Stable Diffusion 1.5 usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Does Stable Diffusion 1.5 work in ComfyUI and Automatic1111?
Stable Diffusion 1.5 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 Stable Diffusion 1.5 on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run Stable Diffusion 1.5 comfortably at FP16. Expected generation time is around ~1s per image at 1024×1024.
Does Stable Diffusion 1.5 support ControlNet?
Yes, Stable Diffusion 1.5 has 8 ControlNet adapters available: Canny Edge, Depth Map, OpenPose, Scribble, Lineart, Normal Map, Tile/Upscale, Inpaint. Each ControlNet adds roughly 0.7 GB of extra VRAM.
Does Stable Diffusion 1.5 have LoRA support?
The largest LoRA ecosystem in AI image generation. Thousands of LoRAs on CivitAI covering every imaginable style, character, concept, and quality modifier. SD 1.5 remains the most customizable image model. The LoRA ecosystem for Stable Diffusion 1.5 is rated as "massive". There are approximately 50,000 LoRAs available on Civitai. Each LoRA adds roughly 0.1 GB of extra VRAM.
How fast is Stable Diffusion 1.5?
On a reference GPU (RTX 4090 24GB), Stable Diffusion 1.5 generates a 1024×1024 image in approximately ~1s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
About Stable Diffusion 1.5
See also