LCM DreamShaper v7
Stableby SimianLuo
Pioneer of Latent Consistency Models (LCM). SD 1.5 based model that generates images in only 1-4 steps, enabling near-real-time generation. Runs on 4GB+ VRAM. MIT licensed.
VRAM requirements, GPU fit, and setup notes for LCM DreamShaper v7, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~2.0 GB at FP16.
- 1-4 step inference — pioneer of Latent Consistency Models
- Near-real-time generation on consumer GPUs
- Runs on 4GB+ VRAM
- MIT licensed — fully open
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for LCM DreamShaper v7 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 LCM DreamShaper v7 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 | 4.8 GB | S● | S● | S● | S● |
| 1024×1024 | 5.9 GB | S● | S● | S● | S● |
Optimization Tips
ControlNets available
Add guided generation with 2 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(
"SimianLuo/LCM_Dreamshaper_v7",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=4,
height=768,
width=768,
).images[0]
image.save("output.png")Get started
Setup instructions for running LCM DreamShaper v7 locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyMemory 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 LCM DreamShaper v7 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 |
| onnxEmpfohlen | FP16 | 2.0 GB | official | Herunterladen |
ControlNet Support
2 ControlNets available for LCM DreamShaper v7. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge (SD 1.5)
+0.7 GB VRAMInherits SD 1.5 base model ControlNet compatibility. Edge-based structural guidance.
Depth Map (SD 1.5)
+0.7 GB VRAMInherits SD 1.5 base model ControlNet compatibility. Depth-based spatial control.
LoRA Ecosystem
Massive EcosystemInherits full SD 1.5 LoRA ecosystem — 50,000+ LoRAs on CivitAI.
Approximately 50,000 LoRAs available on CivitAI. Each LoRA adds ~0.1 GB VRAM.
Browse all LoRAs on CivitAIRelated Workflows
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Frequently asked questions
FAQ — LCM DreamShaper v7 VRAM, Runtimes & Fit
How much VRAM does LCM DreamShaper v7 need?
LCM DreamShaper v7 (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 LCM DreamShaper v7 on an 8GB GPU?
LCM DreamShaper v7 usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Does LCM DreamShaper v7 work in ComfyUI and Diffusers?
LCM DreamShaper v7 is marked for ComfyUI 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 LCM DreamShaper v7 on RTX 4090?
Yes, the RTX 4090 (24 GB VRAM) can run LCM DreamShaper v7 comfortably at FP16. Expected generation time is around 300ms per image at 1024×1024.
Does LCM DreamShaper v7 support ControlNet?
Yes, LCM DreamShaper v7 has 2 ControlNet adapters available: Canny Edge (SD 1.5), Depth Map (SD 1.5). Each ControlNet adds roughly 0.7 GB of extra VRAM.
Does LCM DreamShaper v7 have LoRA support?
Inherits full SD 1.5 LoRA ecosystem — 50,000+ LoRAs on CivitAI. The LoRA ecosystem for LCM DreamShaper v7 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 LCM DreamShaper v7?
On a reference GPU (RTX 4090 24GB), LCM DreamShaper v7 generates a 1024×1024 image in approximately 300ms at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
About LCM DreamShaper v7
See also