LCM_Dreamshaper_v7 by Simian Luo: Open-Weight Model

Runs On

What it takes to serve LCM_Dreamshaper_v7 (860M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	1.7 GB	2.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.9 GB	1.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.4 GB	0.5 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Simian Luo, published under mit, revision a85df6a8bd97.

Latent Consistency Models

Official Repository of the paper: Latent Consistency Models.

Project Page: https://latent-consistency-models.github.io

Try our Hugging Face demos:

Model Descriptions:

Distilled from Dreamshaper v7 fine-tune of Stable-Diffusion v1-5 with only 4,000 training iterations (~32 A100 GPU Hours).

Generation Results:

By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU.

Usage

You can try out Latency Consistency Models directly on:

To run the model yourself, you can leverage the Diffusers library: 1. Install the library:

pip install --upgrade diffusers  # make sure to use at least diffusers >= 0.22
pip install transformers accelerate

Run the model:

Read the full model card (315 words)

Identity and Version

Repository: SimianLuo/LCM_Dreamshaper_v7
Publisher: Simian Luo
Task: Text to image
Modality: Image
Library: diffusers
Parameters: 860M parameters
Languages: en
Revision: a85df6a8bd976cdd08b4fd8f3b73f229c9e54df5
First published: 2023-10-14
Last updated: 2024-03-05

Files and Weights

30 files, 13.2 GB in total. The weights are 9 files totalling 9.7 GB in onnx, safetensors.

Weights9 files · 9.7 GB

Configuration13 files · 42.3 KB

Tokenizer3 files · 1.6 MB

Documentation1 file · 3.5 KB

Other3 files · 3.4 GB

Repository1 file · 1.6 KB

Every file

File	Type	Size	SHA-256
LCM_Dreamshaper_v7_4k.safetensors	Weights	3.4 GB	84feab3a32f1
safety_checker/model.safetensors	Weights	1.2 GB	fb351a5ded81
text_encoder/model.onnx	Weights	492.6 MB	767d3fad6f9d
text_encoder/model.safetensors	Weights	492.3 MB	66262104f509
unet/diffusion_pytorch_model.safetensors	Weights	3.4 GB	7be468469c51
unet/model.onnx	Weights	2.0 MB	01c5869bc7cb
vae/diffusion_pytorch_model.safetensors	Weights	334.6 MB	6bb865b3478d
vae_decoder/model.onnx	Weights	198.1 MB	0ea3df9f76ff
vae_encoder/model.onnx	Weights	136.8 MB	f90150f8c77f
feature_extractor/preprocessor_config.json	Configuration	518 B	—
inference.py	Configuration	2.2 KB	—
lcm_pipeline.py	Configuration	11.1 KB	—
lcm_scheduler.py	Configuration	22.3 KB	—
model_index.json	Configuration	588 B	—
safety_checker/config.json	Configuration	726 B	—
scheduler/scheduler_config.json	Configuration	539 B	—
text_encoder/config.json	Configuration	610 B	—
tokenizer/special_tokens_map.json	Configuration	133 B	—
unet/config.json	Configuration	1.7 KB	—
vae/config.json	Configuration	651 B	—
vae_decoder/config.json	Configuration	651 B	—
vae_encoder/config.json	Configuration	651 B	—
README.md	Documentation	3.5 KB	—
speed_fid.png	Other	56.9 KB	—
teaser.png	Other	4.0 MB	0bed40424a7b
unet/model.onnx_data	Other	3.4 GB	ef99ccc336de
.gitattributes	Repository	1.6 KB	—
tokenizer/merges.txt	Tokenizer	524.6 KB	—
tokenizer/tokenizer_config.json	Tokenizer	765 B	—
tokenizer/vocab.json	Tokenizer	1.1 MB	—

License and Download

License: mit
Access: Open weights, no gate
Download size: 9.7 GB

Download from Simian Luo

Released by Simian Luo through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:2310.04378

Memory Requirements

Precision	Weights in memory
As published	9.7 GB
16-bit	1.7 GB
8-bit	0.9 GB
4-bit	0.4 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About LCM_Dreamshaper_v7

How much GPU memory does LCM_Dreamshaper_v7 need?

About 2.1 GB at 16-bit and 0.5 GB at 4-bit: the weights (860M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run LCM_Dreamshaper_v7 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use LCM_Dreamshaper_v7 commercially?

Yes. LCM_Dreamshaper_v7 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

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