# LCM_Dreamshaper_v7 by Simian Luo: Open-Weight Model Source: https://savrn.com/models/lcm-dreamshaper-v7 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.9 GB | 1.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.4 GB | 0.5 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [LCM_Dreamshaper_v7 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/lcm-dreamshaper-v7/gpus) ## SAVRN's Notes on LCM_Dreamshaper_v7 This is the original Latent Consistency Model demonstration: DreamShaper 7, itself a Stable Diffusion 1.5 fine-tune, distilled so it produces good images in two to four steps instead of dozens. The distillation took about 4,000 training iterations, which showed how cheap this kind of speedup can be. It has 860M parameters and runs quickly on modest GPUs. The MIT license covers this repository, but the DreamShaper and Stable Diffusion weights it derives from carry OpenRAIL terms, so review those before commercial use. SAVRN Research, 2026-09-18 ## Model Card By Simian Luo, published under mit, revision a85df6a8bd97. ### Latent Consistency Models Official Repository of the paper: [Latent Consistency Models](https://arxiv.org/abs/2310.04378). Project Page: https://latent-consistency-models.github.io ### Try our Hugging Face demos: ### Model Descriptions: Distilled from [Dreamshaper v7](https://savrn.com/models/dreamshaper-7) fine-tune of [Stable-Diffusion v1-5](https://huggingface.co/runwayml/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 ``` 1. Run the model: [Read the full model card (315 words)](https://savrn.com/models/lcm-dreamshaper-v7/card) ## 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](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) Released by Simian Luo through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## 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. ## Similar Models Model · Text to image ### [stable-diffusion-v1-5](https://savrn.com/models/stable-diffusion-v1-5) [SD v1.5](https://savrn.com/model-publishers/stable-diffusion-v1-5) Modifications to the original model card are in red or green Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion blog. The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. You can use this both with the Diffusers library and RunwayML GitHub repository ( now deprecated ), ComfyUI… Open weights creativeml-openrail-m 860M parameters diffusers [View model](https://savrn.com/models/stable-diffusion-v1-5) Model · Text to image ### [dreamshaper-7](https://savrn.com/models/dreamshaper-7) [Lykon](https://savrn.com/model-publishers/lykon) lykon/dreamshaper-7 is a Stable Diffusion model that has been fine-tuned on runwayml/stable-diffusion-v1-5. For more general information on how to run text-to-image models with Diffusers, see the docs. - Version 8 focuses on improving what V7 started. Might be harder to do photorealism compared to realism focused models, as it might be hard to do anime compared to anime focused models, but it can do both pretty well if you're skilled enough. Check the examples! - Version 7 improves lora support, NSFW and realism. If you're interested in "absolute" realism, try AbsoluteReality. - Version 6 adds more lora support and more style in general. It should also be better at generating directly at… Open weights creativeml-openrail-m 860M parameters diffusers [View model](https://savrn.com/models/dreamshaper-7) Model · Text to image ### [stable-diffusion-v1-4](https://savrn.com/models/stable-diffusion-v1-4) [CompVis](https://savrn.com/model-publishers/compvis) Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog. The Stable-Diffusion-v1-4 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. This weights here are intended to be used with the Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase… Open weights creativeml-openrail-m 860M parameters diffusers [View model](https://savrn.com/models/stable-diffusion-v1-4) Model · Text to image ### [dreamshaper-8](https://savrn.com/models/dreamshaper-8) [Lykon](https://savrn.com/model-publishers/lykon) lykon/dreamshaper-8 is a Stable Diffusion model that has been fine-tuned on runwayml/stable-diffusion-v1-5. For more general information on how to run text-to-image models with Diffusers, see the docs. - Version 8 focuses on improving what V7 started. Might be harder to do photorealism compared to realism focused models, as it might be hard to do anime compared to anime focused models, but it can do both pretty well if you're skilled enough. Check the examples! - Version 7 improves lora support, NSFW and realism. If you're interested in "absolute" realism, try AbsoluteReality. - Version 6 adds more lora support and more style in general. It should also be better at generating directly at… Open weights creativeml-openrail-m 860M parameters diffusers [View model](https://savrn.com/models/dreamshaper-8) Model · Text to image ### [sd-turbo](https://savrn.com/models/sd-turbo) [Stability AI](https://savrn.com/model-publishers/stabilityai) SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding, we recommend SDXL-Turbo. Please note: For commercial use, please refer to https://stability.ai/license. SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high… Open weights 866M parameters diffusers [View model](https://savrn.com/models/sd-turbo) Model · Text to image ### [controlnet-openpose-sdxl-1.0](https://savrn.com/models/controlnet-openpose-sdxl-1-0) [Qi](https://savrn.com/model-publishers/xinsir) thanks feiyuuu for report the problem. When using the default pose line the performance may be unstable, this is because the pose label use more thick line in training to have a better look. This difference can be fix by using the following method: Find the util.py in controlnetaux python package, usually the path is like: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnetaux/openpose/util.py Replace the drawbodypose function with the following code: Use the code below to get started with the model. HumanArt [https://github.com/IDEA-Research/HumanArt], select 2000 images with ground truth pose annotations to generate images and calculate mAP. We are the SOTA… Open weights apache-2.0 1.3B parameters diffusers [View model](https://savrn.com/models/controlnet-openpose-sdxl-1-0) ## Simian Luo [All models and datasets](https://savrn.com/model-publishers/simianluo) ## Versions - [a85df6a8bd97](https://savrn.com/models/lcm-dreamshaper-v7/versions/a85df6a8bd97) · current 2026-09-18 ## Explore More - [All text to image models](https://savrn.com/models/tasks/text-to-image) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Hugging Face record](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) - [How the hub is built](https://savrn.com/model-hub/methodology)