Instructions to use LucasDash/dash-sd-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LucasDash/dash-sd-mix with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LucasDash/dash-sd-mix", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 1,415 Bytes
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license: creativeml-openrail-m
language:
- en
library_name: diffusers
tags:
- stable-diffusion
---
# Dash Stable Diffusion Mix
This space is where I host models and workflows to assist with the production of digital paintings. The goal is not perfectionism, but rather scalability and adaptability. The intention of my Stable Diffusion 1.5 Dash SD Mix template is to be used in conjunction with control net models as a scribble or line art control net to support a sketch drawing into a final painting.
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# Image Sample

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# Model Details
- **Model Type:** Text-to-Image Generation
- **Base Model:** Stable Diffusion 1.5 (SD 1.5)
- **Format:** safetensors
- **Version:** v1.0
- **Creator:** Lucas Dash
- **File Size:** ~2 GB
- **License:** CreativeML Open RAIL++-M License
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# Limitations
- May produce content that could be considered sensitive; use responsibly.
- Currently lacking a dedicated embedded VAE, so I recommend [this one](https://civitai.com/models/23906/vae-kl-f8-anime2) to use it separately.
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# License
This model is distributed under the **CreativeML Open RAIL++-M License**, which allows commercial and non-commercial use, with proper credit and no malicious usage.
> [License details](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |