Instructions to use SidXXD/custom-diffusion-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/custom-diffusion-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/custom-diffusion-model", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> morning glory--output_dir=model/single-F-5-morning-glory-FLOWER-2" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from SidXXD/custom-diffusion-model: direct link, hf CLI and curl.
- Browser
- Download file 811 Bytes
-
https://huggingface.co/SidXXD/custom-diffusion-model/resolve/main/README.md
- Command line
-
hf download hf://SidXXD/custom-diffusion-model/README.md
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curl -L -o README.md https://huggingface.co/SidXXD/custom-diffusion-model/resolve/main/README.md
811 Bytes
metadata
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: >-
photo of a <new1> morning
glory--output_dir=model/single-F-5-morning-glory-FLOWER-2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
Custom Diffusion - SidXXD/custom-diffusion-model
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a morning glory--output_dir=model/single-F-5-morning-glory-FLOWER-2 using Custom Diffusion. You can find some example images in the following.
For more details on the training, please follow this link.