Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use forkthus/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use forkthus/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("forkthus/model", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks object" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from forkthus/model: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/forkthus/model/resolve/main/README.md
- Command line
-
hf download hf://forkthus/model/README.md
-
curl -L -o README.md https://huggingface.co/forkthus/model/resolve/main/README.md
1.08 kB
metadata
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of sks object
DreamBooth - forkthus/model
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks object using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]