Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use bhuv1-c/db-valid-warehouse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bhuv1-c/db-valid-warehouse with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bhuv1-c/db-valid-warehouse", dtype=torch.bfloat16, device_map="cuda") prompt = "any two tiles of blue, pink, or orange color are connected through a path with non-black tiles, each blue tile is adjacent to at least one black tile,each black tile is adjacent to at least two blue tiles." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth - bhuv1-c/db-valid-warehouse
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on any two tiles of blue, pink, or orange color are connected through a path with non-black tiles, each blue tile is adjacent to at least one black tile,each black tile is adjacent to at least two blue tiles. 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]
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Model tree for bhuv1-c/db-valid-warehouse
Base model
CompVis/stable-diffusion-v1-4