Instructions to use tashfinsami/model_bcc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tashfinsami/model_bcc with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kardbord/stable-diffusion-v1-5-unsafe", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tashfinsami/model_bcc") prompt = "a derm photo of sks basal cell carcinoma lesion" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - tashfinsami/model_bcc
These are LoRA adaption weights for Kardbord/stable-diffusion-v1-5-unsafe. The weights were trained on a derm photo of sks basal cell carcinoma lesion using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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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