Text-to-Image
Diffusers
TensorBoard
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use dsgesd32/lora-trained-sd2-chim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dsgesd32/lora-trained-sd2-chim with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Manojb/stable-diffusion-2-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dsgesd32/lora-trained-sd2-chim") prompt = "a photo of sks person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA DreamBooth - dsgesd32/lora-trained-sd2-chim
These are LoRA adaption weights for Manojb/stable-diffusion-2-base. The weights were trained on a photo of sks person using DreamBooth. You can find some example images in the following.
LoRA 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 dsgesd32/lora-trained-sd2-chim
Base model
Manojb/stable-diffusion-2-base


