Diffusers Dataset Augmentation
Collection
Dataset augmentation project using diffuser SANA model • 3 items • Updated
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("jgilewicz/SANA-LoRA-CIFAR10")
prompt = "a low-resolution photo of an airplane, CIFAR-10 style"
image = pipe(prompt).images[0]Sana DreamBooth LoRA - W1ndrunn3rr/SANA-LoRA-CIFAR10
- Prompt
- a low-resolution photo of an airplane, CIFAR-10 style
- Prompt
- a low-resolution photo of an airplane, CIFAR-10 style
- Prompt
- a low-resolution photo of an airplane, CIFAR-10 style
- Prompt
- a low-resolution photo of an airplane, CIFAR-10 style
Model description
These are W1ndrunn3rr/SANA-LoRA-CIFAR10 DreamBooth LoRA weights for Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers.
The weights were trained using DreamBooth with the Sana diffusers trainer.
Trigger words
You should use a photo to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
TODO
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
License
TODO
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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