Sana DreamBooth FALQON - wsl448/yarn_art_falqon_sana_block_int8_svd_lr1e-3
- Prompt
- a puppy in a pond, yarn art style
- Prompt
- a puppy in a pond, yarn art style
- Prompt
- a puppy in a pond, yarn art style
- Prompt
- a puppy in a pond, yarn art style
Model description
These are wsl448/yarn_art_falqon_sana_block_int8_svd_lr1e-3 DreamBooth FALQON weights for Efficient-Large-Model/Sana_1600M_1024px_diffusers.
The weights were trained using DreamBooth with FALQON (FP8-Accelerated LoRA with Quantization) for efficient fine-tuning.
Trigger words
You should use a puppy, yarn art style to trigger the image generation.
License
Please check the base model license.
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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# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://wsl448/yarn_art_falqon_sana_block_int8_svd_lr1e-3") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, )