Instructions to use amerssun/tww_result_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amerssun/tww_result_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("amerssun/tww_result_lora") prompt = "a photo of tww 1girl" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("amerssun/tww_result_lora")
prompt = "a photo of tww 1girl"
image = pipe(prompt).images[0]YAML Metadata Error:"base_model" with value "/mnt/user/sunzhaoxu/diffusion/cilloutmix/" is not valid. Use a model id from https://hf.co/models.
LoRA DreamBooth - amerssun/tww_result_lora
These are LoRA adaption weights for /mnt/user/sunzhaoxu/diffusion/cilloutmix/. The weights were trained on a photo of tww 1girl using DreamBooth. You can find some example images in the following.
- Downloads last month
- -



