Instructions to use wkplhc/hmanhua with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wkplhc/hmanhua with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LyliaEngine/Pony_Diffusion_V6_XL", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("wkplhc/hmanhua") prompt = "gergh45" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("LyliaEngine/Pony_Diffusion_V6_XL", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("wkplhc/hmanhua")
prompt = "gergh45"
image = pipe(prompt).images[0]ngfny

- Prompt
- gergh45
Trigger words
You should use manhua to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for wkplhc/hmanhua
Base model
Bakanayatsu/Pony-Diffusion-V6-XL-for-Anime Adapter
LyliaEngine/Pony_Diffusion_V6_XL