Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use ngoupeyoukheng/fourmedambert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ngoupeyoukheng/fourmedambert with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ngoupeyoukheng/fourmedambert") prompt = "An image of the fourme dambert cheese" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
AutoTrain SDXL LoRA DreamBooth - haroldng/fourmedambert
Model description
These are haroldng/fourmedambert LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
Trigger words
You should use An image of the fourme dambert cheese 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 ngoupeyoukheng/fourmedambert
Base model
stabilityai/stable-diffusion-xl-base-1.0