Text-to-Image
Diffusers
diffusers-training
dora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Wacim-octo/factory_LoRA_local_RTX_3060", dtype=torch.bfloat16, device_map="cuda")
prompt = "a photo of factory"
image = pipe(prompt).images[0]SDXL LoRA DreamBooth - Wacim-octo/factory_LoRA_local_RTX_3060
Model description
These are Wacim-octo/factory_LoRA_local_RTX_3060 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: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use a photo of factory to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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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Model tree for Wacim-octo/factory_LoRA_local_RTX_3060
Base model
stabilityai/stable-diffusion-xl-base-1.0