import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("linoyts/tarot-z-image-lora")
prompt = "a trtcrd of a person on a computer, on the computer you see a meme being made with an ancient looking trollface, 'the shitposter' arcana, in the style of TOK a trtcrd, tarot style"
image = pipe(prompt).images[0]Z Image DreamBooth LoRA - linoyts/tarot-z-image-lora
Model description
These are linoyts/tarot-z-image-lora DreamBooth LoRA weights for Tongyi-MAI/Z-Image.
The weights were trained using DreamBooth with the Z Image diffusers trainer.
Quant training? None
Trigger words
You should use a trtcrd of a person on a computer, on the computer you see a meme being made with an ancient looking trollface, 'the shitposter' arcana, in the style of TOK a trtcrd, tarot style to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("Tongyi-MAI/Z-Image", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('linoyts/tarot-z-image-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a trtcrd of a person on a computer, on the computer you see a meme being made with an ancient looking trollface, 'the shitposter' arcana, in the style of TOK a trtcrd, tarot style').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
License
Please adhere to the licensing terms as described here.
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 linoyts/tarot-z-image-lora
Base model
Tongyi-MAI/Z-Image