Instructions to use CreeperMZ/weight_tag_loss_sdxl_test_1536 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CreeperMZ/weight_tag_loss_sdxl_test_1536 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CreeperMZ/weight_tag_loss_sdxl_test_1536", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
test train with new loss method
resolution: 1536
sample size:90k
1 epoch
huber loss + ztsnr + vpre
- Downloads last month
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Model tree for CreeperMZ/weight_tag_loss_sdxl_test_1536
Base model
Laxhar/noobai-XL-1.0 Finetuned
Laxhar/noobai-XL-Vpred-0.5 Finetuned
Laxhar/noobai-XL-Vpred-0.6 Finetuned
Laxhar/noobai-XL-Vpred-0.65 Finetuned
Laxhar/noobai-XL-Vpred-0.75 Finetuned
Laxhar/noobai-XL-Vpred-1.0