Instructions to use h1t/oms_l_mixclip_xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use h1t/oms_l_mixclip_xl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("h1t/oms_l_mixclip_xl", 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
File size: 298 Bytes
c490bed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"_class_name": "OMSPipeline",
"_diffusers_version": "0.23.1",
"oms_module": [
"unet_2d_condition_woct",
"UNet2DConditionWoCTModel"
],
"oms_text_encoder": [
"encoder_wrapper",
"SDXLTextEncoder"
],
"oms_tokenizer": [
"tokenizer_wrapper",
"SDXLTokenizer"
]
}
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