Instructions to use SearchingMan/Z-Image-Turbo-student-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SearchingMan/Z-Image-Turbo-student-adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SearchingMan/Z-Image-Turbo-student-adapter", 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
- Draw Things
- DiffusionBee
File size: 1,035 Bytes
0179f45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | from transformers import PretrainedConfig
class StudentAdapterConfig(PretrainedConfig):
model_type = "zimage_student_adapter"
def __init__(
self,
student_config_dict=None,
student_model_type=None,
hs_tap_index=-2,
adapter_dim=1024,
adapter_heads=8,
adapter_blocks=2,
adapter_ff_mult=4,
adapter_dropout=0.1,
teacher_hidden_size=None,
student_hidden_size=None,
**kwargs,
):
super().__init__(**kwargs)
self.student_config_dict = student_config_dict or {}
self.student_model_type = student_model_type
self.hs_tap_index = int(hs_tap_index)
self.adapter_dim = int(adapter_dim)
self.adapter_heads = int(adapter_heads)
self.adapter_blocks = int(adapter_blocks)
self.adapter_ff_mult = int(adapter_ff_mult)
self.adapter_dropout = float(adapter_dropout)
self.teacher_hidden_size = teacher_hidden_size
self.student_hidden_size = student_hidden_size
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