Instructions to use chenzeyang1/T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenzeyang1/T with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chenzeyang1/T", torch_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
File size: 748 Bytes
a8a0738 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | import os
from .clip_encoder import CLIPVisionTower, CLIPVisionTowerS2
def build_vision_tower(vision_tower_cfg, **kwargs):
vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
is_absolute_path_exists = os.path.exists(vision_tower)
use_s2 = getattr(vision_tower_cfg, 's2', False)
if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or "ShareGPT4V" in vision_tower:
if use_s2:
return CLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
else:
return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
raise ValueError(f'Unknown vision tower: {vision_tower}')
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