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: 927 Bytes
a8a0738 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | from transformers import AutoConfig
def auto_upgrade(config):
cfg = AutoConfig.from_pretrained(config)
if 'llava' in config and 'llava' not in cfg.model_type:
assert cfg.model_type == 'llama'
print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.")
print("You must upgrade the checkpoint to the new code base (this can be done automatically).")
confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]")
if confirm.lower() in ["y", "yes"]:
print("Upgrading checkpoint...")
assert len(cfg.architectures) == 1
setattr(cfg.__class__, "model_type", "llava")
cfg.architectures[0] = 'LlavaLlamaForCausalLM'
cfg.save_pretrained(config)
print("Checkpoint upgraded.")
else:
print("Checkpoint upgrade aborted.")
exit(1)
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