Instructions to use Cccccz/HY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cccccz/HY with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cccccz/HY", 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: 738 Bytes
74da989 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # SPDX-License-Identifier: Apache-2.0
# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/layers/utils.py
"""Utility methods for model layers."""
import torch
def get_token_bin_counts_and_mask(
tokens: torch.Tensor,
vocab_size: int,
num_seqs: int,
) -> tuple[torch.Tensor, torch.Tensor]:
# Compute the bin counts for the tokens.
# vocab_size + 1 for padding.
bin_counts = torch.zeros((num_seqs, vocab_size + 1),
dtype=torch.long,
device=tokens.device)
bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
bin_counts = bin_counts[:, :vocab_size]
mask = bin_counts > 0
return bin_counts, mask
|