Instructions to use roshikhan301/NEWONE1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use roshikhan301/NEWONE1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("roshikhan301/NEWONE1", 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: 1,353 Bytes
8a37e0a | 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 32 33 34 35 36 | from contextlib import contextmanager
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
@contextmanager
def patch_vae_tiling_params(
vae: AutoencoderKL | AutoencoderTiny,
tile_sample_min_size: int,
tile_latent_min_size: int,
tile_overlap_factor: float,
):
"""Patch the parameters that control the VAE tiling tile size and overlap.
These parameters are not explicitly exposed in the VAE's API, but they have a significant impact on the quality of
the outputs. As a general rule, bigger tiles produce better results, but this comes at the cost of higher memory
usage.
"""
# Record initial config.
orig_tile_sample_min_size = vae.tile_sample_min_size
orig_tile_latent_min_size = vae.tile_latent_min_size
orig_tile_overlap_factor = vae.tile_overlap_factor
try:
# Apply target config.
vae.tile_sample_min_size = tile_sample_min_size
vae.tile_latent_min_size = tile_latent_min_size
vae.tile_overlap_factor = tile_overlap_factor
yield
finally:
# Restore initial config.
vae.tile_sample_min_size = orig_tile_sample_min_size
vae.tile_latent_min_size = orig_tile_latent_min_size
vae.tile_overlap_factor = orig_tile_overlap_factor
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