Instructions to use lsmpp/kontextrefiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lsmpp/kontextrefiner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lsmpp/kontextrefiner", 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: 1,134 Bytes
1b7e5b9 | 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 37 38 39 40 | import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from ..test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class FlaxAutoencoderKLTests(FlaxModelTesterMixin, unittest.TestCase):
model_class = FlaxAutoencoderKL
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
prng_key = jax.random.PRNGKey(0)
image = jax.random.uniform(prng_key, ((batch_size, num_channels) + sizes))
return {"sample": image, "prng_key": prng_key}
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
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