63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 HuggingFace Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from diffusers.utils import is_flax_available
|
|
from diffusers.utils.testing_utils import require_flax, slow
|
|
|
|
|
|
if is_flax_available():
|
|
import jax
|
|
from diffusers import FlaxStableDiffusionPipeline
|
|
from flax.jax_utils import replicate
|
|
from flax.training.common_utils import shard
|
|
from jax import pmap
|
|
|
|
|
|
@require_flax
|
|
@slow
|
|
class FlaxPipelineTests(unittest.TestCase):
|
|
def test_dummy_all_tpus(self):
|
|
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
|
|
"hf-internal-testing/tiny-stable-diffusion-pipe"
|
|
)
|
|
|
|
prompt = (
|
|
"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
|
|
" field, close up, split lighting, cinematic"
|
|
)
|
|
|
|
prng_seed = jax.random.PRNGKey(0)
|
|
num_inference_steps = 4
|
|
|
|
num_samples = jax.device_count()
|
|
prompt = num_samples * [prompt]
|
|
prompt_ids = pipeline.prepare_inputs(prompt)
|
|
|
|
p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
|
|
|
|
# shard inputs and rng
|
|
params = replicate(params)
|
|
prng_seed = jax.random.split(prng_seed, 8)
|
|
prompt_ids = shard(prompt_ids)
|
|
|
|
images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
|
|
images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
|
|
|
|
assert len(images_pil) == 8
|