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ldm3d-inpainting
/
diffuserslocal
/tests
/pipelines
/stable_diffusion_2
/test_stable_diffusion_flax_inpaint.py
# coding=utf-8 | |
# Copyright 2023 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 gc | |
import unittest | |
from diffusers import FlaxStableDiffusionInpaintPipeline | |
from diffusers.utils import is_flax_available, load_image | |
from diffusers.utils.testing_utils import require_flax, slow | |
if is_flax_available(): | |
import jax | |
import jax.numpy as jnp | |
from flax.jax_utils import replicate | |
from flax.training.common_utils import shard | |
class FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
def test_stable_diffusion_inpaint_pipeline(self): | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/sd2-inpaint/init_image.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" | |
) | |
model_id = "xvjiarui/stable-diffusion-2-inpainting" | |
pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None) | |
prompt = "Face of a yellow cat, high resolution, sitting on a park bench" | |
prng_seed = jax.random.PRNGKey(0) | |
num_inference_steps = 50 | |
num_samples = jax.device_count() | |
prompt = num_samples * [prompt] | |
init_image = num_samples * [init_image] | |
mask_image = num_samples * [mask_image] | |
prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image) | |
# shard inputs and rng | |
params = replicate(params) | |
prng_seed = jax.random.split(prng_seed, jax.device_count()) | |
prompt_ids = shard(prompt_ids) | |
processed_masked_images = shard(processed_masked_images) | |
processed_masks = shard(processed_masks) | |
output = pipeline( | |
prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True | |
) | |
images = output.images.reshape(num_samples, 512, 512, 3) | |
image_slice = images[0, 253:256, 253:256, -1] | |
output_slice = jnp.asarray(jax.device_get(image_slice.flatten())) | |
expected_slice = jnp.array( | |
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] | |
) | |
print(f"output_slice: {output_slice}") | |
assert jnp.abs(output_slice - expected_slice).max() < 1e-2 | |