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| # 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 FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline | |
| from diffusers.utils import is_flax_available, slow | |
| from diffusers.utils.testing_utils import require_flax | |
| 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 FlaxStableDiffusion2PipelineIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| def test_stable_diffusion_flax(self): | |
| sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2", | |
| revision="bf16", | |
| dtype=jnp.bfloat16, | |
| ) | |
| prompt = "A painting of a squirrel eating a burger" | |
| num_samples = jax.device_count() | |
| prompt = num_samples * [prompt] | |
| prompt_ids = sd_pipe.prepare_inputs(prompt) | |
| params = replicate(params) | |
| prompt_ids = shard(prompt_ids) | |
| prng_seed = jax.random.PRNGKey(0) | |
| prng_seed = jax.random.split(prng_seed, jax.device_count()) | |
| images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0] | |
| assert images.shape == (jax.device_count(), 1, 768, 768, 3) | |
| images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) | |
| image_slice = images[0, 253:256, 253:256, -1] | |
| output_slice = jnp.asarray(jax.device_get(image_slice.flatten())) | |
| expected_slice = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]) | |
| print(f"output_slice: {output_slice}") | |
| assert jnp.abs(output_slice - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_dpm_flax(self): | |
| model_id = "stabilityai/stable-diffusion-2" | |
| scheduler, scheduler_params = FlaxDPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained( | |
| model_id, | |
| scheduler=scheduler, | |
| revision="bf16", | |
| dtype=jnp.bfloat16, | |
| ) | |
| params["scheduler"] = scheduler_params | |
| prompt = "A painting of a squirrel eating a burger" | |
| num_samples = jax.device_count() | |
| prompt = num_samples * [prompt] | |
| prompt_ids = sd_pipe.prepare_inputs(prompt) | |
| params = replicate(params) | |
| prompt_ids = shard(prompt_ids) | |
| prng_seed = jax.random.PRNGKey(0) | |
| prng_seed = jax.random.split(prng_seed, jax.device_count()) | |
| images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0] | |
| assert images.shape == (jax.device_count(), 1, 768, 768, 3) | |
| images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) | |
| image_slice = images[0, 253:256, 253:256, -1] | |
| output_slice = jnp.asarray(jax.device_get(image_slice.flatten())) | |
| expected_slice = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]) | |
| print(f"output_slice: {output_slice}") | |
| assert jnp.abs(output_slice - expected_slice).max() < 1e-2 | |