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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 FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline | |
from diffusers.utils import is_flax_available, load_image, 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 FlaxControlNetPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
def test_canny(self): | |
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
"lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.bfloat16 | |
) | |
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( | |
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16 | |
) | |
params["controlnet"] = controlnet_params | |
prompts = "bird" | |
num_samples = jax.device_count() | |
prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) | |
canny_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" | |
) | |
processed_image = pipe.prepare_image_inputs([canny_image] * num_samples) | |
rng = jax.random.PRNGKey(0) | |
rng = jax.random.split(rng, jax.device_count()) | |
p_params = replicate(params) | |
prompt_ids = shard(prompt_ids) | |
processed_image = shard(processed_image) | |
images = pipe( | |
prompt_ids=prompt_ids, | |
image=processed_image, | |
params=p_params, | |
prng_seed=rng, | |
num_inference_steps=50, | |
jit=True, | |
).images | |
assert images.shape == (jax.device_count(), 1, 768, 512, 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( | |
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] | |
) | |
print(f"output_slice: {output_slice}") | |
assert jnp.abs(output_slice - expected_slice).max() < 1e-2 | |
def test_pose(self): | |
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
"lllyasviel/sd-controlnet-openpose", from_pt=True, dtype=jnp.bfloat16 | |
) | |
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( | |
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16 | |
) | |
params["controlnet"] = controlnet_params | |
prompts = "Chef in the kitchen" | |
num_samples = jax.device_count() | |
prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) | |
pose_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" | |
) | |
processed_image = pipe.prepare_image_inputs([pose_image] * num_samples) | |
rng = jax.random.PRNGKey(0) | |
rng = jax.random.split(rng, jax.device_count()) | |
p_params = replicate(params) | |
prompt_ids = shard(prompt_ids) | |
processed_image = shard(processed_image) | |
images = pipe( | |
prompt_ids=prompt_ids, | |
image=processed_image, | |
params=p_params, | |
prng_seed=rng, | |
num_inference_steps=50, | |
jit=True, | |
).images | |
assert images.shape == (jax.device_count(), 1, 768, 512, 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( | |
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] | |
) | |
print(f"output_slice: {output_slice}") | |
assert jnp.abs(output_slice - expected_slice).max() < 1e-2 | |