File size: 4,828 Bytes
43b7e92 |
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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# coding=utf-8
# Copyright 2024 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
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
@slow
@require_flax
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
|