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128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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from diffusers.utils import is_flax_available, load_image
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from diffusers.utils.testing_utils import require_flax, slow
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if is_flax_available():
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import jax
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import jax.numpy as jnp
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from flax.jax_utils import replicate
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from flax.training.common_utils import shard
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@slow
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@require_flax
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class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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def test_canny(self):
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controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
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"lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.bfloat16
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)
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pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
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"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
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)
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params["controlnet"] = controlnet_params
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prompts = "bird"
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num_samples = jax.device_count()
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prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
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canny_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
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)
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processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)
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rng = jax.random.PRNGKey(0)
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rng = jax.random.split(rng, jax.device_count())
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p_params = replicate(params)
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prompt_ids = shard(prompt_ids)
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processed_image = shard(processed_image)
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images = pipe(
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prompt_ids=prompt_ids,
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image=processed_image,
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params=p_params,
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prng_seed=rng,
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num_inference_steps=50,
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jit=True,
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).images
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assert images.shape == (jax.device_count(), 1, 768, 512, 3)
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images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
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image_slice = images[0, 253:256, 253:256, -1]
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output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
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expected_slice = jnp.array(
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[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]
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)
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assert jnp.abs(output_slice - expected_slice).max() < 1e-2
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def test_pose(self):
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controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
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"lllyasviel/sd-controlnet-openpose", from_pt=True, dtype=jnp.bfloat16
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)
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pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
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"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16
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)
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params["controlnet"] = controlnet_params
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prompts = "Chef in the kitchen"
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num_samples = jax.device_count()
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prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
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pose_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"
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)
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processed_image = pipe.prepare_image_inputs([pose_image] * num_samples)
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rng = jax.random.PRNGKey(0)
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rng = jax.random.split(rng, jax.device_count())
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p_params = replicate(params)
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prompt_ids = shard(prompt_ids)
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processed_image = shard(processed_image)
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images = pipe(
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prompt_ids=prompt_ids,
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image=processed_image,
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params=p_params,
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prng_seed=rng,
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num_inference_steps=50,
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jit=True,
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).images
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assert images.shape == (jax.device_count(), 1, 768, 512, 3)
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images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
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image_slice = images[0, 253:256, 253:256, -1]
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output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
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expected_slice = jnp.array(
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[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]
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)
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assert jnp.abs(output_slice - expected_slice).max() < 1e-2
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