diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_flax_controlnet.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 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


@slow
@require_flax
class FlaxStableDiffusionControlNetPipelineIntegrationTests(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