153 lines
5.3 KiB
Python
153 lines
5.3 KiB
Python
# 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
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, Transformer2DModel
|
|
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
|
|
from diffusers.utils.testing_utils import require_torch_gpu
|
|
|
|
from ...pipeline_params import (
|
|
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
|
|
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
|
|
)
|
|
from ...test_pipelines_common import PipelineTesterMixin
|
|
|
|
|
|
torch.backends.cuda.matmul.allow_tf32 = False
|
|
|
|
|
|
class DiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
|
|
pipeline_class = DiTPipeline
|
|
params = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
|
|
required_optional_params = PipelineTesterMixin.required_optional_params - {
|
|
"latents",
|
|
"num_images_per_prompt",
|
|
"callback",
|
|
"callback_steps",
|
|
}
|
|
batch_params = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
|
|
test_cpu_offload = False
|
|
|
|
def get_dummy_components(self):
|
|
torch.manual_seed(0)
|
|
transformer = Transformer2DModel(
|
|
sample_size=16,
|
|
num_layers=2,
|
|
patch_size=4,
|
|
attention_head_dim=8,
|
|
num_attention_heads=2,
|
|
in_channels=4,
|
|
out_channels=8,
|
|
attention_bias=True,
|
|
activation_fn="gelu-approximate",
|
|
num_embeds_ada_norm=1000,
|
|
norm_type="ada_norm_zero",
|
|
norm_elementwise_affine=False,
|
|
)
|
|
vae = AutoencoderKL()
|
|
scheduler = DDIMScheduler()
|
|
components = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
|
|
return components
|
|
|
|
def get_dummy_inputs(self, device, seed=0):
|
|
if str(device).startswith("mps"):
|
|
generator = torch.manual_seed(seed)
|
|
else:
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
inputs = {
|
|
"class_labels": [1],
|
|
"generator": generator,
|
|
"num_inference_steps": 2,
|
|
"output_type": "numpy",
|
|
}
|
|
return inputs
|
|
|
|
def test_inference(self):
|
|
device = "cpu"
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
self.assertEqual(image.shape, (1, 16, 16, 3))
|
|
expected_slice = np.array([0.4380, 0.4141, 0.5159, 0.0000, 0.4282, 0.6680, 0.5485, 0.2545, 0.6719])
|
|
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
|
|
self.assertLessEqual(max_diff, 1e-3)
|
|
|
|
def test_inference_batch_single_identical(self):
|
|
self._test_inference_batch_single_identical(relax_max_difference=True, expected_max_diff=1e-3)
|
|
|
|
@unittest.skipIf(
|
|
torch_device != "cuda" or not is_xformers_available(),
|
|
reason="XFormers attention is only available with CUDA and `xformers` installed",
|
|
)
|
|
def test_xformers_attention_forwardGenerator_pass(self):
|
|
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
|
|
|
|
|
|
@require_torch_gpu
|
|
@slow
|
|
class DiTPipelineIntegrationTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_dit_256(self):
|
|
generator = torch.manual_seed(0)
|
|
|
|
pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256")
|
|
pipe.to("cuda")
|
|
|
|
words = ["vase", "umbrella", "white shark", "white wolf"]
|
|
ids = pipe.get_label_ids(words)
|
|
|
|
images = pipe(ids, generator=generator, num_inference_steps=40, output_type="np").images
|
|
|
|
for word, image in zip(words, images):
|
|
expected_image = load_numpy(
|
|
f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"
|
|
)
|
|
assert np.abs((expected_image - image).max()) < 1e-2
|
|
|
|
def test_dit_512(self):
|
|
pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
|
|
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
|
|
pipe.to("cuda")
|
|
|
|
words = ["vase", "umbrella"]
|
|
ids = pipe.get_label_ids(words)
|
|
|
|
generator = torch.manual_seed(0)
|
|
images = pipe(ids, generator=generator, num_inference_steps=25, output_type="np").images
|
|
|
|
for word, image in zip(words, images):
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
f"/dit/{word}_512.npy"
|
|
)
|
|
|
|
assert np.abs((expected_image - image).max()) < 1e-1
|