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import gc
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, OmniGenPipeline, OmniGenTransformer2DModel
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_torch_accelerator,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
class OmniGenPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = OmniGenPipeline
params = frozenset(["prompt", "guidance_scale"])
batch_params = frozenset(["prompt"])
test_layerwise_casting = True
def get_dummy_components(self):
torch.manual_seed(0)
transformer = OmniGenTransformer2DModel(
hidden_size=16,
num_attention_heads=4,
num_key_value_heads=4,
intermediate_size=32,
num_layers=1,
in_channels=4,
time_step_dim=4,
rope_scaling={"long_factor": list(range(1, 3)), "short_factor": list(range(1, 3))},
)
torch.manual_seed(0)
vae = AutoencoderKL(
sample_size=32,
in_channels=3,
out_channels=3,
block_out_channels=(4, 4, 4, 4),
layers_per_block=1,
latent_channels=4,
norm_num_groups=1,
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
)
scheduler = FlowMatchEulerDiscreteScheduler(invert_sigmas=True, num_train_timesteps=1)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
components = {
"transformer": transformer,
"vae": vae,
"scheduler": scheduler,
"tokenizer": tokenizer,
}
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="cpu").manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 1,
"guidance_scale": 3.0,
"output_type": "np",
"height": 16,
"width": 16,
}
return inputs
def test_inference(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
generated_image = pipe(**inputs).images[0]
self.assertEqual(generated_image.shape, (16, 16, 3))
@slow
@require_torch_accelerator
class OmniGenPipelineSlowTests(unittest.TestCase):
pipeline_class = OmniGenPipeline
repo_id = "shitao/OmniGen-v1-diffusers"
def setUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
return {
"prompt": "A photo of a cat",
"num_inference_steps": 2,
"guidance_scale": 2.5,
"output_type": "np",
"generator": generator,
}
def test_omnigen_inference(self):
pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
[0.05859375, 0.05859375, 0.04492188],
[0.04882812, 0.04101562, 0.03320312],
[0.04882812, 0.04296875, 0.03125],
[0.04296875, 0.0390625, 0.03320312],
[0.04296875, 0.03710938, 0.03125],
[0.04492188, 0.0390625, 0.03320312],
[0.04296875, 0.03710938, 0.03125],
[0.04101562, 0.03710938, 0.02734375],
[0.04101562, 0.03515625, 0.02734375],
[0.04101562, 0.03515625, 0.02929688],
],
dtype=np.float32,
),
("cuda", 7): np.array(
[
[0.1783447, 0.16772744, 0.14339337],
[0.17066911, 0.15521264, 0.13757327],
[0.17072496, 0.15531206, 0.13524258],
[0.16746324, 0.1564025, 0.13794944],
[0.16490817, 0.15258026, 0.13697758],
[0.16971767, 0.15826806, 0.13928896],
[0.16782972, 0.15547255, 0.13783783],
[0.16464645, 0.15281534, 0.13522372],
[0.16535294, 0.15301755, 0.13526791],
[0.16365296, 0.15092957, 0.13443318],
],
dtype=np.float32,
),
("cuda", 8): np.array(
[
[0.0546875, 0.05664062, 0.04296875],
[0.046875, 0.04101562, 0.03320312],
[0.05078125, 0.04296875, 0.03125],
[0.04296875, 0.04101562, 0.03320312],
[0.0390625, 0.03710938, 0.02929688],
[0.04296875, 0.03710938, 0.03125],
[0.0390625, 0.03710938, 0.02929688],
[0.0390625, 0.03710938, 0.02734375],
[0.0390625, 0.03320312, 0.02734375],
[0.0390625, 0.03320312, 0.02734375],
],
dtype=np.float32,
),
}
)
expected_slice = expected_slices.get_expectation()
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
assert max_diff < 1e-4
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