# 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 unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import AmusedPipeline, AmusedScheduler, UVit2DModel, VQModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_accelerator,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AmusedPipeline
params = TEXT_TO_IMAGE_PARAMS | {"encoder_hidden_states", "negative_encoder_hidden_states"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
test_layerwise_casting = True
def get_dummy_components(self):
torch.manual_seed(0)
transformer = UVit2DModel(
hidden_size=8,
use_bias=False,
hidden_dropout=0.0,
cond_embed_dim=8,
micro_cond_encode_dim=2,
micro_cond_embed_dim=10,
encoder_hidden_size=8,
vocab_size=32,
codebook_size=8,
in_channels=8,
block_out_channels=8,
num_res_blocks=1,
downsample=True,
upsample=True,
block_num_heads=1,
num_hidden_layers=1,
num_attention_heads=1,
attention_dropout=0.0,
intermediate_size=8,
layer_norm_eps=1e-06,
ln_elementwise_affine=True,
)
scheduler = AmusedScheduler(mask_token_id=31)
torch.manual_seed(0)
vqvae = VQModel(
act_fn="silu",
block_out_channels=[8],
down_block_types=["DownEncoderBlock2D"],
in_channels=3,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=8,
out_channels=3,
sample_size=8,
up_block_types=["UpDecoderBlock2D"],
mid_block_add_attention=False,
lookup_from_codebook=True,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=8,
intermediate_size=8,
layer_norm_eps=1e-05,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=8,
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"transformer": transformer,
"scheduler": scheduler,
"vqvae": vqvae,
"text_encoder": text_encoder,
"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=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
"height": 4,
"width": 4,
}
return inputs
def test_inference_batch_consistent(self, batch_sizes=[2]):
self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
@unittest.skip("aMUSEd does not support lists of generators")
def test_inference_batch_single_identical(self):
...
@slow
@require_torch_accelerator
class AmusedPipelineSlowTests(unittest.TestCase):
def test_amused_256(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-256")
pipe.to(torch_device)
image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.4011, 0.3992, 0.379, 0.3856, 0.3772, 0.3711, 0.3919, 0.385, 0.3625])
assert np.abs(image_slice - expected_slice).max() < 0.003
def test_amused_256_fp16(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16)
pipe.to(torch_device)
image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.0554, 0.05129, 0.0344, 0.0452, 0.0476, 0.0271, 0.0495, 0.0527, 0.0158])
assert np.abs(image_slice - expected_slice).max() < 0.007
def test_amused_512(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-512")
pipe.to(torch_device)
image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1199, 0.1171, 0.1229, 0.1188, 0.1210, 0.1147, 0.1260, 0.1346, 0.1152])
assert np.abs(image_slice - expected_slice).max() < 0.003
def test_amused_512_fp16(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
pipe.to(torch_device)
image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1509, 0.1492, 0.1531, 0.1485, 0.1501, 0.1465, 0.1581, 0.1690, 0.1499])
assert np.abs(image_slice - expected_slice).max() < 0.003