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# 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 transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNet2DConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ...test_pipelines_common import PipelineTesterMixin
@skip_mps
class StableDiffusionAttendAndExcitePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionAttendAndExcitePipeline
test_attention_slicing = False
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"})
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
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 = inputs = {
"prompt": "a cat and a frog",
"token_indices": [2, 5],
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"max_iter_to_alter": 2,
"thresholds": {0: 0.7},
}
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, 64, 64, 3))
expected_slice = np.array(
[0.5644937, 0.60543084, 0.48239064, 0.5206757, 0.55623394, 0.46045133, 0.5100435, 0.48919064, 0.4759359]
)
max_diff = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(max_diff, 1e-3)
def test_inference_batch_consistent(self):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[2, 4])
@require_torch_gpu
@slow
class StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_attend_and_excite_fp16(self):
generator = torch.manual_seed(51)
pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe.to("cuda")
prompt = "a painting of an elephant with glasses"
token_indices = [5, 7]
image = pipe(
prompt=prompt,
token_indices=token_indices,
guidance_scale=7.5,
generator=generator,
num_inference_steps=5,
max_iter_to_alter=5,
output_type="numpy",
).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"
)
assert np.abs((expected_image - image).max()) < 5e-1
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