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# 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 gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
StableDiffusionSAGPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
IPAdapterTesterMixin,
PipelineFromPipeTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class StableDiffusionSAGPipelineFastTests(
IPAdapterTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
PipelineFromPipeTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionSAGPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=2,
sample_size=8,
norm_num_groups=1,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=8,
)
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=[4, 8],
norm_num_groups=1,
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=8,
num_hidden_layers=2,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
pad_token_id=1,
vocab_size=1000,
)
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,
"image_encoder": 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 = {
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "np",
}
return inputs
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@unittest.skip("Not necessary to test here.")
def test_xformers_attention_forwardGenerator_pass(self):
pass
def test_pipeline_different_schedulers(self):
pipeline = self.pipeline_class(**self.get_dummy_components())
inputs = self.get_dummy_inputs("cpu")
expected_image_size = (16, 16, 3)
for scheduler_cls in [DDIMScheduler, DEISMultistepScheduler, DPMSolverMultistepScheduler]:
pipeline.scheduler = scheduler_cls.from_config(pipeline.scheduler.config)
image = pipeline(**inputs).images[0]
shape = image.shape
assert shape == expected_image_size
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
with self.assertRaises(ValueError):
# Karras schedulers are not supported
image = pipeline(**inputs).images[0]
@nightly
@require_torch_gpu
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_diffusion_1(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np"
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def test_stable_diffusion_2(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np"
)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def test_stable_diffusion_2_non_square(self):
sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
sag_pipe = sag_pipe.to(torch_device)
sag_pipe.set_progress_bar_config(disable=None)
prompt = "."
generator = torch.manual_seed(0)
output = sag_pipe(
[prompt],
width=768,
height=512,
generator=generator,
guidance_scale=7.5,
sag_scale=1.0,
num_inference_steps=20,
output_type="np",
)
image = output.images
assert image.shape == (1, 512, 768, 3)
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