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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 random | |
import unittest | |
import numpy as np | |
import torch | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import ( | |
AutoencoderKL, | |
EulerDiscreteScheduler, | |
StableDiffusionLatentUpscalePipeline, | |
StableDiffusionPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
from ...pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
from ...test_pipelines_common import PipelineTesterMixin | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionLatentUpscalePipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = StableDiffusionLatentUpscalePipeline | |
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - { | |
"height", | |
"width", | |
"cross_attention_kwargs", | |
"negative_prompt_embeds", | |
"prompt_embeds", | |
} | |
required_optional_params = PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"} | |
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS | |
test_cpu_offload = True | |
def dummy_image(self): | |
batch_size = 1 | |
num_channels = 4 | |
sizes = (16, 16) | |
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) | |
return image | |
def get_dummy_components(self): | |
torch.manual_seed(0) | |
model = UNet2DConditionModel( | |
act_fn="gelu", | |
attention_head_dim=8, | |
norm_num_groups=None, | |
block_out_channels=[32, 32, 64, 64], | |
time_cond_proj_dim=160, | |
conv_in_kernel=1, | |
conv_out_kernel=1, | |
cross_attention_dim=32, | |
down_block_types=( | |
"KDownBlock2D", | |
"KCrossAttnDownBlock2D", | |
"KCrossAttnDownBlock2D", | |
"KCrossAttnDownBlock2D", | |
), | |
in_channels=8, | |
mid_block_type=None, | |
only_cross_attention=False, | |
out_channels=5, | |
resnet_time_scale_shift="scale_shift", | |
time_embedding_type="fourier", | |
timestep_post_act="gelu", | |
up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"), | |
) | |
vae = AutoencoderKL( | |
block_out_channels=[32, 32, 64, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=[ | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
"DownEncoderBlock2D", | |
], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=4, | |
) | |
scheduler = EulerDiscreteScheduler(prediction_type="sample") | |
text_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, | |
hidden_act="quick_gelu", | |
projection_dim=512, | |
) | |
text_encoder = CLIPTextModel(text_config) | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
components = { | |
"unet": model.eval(), | |
"vae": vae.eval(), | |
"scheduler": scheduler, | |
"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", | |
"image": self.dummy_image.cpu(), | |
"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, 256, 256, 3)) | |
expected_slice = np.array( | |
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] | |
) | |
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=False) | |
class StableDiffusionLatentUpscalePipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_latent_upscaler_fp16(self): | |
generator = torch.manual_seed(33) | |
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16) | |
pipe.to("cuda") | |
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained( | |
"stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16 | |
) | |
upscaler.to("cuda") | |
prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic" | |
low_res_latents = pipe(prompt, generator=generator, output_type="latent").images | |
image = upscaler( | |
prompt=prompt, | |
image=low_res_latents, | |
num_inference_steps=20, | |
guidance_scale=0, | |
generator=generator, | |
output_type="np", | |
).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" | |
) | |
assert np.abs((expected_image - image).mean()) < 5e-2 | |
def test_latent_upscaler_fp16_image(self): | |
generator = torch.manual_seed(33) | |
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained( | |
"stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16 | |
) | |
upscaler.to("cuda") | |
prompt = "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas" | |
low_res_img = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" | |
) | |
image = upscaler( | |
prompt=prompt, | |
image=low_res_img, | |
num_inference_steps=20, | |
guidance_scale=0, | |
generator=generator, | |
output_type="np", | |
).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" | |
) | |
assert np.abs((expected_image - image).max()) < 5e-2 | |