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diffusers
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_k_diffusion.py
# 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 diffusers import StableDiffusionKDiffusionPipeline | |
from diffusers.utils import slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_stable_diffusion_1(self): | |
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") | |
sd_pipe = sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
sd_pipe.set_scheduler("sample_euler") | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.manual_seed(0) | |
output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, 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([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_2(self): | |
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") | |
sd_pipe = sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
sd_pipe.set_scheduler("sample_euler") | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.manual_seed(0) | |
output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, 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([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1 | |
def test_stable_diffusion_karras_sigmas(self): | |
sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") | |
sd_pipe = sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
sd_pipe.set_scheduler("sample_dpmpp_2m") | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.manual_seed(0) | |
output = sd_pipe( | |
[prompt], | |
generator=generator, | |
guidance_scale=7.5, | |
num_inference_steps=15, | |
output_type="np", | |
use_karras_sigmas=True, | |
) | |
image = output.images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array( | |
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] | |
) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |