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CivitAi-to-Diffusers
/
diffusers
/tests
/pipelines
/versatile_diffusion
/test_versatile_diffusion_mega.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 tempfile | |
import unittest | |
import numpy as np | |
import torch | |
from diffusers import VersatileDiffusionPipeline | |
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class VersatileDiffusionMegaPipelineFastTests(unittest.TestCase): | |
pass | |
class VersatileDiffusionMegaPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_from_save_pretrained(self): | |
pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
prompt_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" | |
) | |
generator = torch.manual_seed(0) | |
image = pipe.dual_guided( | |
prompt="first prompt", | |
image=prompt_image, | |
text_to_image_strength=0.75, | |
generator=generator, | |
guidance_scale=7.5, | |
num_inference_steps=2, | |
output_type="numpy", | |
).images | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
pipe.save_pretrained(tmpdirname) | |
pipe = VersatileDiffusionPipeline.from_pretrained(tmpdirname, torch_dtype=torch.float16) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
generator = generator.manual_seed(0) | |
new_image = pipe.dual_guided( | |
prompt="first prompt", | |
image=prompt_image, | |
text_to_image_strength=0.75, | |
generator=generator, | |
guidance_scale=7.5, | |
num_inference_steps=2, | |
output_type="numpy", | |
).images | |
assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass" | |
def test_inference_dual_guided_then_text_to_image(self): | |
pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
prompt = "cyberpunk 2077" | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" | |
) | |
generator = torch.manual_seed(0) | |
image = pipe.dual_guided( | |
prompt=prompt, | |
image=init_image, | |
text_to_image_strength=0.75, | |
generator=generator, | |
guidance_scale=7.5, | |
num_inference_steps=50, | |
output_type="numpy", | |
).images | |
image_slice = image[0, 253:256, 253:256, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 | |
prompt = "A painting of a squirrel eating a burger " | |
generator = torch.manual_seed(0) | |
image = pipe.text_to_image( | |
prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=50, output_type="numpy" | |
).images | |
image_slice = image[0, 253:256, 253:256, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 | |
image = pipe.image_variation(init_image, generator=generator, output_type="numpy").images | |
image_slice = image[0, 253:256, 253:256, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 | |