130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import tempfile
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import unittest
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import numpy as np
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import torch
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from diffusers import VersatileDiffusionPipeline
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from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class VersatileDiffusionMegaPipelineFastTests(unittest.TestCase):
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pass
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@slow
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@require_torch_gpu
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class VersatileDiffusionMegaPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_from_save_pretrained(self):
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pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
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)
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generator = torch.manual_seed(0)
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image = pipe.dual_guided(
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prompt="first prompt",
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image=prompt_image,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=2,
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output_type="numpy",
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).images
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe = VersatileDiffusionPipeline.from_pretrained(tmpdirname, torch_dtype=torch.float16)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator = generator.manual_seed(0)
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new_image = pipe.dual_guided(
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prompt="first prompt",
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image=prompt_image,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=2,
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output_type="numpy",
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).images
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assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"
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def test_inference_dual_guided_then_text_to_image(self):
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pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "cyberpunk 2077"
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
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)
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generator = torch.manual_seed(0)
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image = pipe.dual_guided(
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prompt=prompt,
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image=init_image,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=50,
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output_type="numpy",
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).images
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image_slice = image[0, 253:256, 253:256, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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prompt = "A painting of a squirrel eating a burger "
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generator = torch.manual_seed(0)
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image = pipe.text_to_image(
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prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=50, output_type="numpy"
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).images
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image_slice = image[0, 253:256, 253:256, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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image = pipe.image_variation(init_image, generator=generator, output_type="numpy").images
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image_slice = image[0, 253:256, 253:256, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
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