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https://github.com/huggingface/diffusers.git
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b6e0b016ce
* initial commit * move modules to import struct * add dummy objects and _LazyModule * add lazy import to schedulers * clean up unused imports * lazy import on models module * lazy import for schedulers module * add lazy import to pipelines module * lazy import altdiffusion * lazy import audio diffusion * lazy import audioldm * lazy import consistency model * lazy import controlnet * lazy import dance diffusion ddim ddpm * lazy import deepfloyd * lazy import kandinksy * lazy imports * lazy import semantic diffusion * lazy imports * lazy import stable diffusion * move sd output to its own module * clean up * lazy import t2iadapter * lazy import unclip * lazy import versatile and vq diffsuion * lazy import vq diffusion * helper to fetch objects from modules * lazy import sdxl * lazy import txt2vid * lazy import stochastic karras * fix model imports * fix bug * lazy import * clean up * clean up * fixes for tests * fixes for tests * clean up * remove import of torch_utils from utils module * clean up * clean up * fix mistake import statement * dedicated modules for exporting and loading * remove testing utils from utils module * fixes from merge conflicts * Update src/diffusers/pipelines/kandinsky2_2/__init__.py * fix docs * fix alt diffusion copied from * fix check dummies * fix more docs * remove accelerate import from utils module * add type checking * make style * fix check dummies * remove torch import from xformers check * clean up error message * fixes after upstream merges * dummy objects fix * fix tests * remove unused module import --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
# coding=utf-8
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# Copyright 2023 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 unittest
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import numpy as np
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import torch
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from diffusers import StableDiffusionKDiffusionPipeline
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from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
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enable_full_determinism()
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@slow
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@require_torch_gpu
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class StableDiffusionPipelineIntegrationTests(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_stable_diffusion_1(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_euler")
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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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output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_2(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_euler")
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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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output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1
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def test_stable_diffusion_karras_sigmas(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_dpmpp_2m")
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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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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=15,
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output_type="np",
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use_karras_sigmas=True,
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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_noise_sampler_seed(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_dpmpp_sde")
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prompt = "A painting of a squirrel eating a burger"
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seed = 0
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images1 = sd_pipe(
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[prompt],
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generator=torch.manual_seed(seed),
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noise_sampler_seed=seed,
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guidance_scale=9.0,
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num_inference_steps=20,
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output_type="np",
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).images
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images2 = sd_pipe(
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[prompt],
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generator=torch.manual_seed(seed),
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noise_sampler_seed=seed,
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guidance_scale=9.0,
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num_inference_steps=20,
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output_type="np",
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).images
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assert images1.shape == (1, 512, 512, 3)
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assert images2.shape == (1, 512, 512, 3)
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assert np.abs(images1.flatten() - images2.flatten()).max() < 1e-2
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