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https://github.com/huggingface/diffusers.git
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14e3a28c12
The 'CLIPFeatureExtractor' class name has been renamed to 'CLIPImageProcessor' in order to comply with future deprecation. This commit includes the necessary changes to the affected files.
1169 lines
49 KiB
Python
1169 lines
49 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 json
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import os
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import random
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import shutil
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import sys
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import tempfile
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import unittest
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import unittest.mock as mock
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import numpy as np
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import PIL
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import requests_mock
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import safetensors.torch
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import torch
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from parameterized import parameterized
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from PIL import Image
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from requests.exceptions import HTTPError
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from transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import (
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AutoencoderKL,
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DDIMPipeline,
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DDIMScheduler,
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DDPMPipeline,
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DDPMScheduler,
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DiffusionPipeline,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionImg2ImgPipeline,
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StableDiffusionInpaintPipelineLegacy,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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UNet2DModel,
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UniPCMultistepScheduler,
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logging,
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)
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from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
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from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, is_flax_available, nightly, slow, torch_device
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from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, load_numpy, require_compel, require_torch_gpu
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torch.backends.cuda.matmul.allow_tf32 = False
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class DownloadTests(unittest.TestCase):
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def test_one_request_upon_cached(self):
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# TODO: For some reason this test fails on MPS where no HEAD call is made.
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if torch_device == "mps":
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return
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with tempfile.TemporaryDirectory() as tmpdirname:
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with requests_mock.mock(real_http=True) as m:
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DiffusionPipeline.download(
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"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
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)
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download_requests = [r.method for r in m.request_history]
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assert download_requests.count("HEAD") == 15, "15 calls to files"
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assert download_requests.count("GET") == 17, "15 calls to files + model_info + model_index.json"
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assert (
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len(download_requests) == 32
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), "2 calls per file (15 files) + send_telemetry, model_info and model_index.json"
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with requests_mock.mock(real_http=True) as m:
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DiffusionPipeline.download(
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"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
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)
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cache_requests = [r.method for r in m.request_history]
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assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD"
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assert cache_requests.count("GET") == 1, "model info is only GET"
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assert (
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len(cache_requests) == 2
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), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
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def test_download_only_pytorch(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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# pipeline has Flax weights
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tmpdirname = DiffusionPipeline.download(
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"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
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)
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all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a flax file even if we have some here:
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# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
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assert not any(f.endswith(".msgpack") for f in files)
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# We need to never convert this tiny model to safetensors for this test to pass
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assert not any(f.endswith(".safetensors") for f in files)
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def test_force_safetensors_error(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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# pipeline has Flax weights
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with self.assertRaises(EnvironmentError):
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tmpdirname = DiffusionPipeline.download(
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"hf-internal-testing/tiny-stable-diffusion-pipe-no-safetensors",
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safety_checker=None,
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cache_dir=tmpdirname,
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use_safetensors=True,
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)
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def test_returned_cached_folder(self):
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prompt = "hello"
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
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)
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_, local_path = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None, return_cached_folder=True
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)
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pipe_2 = StableDiffusionPipeline.from_pretrained(local_path)
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pipe = pipe.to(torch_device)
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pipe_2 = pipe_2.to(torch_device)
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generator = torch.manual_seed(0)
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out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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generator = torch.manual_seed(0)
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out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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assert np.max(np.abs(out - out_2)) < 1e-3
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def test_download_safetensors(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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# pipeline has Flax weights
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tmpdirname = DiffusionPipeline.download(
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"hf-internal-testing/tiny-stable-diffusion-pipe-safetensors",
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safety_checker=None,
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cache_dir=tmpdirname,
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)
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all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a pytorch file even if we have some here:
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# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
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assert not any(f.endswith(".bin") for f in files)
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def test_download_no_safety_checker(self):
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prompt = "hello"
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
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)
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pipe = pipe.to(torch_device)
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generator = torch.manual_seed(0)
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out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
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pipe_2 = pipe_2.to(torch_device)
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generator = torch.manual_seed(0)
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out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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assert np.max(np.abs(out - out_2)) < 1e-3
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def test_load_no_safety_checker_explicit_locally(self):
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prompt = "hello"
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
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)
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pipe = pipe.to(torch_device)
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generator = torch.manual_seed(0)
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out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)
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pipe_2 = pipe_2.to(torch_device)
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generator = torch.manual_seed(0)
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out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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assert np.max(np.abs(out - out_2)) < 1e-3
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def test_load_no_safety_checker_default_locally(self):
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prompt = "hello"
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pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
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pipe = pipe.to(torch_device)
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generator = torch.manual_seed(0)
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out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)
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pipe_2 = pipe_2.to(torch_device)
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generator = torch.manual_seed(0)
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out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
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assert np.max(np.abs(out - out_2)) < 1e-3
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def test_cached_files_are_used_when_no_internet(self):
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# A mock response for an HTTP head request to emulate server down
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response_mock = mock.Mock()
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response_mock.status_code = 500
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response_mock.headers = {}
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response_mock.raise_for_status.side_effect = HTTPError
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response_mock.json.return_value = {}
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# Download this model to make sure it's in the cache.
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orig_pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
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)
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orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")}
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# Under the mock environment we get a 500 error when trying to reach the model.
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with mock.patch("requests.request", return_value=response_mock):
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# Download this model to make sure it's in the cache.
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None, local_files_only=True
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)
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comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")}
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for m1, m2 in zip(orig_comps.values(), comps.values()):
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for p1, p2 in zip(m1.parameters(), m2.parameters()):
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if p1.data.ne(p2.data).sum() > 0:
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assert False, "Parameters not the same!"
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def test_download_from_variant_folder(self):
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for safe_avail in [False, True]:
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import diffusers
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diffusers.utils.import_utils._safetensors_available = safe_avail
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other_format = ".bin" if safe_avail else ".safetensors"
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with tempfile.TemporaryDirectory() as tmpdirname:
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tmpdirname = StableDiffusionPipeline.download(
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"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname
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)
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all_root_files = [t[-1] for t in os.walk(tmpdirname)]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a variant file even if we have some here:
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# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
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assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
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assert not any(f.endswith(other_format) for f in files)
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# no variants
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assert not any(len(f.split(".")) == 3 for f in files)
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diffusers.utils.import_utils._safetensors_available = True
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def test_download_variant_all(self):
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for safe_avail in [False, True]:
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import diffusers
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diffusers.utils.import_utils._safetensors_available = safe_avail
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other_format = ".bin" if safe_avail else ".safetensors"
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this_format = ".safetensors" if safe_avail else ".bin"
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variant = "fp16"
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with tempfile.TemporaryDirectory() as tmpdirname:
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tmpdirname = StableDiffusionPipeline.download(
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"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
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)
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all_root_files = [t[-1] for t in os.walk(tmpdirname)]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a non-variant file even if we have some here:
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# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
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assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
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# unet, vae, text_encoder, safety_checker
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assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 4
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# all checkpoints should have variant ending
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assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files)
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assert not any(f.endswith(other_format) for f in files)
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diffusers.utils.import_utils._safetensors_available = True
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def test_download_variant_partly(self):
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for safe_avail in [False, True]:
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import diffusers
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diffusers.utils.import_utils._safetensors_available = safe_avail
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other_format = ".bin" if safe_avail else ".safetensors"
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this_format = ".safetensors" if safe_avail else ".bin"
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variant = "no_ema"
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with tempfile.TemporaryDirectory() as tmpdirname:
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tmpdirname = StableDiffusionPipeline.download(
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"hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
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)
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all_root_files = [t[-1] for t in os.walk(tmpdirname)]
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files = [item for sublist in all_root_files for item in sublist]
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unet_files = os.listdir(os.path.join(tmpdirname, "unet"))
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# Some of the downloaded files should be a non-variant file, check:
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# https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
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assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
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# only unet has "no_ema" variant
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assert f"diffusion_pytorch_model.{variant}{this_format}" in unet_files
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assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 1
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# vae, safety_checker and text_encoder should have no variant
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assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3
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assert not any(f.endswith(other_format) for f in files)
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diffusers.utils.import_utils._safetensors_available = True
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def test_download_broken_variant(self):
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for safe_avail in [False, True]:
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import diffusers
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diffusers.utils.import_utils._safetensors_available = safe_avail
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# text encoder is missing no variant and "no_ema" variant weights, so the following can't work
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for variant in [None, "no_ema"]:
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with self.assertRaises(OSError) as error_context:
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with tempfile.TemporaryDirectory() as tmpdirname:
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tmpdirname = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/stable-diffusion-broken-variants",
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cache_dir=tmpdirname,
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variant=variant,
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)
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assert "Error no file name" in str(error_context.exception)
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# text encoder has fp16 variants so we can load it
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with tempfile.TemporaryDirectory() as tmpdirname:
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tmpdirname = StableDiffusionPipeline.download(
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"hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant="fp16"
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)
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all_root_files = [t[-1] for t in os.walk(tmpdirname)]
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files = [item for sublist in all_root_files for item in sublist]
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# None of the downloaded files should be a non-variant file even if we have some here:
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# https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet
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assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
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# only unet has "no_ema" variant
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diffusers.utils.import_utils._safetensors_available = True
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class CustomPipelineTests(unittest.TestCase):
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def test_load_custom_pipeline(self):
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
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)
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pipeline = pipeline.to(torch_device)
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# NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
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# under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
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assert pipeline.__class__.__name__ == "CustomPipeline"
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def test_load_custom_github(self):
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main"
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)
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# make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690
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with torch.no_grad():
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output = pipeline()
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assert output.numel() == output.sum()
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# hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python
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# Could in the future work with hashes instead.
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del sys.modules["diffusers_modules.git.one_step_unet"]
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2"
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)
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with torch.no_grad():
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output = pipeline()
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assert output.numel() != output.sum()
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assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline"
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def test_run_custom_pipeline(self):
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
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)
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pipeline = pipeline.to(torch_device)
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images, output_str = pipeline(num_inference_steps=2, output_type="np")
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assert images[0].shape == (1, 32, 32, 3)
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# compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
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assert output_str == "This is a test"
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def test_local_custom_pipeline_repo(self):
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local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
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)
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pipeline = pipeline.to(torch_device)
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images, output_str = pipeline(num_inference_steps=2, output_type="np")
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assert pipeline.__class__.__name__ == "CustomLocalPipeline"
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assert images[0].shape == (1, 32, 32, 3)
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# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
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assert output_str == "This is a local test"
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def test_local_custom_pipeline_file(self):
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local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
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|
local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py")
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
"google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
|
|
)
|
|
pipeline = pipeline.to(torch_device)
|
|
images, output_str = pipeline(num_inference_steps=2, output_type="np")
|
|
|
|
assert pipeline.__class__.__name__ == "CustomLocalPipeline"
|
|
assert images[0].shape == (1, 32, 32, 3)
|
|
# compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
|
|
assert output_str == "This is a local test"
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
def test_download_from_git(self):
|
|
clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
|
|
|
|
feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
|
|
clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
|
|
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
custom_pipeline="clip_guided_stable_diffusion",
|
|
clip_model=clip_model,
|
|
feature_extractor=feature_extractor,
|
|
torch_dtype=torch.float16,
|
|
)
|
|
pipeline.enable_attention_slicing()
|
|
pipeline = pipeline.to(torch_device)
|
|
|
|
# NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
|
|
# https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
|
|
assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
|
|
|
|
image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
|
|
assert image.shape == (512, 512, 3)
|
|
|
|
|
|
class PipelineFastTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
import diffusers
|
|
|
|
diffusers.utils.import_utils._safetensors_available = True
|
|
|
|
def dummy_image(self):
|
|
batch_size = 1
|
|
num_channels = 3
|
|
sizes = (32, 32)
|
|
|
|
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
|
|
return image
|
|
|
|
def dummy_uncond_unet(self, sample_size=32):
|
|
torch.manual_seed(0)
|
|
model = UNet2DModel(
|
|
block_out_channels=(32, 64),
|
|
layers_per_block=2,
|
|
sample_size=sample_size,
|
|
in_channels=3,
|
|
out_channels=3,
|
|
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
|
|
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
|
|
)
|
|
return model
|
|
|
|
def dummy_cond_unet(self, sample_size=32):
|
|
torch.manual_seed(0)
|
|
model = UNet2DConditionModel(
|
|
block_out_channels=(32, 64),
|
|
layers_per_block=2,
|
|
sample_size=sample_size,
|
|
in_channels=4,
|
|
out_channels=4,
|
|
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
|
|
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
|
|
cross_attention_dim=32,
|
|
)
|
|
return model
|
|
|
|
@property
|
|
def dummy_vae(self):
|
|
torch.manual_seed(0)
|
|
model = AutoencoderKL(
|
|
block_out_channels=[32, 64],
|
|
in_channels=3,
|
|
out_channels=3,
|
|
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
|
|
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
|
|
latent_channels=4,
|
|
)
|
|
return model
|
|
|
|
@property
|
|
def dummy_text_encoder(self):
|
|
torch.manual_seed(0)
|
|
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,
|
|
)
|
|
return CLIPTextModel(config)
|
|
|
|
@property
|
|
def dummy_extractor(self):
|
|
def extract(*args, **kwargs):
|
|
class Out:
|
|
def __init__(self):
|
|
self.pixel_values = torch.ones([0])
|
|
|
|
def to(self, device):
|
|
self.pixel_values.to(device)
|
|
return self
|
|
|
|
return Out()
|
|
|
|
return extract
|
|
|
|
@parameterized.expand(
|
|
[
|
|
[DDIMScheduler, DDIMPipeline, 32],
|
|
[DDPMScheduler, DDPMPipeline, 32],
|
|
[DDIMScheduler, DDIMPipeline, (32, 64)],
|
|
[DDPMScheduler, DDPMPipeline, (64, 32)],
|
|
]
|
|
)
|
|
def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
|
|
unet = self.dummy_uncond_unet(sample_size)
|
|
scheduler = scheduler_fn()
|
|
pipeline = pipeline_fn(unet, scheduler).to(torch_device)
|
|
|
|
generator = torch.manual_seed(0)
|
|
out_image = pipeline(
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
).images
|
|
sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
|
|
assert out_image.shape == (1, *sample_size, 3)
|
|
|
|
def test_stable_diffusion_components(self):
|
|
"""Test that components property works correctly"""
|
|
unet = self.dummy_cond_unet()
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
inpaint = StableDiffusionInpaintPipelineLegacy(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
).to(torch_device)
|
|
img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
|
|
text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_inpaint = inpaint(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
).images
|
|
image_img2img = img2img(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
image=init_image,
|
|
).images
|
|
image_text2img = text2img(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
).images
|
|
|
|
assert image_inpaint.shape == (1, 32, 32, 3)
|
|
assert image_img2img.shape == (1, 32, 32, 3)
|
|
assert image_text2img.shape == (1, 64, 64, 3)
|
|
|
|
@require_torch_gpu
|
|
def test_pipe_false_offload_warn(self):
|
|
unet = self.dummy_cond_unet()
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
|
|
sd.enable_model_cpu_offload()
|
|
|
|
logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
|
|
with CaptureLogger(logger) as cap_logger:
|
|
sd.to("cuda")
|
|
|
|
assert "It is strongly recommended against doing so" in str(cap_logger)
|
|
|
|
sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
|
|
def test_set_scheduler(self):
|
|
unet = self.dummy_cond_unet()
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
|
|
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, DDIMScheduler)
|
|
sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, DDPMScheduler)
|
|
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, PNDMScheduler)
|
|
sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, LMSDiscreteScheduler)
|
|
sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, EulerDiscreteScheduler)
|
|
sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)
|
|
sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
|
|
assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)
|
|
|
|
def test_set_scheduler_consistency(self):
|
|
unet = self.dummy_cond_unet()
|
|
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
|
|
ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=pndm,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
|
|
pndm_config = sd.scheduler.config
|
|
sd.scheduler = DDPMScheduler.from_config(pndm_config)
|
|
sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
|
|
pndm_config_2 = sd.scheduler.config
|
|
pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}
|
|
|
|
assert dict(pndm_config) == dict(pndm_config_2)
|
|
|
|
sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=ddim,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
|
|
ddim_config = sd.scheduler.config
|
|
sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)
|
|
sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
|
|
ddim_config_2 = sd.scheduler.config
|
|
ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}
|
|
|
|
assert dict(ddim_config) == dict(ddim_config_2)
|
|
|
|
def test_save_safe_serialization(self):
|
|
pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pipeline.save_pretrained(tmpdirname, safe_serialization=True)
|
|
|
|
# Validate that the VAE safetensor exists and are of the correct format
|
|
vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors")
|
|
assert os.path.exists(vae_path), f"Could not find {vae_path}"
|
|
_ = safetensors.torch.load_file(vae_path)
|
|
|
|
# Validate that the UNet safetensor exists and are of the correct format
|
|
unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors")
|
|
assert os.path.exists(unet_path), f"Could not find {unet_path}"
|
|
_ = safetensors.torch.load_file(unet_path)
|
|
|
|
# Validate that the text encoder safetensor exists and are of the correct format
|
|
text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors")
|
|
assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
|
|
_ = safetensors.torch.load_file(text_encoder_path)
|
|
|
|
pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)
|
|
assert pipeline.unet is not None
|
|
assert pipeline.vae is not None
|
|
assert pipeline.text_encoder is not None
|
|
assert pipeline.scheduler is not None
|
|
assert pipeline.feature_extractor is not None
|
|
|
|
def test_no_pytorch_download_when_doing_safetensors(self):
|
|
# by default we don't download
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
_ = StableDiffusionPipeline.from_pretrained(
|
|
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
|
|
)
|
|
|
|
path = os.path.join(
|
|
tmpdirname,
|
|
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
|
|
"snapshots",
|
|
"07838d72e12f9bcec1375b0482b80c1d399be843",
|
|
"unet",
|
|
)
|
|
# safetensors exists
|
|
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
|
|
# pytorch does not
|
|
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
|
|
|
|
def test_no_safetensors_download_when_doing_pytorch(self):
|
|
# mock diffusers safetensors not available
|
|
import diffusers
|
|
|
|
diffusers.utils.import_utils._safetensors_available = False
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
_ = StableDiffusionPipeline.from_pretrained(
|
|
"hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
|
|
)
|
|
|
|
path = os.path.join(
|
|
tmpdirname,
|
|
"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
|
|
"snapshots",
|
|
"07838d72e12f9bcec1375b0482b80c1d399be843",
|
|
"unet",
|
|
)
|
|
# safetensors does not exists
|
|
assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
|
|
# pytorch does
|
|
assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
|
|
|
|
diffusers.utils.import_utils._safetensors_available = True
|
|
|
|
def test_optional_components(self):
|
|
unet = self.dummy_cond_unet()
|
|
pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
|
|
vae = self.dummy_vae
|
|
bert = self.dummy_text_encoder
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
orig_sd = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=pndm,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=unet,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd = orig_sd
|
|
|
|
assert sd.config.requires_safety_checker is True
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
sd.save_pretrained(tmpdirname)
|
|
|
|
# Test that passing None works
|
|
sd = StableDiffusionPipeline.from_pretrained(
|
|
tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False
|
|
)
|
|
|
|
assert sd.config.requires_safety_checker is False
|
|
assert sd.config.safety_checker == (None, None)
|
|
assert sd.config.feature_extractor == (None, None)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
sd.save_pretrained(tmpdirname)
|
|
|
|
# Test that loading previous None works
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
|
|
|
|
assert sd.config.requires_safety_checker is False
|
|
assert sd.config.safety_checker == (None, None)
|
|
assert sd.config.feature_extractor == (None, None)
|
|
|
|
orig_sd.save_pretrained(tmpdirname)
|
|
|
|
# Test that loading without any directory works
|
|
shutil.rmtree(os.path.join(tmpdirname, "safety_checker"))
|
|
with open(os.path.join(tmpdirname, sd.config_name)) as f:
|
|
config = json.load(f)
|
|
config["safety_checker"] = [None, None]
|
|
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
|
|
json.dump(config, f)
|
|
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)
|
|
sd.save_pretrained(tmpdirname)
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
|
|
|
|
assert sd.config.requires_safety_checker is False
|
|
assert sd.config.safety_checker == (None, None)
|
|
assert sd.config.feature_extractor == (None, None)
|
|
|
|
# Test that loading from deleted model index works
|
|
with open(os.path.join(tmpdirname, sd.config_name)) as f:
|
|
config = json.load(f)
|
|
del config["safety_checker"]
|
|
del config["feature_extractor"]
|
|
with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
|
|
json.dump(config, f)
|
|
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
|
|
|
|
assert sd.config.requires_safety_checker is False
|
|
assert sd.config.safety_checker == (None, None)
|
|
assert sd.config.feature_extractor == (None, None)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
sd.save_pretrained(tmpdirname)
|
|
|
|
# Test that partially loading works
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
|
|
|
|
assert sd.config.requires_safety_checker is False
|
|
assert sd.config.safety_checker == (None, None)
|
|
assert sd.config.feature_extractor != (None, None)
|
|
|
|
# Test that partially loading works
|
|
sd = StableDiffusionPipeline.from_pretrained(
|
|
tmpdirname,
|
|
feature_extractor=self.dummy_extractor,
|
|
safety_checker=unet,
|
|
requires_safety_checker=[True, True],
|
|
)
|
|
|
|
assert sd.config.requires_safety_checker == [True, True]
|
|
assert sd.config.safety_checker != (None, None)
|
|
assert sd.config.feature_extractor != (None, None)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
sd.save_pretrained(tmpdirname)
|
|
sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
|
|
|
|
assert sd.config.requires_safety_checker == [True, True]
|
|
assert sd.config.safety_checker != (None, None)
|
|
assert sd.config.feature_extractor != (None, None)
|
|
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
class PipelineSlowTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_smart_download(self):
|
|
model_id = "hf-internal-testing/unet-pipeline-dummy"
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
_ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
|
|
local_repo_name = "--".join(["models"] + model_id.split("/"))
|
|
snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
|
|
snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
|
|
|
|
# inspect all downloaded files to make sure that everything is included
|
|
assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
|
|
assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
|
|
# let's make sure the super large numpy file:
|
|
# https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
|
|
# is not downloaded, but all the expected ones
|
|
assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
|
|
|
|
def test_warning_unused_kwargs(self):
|
|
model_id = "hf-internal-testing/unet-pipeline-dummy"
|
|
logger = logging.get_logger("diffusers.pipelines")
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
with CaptureLogger(logger) as cap_logger:
|
|
DiffusionPipeline.from_pretrained(
|
|
model_id,
|
|
not_used=True,
|
|
cache_dir=tmpdirname,
|
|
force_download=True,
|
|
)
|
|
|
|
assert (
|
|
cap_logger.out.strip().split("\n")[-1]
|
|
== "Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored."
|
|
)
|
|
|
|
def test_from_save_pretrained(self):
|
|
# 1. Load models
|
|
model = UNet2DModel(
|
|
block_out_channels=(32, 64),
|
|
layers_per_block=2,
|
|
sample_size=32,
|
|
in_channels=3,
|
|
out_channels=3,
|
|
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
|
|
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
|
|
)
|
|
schedular = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
ddpm = DDPMPipeline(model, schedular)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
ddpm.save_pretrained(tmpdirname)
|
|
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
|
|
new_ddpm.to(torch_device)
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
def test_from_pretrained_hub(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
scheduler = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm = ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm_from_hub = ddpm_from_hub.to(torch_device)
|
|
ddpm_from_hub.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
def test_from_pretrained_hub_pass_model(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
scheduler = DDPMScheduler(num_train_timesteps=10)
|
|
|
|
# pass unet into DiffusionPipeline
|
|
unet = UNet2DModel.from_pretrained(model_path)
|
|
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
|
|
ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)
|
|
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
|
|
|
|
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
ddpm_from_hub = ddpm_from_hub.to(torch_device)
|
|
ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
|
|
|
|
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
def test_output_format(self):
|
|
model_path = "google/ddpm-cifar10-32"
|
|
|
|
scheduler = DDIMScheduler.from_pretrained(model_path)
|
|
pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
images = pipe(output_type="numpy").images
|
|
assert images.shape == (1, 32, 32, 3)
|
|
assert isinstance(images, np.ndarray)
|
|
|
|
images = pipe(output_type="pil", num_inference_steps=4).images
|
|
assert isinstance(images, list)
|
|
assert len(images) == 1
|
|
assert isinstance(images[0], PIL.Image.Image)
|
|
|
|
# use PIL by default
|
|
images = pipe(num_inference_steps=4).images
|
|
assert isinstance(images, list)
|
|
assert isinstance(images[0], PIL.Image.Image)
|
|
|
|
def test_from_flax_from_pt(self):
|
|
pipe_pt = StableDiffusionPipeline.from_pretrained(
|
|
"hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
|
|
)
|
|
pipe_pt.to(torch_device)
|
|
|
|
if not is_flax_available():
|
|
raise ImportError("Make sure flax is installed.")
|
|
|
|
from diffusers import FlaxStableDiffusionPipeline
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pipe_pt.save_pretrained(tmpdirname)
|
|
|
|
pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained(
|
|
tmpdirname, safety_checker=None, from_pt=True
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pipe_flax.save_pretrained(tmpdirname, params=params)
|
|
pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True)
|
|
pipe_pt_2.to(torch_device)
|
|
|
|
prompt = "Hello"
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_0 = pipe_pt(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
).images[0]
|
|
|
|
generator = torch.manual_seed(0)
|
|
image_1 = pipe_pt_2(
|
|
[prompt],
|
|
generator=generator,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
).images[0]
|
|
|
|
assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass"
|
|
|
|
@require_compel
|
|
def test_weighted_prompts_compel(self):
|
|
from compel import Compel
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
|
|
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
|
|
pipe.enable_model_cpu_offload()
|
|
pipe.enable_attention_slicing()
|
|
|
|
compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
|
|
|
|
prompt = "a red cat playing with a ball{}"
|
|
|
|
prompts = [prompt.format(s) for s in ["", "++", "--"]]
|
|
|
|
prompt_embeds = compel(prompts)
|
|
|
|
generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])]
|
|
|
|
images = pipe(
|
|
prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="numpy"
|
|
).images
|
|
|
|
for i, image in enumerate(images):
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
f"/compel/forest_{i}.npy"
|
|
)
|
|
|
|
assert np.abs(image - expected_image).max() < 1e-2
|
|
|
|
|
|
@nightly
|
|
@require_torch_gpu
|
|
class PipelineNightlyTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_ddpm_ddim_equality_batched(self):
|
|
seed = 0
|
|
model_id = "google/ddpm-cifar10-32"
|
|
|
|
unet = UNet2DModel.from_pretrained(model_id)
|
|
ddpm_scheduler = DDPMScheduler()
|
|
ddim_scheduler = DDIMScheduler()
|
|
|
|
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
|
ddpm.to(torch_device)
|
|
ddpm.set_progress_bar_config(disable=None)
|
|
|
|
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
|
ddim.to(torch_device)
|
|
ddim.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(seed)
|
|
ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(seed)
|
|
ddim_images = ddim(
|
|
batch_size=2,
|
|
generator=generator,
|
|
num_inference_steps=1000,
|
|
eta=1.0,
|
|
output_type="numpy",
|
|
use_clipped_model_output=True, # Need this to make DDIM match DDPM
|
|
).images
|
|
|
|
# the values aren't exactly equal, but the images look the same visually
|
|
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
|