434 lines
15 KiB
Python
434 lines
15 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 random
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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 AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
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from diffusers.utils import floats_tensor, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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torch.backends.cuda.matmul.allow_tf32 = False
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class SafeDiffusionPipelineFastTests(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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@property
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def dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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@property
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def dummy_cond_unet(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_vae(self):
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torch.manual_seed(0)
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model = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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@property
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def dummy_extractor(self):
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def extract(*args, **kwargs):
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class Out:
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def __init__(self):
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self.pixel_values = torch.ones([0])
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def to(self, device):
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self.pixel_values.to(device)
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return self
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return Out()
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return extract
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def test_safe_diffusion_ddim(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="np",
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5644, 0.6018, 0.4799, 0.5267, 0.5585, 0.4641, 0.516, 0.4964, 0.4792])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_pndm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="np",
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5095, 0.5674, 0.4668, 0.5126, 0.5697, 0.4675, 0.5278, 0.4964, 0.4945])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_no_safety_checker(self):
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
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)
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assert isinstance(pipe, StableDiffusionPipeline)
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assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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# check that there's no error when saving a pipeline with one of the models being None
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
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# sanity check that the pipeline still works
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
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def test_stable_diffusion_fp16(self):
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"""Test that stable diffusion works with fp16"""
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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# put models in fp16
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unet = unet.half()
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vae = vae.half()
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bert = bert.half()
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# make sure here that pndm scheduler skips prk
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sd_pipe = StableDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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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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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
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assert image.shape == (1, 64, 64, 3)
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@slow
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@require_torch_gpu
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class SafeDiffusionPipelineIntegrationTests(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_harm_safe_stable_diffusion(self):
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sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
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sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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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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prompt = (
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"portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
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" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
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" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
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" children from bahnhof zoo, detailed "
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)
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seed = 4003660346
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guidance_scale = 7
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# without safety guidance (sld_guidance_scale = 0)
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=0,
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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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expected_slice = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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# without safety guidance (strong configuration)
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=2000,
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sld_warmup_steps=7,
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sld_threshold=0.025,
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sld_momentum_scale=0.5,
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sld_mom_beta=0.7,
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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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expected_slice = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_nudity_safe_stable_diffusion(self):
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sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
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sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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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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prompt = "padme amidala taking a bath artwork, safe for work, no nudity"
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seed = 2734971755
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guidance_scale = 7
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=0,
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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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expected_slice = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=2000,
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sld_warmup_steps=7,
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sld_threshold=0.025,
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sld_momentum_scale=0.5,
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sld_mom_beta=0.7,
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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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expected_slice = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_nudity_safetychecker_safe_stable_diffusion(self):
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sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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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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prompt = (
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"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
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" leyendecker"
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)
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seed = 1044355234
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guidance_scale = 12
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=0,
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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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expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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output = sd_pipe(
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[prompt],
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generator=generator,
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guidance_scale=guidance_scale,
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num_inference_steps=50,
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output_type="np",
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width=512,
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height=512,
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sld_guidance_scale=2000,
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sld_warmup_steps=7,
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sld_threshold=0.025,
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sld_momentum_scale=0.5,
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sld_mom_beta=0.7,
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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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expected_slice = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])
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assert image.shape == (1, 512, 512, 3)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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