602 lines
18 KiB
Python
602 lines
18 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 transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
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from diffusers.utils import floats_tensor, nightly, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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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_semantic_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_semantic_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_semantic_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_semantic_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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image = sd_pipe([prompt], num_inference_steps=2, output_type="np").images
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assert image.shape == (1, 64, 64, 3)
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@nightly
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@require_torch_gpu
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class SemanticDiffusionPipelineIntegrationTests(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_positive_guidance(self):
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torch_device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "a photo of a cat"
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edit = {
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"editing_prompt": ["sunglasses"],
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"reverse_editing_direction": [False],
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"edit_warmup_steps": 10,
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"edit_guidance_scale": 6,
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"edit_threshold": 0.95,
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"edit_momentum_scale": 0.5,
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"edit_mom_beta": 0.6,
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}
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seed = 3
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guidance_scale = 7
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# no sega enabled
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generator = torch.Generator(torch_device)
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generator.manual_seed(seed)
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output = 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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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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expected_slice = [
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0.34673113,
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0.38492733,
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0.37597352,
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0.34086335,
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0.35650748,
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0.35579205,
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0.3384763,
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0.34340236,
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0.3573271,
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]
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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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# with sega enabled
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# generator = torch.manual_seed(seed)
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generator.manual_seed(seed)
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output = 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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**edit,
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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 = [
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0.41887826,
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0.37728766,
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0.30138272,
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0.41416335,
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0.41664985,
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0.36283392,
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0.36191246,
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0.43364465,
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0.43001732,
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]
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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_negative_guidance(self):
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torch_device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "an image of a crowded boulevard, realistic, 4k"
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edit = {
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"editing_prompt": "crowd, crowded, people",
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"reverse_editing_direction": True,
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"edit_warmup_steps": 10,
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"edit_guidance_scale": 8.3,
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"edit_threshold": 0.9,
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"edit_momentum_scale": 0.5,
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"edit_mom_beta": 0.6,
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}
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seed = 9
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guidance_scale = 7
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# no sega enabled
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generator = torch.Generator(torch_device)
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generator.manual_seed(seed)
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output = 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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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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expected_slice = [
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0.43497998,
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0.91814065,
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0.7540739,
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0.55580205,
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0.8467265,
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0.5389691,
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0.62574506,
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0.58897763,
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0.50926757,
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]
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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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# with sega enabled
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# generator = torch.manual_seed(seed)
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generator.manual_seed(seed)
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output = 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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**edit,
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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 = [
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0.3089719,
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0.30500144,
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0.29016042,
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0.30630964,
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0.325687,
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0.29419225,
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0.2908091,
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0.28723598,
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0.27696294,
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]
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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_multi_cond_guidance(self):
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torch_device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "a castle next to a river"
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edit = {
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"editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
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"reverse_editing_direction": False,
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"edit_warmup_steps": [15, 18],
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"edit_guidance_scale": 6,
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"edit_threshold": [0.9, 0.8],
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"edit_momentum_scale": 0.5,
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"edit_mom_beta": 0.6,
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}
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seed = 48
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guidance_scale = 7
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# no sega enabled
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generator = torch.Generator(torch_device)
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generator.manual_seed(seed)
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output = 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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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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expected_slice = [
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0.75163555,
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0.76037145,
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0.61785,
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0.9189673,
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0.8627701,
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0.85189694,
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0.8512813,
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0.87012076,
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0.8312857,
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]
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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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# with sega enabled
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# generator = torch.manual_seed(seed)
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generator.manual_seed(seed)
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output = 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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**edit,
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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 = [
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0.73553365,
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0.7537271,
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0.74341905,
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0.66480356,
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0.6472925,
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0.63039416,
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0.64812905,
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0.6749717,
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0.6517102,
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]
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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_guidance_fp16(self):
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torch_device = "cuda"
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pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
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pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a photo of a cat"
|
|
edit = {
|
|
"editing_prompt": ["sunglasses"],
|
|
"reverse_editing_direction": [False],
|
|
"edit_warmup_steps": 10,
|
|
"edit_guidance_scale": 6,
|
|
"edit_threshold": 0.95,
|
|
"edit_momentum_scale": 0.5,
|
|
"edit_mom_beta": 0.6,
|
|
}
|
|
|
|
seed = 3
|
|
guidance_scale = 7
|
|
|
|
# no sega enabled
|
|
generator = torch.Generator(torch_device)
|
|
generator.manual_seed(seed)
|
|
output = pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=50,
|
|
output_type="np",
|
|
width=512,
|
|
height=512,
|
|
)
|
|
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
expected_slice = [
|
|
0.34887695,
|
|
0.3876953,
|
|
0.375,
|
|
0.34423828,
|
|
0.3581543,
|
|
0.35717773,
|
|
0.3383789,
|
|
0.34570312,
|
|
0.359375,
|
|
]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
# with sega enabled
|
|
# generator = torch.manual_seed(seed)
|
|
generator.manual_seed(seed)
|
|
output = pipe(
|
|
[prompt],
|
|
generator=generator,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=50,
|
|
output_type="np",
|
|
width=512,
|
|
height=512,
|
|
**edit,
|
|
)
|
|
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
expected_slice = [
|
|
0.42285156,
|
|
0.36914062,
|
|
0.29077148,
|
|
0.42041016,
|
|
0.41918945,
|
|
0.35498047,
|
|
0.3618164,
|
|
0.4423828,
|
|
0.43115234,
|
|
]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|