860 lines
32 KiB
Python
860 lines
32 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 time
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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 (
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AutoencoderKL,
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DDIMScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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UNet2DModel,
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VQModel,
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)
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from diffusers.utils import floats_tensor, load_image, 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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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class StableDiffusionPipelineFastTests(PipelineTesterMixin, 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_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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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=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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return model
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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_cond_unet_inpaint(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=9,
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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_vq_model(self):
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torch.manual_seed(0)
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model = VQModel(
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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=3,
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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_stable_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, 128, 128, 3)
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expected_slice = np.array([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755])
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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_ddim_factor_8(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(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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height=536,
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width=536,
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num_inference_steps=2,
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output_type="np",
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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 134, 134, 3)
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expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557])
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assert np.abs(image_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, 128, 128, 3)
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expected_slice = np.array([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738])
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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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def test_stable_diffusion_k_lms(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 = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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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, 128, 128, 3)
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expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
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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_k_euler_ancestral(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 = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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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, 128, 128, 3)
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expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
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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_k_euler(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 = EulerDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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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, 128, 128, 3)
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expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
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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_attention_chunk(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 = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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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"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
# make sure chunking the attention yields the same result
|
|
sd_pipe.enable_attention_slicing(slice_size=1)
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
|
|
|
|
assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4
|
|
|
|
def test_stable_diffusion_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
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")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
negative_prompt = "french fries"
|
|
generator = torch.Generator(device=device).manual_seed(0)
|
|
output = sd_pipe(
|
|
prompt,
|
|
negative_prompt=negative_prompt,
|
|
generator=generator,
|
|
guidance_scale=6.0,
|
|
num_inference_steps=2,
|
|
output_type="np",
|
|
)
|
|
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_num_images_per_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
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")
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
|
|
# test num_images_per_prompt=1 (default)
|
|
images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
|
|
|
|
assert images.shape == (1, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt=1 (default) for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
|
|
|
|
assert images.shape == (batch_size, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt for single prompt
|
|
num_images_per_prompt = 2
|
|
images = sd_pipe(
|
|
prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
|
|
).images
|
|
|
|
assert images.shape == (num_images_per_prompt, 128, 128, 3)
|
|
|
|
# test num_images_per_prompt for batch of prompts
|
|
batch_size = 2
|
|
images = sd_pipe(
|
|
[prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
|
|
).images
|
|
|
|
assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3)
|
|
|
|
@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
|
|
def test_stable_diffusion_fp16(self):
|
|
"""Test that stable diffusion works with fp16"""
|
|
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")
|
|
|
|
# put models in fp16
|
|
unet = unet.half()
|
|
vae = vae.half()
|
|
bert = bert.half()
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline(
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=bert,
|
|
tokenizer=tokenizer,
|
|
safety_checker=None,
|
|
feature_extractor=self.dummy_extractor,
|
|
)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_stable_diffusion(self):
|
|
# make sure here that pndm scheduler skips prk
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto")
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast("cuda"):
|
|
output = sd_pipe(
|
|
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
|
|
)
|
|
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_fast_ddim(self):
|
|
scheduler = DDIMScheduler.from_config("CompVis/stable-diffusion-v1-1", subfolder="scheduler")
|
|
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-1", scheduler=scheduler, device_map="auto"
|
|
)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
|
|
with torch.autocast("cuda"):
|
|
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.9326, 0.923, 0.951, 0.9365, 0.9214, 0.951, 0.9365, 0.9414, 0.918])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_lms_stable_diffusion_pipeline(self):
|
|
model_id = "CompVis/stable-diffusion-v1-1"
|
|
pipe = StableDiffusionPipeline.from_pretrained(model_id, device_map="auto").to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
|
|
pipe.scheduler = scheduler
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
image = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
).images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_memory_chunking(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
model_id, revision="fp16", torch_dtype=torch.float16, device_map="auto"
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
|
|
# make attention efficient
|
|
pipe.enable_attention_slicing()
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output_chunked = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image_chunked = output_chunked.images
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
# make sure that less than 3.75 GB is allocated
|
|
assert mem_bytes < 3.75 * 10**9
|
|
|
|
# disable chunking
|
|
pipe.disable_attention_slicing()
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image = output.images
|
|
|
|
# make sure that more than 3.75 GB is allocated
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
assert mem_bytes > 3.75 * 10**9
|
|
assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3
|
|
|
|
def test_stable_diffusion_text2img_pipeline_fp16(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
model_id, revision="fp16", device_map="auto", torch_dtype=torch.float16
|
|
)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a photograph of an astronaut riding a horse"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output_chunked = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image_chunked = output_chunked.images
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
output = pipe(
|
|
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
|
|
)
|
|
image = output.images
|
|
|
|
# Make sure results are close enough
|
|
diff = np.abs(image_chunked.flatten() - image.flatten())
|
|
# They ARE different since ops are not run always at the same precision
|
|
# however, they should be extremely close.
|
|
assert diff.mean() < 2e-2
|
|
|
|
def test_stable_diffusion_text2img_pipeline(self):
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/text2img/astronaut_riding_a_horse.png"
|
|
)
|
|
expected_image = np.array(expected_image, dtype=np.float32) / 255.0
|
|
|
|
model_id = "CompVis/stable-diffusion-v1-4"
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=None,
|
|
device_map="auto",
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "astronaut riding a horse"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np")
|
|
image = output.images[0]
|
|
|
|
assert image.shape == (512, 512, 3)
|
|
assert np.abs(expected_image - image).max() < 1e-2
|
|
|
|
def test_stable_diffusion_text2img_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 50:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[1.1078, 1.5803, 0.2773, -0.0589, -1.7928, -0.3665, -0.4695, -1.0727, -1.1601]
|
|
)
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, device_map="auto"
|
|
)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
prompt = "Andromeda galaxy in a bottle"
|
|
|
|
generator = torch.Generator(device=torch_device).manual_seed(0)
|
|
with torch.autocast(torch_device):
|
|
pipe(
|
|
prompt=prompt,
|
|
num_inference_steps=50,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
callback=test_callback_fn,
|
|
callback_steps=1,
|
|
)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 51
|
|
|
|
def test_stable_diffusion_accelerate_auto_device(self):
|
|
pipeline_id = "CompVis/stable-diffusion-v1-4"
|
|
|
|
start_time = time.time()
|
|
pipeline_normal_load = StableDiffusionPipeline.from_pretrained(
|
|
pipeline_id, revision="fp16", torch_dtype=torch.float16
|
|
)
|
|
pipeline_normal_load.to(torch_device)
|
|
normal_load_time = time.time() - start_time
|
|
|
|
start_time = time.time()
|
|
_ = StableDiffusionPipeline.from_pretrained(
|
|
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
|
|
)
|
|
meta_device_load_time = time.time() - start_time
|
|
|
|
assert 2 * meta_device_load_time < normal_load_time
|
|
|
|
@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
|
|
def test_stable_diffusion_pipeline_with_unet_on_gpu_only(self):
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.reset_max_memory_allocated()
|
|
|
|
pipeline_id = "CompVis/stable-diffusion-v1-4"
|
|
prompt = "Andromeda galaxy in a bottle"
|
|
|
|
pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16)
|
|
pipeline.enable_attention_slicing(1)
|
|
pipeline.enable_sequential_cpu_offload()
|
|
|
|
_ = pipeline(prompt, num_inference_steps=5)
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
# make sure that less than 1.5 GB is allocated
|
|
assert mem_bytes < 1.5 * 10**9
|