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Support SD2 attention slicing (#1397)

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* Support SD2 attention slicing

* Support SD2 attention slicing

* Add more copies

* Use attn_num_head_channels in blocks

* fix-copies

* Update tests

* fix imports
This commit is contained in:
Anton Lozhkov
2022-11-24 22:42:59 +01:00
committed by GitHub
parent 8e2c4cd56c
commit d50e321745
16 changed files with 892 additions and 81 deletions
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42
src/diffusers/models/unet_2d_blocks.py
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@@ -404,15 +404,17 @@ class UNetMidBlock2DCrossAttn(nn.Module):
self.resnets = nn.ModuleList(resnets)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
@@ -600,15 +602,17 @@ class CrossAttnDownBlock2D(nn.Module):
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
@@ -1197,15 +1201,17 @@ class CrossAttnUpBlock2D(nn.Module):
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:

14
src/diffusers/models/unet_2d_condition.py
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@@ -229,15 +229,17 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin):
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.config.attention_head_dim % slice_size != 0:
head_dims = self.config.attention_head_dim
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.config.attention_head_dim:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for block in self.down_blocks:

11
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py
View File

@@ -198,9 +198,14 @@ class AltDiffusionPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):

11
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
View File

@@ -193,9 +193,14 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):

11
src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
View File

@@ -224,9 +224,14 @@ class CycleDiffusionPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
View File

@@ -197,9 +197,14 @@ class StableDiffusionPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):

11
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py
View File

@@ -169,9 +169,14 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
View File

@@ -193,9 +193,14 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
View File

@@ -258,9 +258,14 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
View File

@@ -206,9 +206,14 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
if isinstance(self.unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.unet.config.attention_head_dim)
self.unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

56
src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
View File

@@ -307,15 +307,17 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.config.attention_head_dim % slice_size != 0:
head_dims = self.config.attention_head_dim
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.config.attention_head_dim:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.config.attention_head_dim}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for block in self.down_blocks:
@@ -743,15 +745,17 @@ class CrossAttnDownBlockFlat(nn.Module):
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
@@ -954,15 +958,17 @@ class CrossAttnUpBlockFlat(nn.Module):
self.gradient_checkpointing = False
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:
@@ -1101,15 +1107,17 @@ class UNetMidBlockFlatCrossAttn(nn.Module):
self.resnets = nn.ModuleList(resnets)
def set_attention_slice(self, slice_size):
if slice_size is not None and self.attn_num_head_channels % slice_size != 0:
head_dims = self.attn_num_head_channels
head_dims = [head_dims] if isinstance(head_dims, int) else head_dims
if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims):
raise ValueError(
f"Make sure slice_size {slice_size} is a divisor of "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"Make sure slice_size {slice_size} is a common divisor of "
f"the number of heads used in cross_attention: {head_dims}"
)
if slice_size is not None and slice_size > self.attn_num_head_channels:
if slice_size is not None and slice_size > min(head_dims):
raise ValueError(
f"Chunk_size {slice_size} has to be smaller or equal to "
f"the number of heads used in cross_attention {self.attn_num_head_channels}"
f"slice_size {slice_size} has to be smaller or equal to "
f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}"
)
for attn in self.attentions:

11
src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py
View File

@@ -178,9 +178,14 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
if isinstance(self.image_unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.image_unet.config.attention_head_dim)
self.image_unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py
View File

@@ -108,9 +108,14 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
if isinstance(self.image_unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.image_unet.config.attention_head_dim)
self.image_unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

11
src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py
View File

@@ -131,9 +131,14 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
if isinstance(self.image_unet.config.attention_head_dim, int):
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.image_unet.config.attention_head_dim // 2
else:
# if `attention_head_dim` is a list, take the smallest head size
slice_size = min(self.image_unet.config.attention_head_dim)
self.image_unet.set_attention_slice(slice_size)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing

0
tests/pipelines/stable_diffusion_2/__init__.py Normal file
View File

740
tests/pipelines/stable_diffusion_2/test_stable_diffusion.py Normal file
View File

@@ -0,0 +1,740 @@
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import time
import unittest
import numpy as np
import torch
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
logging,
)
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
from transformers import CLIPFeatureExtractor, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class StableDiffusion2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def dummy_cond_unet(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
# SD2-specific config below
attention_head_dim=(2, 4, 8, 8),
use_linear_projection=True,
)
return model
@property
def dummy_vae(self):
torch.manual_seed(0)
model = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
return model
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=512,
)
return CLIPTextModel(config)
@property
def dummy_extractor(self):
def extract(*args, **kwargs):
class Out:
def __init__(self):
self.pixel_values = torch.ones([0])
def to(self, device):
self.pixel_values.to(device)
return self
return Out()
return extract
def test_save_pretrained_from_pretrained(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
feature_extractor = CLIPFeatureExtractor.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=feature_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
with tempfile.TemporaryDirectory() as tmpdirname:
sd_pipe.save_pretrained(tmpdirname)
sd_pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
generator = generator.manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
new_image = output.images
assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
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"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5649, 0.6022, 0.4804, 0.5270, 0.5585, 0.4643, 0.5159, 0.4963, 0.4793])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_pndm(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"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5099, 0.5677, 0.4671, 0.5128, 0.5697, 0.4676, 0.5277, 0.4964, 0.4946])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4715, 0.5376, 0.4569, 0.5224, 0.5734, 0.4797, 0.5465, 0.5074, 0.5046])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = EulerDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
image = output.images
generator = torch.Generator(device=device).manual_seed(0)
image_from_tuple = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_attention_chunk(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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"
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
@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, 64, 64, 3)
def test_stable_diffusion_long_prompt(self):
unet = self.dummy_cond_unet
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
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(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
do_classifier_free_guidance = True
negative_prompt = None
num_images_per_prompt = 1
logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion")
prompt = 25 * "@"
with CaptureLogger(logger) as cap_logger_3:
text_embeddings_3 = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
prompt = 100 * "@"
with CaptureLogger(logger) as cap_logger:
text_embeddings = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
negative_prompt = "Hello"
with CaptureLogger(logger) as cap_logger_2:
text_embeddings_2 = sd_pipe._encode_prompt(
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
assert text_embeddings.shape[1] == 77
assert cap_logger.out == cap_logger_2.out
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
assert cap_logger.out.count("@") == 25
assert cap_logger_3.out == ""
@slow
@require_torch_gpu
class StableDiffusion2PipelineIntegrationTests(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):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
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)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np")
image = output.images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0788, 0.0823, 0.1091, 0.1165, 0.1263, 0.1459, 0.1317, 0.1507, 0.1551])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_ddim(self):
scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-base", subfolder="scheduler")
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base", scheduler=scheduler)
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)
output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy")
image = output.images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0642, 0.0382, 0.0408, 0.0395, 0.0227, 0.0942, 0.0749, 0.0669, 0.0248])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_k_lms(self):
scheduler = LMSDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2-base", subfolder="scheduler")
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base", scheduler=scheduler)
sd_pipe = sd_pipe.to(torch_device)
sd_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)
image = sd_pipe(
[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy"
).images
image_slice = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0548, 0.0626, 0.0612, 0.0611, 0.0706, 0.0586, 0.0843, 0.0333, 0.1197])
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 = "stabilityai/stable-diffusion-2-base"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
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 = "stabilityai/stable-diffusion-2-base"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", 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_default(self):
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-text2img/astronaut_riding_a_horse.npy"
)
model_id = "stabilityai/stable-diffusion-2-base"
pipe = StableDiffusionPipeline.from_pretrained(model_id, safety_checker=None)
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() < 5e-3
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([1.8606, 1.3169, -0.0691, 1.2374, -2.309, 1.077, -0.1084, -0.6774, -2.9594])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
elif step == 20:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([1.078, 1.1804, 1.1339, 0.4664, -0.2354, 0.6097, -0.7749, -0.8784, -0.9465])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
test_callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", revision="fp16", torch_dtype=torch.float16
)
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=20,
guidance_scale=7.5,
generator=generator,
callback=test_callback_fn,
callback_steps=1,
)
assert test_callback_fn.has_been_called
assert number_of_steps == 21
def test_stable_diffusion_low_cpu_mem_usage(self):
pipeline_id = "stabilityai/stable-diffusion-2-base"
start_time = time.time()
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16
)
pipeline_low_cpu_mem_usage.to(torch_device)
low_cpu_mem_usage_time = time.time() - start_time
start_time = time.time()
_ = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, low_cpu_mem_usage=False
)
normal_load_time = time.time() - start_time
assert 2 * low_cpu_mem_usage_time < normal_load_time
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipeline_id = "stabilityai/stable-diffusion-2-base"
prompt = "Andromeda galaxy in a bottle"
pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16)
pipeline = pipeline.to(torch_device)
pipeline.enable_attention_slicing(1)
pipeline.enable_sequential_cpu_offload()
generator = torch.Generator(device=torch_device).manual_seed(0)
_ = pipeline(prompt, generator=generator, num_inference_steps=5)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9