Support SD2 attention slicing (#1397)

* Support SD2 attention slicing

* Support SD2 attention slicing

* Add more copies

* Use attn_num_head_channels in blocks

* fix-copies

* Update tests

* fix imports

src/diffusers/models/unet_2d_blocks.py

@@ -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:

src/diffusers/models/unet_2d_condition.py

@@ -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:

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -198,9 +198,14 @@ class AltDiffusionPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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):

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -193,9 +193,14 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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):

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -224,9 +224,14 @@ class CycleDiffusionPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -197,9 +197,14 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py

@@ -169,9 +169,14 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -193,9 +193,14 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -258,9 +258,14 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -206,9 +206,14 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -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:

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py

@@ -178,9 +178,14 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -108,9 +108,14 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py

@@ -131,9 +131,14 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
                 `attention_head_dim` must be a multiple of `slice_size`.
         """
         if slice_size == "auto":
+            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

tests/pipelines/stable_diffusion_2/test_stable_diffusion.py

+# 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