StableDiffusionDepth2ImgPipeline (#1531)

* begin depth pipeline

* add depth estimation model

* fix prepare_depth_mask

* add a comment about autocast

* copied from, quality, cleanup

* begin tests

* handle tensors

* norm image tensor

* fix batch size

* fix tests

* fix enable_sequential_cpu_offload

* fix save load

* fix test_save_load_float16

* fix test_save_load_optional_components

* fix test_float16_inference

* fix test_cpu_offload_forward_pass

* fix test_dict_tuple_outputs_equivalent

* up

* fix fast tests

* fix test_stable_diffusion_img2img_multiple_init_images

* fix few more fast tests

* don't use device map for DPT

* fix test_stable_diffusion_pipeline_with_sequential_cpu_offloading

* accept external depth maps

* prepare_depth_mask -> prepare_depth_map

* fix file name

* fix file name

* quality

* check transformers version

* fix test names

* use skipif

* fix import

* add docs

* skip tests on mps

* correct version

* uP

* Update docs/source/api/pipelines/stable_diffusion_2.mdx

* fix fix-copies

* fix fix-copies

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: anton- <anton@huggingface.co>

docs/source/api/pipelines/stable_diffusion.mdx

@@ -97,6 +97,14 @@ If you want to use all possible use cases in a single `DiffusionPipeline` you ca
 	- enable_xformers_memory_efficient_attention
 	- disable_xformers_memory_efficient_attention
 
+## StableDiffusionDepth2ImgPipeline
+[[autodoc]] StableDiffusionDepth2ImgPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
 ## StableDiffusionImageVariationPipeline
 [[autodoc]] StableDiffusionImageVariationPipeline
 	- __call__

docs/source/api/pipelines/stable_diffusion_2.mdx

@@ -30,6 +30,7 @@ Note that the architecture is more or less identical to [Stable Diffusion 1](./a
 - *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
 - *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
 - *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
+- *Depth-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with [`StableDiffusionDepth2ImagePipeline`]
 
 We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest scheduler there is.
 
@@ -125,6 +126,37 @@ upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
 upscaled_image.save("upsampled_cat.png")
 ```
 
+- *Depth-Guided Text-to-Image*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) [`StableDiffusionDepth2ImagePipeline`]
+
+**Installation**
+
+```bash
+!pip install -U git+https://github.com/huggingface/transformers.git
+!pip install diffusers[torch]
+```
+
+**Example**
+
+```python
+import torch
+import requests
+from PIL import Image
+
+from diffusers import StableDiffusionDepth2ImgPipeline
+
+pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-2-depth",
+    torch_dtype=torch.float16,
+).to("cuda")
+
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+init_image = Image.open(requests.get(url, stream=True).raw)
+prompt = "two tigers"
+n_propmt = "bad, deformed, ugly, bad anotomy"
+image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
+```
+
 ### How to load and use different schedulers.
 
 The stable diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.

setup.py

@@ -107,7 +107,7 @@ _deps = [
     "tensorboard",
     "torch>=1.4",
     "torchvision",
-    "transformers>=4.21.0",
+    "transformers>=4.25.1",
 ]
 
 # this is a lookup table with items like:

src/diffusers/__init__.py

@@ -12,11 +12,24 @@ from .utils import (
     is_scipy_available,
     is_torch_available,
     is_transformers_available,
+    is_transformers_version,
     is_unidecode_available,
     logging,
 )
 
 
+# Make sure `transformers` is up to date
+if is_transformers_available():
+    import transformers
+
+    if is_transformers_version("<", "4.25.1"):
+        raise ImportError(
+            f"`diffusers` requires transformers >= 4.25.1 to function correctly, but {transformers.__version__} was"
+            " found in your environment. You can upgrade it with pip: `pip install transformers --upgrade`"
+        )
+else:
+    pass
+
 try:
     if not is_torch_available():
         raise OptionalDependencyNotAvailable()
@@ -87,6 +100,7 @@ else:
         CycleDiffusionPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
+        StableDiffusionDepth2ImgPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImg2ImgPipeline,
         StableDiffusionInpaintPipeline,

src/diffusers/dependency_versions_table.py

@@ -31,5 +31,5 @@ deps = {
     "tensorboard": "tensorboard",
     "torch": "torch>=1.4",
     "torchvision": "torchvision",
-    "transformers": "transformers>=4.21.0",
+    "transformers": "transformers>=4.25.1",
 }

src/diffusers/pipelines/__init__.py

@@ -44,6 +44,7 @@ else:
     from .paint_by_example import PaintByExamplePipeline
     from .stable_diffusion import (
         CycleDiffusionPipeline,
+        StableDiffusionDepth2ImgPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImg2ImgPipeline,
         StableDiffusionInpaintPipeline,

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -46,13 +46,23 @@ if is_transformers_available() and is_torch_available():
     from .safety_checker import StableDiffusionSafetyChecker
 
 try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0.dev0")):
+    if not (is_transformers_available() and is_torch_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
 else:
     from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
 
+
+try:
+    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0.dev0")):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import StableDiffusionDepth2ImgPipeline
+else:
+    from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline
+
+
 try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
         raise OptionalDependencyNotAvailable()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -0,0 +1,564 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# 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 contextlib
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+import PIL
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
+
+from ...configuration_utils import FrozenDict
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ...schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from ...utils import PIL_INTERPOLATION, deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def preprocess(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+class StableDiffusionDepth2ImgPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-guided image to image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        depth_estimator: DPTForDepthEstimation,
+        feature_extractor: DPTFeatureExtractor,
+    ):
+        super().__init__()
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            depth_estimator=depth_estimator,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.depth_estimator]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+
+        if not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(self, prompt, strength, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        offset = self.scheduler.config.get("steps_offset", 0)
+        init_timestep = int(num_inference_steps * strength) + offset
+        init_timestep = min(init_timestep, num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
+    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
+        image = image.to(device=device, dtype=dtype)
+        init_latent_dist = self.vae.encode(image).latent_dist
+        init_latents = init_latent_dist.sample(generator=generator)
+        init_latents = 0.18215 * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            deprecation_message = (
+                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
+                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+                " your script to pass as many initial images as text prompts to suppress this warning."
+            )
+            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
+
+        # add noise to latents using the timesteps
+        noise = torch.randn(init_latents.shape, generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
+        if isinstance(image, PIL.Image.Image):
+            width, height = image.size
+            width, height = map(lambda dim: dim - dim % 32, (width, height))  # resize to integer multiple of 32
+            image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
+            width, height = image.size
+        else:
+            image = [img for img in image]
+            width, height = image[0].shape[-2:]
+
+        if depth_map is None:
+            pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
+            pixel_values = pixel_values.to(device=device)
+            # The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.
+            # So we use `torch.autocast` here for half precision inference.
+            context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext()
+            with context_manger:
+                depth_map = self.depth_estimator(pixel_values).predicted_depth
+        else:
+            depth_map = depth_map.to(device=device, dtype=dtype)
+
+        depth_map = torch.nn.functional.interpolate(
+            depth_map.unsqueeze(1),
+            size=(height // self.vae_scale_factor, width // self.vae_scale_factor),
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+        depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+        depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
+        depth_map = depth_map.to(dtype)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if depth_map.shape[0] < batch_size:
+            depth_map = depth_map.repeat(batch_size, 1, 1, 1)
+
+        depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map
+        return depth_map
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        depth_map: Optional[torch.FloatTensor] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter will be modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 1. Check inputs
+        self.check_inputs(prompt, strength, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # 4. Prepare depth mask
+        depth_mask = self.prepare_depth_map(
+            image,
+            depth_map,
+            batch_size * num_images_per_prompt,
+            do_classifier_free_guidance,
+            text_embeddings.dtype,
+            device,
+        )
+
+        # 5. Preprocess image
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess(image)
+        else:
+            image = 2.0 * (image / 255.0) - 1.0
+
+        # 6. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 7. Prepare latent variables
+        latents = self.prepare_latents(
+            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, device, generator
+        )
+
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # 10. Post-processing
+        image = self.decode_latents(latents)
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

src/diffusers/pipelines/versatile_diffusion/__init__.py

@@ -7,7 +7,7 @@ from ...utils import (
 
 
 try:
-    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0.dev0")):
+    if not (is_transformers_available() and is_torch_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -79,6 +79,21 @@ class PaintByExamplePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class StableDiffusionDepth2ImgPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class StableDiffusionImageVariationPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

src/diffusers/utils/import_utils.py

@@ -310,12 +310,6 @@ LIBROSA_IMPORT_ERROR = """
 installation page: https://librosa.org/doc/latest/install.html and follow the ones that match your environment.
 """
 
-# docstyle-ignore
-TENSORFLOW_IMPORT_ERROR = """
-{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
-installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
-"""
-
 # docstyle-ignore
 TRANSFORMERS_IMPORT_ERROR = """
 {0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
@@ -341,7 +335,6 @@ BACKENDS_MAPPING = OrderedDict(
         ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
         ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
         ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
-        ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
         ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
         ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
         ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
@@ -361,12 +354,7 @@ def requires_backends(obj, backends):
     if failed:
         raise ImportError("".join(failed))
 
-    if name in [
+    if name in ["StableDiffusionDepth2ImgPipeline"] and is_transformers_version("<", "4.26.0.dev0"):
-        "VersatileDiffusionTextToImagePipeline",
-        "VersatileDiffusionPipeline",
-        "VersatileDiffusionDualGuidedPipeline",
-        "StableDiffusionImageVariationPipeline",
-    ] and is_transformers_version("<", "4.25.0.dev0"):
         raise ImportError(
             f"You need to install `transformers` from 'main' in order to use {name}: \n```\n pip install"
             " git+https://github.com/huggingface/transformers \n```"

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -0,0 +1,573 @@
+# 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 random
+import tempfile
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionDepth2ImgPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
+from diffusers.utils.import_utils import is_accelerate_available
+from diffusers.utils.testing_utils import require_torch_gpu
+from PIL import Image
+from transformers import (
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTokenizer,
+    DPTConfig,
+    DPTFeatureExtractor,
+    DPTForDepthEstimation,
+)
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+@unittest.skipIf(torch_device == "mps", reason="The depth model does not support MPS yet")
+class StableDiffusiondepth2imgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = StableDiffusionDepth2ImgPipeline
+    test_save_load_optional_components = False
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=5,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+            attention_head_dim=(2, 4, 8, 8),
+            use_linear_projection=True,
+        )
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        torch.manual_seed(0)
+        vae = 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,
+        )
+        torch.manual_seed(0)
+        text_encoder_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,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        backbone_config = {
+            "global_padding": "same",
+            "layer_type": "bottleneck",
+            "depths": [3, 4, 9],
+            "out_features": ["stage1", "stage2", "stage3"],
+            "embedding_dynamic_padding": True,
+            "hidden_sizes": [96, 192, 384, 768],
+            "num_groups": 2,
+        }
+        depth_estimator_config = DPTConfig(
+            image_size=32,
+            patch_size=16,
+            num_channels=3,
+            hidden_size=32,
+            num_hidden_layers=4,
+            backbone_out_indices=(0, 1, 2, 3),
+            num_attention_heads=4,
+            intermediate_size=37,
+            hidden_act="gelu",
+            hidden_dropout_prob=0.1,
+            attention_probs_dropout_prob=0.1,
+            is_decoder=False,
+            initializer_range=0.02,
+            is_hybrid=True,
+            backbone_config=backbone_config,
+            backbone_featmap_shape=[1, 384, 24, 24],
+        )
+        depth_estimator = DPTForDepthEstimation(depth_estimator_config)
+        feature_extractor = DPTFeatureExtractor.from_pretrained(
+            "hf-internal-testing/tiny-random-DPTForDepthEstimation"
+        )
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "depth_estimator": depth_estimator,
+            "feature_extractor": feature_extractor,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "image": image,
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 6.0,
+            "output_type": "numpy",
+        }
+        return inputs
+
+    @unittest.skipIf(torch_device == "mps", reason="The depth model does not support MPS yet")
+    def test_save_load_local(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
+            pipe_loaded.to(torch_device)
+            pipe_loaded.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_loaded = pipe_loaded(**inputs)[0]
+
+        max_diff = np.abs(output - output_loaded).max()
+        self.assertLess(max_diff, 3e-5)
+
+    @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
+    def test_save_load_float16(self):
+        components = self.get_dummy_components()
+        for name, module in components.items():
+            if hasattr(module, "half"):
+                components[name] = module.to(torch_device).half()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
+            pipe_loaded.to(torch_device)
+            pipe_loaded.set_progress_bar_config(disable=None)
+
+        for name, component in pipe_loaded.components.items():
+            if hasattr(component, "dtype"):
+                self.assertTrue(
+                    component.dtype == torch.float16,
+                    f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
+                )
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_loaded = pipe_loaded(**inputs)[0]
+
+        max_diff = np.abs(output - output_loaded).max()
+        self.assertLess(max_diff, 2e-2, "The output of the fp16 pipeline changed after saving and loading.")
+
+    @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
+    def test_float16_inference(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        for name, module in components.items():
+            if hasattr(module, "half"):
+                components[name] = module.half()
+        pipe_fp16 = self.pipeline_class(**components)
+        pipe_fp16.to(torch_device)
+        pipe_fp16.set_progress_bar_config(disable=None)
+
+        output = pipe(**self.get_dummy_inputs(torch_device))[0]
+        output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0]
+
+        max_diff = np.abs(output - output_fp16).max()
+        self.assertLess(max_diff, 1.3e-2, "The outputs of the fp16 and fp32 pipelines are too different.")
+
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_accelerate_available(),
+        reason="CPU offload is only available with CUDA and `accelerate` installed",
+    )
+    def test_cpu_offload_forward_pass(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_without_offload = pipe(**inputs)[0]
+
+        pipe.enable_sequential_cpu_offload()
+        inputs = self.get_dummy_inputs(torch_device)
+        output_with_offload = pipe(**inputs)[0]
+
+        max_diff = np.abs(output_with_offload - output_without_offload).max()
+        self.assertLess(max_diff, 3e-5, "CPU offloading should not affect the inference results")
+
+    @unittest.skipIf(torch_device == "mps", reason="The depth model does not support MPS yet")
+    def test_dict_tuple_outputs_equivalent(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        # Warmup pass when using mps (see #372)
+        if torch_device == "mps":
+            _ = pipe(**self.get_dummy_inputs(torch_device))
+
+        output = pipe(**self.get_dummy_inputs(torch_device))[0]
+        output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0]
+
+        max_diff = np.abs(output - output_tuple).max()
+        self.assertLess(max_diff, 3e-5)
+
+    @unittest.skipIf(torch_device == "mps", reason="The depth model does not support MPS yet")
+    def test_num_inference_steps_consistent(self):
+        super().test_num_inference_steps_consistent()
+
+    @unittest.skipIf(torch_device == "mps", reason="The depth model does not support MPS yet")
+    def test_progress_bar(self):
+        super().test_progress_bar()
+
+    def test_stable_diffusion_depth2img_default_case(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionDepth2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        if torch_device == "mps":
+            expected_slice = np.array([0.6071, 0.5035, 0.4378, 0.5776, 0.5753, 0.4316, 0.4513, 0.5263, 0.4546])
+        else:
+            expected_slice = np.array([0.6907, 0.5135, 0.4688, 0.5169, 0.5738, 0.4600, 0.4435, 0.5640, 0.4653])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionDepth2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        negative_prompt = "french fries"
+        output = sd_pipe(**inputs, negative_prompt=negative_prompt)
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        if torch_device == "mps":
+            expected_slice = np.array([0.5825, 0.5135, 0.4095, 0.5452, 0.6059, 0.4211, 0.3994, 0.5177, 0.4335])
+        else:
+            expected_slice = np.array([0.755, 0.521, 0.473, 0.554, 0.629, 0.442, 0.440, 0.582, 0.449])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_multiple_init_images(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionDepth2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        inputs["prompt"] = [inputs["prompt"]] * 2
+        inputs["image"] = inputs["image"].repeat(2, 1, 1, 1)
+        image = sd_pipe(**inputs).images
+        image_slice = image[-1, -3:, -3:, -1]
+
+        assert image.shape == (2, 32, 32, 3)
+
+        if torch_device == "mps":
+            expected_slice = np.array([0.6501, 0.5150, 0.4939, 0.6688, 0.5437, 0.5758, 0.5115, 0.4406, 0.4551])
+        else:
+            expected_slice = np.array([0.6475, 0.6302, 0.5627, 0.5222, 0.4318, 0.5489, 0.5079, 0.4419, 0.4494])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_num_images_per_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionDepth2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        # test num_images_per_prompt=1 (default)
+        inputs = self.get_dummy_inputs(device)
+        images = sd_pipe(**inputs).images
+
+        assert images.shape == (1, 32, 32, 3)
+
+        # test num_images_per_prompt=1 (default) for batch of prompts
+        batch_size = 2
+        inputs = self.get_dummy_inputs(device)
+        inputs["prompt"] = [inputs["prompt"]] * batch_size
+        images = sd_pipe(**inputs).images
+
+        assert images.shape == (batch_size, 32, 32, 3)
+
+        # test num_images_per_prompt for single prompt
+        num_images_per_prompt = 2
+        inputs = self.get_dummy_inputs(device)
+        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
+
+        assert images.shape == (num_images_per_prompt, 32, 32, 3)
+
+        # test num_images_per_prompt for batch of prompts
+        batch_size = 2
+        inputs = self.get_dummy_inputs(device)
+        inputs["prompt"] = [inputs["prompt"]] * batch_size
+        images = sd_pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images
+
+        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
+
+    def test_stable_diffusion_depth2img_pil(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionDepth2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+
+        inputs["image"] = Image.fromarray(inputs["image"][0].permute(1, 2, 0).numpy().astype(np.uint8))
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        if torch_device == "mps":
+            expected_slice = np.array([0.53232, 0.47015, 0.40868, 0.45651, 0.4891, 0.4668, 0.4287, 0.48822, 0.47439])
+        else:
+            expected_slice = np.array([0.6853, 0.3740, 0.4856, 0.7130, 0.7402, 0.5535, 0.4828, 0.6182, 0.5053])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
+
+@slow
+@require_torch_gpu
+class StableDiffusionDepth2ImgPipelineIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_stable_diffusion_depth2img_pipeline_default(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
+        )
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.npy"
+        )
+
+        model_id = "stabilityai/stable-diffusion-2-depth"
+        pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "two tigers"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (480, 640, 3)
+        # depth2img is flaky across GPUs even in fp32, so using MAE here
+        assert np.abs(expected_image - image).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_pipeline_k_lms(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
+        )
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats_k_lms.npy"
+        )
+
+        model_id = "stabilityai/stable-diffusion-2-depth"
+        lms = LMSDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+        pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id, scheduler=lms)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "two tigers"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (480, 640, 3)
+        assert np.abs(expected_image - image).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_pipeline_ddim(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
+        )
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats_ddim.npy"
+        )
+
+        model_id = "stabilityai/stable-diffusion-2-depth"
+        ddim = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
+        pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id, scheduler=ddim)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "two tigers"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (480, 640, 3)
+        assert np.abs(expected_image - image).max() < 1e-3
+
+    def test_stable_diffusion_depth2img_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 == 1:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 60, 80)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array(
+                    [-0.7825, 0.5786, -0.9125, -0.9885, -1.0071, 2.7126, -0.8490, 0.3776, -0.0791]
+                )
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
+            elif step == 37:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 60, 80)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array(
+                    [-0.6110, -0.2347, -0.5115, -1.1383, -1.4755, -0.5970, -0.9050, -0.7199, -0.8417]
+                )
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
+
+        test_callback_fn.has_been_called = False
+
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
+        )
+
+        pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "two tigers"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            num_inference_steps=50,
+            generator=generator,
+            callback=test_callback_fn,
+            callback_steps=1,
+        )
+        assert test_callback_fn.has_been_called
+        assert number_of_steps == 37
+
+    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()
+
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/depth2img/sketch-mountains-input.jpg"
+        )
+        init_image = init_image.resize((768, 512))
+
+        model_id = "stabilityai/stable-diffusion-2-depth"
+        lms = LMSDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+        pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
+            model_id, scheduler=lms, safety_checker=None, revision="fp16", torch_dtype=torch.float16
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing(1)
+        pipe.enable_sequential_cpu_offload()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        _ = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+            num_inference_steps=5,
+        )
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        # make sure that less than 2.9 GB is allocated
+        assert mem_bytes < 2.9 * 10**9

tests/test_pipelines.py

@@ -26,7 +26,6 @@ import torch
 
 import PIL
 import safetensors.torch
-import transformers
 from diffusers import (
     AutoencoderKL,
     DDIMPipeline,
@@ -533,9 +532,8 @@ class PipelineFastTests(unittest.TestCase):
 
             # Validate that the text encoder safetensor exists and are of the correct format
             text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors")
-            if transformers.__version__ >= "4.25.1":
+            assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
-                assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
+            _ = safetensors.torch.load_file(text_encoder_path)
-                _ = safetensors.torch.load_file(text_encoder_path)
 
             pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)
             assert pipeline.unet is not None

tests/test_pipelines_common.py

@@ -11,7 +11,13 @@ from typing import Callable, Union
 import numpy as np
 import torch
 
-from diffusers import CycleDiffusionPipeline, DanceDiffusionPipeline, DiffusionPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import (
+    CycleDiffusionPipeline,
+    DanceDiffusionPipeline,
+    DiffusionPipeline,
+    StableDiffusionDepth2ImgPipeline,
+    StableDiffusionImg2ImgPipeline,
+)
 from diffusers.utils.import_utils import is_accelerate_available, is_xformers_available
 from diffusers.utils.testing_utils import require_torch, torch_device
 
@@ -281,6 +287,7 @@ class PipelineTesterMixin:
             DanceDiffusionPipeline,
             CycleDiffusionPipeline,
             StableDiffusionImg2ImgPipeline,
+            StableDiffusionDepth2ImgPipeline,
         ):
             # FIXME: inconsistent outputs on MPS
             return

utils/check_dummies.py

@@ -91,7 +91,8 @@ def read_init():
                     objects.append(line[8:-2])
                 line_index += 1
 
-            backend_specific_objects[backend] = objects
+            if len(objects) > 0:
+                backend_specific_objects[backend] = objects
         else:
             line_index += 1