Upload pipeline.py

pipeline.py

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+# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.
+# Copyright 2024 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.
+# --------------------------------------------------------------------------
+# More information and citation instructions are available on the
+# Marigold project website: https://marigoldmonodepth.github.io
+# --------------------------------------------------------------------------
+
+# @GonzaloMartinGarcia
+# Inference Pipeline for End-to-End Marigold and Stable Diffusion Depth Estimators
+# ----------------------------------------------------------------------------------
+# A streamlined version of the official MarigoldDepthPipeline from diffusers:
+# https://github.com/huggingface/diffusers/blob/a98a839de75f1ad82d8d200c3bc2e4ff89929081/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py#L96
+#
+# This implementation is meant for use with the diffusers custom_pipeline feature.
+# Modifications from the original code are marked with '# add' comments.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers.image_processor import PipelineImageInput
+from diffusers.models import (
+    AutoencoderKL,
+    UNet2DConditionModel,
+)
+from diffusers.schedulers import (
+    DDIMScheduler,
+)
+from diffusers.utils import (
+    BaseOutput,
+    logging,
+)
+from diffusers import DiffusionPipeline
+from diffusers.pipelines.marigold.marigold_image_processing import MarigoldImageProcessor
+
+# add
+def zeros_tensor(
+    shape: Union[Tuple, List],
+    device: Optional["torch.device"] = None,
+    dtype: Optional["torch.dtype"] = None,
+    layout: Optional["torch.layout"] = None,
+):
+    """
+    A helper function to create tensors of zeros on the desired `device`.
+    Mirrors randn_tensor from diffusers.utils.torch_utils.
+    """
+    layout = layout or torch.strided
+    device = device or torch.device("cpu")
+    latents = torch.zeros(list(shape), dtype=dtype, layout=layout).to(device)
+    return latents
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+@dataclass
+class E2EMarigoldDepthOutput(BaseOutput):
+    """
+    Output class for Marigold monocular depth prediction pipeline.
+
+    Args:
+        prediction (`np.ndarray`, `torch.Tensor`):
+            Predicted depth maps with values in the range [0, 1]. The shape is always $numimages \times 1 \times height
+            \times width$, regardless of whether the images were passed as a 4D array or a list.
+        latent (`None`, `torch.Tensor`):
+            Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
+            The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
+    """
+    
+    prediction: Union[np.ndarray, torch.Tensor]
+    latent: Union[None, torch.Tensor]
+
+
+class E2EMarigoldDepthPipeline(DiffusionPipeline):
+    """
+    # add
+    Pipeline for monocular depth estimation using the E2E FT Marigold and SD method: https://gonzalomartingarcia.github.io/diffusion-e2e-ft/
+    Implementation is built upon Marigold: https://marigoldmonodepth.github.io
+
+    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:
+        unet (`UNet2DConditionModel`):
+            Conditional U-Net to denoise the depth latent, conditioned on image latent.
+        vae (`AutoencoderKL`):
+            Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent
+            representations.
+        scheduler (`DDIMScheduler`):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
+        text_encoder (`CLIPTextModel`):
+            Text-encoder, for empty text embedding.
+        tokenizer (`CLIPTokenizer`):
+            CLIP tokenizer.
+        prediction_type (`str`, *optional*):
+            Type of predictions made by the model.
+        default_processing_resolution (`int`, *optional*):
+            The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in
+            the model config. When the pipeline is called without explicitly setting `processing_resolution`, the
+            default value is used. This is required to ensure reasonable results with various model flavors trained
+            with varying optimal processing resolution values.
+    """
+    
+    model_cpu_offload_seq = "text_encoder->unet->vae"
+    supported_prediction_types = ("depth", "disparity")
+
+    def __init__(
+        self,
+        unet: UNet2DConditionModel,
+        vae: AutoencoderKL,
+        scheduler: Union[DDIMScheduler],
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        prediction_type: Optional[str] = None,
+        default_processing_resolution: Optional[int] = None,
+    ):
+        super().__init__()
+
+        if prediction_type not in self.supported_prediction_types:
+            logger.warning(
+                f"Potentially unsupported `prediction_type='{prediction_type}'`; values supported by the pipeline: "
+                f"{self.supported_prediction_types}."
+            )
+
+        self.register_modules(
+            unet=unet,
+            vae=vae,
+            scheduler=scheduler,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+        )
+        self.register_to_config(
+            prediction_type=prediction_type,
+            default_processing_resolution=default_processing_resolution,
+        )
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.default_processing_resolution = default_processing_resolution
+        self.empty_text_embedding = None
+
+        self.image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def check_inputs(
+        self,
+        image: PipelineImageInput,
+        processing_resolution: int,
+        resample_method_input: str,
+        resample_method_output: str,
+        batch_size: int,
+        output_type: str,
+    ) -> int:
+        if processing_resolution is None:
+            raise ValueError(
+                "`processing_resolution` is not specified and could not be resolved from the model config."
+            )
+        if processing_resolution < 0:
+            raise ValueError(
+                "`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for "
+                "downsampled processing."
+            )
+        if processing_resolution % self.vae_scale_factor != 0:
+            raise ValueError(f"`processing_resolution` must be a multiple of {self.vae_scale_factor}.")
+        if resample_method_input not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
+            raise ValueError(
+                "`resample_method_input` takes string values compatible with PIL library: "
+                "nearest, nearest-exact, bilinear, bicubic, area."
+            )
+        if resample_method_output not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
+            raise ValueError(
+                "`resample_method_output` takes string values compatible with PIL library: "
+                "nearest, nearest-exact, bilinear, bicubic, area."
+            )
+        if batch_size < 1:
+            raise ValueError("`batch_size` must be positive.")
+        if output_type not in ["pt", "np"]:
+            raise ValueError("`output_type` must be one of `pt` or `np`.")
+
+        # image checks
+        num_images = 0
+        W, H = None, None
+        if not isinstance(image, list):
+            image = [image]
+        for i, img in enumerate(image):
+            if isinstance(img, np.ndarray) or torch.is_tensor(img):
+                if img.ndim not in (2, 3, 4):
+                    raise ValueError(f"`image[{i}]` has unsupported dimensions or shape: {img.shape}.")
+                H_i, W_i = img.shape[-2:]
+                N_i = 1
+                if img.ndim == 4:
+                    N_i = img.shape[0]
+            elif isinstance(img, Image.Image):
+                W_i, H_i = img.size
+                N_i = 1
+            else:
+                raise ValueError(f"Unsupported `image[{i}]` type: {type(img)}.")
+            if W is None:
+                W, H = W_i, H_i
+            elif (W, H) != (W_i, H_i):
+                raise ValueError(
+                    f"Input `image[{i}]` has incompatible dimensions {(W_i, H_i)} with the previous images {(W, H)}"
+                )
+            num_images += N_i
+
+        return num_images
+
+    def progress_bar(self, iterable=None, total=None, desc=None, leave=True):
+        if not hasattr(self, "_progress_bar_config"):
+            self._progress_bar_config = {}
+        elif not isinstance(self._progress_bar_config, dict):
+            raise ValueError(
+                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
+            )
+
+        progress_bar_config = dict(**self._progress_bar_config)
+        progress_bar_config["desc"] = progress_bar_config.get("desc", desc)
+        progress_bar_config["leave"] = progress_bar_config.get("leave", leave)
+        if iterable is not None:
+            return tqdm(iterable, **progress_bar_config)
+        elif total is not None:
+            return tqdm(total=total, **progress_bar_config)
+        else:
+            raise ValueError("Either `total` or `iterable` has to be defined.")
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        processing_resolution: Optional[int] = None,
+        match_input_resolution: bool = True,
+        resample_method_input: str = "bilinear",
+        resample_method_output: str = "bilinear",
+        batch_size: int = 1,
+        output_type: str = "np",
+        output_latent: bool = False,
+        return_dict: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline.
+
+        Args:
+            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`),
+                `List[torch.Tensor]`: An input image or images used as an input for the depth estimation task. For
+                arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible
+                by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or
+                three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the
+                same width and height.
+            processing_resolution (`int`, *optional*, defaults to `None`):
+                Effective processing resolution. When set to `0`, matches the larger input image dimension. This
+                produces crisper predictions, but may also lead to the overall loss of global context. The default
+                value `None` resolves to the optimal value from the model config.
+            match_input_resolution (`bool`, *optional*, defaults to `True`):
+                When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer
+                side of the output will equal to `processing_resolution`.
+            resample_method_input (`str`, *optional*, defaults to `"bilinear"`):
+                Resampling method used to resize input images to `processing_resolution`. The accepted values are:
+                `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
+            resample_method_output (`str`, *optional*, defaults to `"bilinear"`):
+                Resampling method used to resize output predictions to match the input resolution. The accepted values
+                are `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
+            batch_size (`int`, *optional*, defaults to `1`):
+                Batch size; only matters passing a tensor of images.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted
+                values are: `"np"` (numpy array) or `"pt"` (torch tensor).
+            output_latent (`bool`, *optional*, defaults to `False`):
+                When enabled, the output's `latent` field contains the latent codes corresponding to the predictions
+                within the ensemble. These codes can be saved, modified, and used for subsequent calls with the
+                `latents` argument.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.marigold.E2EMarigoldDepthOutput`] instead of a plain tuple.
+
+        # add
+        E2E FT models are deterministic single step models involving no ensembling, i.e. E=1.
+        """
+
+        # 0. Resolving variables.
+        device = self._execution_device
+        dtype = self.dtype
+
+        # Model-specific optimal default values leading to fast and reasonable results.
+        if processing_resolution is None:
+            processing_resolution = self.default_processing_resolution
+
+        # 1. Check inputs.
+        num_images = self.check_inputs(
+            image,
+            processing_resolution,
+            resample_method_input,
+            resample_method_output,
+            batch_size,
+            output_type,
+        )
+
+        # 2. Prepare empty text conditioning.
+        # Model invocation: self.tokenizer, self.text_encoder.
+        if self.empty_text_embedding is None:
+            prompt = ""
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="do_not_pad",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids.to(device)
+            self.empty_text_embedding = self.text_encoder(text_input_ids)[0]  # [1,2,1024]
+
+        # 3. Preprocess input images. This function loads input image or images of compatible dimensions `(H, W)`,
+        # optionally downsamples them to the `processing_resolution` `(PH, PW)`, where
+        # `max(PH, PW) == processing_resolution`, and pads the dimensions to `(PPH, PPW)` such that these values are
+        # divisible by the latent space downscaling factor (typically 8 in Stable Diffusion). The default value `None`
+        # of `processing_resolution` resolves to the optimal value from the model config. It is a recommended mode of
+        # operation and leads to the most reasonable results. Using the native image resolution or any other processing
+        # resolution can lead to loss of either fine details or global context in the output predictions.
+        image, padding, original_resolution = self.image_processor.preprocess(
+            image, processing_resolution, resample_method_input, device, dtype
+        )  # [N,3,PPH,PPW]
+
+        # 4. Encode input image into latent space. At this step, each of the `N` input images is represented with `E`
+        # ensemble members. Each ensemble member is an independent diffused prediction, just initialized independently.
+        # Latents of each such predictions across all input images and all ensemble members are represented in the
+        # `pred_latent` variable. The variable `image_latent` is of the same shape: it contains each input image encoded
+        # into latent space and replicated `E` times. Encoding into latent space happens in batches of size `batch_size`.
+        # Model invocation: self.vae.encoder.
+        image_latent, pred_latent = self.prepare_latents(
+            image, batch_size
+        )  # [N*E,4,h,w], [N*E,4,h,w]
+
+        del image
+
+        batch_empty_text_embedding = self.empty_text_embedding.to(device=device, dtype=dtype).repeat(
+            batch_size, 1, 1
+        )  # [B,1024,2]
+
+        # 5. Process the denoising loop. All `N * E` latents are processed sequentially in batches of size `batch_size`.
+        # The unet model takes concatenated latent spaces of the input image and the predicted modality as an input, and
+        # outputs noise for the predicted modality's latent space. The number of denoising diffusion steps is defined by
+        # `num_inference_steps`. It is either set directly, or resolves to the optimal value specific to the loaded
+        # model.
+        # Model invocation: self.unet.
+        pred_latents = []
+
+        for i in self.progress_bar(
+            range(0, num_images, batch_size), leave=True, desc="E2E FT predictions..."
+        ):
+            batch_image_latent = image_latent[i : i + batch_size]  # [B,4,h,w]
+            batch_pred_latent = pred_latent[i : i + batch_size]    # [B,4,h,w]
+            effective_batch_size = batch_image_latent.shape[0]
+            text = batch_empty_text_embedding[:effective_batch_size]  # [B,2,1024]
+            
+            # add
+            # Single step inference for E2E FT models
+            self.scheduler.set_timesteps(1, device=device)
+            for t in self.progress_bar(self.scheduler.timesteps, leave=False, desc="Diffusion steps..."):
+                batch_latent = torch.cat([batch_image_latent, batch_pred_latent], dim=1)  # [B,8,h,w]
+                noise = self.unet(batch_latent, t, encoder_hidden_states=text, return_dict=False)[0]  # [B,4,h,w]
+                batch_pred_latent = self.scheduler.step(
+                    noise, t, batch_pred_latent
+                ).pred_original_sample  # [B,4,h,w], # add
+                                                     # directly take pred_original_sample rather than prev_sample
+
+            pred_latents.append(batch_pred_latent)
+
+        pred_latent = torch.cat(pred_latents, dim=0)  # [N*E,4,h,w]
+
+        del (
+            pred_latents,
+            image_latent,
+            batch_empty_text_embedding,
+            batch_image_latent,
+            batch_pred_latent,
+            text,
+            batch_latent,
+            noise,
+        )
+
+        # 6. Decode predictions from latent into pixel space. The resulting `N * E` predictions have shape `(PPH, PPW)`,
+        # which requires slight postprocessing. Decoding into pixel space happens in batches of size `batch_size`.
+        # Model invocation: self.vae.decoder.
+        prediction = torch.cat(
+            [
+                self.decode_prediction(pred_latent[i : i + batch_size])
+                for i in range(0, pred_latent.shape[0], batch_size)
+            ],
+            dim=0,
+        )  # [N*E,1,PPH,PPW]
+
+        if not output_latent:
+            pred_latent = None
+
+        # 7. Remove padding. The output shape is (PH, PW).
+        prediction = self.image_processor.unpad_image(prediction, padding)  # [N*E,1,PH,PW]
+
+        # 9. If `match_input_resolution` is set, the output prediction and the uncertainty are upsampled to match the
+        # input resolution `(H, W)`. This step may introduce upsampling artifacts, and therefore can be disabled.
+        # Depending on the downstream use-case, upsampling can be also chosen based on the tolerated artifacts by
+        # setting the `resample_method_output` parameter (e.g., to `"nearest"`).
+        if match_input_resolution:
+            prediction = self.image_processor.resize_antialias(
+                prediction, original_resolution, resample_method_output, is_aa=False
+            )  # [N,1,H,W]
+
+        # 10. Prepare the final outputs.
+        if output_type == "np":
+            prediction = self.image_processor.pt_to_numpy(prediction)  # [N,H,W,1]
+
+        # 11. Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (prediction, pred_latent)
+
+        return E2EMarigoldDepthOutput(
+            prediction=prediction,
+            latent=pred_latent,
+        )
+
+    def prepare_latents(
+        self,
+        image: torch.Tensor,
+        batch_size: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        def retrieve_latents(encoder_output):
+            if hasattr(encoder_output, "latent_dist"):
+                return encoder_output.latent_dist.mode()
+            elif hasattr(encoder_output, "latents"):
+                return encoder_output.latents
+            else:
+                raise AttributeError("Could not access latents of provided encoder_output")
+
+        image_latent = torch.cat(
+            [
+                retrieve_latents(self.vae.encode(image[i : i + batch_size]))
+                for i in range(0, image.shape[0], batch_size)
+            ],
+            dim=0,
+        )  # [N,4,h,w]
+        image_latent = image_latent * self.vae.config.scaling_factor # [N*E,4,h,w]
+
+        # add
+        # provide zeros as noised latent
+        pred_latent = zeros_tensor(
+            image_latent.shape,
+            device=image_latent.device,
+            dtype=image_latent.dtype,
+        )  # [N*E,4,h,w]
+
+        return image_latent, pred_latent
+
+    def decode_prediction(self, pred_latent: torch.Tensor) -> torch.Tensor:
+        if pred_latent.dim() != 4 or pred_latent.shape[1] != self.vae.config.latent_channels:
+            raise ValueError(
+                f"Expecting 4D tensor of shape [B,{self.vae.config.latent_channels},H,W]; got {pred_latent.shape}."
+            )
+
+        prediction = self.vae.decode(pred_latent / self.vae.config.scaling_factor, return_dict=False)[0]  # [B,3,H,W]
+
+        prediction = prediction.mean(dim=1, keepdim=True)  # [B,1,H,W]
+        prediction = torch.clip(prediction, -1.0, 1.0)  # [B,1,H,W]
+        prediction = (prediction + 1.0) / 2.0
+
+        return prediction  # [B,1,H,W]