# Copyright 2023 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 inspect from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Union import numpy as np import PIL.Image import torch from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection # from models.controlnet_sdv import ControlNetSDVModel from models.DragAnything import DragAnythingSDVModel from diffusers.image_processor import VaeImageProcessor from diffusers.models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel from diffusers.utils import BaseOutput, logging from diffusers.utils.torch_utils import randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline from models.unet_spatio_temporal_condition_controlnet import UNetSpatioTemporalConditionControlNetModel from utils.scheduling_euler_discrete_karras_fix import EulerDiscreteScheduler #from diffusers.pipelines.utils import PIL_INTERPOLATION, BaseOutput, logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name def _get_add_time_ids( noise_aug_strength, dtype, batch_size, fps=4, motion_bucket_id=128, unet=None, ): add_time_ids = [fps, motion_bucket_id, noise_aug_strength] passed_add_embed_dim = unet.config.addition_time_embed_dim * len(add_time_ids) expected_add_embed_dim = unet.add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor([add_time_ids], dtype=dtype) # add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1) return add_time_ids def _append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") return x[(...,) + (None,) * dims_to_append] def tensor2vid(video: torch.Tensor, processor, output_type="np"): # Based on: # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 batch_size, channels, num_frames, height, width = video.shape outputs = [] for batch_idx in range(batch_size): batch_vid = video[batch_idx].permute(1, 0, 2, 3) batch_output = processor.postprocess(batch_vid, output_type) outputs.append(batch_output) return outputs def load_dinov2(): dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14').cuda() dinov2_vitl14.eval() dinov2_vitl14.requires_grad_(False) return dinov2_vitl14 @dataclass class StableVideoDiffusionPipelineOutput(BaseOutput): r""" Output class for zero-shot text-to-video pipeline. Args: frames (`[List[PIL.Image.Image]`, `np.ndarray`]): List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`. """ frames: Union[List[PIL.Image.Image], np.ndarray] class StableVideoDiffusionPipeline(DiffusionPipeline): r""" Pipeline to generate video from an input image using Stable Video Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, 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. image_encoder ([`~transformers.CLIPVisionModelWithProjection`]): Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)). unet ([`UNetSpatioTemporalConditionModel`]): A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents. scheduler ([`EulerDiscreteScheduler`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images. """ model_cpu_offload_seq = "image_encoder->unet->vae" _callback_tensor_inputs = ["latents"] def __init__( self, vae: AutoencoderKLTemporalDecoder, image_encoder: CLIPVisionModelWithProjection, unet: UNetSpatioTemporalConditionControlNetModel, controlnet: DragAnythingSDVModel, scheduler: EulerDiscreteScheduler, feature_extractor: CLIPImageProcessor, ): super().__init__() self.register_modules( vae=vae, image_encoder=image_encoder, controlnet=controlnet, unet=unet, scheduler=scheduler, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.image_processor.pil_to_numpy(image) image = self.image_processor.numpy_to_pt(image) #image = image.unsqueeze(0) image = _resize_with_antialiasing(image, (224, 224)) image = image.to(device=device, dtype=dtype) image_embeddings = self.image_encoder(image).image_embeds image_embeddings = image_embeddings.unsqueeze(1) # duplicate image embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = image_embeddings.shape image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1) image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) if do_classifier_free_guidance: negative_image_embeddings = torch.zeros_like(image_embeddings) # 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 image_embeddings = torch.cat([negative_image_embeddings, image_embeddings]) return image_embeddings def _encode_vae_image( self, image: torch.Tensor, device, num_videos_per_prompt, do_classifier_free_guidance, ): image = image.to(device=device) image_latents = self.vae.encode(image).latent_dist.mode() if do_classifier_free_guidance: negative_image_latents = torch.zeros_like(image_latents) # 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 image_latents = torch.cat([negative_image_latents, image_latents]) # duplicate image_latents for each generation per prompt, using mps friendly method image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1) return image_latents def _get_add_time_ids( self, fps, motion_bucket_id, noise_aug_strength, dtype, batch_size, num_videos_per_prompt, do_classifier_free_guidance, ): add_time_ids = [fps, motion_bucket_id, noise_aug_strength] passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids) expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor([add_time_ids], dtype=dtype) add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1) if do_classifier_free_guidance: add_time_ids = torch.cat([add_time_ids, add_time_ids]) return add_time_ids def decode_latents(self, latents, num_frames, decode_chunk_size=14): # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width] latents = latents.flatten(0, 1) latents = 1 / self.vae.config.scaling_factor * latents accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys()) # decode decode_chunk_size frames at a time to avoid OOM frames = [] for i in range(0, latents.shape[0], decode_chunk_size): num_frames_in = latents[i : i + decode_chunk_size].shape[0] decode_kwargs = {} if accepts_num_frames: # we only pass num_frames_in if it's expected decode_kwargs["num_frames"] = num_frames_in frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample frames.append(frame) frames = torch.cat(frames, dim=0) # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width] frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 frames = frames.float() return frames def check_inputs(self, image, height, width): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") def prepare_latents( self, batch_size, num_frames, num_channels_latents, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size, num_frames, num_channels_latents // 2, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents @property def guidance_scale(self): return self._guidance_scale # 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. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property def num_timesteps(self): return self._num_timesteps def normalize_sam(self, images): """ Normalize an image array to [-1,1]. """ return (images - torch.tensor([0.485, 0.456, 0.406]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1))/torch.tensor([0.229, 0.224, 0.225]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) @torch.no_grad() def __call__( self, image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor], controlnet_condition:[torch.FloatTensor] = None, ids_embedding:[torch.FloatTensor] = None, height: int = 576, width: int = 1024, num_frames: Optional[int] = None, num_inference_steps: int = 25, min_guidance_scale: float = 1.0, max_guidance_scale: float = 3.0, fps: int = 7, motion_bucket_id: int = 127, noise_aug_strength: int = 0.02, decode_chunk_size: Optional[int] = None, num_videos_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], return_dict: bool = True, controlnet_cond_scale=1.0, batch_size=1, ): r""" The call function to the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): Image or images to guide image generation. If you provide a tensor, it needs to be compatible with [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json). height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. num_frames (`int`, *optional*): The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt` num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter is modulated by `strength`. min_guidance_scale (`float`, *optional*, defaults to 1.0): The minimum guidance scale. Used for the classifier free guidance with first frame. max_guidance_scale (`float`, *optional*, defaults to 3.0): The maximum guidance scale. Used for the classifier free guidance with last frame. fps (`int`, *optional*, defaults to 7): Frames per second. The rate at which the generated images shall be exported to a video after generation. Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training. motion_bucket_id (`int`, *optional*, defaults to 127): The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video. noise_aug_strength (`int`, *optional*, defaults to 0.02): The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion. decode_chunk_size (`int`, *optional*): The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once for maximal quality. Reduce `decode_chunk_size` to reduce memory usage. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list of list with the generated frames. Examples: ```py from diffusers import StableVideoDiffusionPipeline from diffusers.utils import load_image, export_to_video pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200") image = image.resize((1024, 576)) frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0] export_to_video(frames, "generated.mp4", fps=7) ``` """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor num_frames = num_frames if num_frames is not None else self.unet.config.num_frames decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames # 1. Check inputs. Raise error if not correct self.check_inputs(image, height, width) # 2. Define call parameters #if isinstance(image, PIL.Image.Image): # batch_size = 1 #elif isinstance(image, list): # batch_size = len(image) #else: # batch_size = image.shape[0] 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 = max_guidance_scale > 1.0 # 3. Encode input image image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance) # NOTE: Stable Diffusion Video was conditioned on fps - 1, which # is why it is reduced here. # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188 fps = fps - 1 # 4. Encode input image using VAE image = self.image_processor.preprocess(image, height=height, width=width) noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype) image = image + noise_aug_strength * noise needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: self.vae.to(dtype=torch.float32) self.dinov2.to(dtype=torch.float32) print(image.shape) image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance) image_latents = image_latents.to(image_embeddings.dtype) # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) self.dinov2.to(dtype=torch.float16) # Repeat the image latents for each frame so we can concatenate them with the noise # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width] image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1) #image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents # 5. Get Added Time IDs added_time_ids = self._get_add_time_ids( fps, motion_bucket_id, noise_aug_strength, image_embeddings.dtype, batch_size, num_videos_per_prompt, do_classifier_free_guidance, ) added_time_ids = added_time_ids.to(device) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_videos_per_prompt, num_frames, num_channels_latents, height, width, image_embeddings.dtype, device, generator, latents, ) #prepare controlnet condition # print(height,width) controlnet_condition = self.image_processor.preprocess(controlnet_condition, height=height, width=width,is_mask=True) controlnet_condition = controlnet_condition.unsqueeze(0) controlnet_condition = torch.cat([controlnet_condition] * 2) controlnet_condition = controlnet_condition.to(device, latents.dtype) # 7. Prepare guidance scale guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0) guidance_scale = guidance_scale.to(device, latents.dtype) guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1) guidance_scale = _append_dims(guidance_scale, latents.ndim) self._guidance_scale = guidance_scale noise_aug_strength = 0.02 #"¯\_(ツ)_/¯ added_time_ids = _get_add_time_ids( noise_aug_strength, image_embeddings.dtype, batch_size, 6, 128, unet=self.unet, ) added_time_ids = torch.cat([added_time_ids] * 2) added_time_ids = added_time_ids.to(latents.device) # # ID_images = self.normalize_sam(ID_images) # # print(image.device) # ID_images = ID_images.to(dtype=image.dtype) # ID_images = ID_images.to(device) # ID_feature = self.dinov2.forward_features(ID_images) ids_embedding = ids_embedding.unsqueeze(0) ids_embedding = torch.cat([ids_embedding] * 2) ids_embedding = ids_embedding.to(device, latents.dtype) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) 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) # Concatenate image_latents over channels dimention # print(latent_model_input.shape) # print(image_latents.shape) latent_model_input = torch.cat([latent_model_input, image_latents], dim=2) # print(latent_model_input.shape) down_block_res_samples, mid_block_res_sample = self.controlnet( latent_model_input, t, encoder_hidden_states=image_embeddings, controlnet_cond=controlnet_condition, added_time_ids=added_time_ids, ID_feature=ids_embedding, conditioning_scale=controlnet_cond_scale, guess_mode=False, return_dict=False, do_classifier_free_guidance=do_classifier_free_guidance, ) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=image_embeddings, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, added_time_ids=added_time_ids, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents).prev_sample if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if not output_type == "latent": # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) frames = self.decode_latents(latents, num_frames, decode_chunk_size) frames = tensor2vid(frames, self.image_processor, output_type=output_type) else: frames = latents self.maybe_free_model_hooks() if not return_dict: return frames return StableVideoDiffusionPipelineOutput(frames=frames) # resizing utils # TODO: clean up later def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True): if input.ndim == 3: input = input.unsqueeze(0) # Add a batch dimension h, w = input.shape[-2:] factors = (h / size[0], w / size[1]) # First, we have to determine sigma # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 sigmas = ( max((factors[0] - 1.0) / 2.0, 0.001), max((factors[1] - 1.0) / 2.0, 0.001), ) # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) # Make sure it is odd if (ks[0] % 2) == 0: ks = ks[0] + 1, ks[1] if (ks[1] % 2) == 0: ks = ks[0], ks[1] + 1 input = _gaussian_blur2d(input, ks, sigmas) output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) return output def _compute_padding(kernel_size): """Compute padding tuple.""" # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad if len(kernel_size) < 2: raise AssertionError(kernel_size) computed = [k - 1 for k in kernel_size] # for even kernels we need to do asymmetric padding :( out_padding = 2 * len(kernel_size) * [0] for i in range(len(kernel_size)): computed_tmp = computed[-(i + 1)] pad_front = computed_tmp // 2 pad_rear = computed_tmp - pad_front out_padding[2 * i + 0] = pad_front out_padding[2 * i + 1] = pad_rear return out_padding def _filter2d(input, kernel): # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) height, width = tmp_kernel.shape[-2:] padding_shape: list[int] = _compute_padding([height, width]) input = torch.nn.functional.pad(input, padding_shape, mode="reflect") # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) # convolve the tensor with the kernel. output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) out = output.view(b, c, h, w) return out def _gaussian(window_size: int, sigma): if isinstance(sigma, float): sigma = torch.tensor([[sigma]]) batch_size = sigma.shape[0] x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) return gauss / gauss.sum(-1, keepdim=True) def _gaussian_blur2d(input, kernel_size, sigma): if isinstance(sigma, tuple): sigma = torch.tensor([sigma], dtype=input.dtype) else: sigma = sigma.to(dtype=input.dtype) ky, kx = int(kernel_size[0]), int(kernel_size[1]) bs = sigma.shape[0] kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1)) kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1)) out_x = _filter2d(input, kernel_x[..., None, :]) out = _filter2d(out_x, kernel_y[..., None]) return out