diff --git a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py index 5c40f56..4c68f8c 100644 --- a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py +++ b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py @@ -251,6 +251,7 @@ class HunyuanVideoPipeline(DiffusionPipeline): f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=self.base_dtype) + if freenoise: logger.info("Applying FreeNoise") # code and comments from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) @@ -285,28 +286,26 @@ class HunyuanVideoPipeline(DiffusionPipeline): # apply shuffled indexes #print("place_idx:", place_idx, "delta:", delta, "list_idx:", list_idx) noise[:, :, place_idx:place_idx + delta, :, :] = noise[:, :, list_idx, :, :] + i2v_mask = None + if official_i2v: + # Create mask + i2v_mask = torch.zeros(shape[0], 1, shape[2], shape[3], shape[4], device=device) + i2v_mask[:, :, 0, ...] = 1.0 + if image_cond_latents is not None: if image_cond_latents.shape[2] == 1: padding = torch.zeros(shape, device=device) padding[:, :, 0:1, :, :] = image_cond_latents image_cond_latents = padding - if official_i2v: - # Create mask - i2v_mask = torch.zeros(shape[0], 1, shape[2], shape[3], shape[4], device=device) - i2v_mask[:, :, 0, ...] = 1.0 - t = torch.tensor([0.999]).to(device=device) - latents = noise * t + image_cond_latents * (1 - t) - latents = latents.to(dtype=self.base_dtype) - elif latents is None: - print("No latents provided, generating noise and using it as latents") - latents = noise - elif denoise_strength < 1.0: - latents = latents.to(device) + + if denoise_strength < 1.0: + if official_i2v: + latents = torch.cat((latents[:,:,0].unsqueeze(2), latents), dim=2) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, denoise_strength, device) latent_timestep = timesteps[:1] - frames_needed = noise.shape[1] - current_frames = latents.shape[1] + frames_needed = noise.shape[2] + current_frames = latents.shape[2] if frames_needed > current_frames: repeat_factor = frames_needed - current_frames @@ -316,8 +315,13 @@ class HunyuanVideoPipeline(DiffusionPipeline): elif frames_needed < current_frames: latents = latents[:, :frames_needed, :, :, :] latents = latents * (1 - latent_timestep / 1000) + latent_timestep / 1000 * noise + print("latents shape:", latents.shape) + elif official_i2v: + t = torch.tensor([0.999]).to(device=device) + latents = noise * t + image_cond_latents * (1 - t) + latents = latents.to(dtype=self.base_dtype) else: - latents = latents.to(device) + latents = noise # Check existence to make it compatible with FlowMatchEulerDiscreteScheduler if hasattr(self.scheduler, "init_noise_sigma"): diff --git a/hyvideo/text_encoder/__init__.py b/hyvideo/text_encoder/__init__.py index 873a739..e72dc87 100644 --- a/hyvideo/text_encoder/__init__.py +++ b/hyvideo/text_encoder/__init__.py @@ -6,6 +6,7 @@ import torch import torch.nn as nn from transformers import CLIPTextModel, CLIPTokenizer, AutoTokenizer, AutoModel, AutoProcessor, CLIPImageProcessor #LlavaForConditionalGeneration from .modeling_llava import LlavaForConditionalGeneration +from .processing_llava import LlavaProcessor from transformers.utils import ModelOutput from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH @@ -166,8 +167,11 @@ class TextEncoder(nn.Module): elif "llm" in text_encoder_type or "glm" in text_encoder_type or "vlm" in text_encoder_type: self.output_key = output_key or "last_hidden_state" if "glm" in text_encoder_type or "vlm" in text_encoder_type: - #self.processor = AutoProcessor.from_pretrained(text_encoder_path, device=device) - self.processor = CLIPImageProcessor.from_pretrained(text_encoder_path, use_fast=False) + self.processor_ip2v = LlavaProcessor.from_pretrained(text_encoder_path, device=device) + self.processor_ip2v.patch_size = None + self.processor_ip2v.vision_feature_select_strategy = None + + self.processor = CLIPImageProcessor.from_pretrained(text_encoder_path, device=device) self.processor.patch_size = None self.processor.vision_feature_select_strategy = None else: @@ -259,7 +263,7 @@ class TextEncoder(nn.Module): raw_images.append(image1.squeeze(0)*255) if image2 is not None: raw_images.append(image2.squeeze(0)*255) - text_tokens = self.processor( + text_tokens = self.processor_ip2v( raw_images, text, **kwargs, diff --git a/hyvideo/text_encoder/processing_llava.py b/hyvideo/text_encoder/processing_llava.py new file mode 100644 index 0000000..8b2e619 --- /dev/null +++ b/hyvideo/text_encoder/processing_llava.py @@ -0,0 +1,203 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# 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. +""" +Processor class for Llava. +""" + +from typing import List, Union + +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_utils import ImageInput, get_image_size, to_numpy_array +from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order +from transformers.tokenization_utils_base import PreTokenizedInput, TextInput +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class LlavaProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + }, + "images_kwargs": {}, + } + + +class LlavaProcessor(ProcessorMixin): + r""" + Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor. + + [`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the + [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information. + + Args: + image_processor ([`CLIPImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`LlamaTokenizerFast`], *optional*): + The tokenizer is a required input. + patch_size (`int`, *optional*): + Patch size from the vision tower. + vision_feature_select_strategy (`str`, *optional*): + The feature selection strategy used to select the vision feature from the vision backbone. + Shoudl be same as in model's config + chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages + in a chat into a tokenizable string. + image_token (`str`, *optional*, defaults to `""`): + Special token used to denote image location. + num_additional_image_tokens (`int`, *optional*, defaults to 0): + Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other + extra tokens appended, no need to set this arg. + """ + + attributes = ["image_processor", "tokenizer"] + valid_kwargs = [ + "chat_template", + "patch_size", + "vision_feature_select_strategy", + "image_token", + "num_additional_image_tokens", + ] + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__( + self, + image_processor=None, + tokenizer=None, + patch_size=None, + vision_feature_select_strategy=None, + chat_template=None, + image_token="", # set the default and let users change if they have peculiar special tokens in rare cases + num_additional_image_tokens=0, + **kwargs, + ): + self.patch_size = patch_size + self.num_additional_image_tokens = num_additional_image_tokens + self.vision_feature_select_strategy = vision_feature_select_strategy + self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + audio=None, + videos=None, + **kwargs: Unpack[LlavaProcessorKwargs], + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to + CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring + of the above two methods for more information. + + Args: + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + if images is None and text is None: + raise ValueError("You have to specify at least one of `images` or `text`.") + + # check if images and text inputs are reversed for BC + images, text = _validate_images_text_input_order(images, text) + + output_kwargs = self._merge_kwargs( + LlavaProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + if images is not None: + image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"]) + else: + image_inputs = {} + + if isinstance(text, str): + text = [text] + elif not isinstance(text, list) and not isinstance(text[0], str): + raise ValueError("Invalid input text. Please provide a string, or a list of strings") + + # try to expand inputs in processing if we have the necessary parts + prompt_strings = text + if image_inputs.get("pixel_values") is not None: + if self.patch_size is not None and self.vision_feature_select_strategy is not None: + # Replace the image token with the expanded image token sequence + pixel_values = image_inputs["pixel_values"] + height, width = get_image_size(to_numpy_array(pixel_values[0])) + num_image_tokens = (height // self.patch_size) * ( + width // self.patch_size + ) + self.num_additional_image_tokens + if self.vision_feature_select_strategy == "default": + num_image_tokens -= self.num_additional_image_tokens + + prompt_strings = [] + for sample in text: + sample = sample.replace(self.image_token, self.image_token * num_image_tokens) + prompt_strings.append(sample) + else: + logger.warning_once( + "Expanding inputs for image tokens in LLaVa should be done in processing. " + "Please add `patch_size` and `vision_feature_select_strategy` to the model's processing config or set directly " + "with `processor.patch_size = {{patch_size}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. " + "Using processors without these attributes in the config is deprecated and will throw an error in v4.50." + ) + + text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) + return BatchFeature(data={**text_inputs, **image_inputs}) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))