initial

README.md

@@ -1,12 +1,17 @@
----
-title: Video P2P Demo
-emoji: 💩
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Video-P2P
+
+## Setup
+
+All required packages are listed in the requirements file.
+The code was tested on a Tesla V100 32GB but should work on other cards with at least **16GB** VRAM.
+
+## Quickstart
+
+``` bash
+bash script.sh
+```
+
+## References
+* prompt-to-prompt: https://github.com/google/prompt-to-prompt
+* Tune-A-Video: https://github.com/showlab/Tune-A-Video
+* diffusers: https://github.com/huggingface/diffusers

configs/man-motor-tune.yaml

@@ -0,0 +1,41 @@
+pretrained_model_path: "/data/stable-diffusion/stable-diffusion-v1-5"
+output_dir: "./outputs/man-motor"
+
+train_data:
+  video_path: "./data/motorbike"
+  prompt: "a man is driving a motorbike in the forest"
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+
+validation_data:
+  prompts:
+    - "a man is driving a motorbike in the forest"
+    - "a Spider-Man is driving a motorbike in the forest"
+    - "a Bat-Man is driving a motorbike in the forest"
+    - "an Iron-Man is driving a motorbike in the forest"
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 12.5
+  use_inv_latent: True
+  num_inv_steps: 50
+
+learning_rate: 3e-5
+train_batch_size: 1
+max_train_steps: 500
+checkpointing_steps: 1000
+validation_steps: 100
+trainable_modules:
+  - "attn1.to_q"
+  - "attn2.to_q"
+  - "attn_temp"
+
+seed: 33
+mixed_precision: fp16
+use_8bit_adam: False
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True

configs/rabbit-jump-p2p.yaml

@@ -0,0 +1,16 @@
+pretrained_model_path: "./outputs/rabbit-jump"
+image_path: "./data/rabbit"
+prompt: "a rabbit is jumping on the grass"
+prompts:
+  - "a rabbit is jumping on the grass"
+  - "a origami rabbit is jumping on the grass"
+blend_word:
+  - 'rabbit'
+  - 'rabbit'
+eq_params:
+  words: "origami"
+  values: 2
+gif_folder: "./outputs/rabbit-jump/results"
+gif_name_1: "./outputs/rabbit-jump/results/original_name.gif"
+gif_name_2: "./outputs/rabbit-jump/results/origami_name.gif"
+IRC: False

configs/rabbit-jump-tune.yaml

@@ -0,0 +1,41 @@
+pretrained_model_path: "/data/stable-diffusion/stable-diffusion-v1-5"
+output_dir: "./outputs/rabbit-jump"
+
+train_data:
+  video_path: "./data/rabbit"
+  prompt: "a rabbit is jumping on the grass"
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+
+validation_data:
+  prompts:
+    - "a rabbit is jumping on the grass"
+    - "a Lego rabbit is jumping on the grass"
+    - "a origami rabbit is jumping on the grass"
+    - "a crochet rabbit is jumping on the grass"
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 12.5
+  use_inv_latent: True
+  num_inv_steps: 50
+
+learning_rate: 3e-5
+train_batch_size: 1
+max_train_steps: 500
+checkpointing_steps: 1000
+validation_steps: 100
+trainable_modules:
+  - "attn1.to_q"
+  - "attn2.to_q"
+  - "attn_temp"
+
+seed: 33
+mixed_precision: fp16
+use_8bit_adam: False
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True

ptp_utils.py

@@ -0,0 +1,309 @@
+# Copyright 2022 Google LLC
+#
+# 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 numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+from typing import Optional, Union, Tuple, List, Callable, Dict
+from IPython.display import display
+from tqdm.notebook import tqdm
+
+
+def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
+    h, w, c = image.shape
+    offset = int(h * .2)
+    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    img[:h] = image
+    textsize = cv2.getTextSize(text, font, 1, 2)[0]
+    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
+    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
+    return img
+
+
+def view_images(images, num_rows=1, offset_ratio=0.02):
+    if type(images) is list:
+        num_empty = len(images) % num_rows
+    elif images.ndim == 4:
+        num_empty = images.shape[0] % num_rows
+    else:
+        images = [images]
+        num_empty = 0
+
+    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
+    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
+    num_items = len(images)
+
+    h, w, c = images[0].shape
+    offset = int(h * offset_ratio)
+    num_cols = num_items // num_rows
+    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
+                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
+    for i in range(num_rows):
+        for j in range(num_cols):
+            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
+                i * num_cols + j]
+
+    pil_img = Image.fromarray(image_)
+    display(pil_img)
+
+
+def diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False, simple=False):
+    if low_resource:
+        noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
+        noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
+    else:
+        latents_input = torch.cat([latents] * 2)
+        noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
+        noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
+    noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
+    if simple:
+        noise_pred[0] = noise_prediction_text[0]
+    latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
+    # first latents: torch.Size([1, 4, 4, 64, 64])
+    latents = controller.step_callback(latents)
+    return latents
+
+
+def latent2image(vae, latents):
+    latents = 1 / 0.18215 * latents
+    image = vae.decode(latents)['sample']
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()
+    image = (image * 255).astype(np.uint8)
+    return image
+
+
+@torch.no_grad()
+def latent2image_video(vae, latents):
+    latents = 1 / 0.18215 * latents
+    latents = latents[0].permute(1, 0, 2, 3)
+    image = vae.decode(latents)['sample']
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()
+    image = (image * 255).astype(np.uint8)
+    return image
+
+
+def init_latent(latent, model, height, width, generator, batch_size):
+    if latent is None:
+        latent = torch.randn(
+            (1, model.unet.in_channels, height // 8, width // 8),
+            generator=generator,
+        )
+    latents = latent.expand(batch_size,  model.unet.in_channels, height // 8, width // 8).to(model.device)
+    return latent, latents
+
+
+@torch.no_grad()
+def text2image_ldm(
+    model,
+    prompt:  List[str],
+    controller,
+    num_inference_steps: int = 50,
+    guidance_scale: Optional[float] = 7.,
+    generator: Optional[torch.Generator] = None,
+    latent: Optional[torch.FloatTensor] = None,
+):
+    register_attention_control(model, controller)
+    height = width = 256
+    batch_size = len(prompt)
+    
+    uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
+    uncond_embeddings = model.bert(uncond_input.input_ids.to(model.device))[0]
+    
+    text_input = model.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
+    text_embeddings = model.bert(text_input.input_ids.to(model.device))[0]
+    latent, latents = init_latent(latent, model, height, width, generator, batch_size)
+    context = torch.cat([uncond_embeddings, text_embeddings])
+    
+    model.scheduler.set_timesteps(num_inference_steps)
+    for t in tqdm(model.scheduler.timesteps):
+        latents = diffusion_step(model, controller, latents, context, t, guidance_scale)
+    
+    image = latent2image(model.vqvae, latents)
+   
+    return image, latent
+
+
+@torch.no_grad()
+def text2image_ldm_stable(
+    model,
+    prompt: List[str],
+    controller,
+    num_inference_steps: int = 50,
+    guidance_scale: float = 7.5,
+    generator: Optional[torch.Generator] = None,
+    latent: Optional[torch.FloatTensor] = None,
+    low_resource: bool = False,
+):
+    register_attention_control(model, controller)
+    height = width = 512
+    batch_size = len(prompt)
+
+    text_input = model.tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=model.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+    text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = model.tokenizer(
+        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+    )
+    uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
+    
+    context = [uncond_embeddings, text_embeddings]
+    if not low_resource:
+        context = torch.cat(context)
+    
+    latent, latents = init_latent(latent, model, height, width, generator, batch_size)
+    
+    # set timesteps
+    extra_set_kwargs = {"offset": 1}
+    model.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+    for t in tqdm(model.scheduler.timesteps):
+        latents = diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource)
+    
+    image = latent2image(model.vae, latents)
+  
+    return image, latent
+
+
+def register_attention_control(model, controller):
+    def ca_forward(self, place_in_unet):
+        to_out = self.to_out
+        if type(to_out) is torch.nn.modules.container.ModuleList:
+            to_out = self.to_out[0]
+        else:
+            to_out = self.to_out
+
+        def forward(x, encoder_hidden_states=None, attention_mask=None):
+            context = encoder_hidden_states
+            mask = attention_mask
+            batch_size, sequence_length, dim = x.shape
+            h = self.heads
+            q = self.to_q(x)
+            is_cross = context is not None
+            context = context if is_cross else x
+            k = self.to_k(context)
+            v = self.to_v(context)
+            q = self.reshape_heads_to_batch_dim(q)
+            k = self.reshape_heads_to_batch_dim(k)
+            v = self.reshape_heads_to_batch_dim(v)
+            sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale # q: torch.Size([128, 4096, 40]); k: torch.Size([64, 77, 40])
+
+            if mask is not None:
+                mask = mask.reshape(batch_size, -1)
+                max_neg_value = -torch.finfo(sim.dtype).max
+                mask = mask[:, None, :].repeat(h, 1, 1)
+                sim.masked_fill_(~mask, max_neg_value)
+
+            attn = torch.exp(sim-torch.max(sim)) / torch.sum(torch.exp(sim-torch.max(sim)), axis=-1).unsqueeze(-1)
+            attn = controller(attn, is_cross, place_in_unet)
+            out = torch.einsum("b i j, b j d -> b i d", attn, v)
+            out = self.reshape_batch_dim_to_heads(out)
+            return to_out(out)
+
+        return forward
+
+    class DummyController:
+
+        def __call__(self, *args):
+            return args[0]
+
+        def __init__(self):
+            self.num_att_layers = 0
+
+    if controller is None:
+        controller = DummyController()
+
+    def register_recr(net_, count, place_in_unet):
+        if net_.__class__.__name__ == 'CrossAttention':
+            net_.forward = ca_forward(net_, place_in_unet)
+            return count + 1
+        elif hasattr(net_, 'children'):
+            for net__ in net_.children():
+                count = register_recr(net__, count, place_in_unet)
+        return count
+
+    cross_att_count = 0
+    sub_nets = model.unet.named_children()
+    for net in sub_nets:
+        if "down" in net[0]:
+            cross_att_count += register_recr(net[1], 0, "down")
+        elif "up" in net[0]:
+            cross_att_count += register_recr(net[1], 0, "up")
+        elif "mid" in net[0]:
+            cross_att_count += register_recr(net[1], 0, "mid")
+
+    controller.num_att_layers = cross_att_count
+
+    
+def get_word_inds(text: str, word_place: int, tokenizer):
+    split_text = text.split(" ")
+    if type(word_place) is str:
+        word_place = [i for i, word in enumerate(split_text) if word_place == word]
+    elif type(word_place) is int:
+        word_place = [word_place]
+    out = []
+    if len(word_place) > 0:
+        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
+        cur_len, ptr = 0, 0
+
+        for i in range(len(words_encode)):
+            cur_len += len(words_encode[i])
+            if ptr in word_place:
+                out.append(i + 1)
+            if cur_len >= len(split_text[ptr]):
+                ptr += 1
+                cur_len = 0
+    return np.array(out)
+
+
+def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
+                           word_inds: Optional[torch.Tensor]=None):
+    if type(bounds) is float:
+        bounds = 0, bounds
+    start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
+    if word_inds is None:
+        word_inds = torch.arange(alpha.shape[2])
+    alpha[: start, prompt_ind, word_inds] = 0
+    alpha[start: end, prompt_ind, word_inds] = 1
+    alpha[end:, prompt_ind, word_inds] = 0
+    return alpha
+
+
+def get_time_words_attention_alpha(prompts, num_steps,
+                                   cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
+                                   tokenizer, max_num_words=77):
+    if type(cross_replace_steps) is not dict:
+        cross_replace_steps = {"default_": cross_replace_steps}
+    if "default_" not in cross_replace_steps:
+        cross_replace_steps["default_"] = (0., 1.)
+    alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
+    for i in range(len(prompts) - 1): # 2
+        alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], # {'default_': 0.8}
+                                                  i)
+    for key, item in cross_replace_steps.items():
+        if key != "default_":
+             inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
+             for i, ind in enumerate(inds):
+                 if len(ind) > 0:
+                    alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
+    alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
+    return alpha_time_words

requirements.txt

@@ -0,0 +1,15 @@
+torch==1.12.1
+torchvision==0.13.1
+diffusers[torch]==0.11.1
+transformers>=4.25.1
+bitsandbytes==0.35.4
+decord==0.6.0
+accelerate
+tensorboard
+modelcards
+omegaconf
+einops
+imageio
+ftfy
+opencv-python
+ipywidgets

run_tuning.py

@@ -0,0 +1,367 @@
+import argparse
+import datetime
+import logging
+import inspect
+import math
+import os
+from typing import Dict, Optional, Tuple
+from omegaconf import OmegaConf
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+import diffusers
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from tuneavideo.models.unet import UNet3DConditionModel
+from tuneavideo.data.dataset import TuneAVideoDataset
+from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
+from tuneavideo.util import save_videos_grid, ddim_inversion
+from einops import rearrange
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+
+def main(
+    pretrained_model_path: str,
+    output_dir: str,
+    train_data: Dict,
+    validation_data: Dict,
+    validation_steps: int = 100,
+    trainable_modules: Tuple[str] = (
+        "attn1.to_q",
+        "attn2.to_q",
+        "attn_temp",
+    ),
+    train_batch_size: int = 1,
+    max_train_steps: int = 500,
+    learning_rate: float = 3e-5,
+    scale_lr: bool = False,
+    lr_scheduler: str = "constant",
+    lr_warmup_steps: int = 0,
+    adam_beta1: float = 0.9,
+    adam_beta2: float = 0.999,
+    adam_weight_decay: float = 1e-2,
+    adam_epsilon: float = 1e-08,
+    max_grad_norm: float = 1.0,
+    gradient_accumulation_steps: int = 1,
+    gradient_checkpointing: bool = True,
+    checkpointing_steps: int = 500,
+    resume_from_checkpoint: Optional[str] = None,
+    mixed_precision: Optional[str] = "fp16",
+    use_8bit_adam: bool = False,
+    enable_xformers_memory_efficient_attention: bool = True,
+    seed: Optional[int] = None,
+):
+    *_, config = inspect.getargvalues(inspect.currentframe())
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if seed is not None:
+        set_seed(seed)
+
+    # Handle the output folder creation
+    if accelerator.is_main_process:
+        # now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+        # output_dir = os.path.join(output_dir, now)
+        os.makedirs(output_dir, exist_ok=True)
+        os.makedirs(f"{output_dir}/samples", exist_ok=True)
+        os.makedirs(f"{output_dir}/inv_latents", exist_ok=True)
+        OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
+    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
+    unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet")
+
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+
+    unet.requires_grad_(False)
+    for name, module in unet.named_modules():
+        if name.endswith(tuple(trainable_modules)):
+            for params in module.parameters():
+                params.requires_grad = True
+
+    if enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    if gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    if scale_lr:
+        learning_rate = (
+            learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    if use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+            )
+
+        optimizer_cls = bnb.optim.AdamW8bit
+    else:
+        optimizer_cls = torch.optim.AdamW
+
+    optimizer = optimizer_cls(
+        unet.parameters(),
+        lr=learning_rate,
+        betas=(adam_beta1, adam_beta2),
+        weight_decay=adam_weight_decay,
+        eps=adam_epsilon,
+    )
+
+    # Get the training dataset
+    train_dataset = TuneAVideoDataset(**train_data)
+
+    # Preprocessing the dataset
+    train_dataset.prompt_ids = tokenizer(
+        train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids[0]
+
+    # DataLoaders creation:
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=train_batch_size
+    )
+
+    # Get the validation pipeline
+    validation_pipeline = TuneAVideoPipeline(
+        vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
+        scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
+    )
+    validation_pipeline.enable_vae_slicing()
+    ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
+    ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
+
+    # Scheduler
+    lr_scheduler = get_scheduler(
+        lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
+        num_training_steps=max_train_steps * gradient_accumulation_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        unet, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move text_encode and vae to gpu and cast to weight_dtype
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        accelerator.init_trackers("text2video-fine-tune")
+
+    # Train!
+    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if resume_from_checkpoint:
+        if resume_from_checkpoint != "latest":
+            path = os.path.basename(resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        accelerator.print(f"Resuming from checkpoint {path}")
+        accelerator.load_state(os.path.join(output_dir, path))
+        global_step = int(path.split("-")[1])
+
+        first_epoch = global_step // num_update_steps_per_epoch
+        resume_step = global_step % num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    for epoch in range(first_epoch, num_train_epochs):
+        unet.train()
+        train_loss = 0.0
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
+            with accelerator.accumulate(unet):
+                # Convert videos to latent space
+                pixel_values = batch["pixel_values"].to(weight_dtype)
+                video_length = pixel_values.shape[1]
+                pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
+                latents = vae.encode(pixel_values).latent_dist.sample()
+                latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
+                latents = latents * 0.18215
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each video
+                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["prompt_ids"])[0]
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}")
+
+                # Predict the noise residual and compute loss
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
+                train_loss += avg_loss.item() / gradient_accumulation_steps
+
+                # Backpropagate
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+                accelerator.log({"train_loss": train_loss}, step=global_step)
+                train_loss = 0.0
+
+                if global_step % checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                if global_step % validation_steps == 0:
+                    if accelerator.is_main_process:
+                        samples = []
+                        generator = torch.Generator(device=latents.device)
+                        generator.manual_seed(seed)
+
+                        ddim_inv_latent = None
+                        if validation_data.use_inv_latent:
+                            inv_latents_path = os.path.join(output_dir, f"inv_latents/ddim_latent-{global_step}.pt")
+                            ddim_inv_latent = ddim_inversion(
+                                validation_pipeline, ddim_inv_scheduler, video_latent=latents,
+                                num_inv_steps=validation_data.num_inv_steps, prompt="")[-1].to(weight_dtype)
+                            torch.save(ddim_inv_latent, inv_latents_path)
+
+                        for idx, prompt in enumerate(validation_data.prompts):
+                            sample = validation_pipeline(prompt, generator=generator, latents=ddim_inv_latent,
+                                                         **validation_data).videos
+                            save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{prompt}.gif")
+                            samples.append(sample)
+                        samples = torch.concat(samples)
+                        save_path = f"{output_dir}/samples/sample-{global_step}.gif"
+                        save_videos_grid(samples, save_path)
+                        logger.info(f"Saved samples to {save_path}")
+
+            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= max_train_steps:
+                break
+
+    # Create the pipeline using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = accelerator.unwrap_model(unet)
+        pipeline = TuneAVideoPipeline.from_pretrained(
+            pretrained_model_path,
+            text_encoder=text_encoder,
+            vae=vae,
+            unet=unet,
+        )
+        pipeline.save_pretrained(output_dir)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml")
+    args = parser.parse_args()
+
+    main(**OmegaConf.load(args.config))

run_videop2p.py

@@ -0,0 +1,707 @@
+import os
+from typing import Optional, Union, Tuple, List, Callable, Dict
+from tqdm.notebook import tqdm
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+import torch.nn.functional as nnf
+import numpy as np
+import abc
+import ptp_utils
+import seq_aligner
+import shutil
+from torch.optim.adam import Adam
+from PIL import Image
+from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer
+from einops import rearrange
+
+from tuneavideo.models.unet import UNet3DConditionModel
+from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
+
+import cv2
+import argparse
+from omegaconf import OmegaConf
+
+scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+MY_TOKEN = ''
+LOW_RESOURCE = False
+NUM_DDIM_STEPS = 50
+GUIDANCE_SCALE = 7.5
+MAX_NUM_WORDS = 77
+device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+IRC = True
+
+# need to adjust
+cross_replace_steps = {'default_': .2,}
+self_replace_steps = .5
+mask_th = (.3, .3)
+video_len = 8
+
+def main(
+    pretrained_model_path: str,
+    image_path: str,
+    prompt: str,
+    prompts: Tuple[str],
+    blend_word: Tuple[str],
+    eq_params: Dict,
+    gif_folder: str,
+    gif_name_1: str,
+    gif_name_2: str,
+    IRC: bool,
+):
+    blend_word = (((blend_word[0],), (blend_word[1],)))
+    eq_params["words"] = (eq_params["words"],)
+    eq_params["values"] = (eq_params["values"],)
+    eq_params = dict(eq_params)
+    prompts = list(prompts)
+    if not os.path.exists(gif_folder):
+        os.makedirs(gif_folder)
+
+    # Load the tokenizer
+    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
+    # Load models and create wrapper for stable diffusion
+    text_encoder = CLIPTextModel.from_pretrained(
+        pretrained_model_path,
+        subfolder="text_encoder",
+    )
+    vae = AutoencoderKL.from_pretrained(
+        pretrained_model_path,
+        subfolder="vae",
+    )
+    unet = UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet")
+    ldm_stable = TuneAVideoPipeline(
+        vae=vae,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        unet=unet,
+        scheduler=scheduler,
+    ).to(device)
+
+    try:
+        ldm_stable.disable_xformers_memory_efficient_attention()
+    except AttributeError:
+        print("Attribute disable_xformers_memory_efficient_attention() is missing")
+    tokenizer = ldm_stable.tokenizer # Tokenizer of class: [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
+    # A tokenizer breaks a stream of text into tokens, usually by looking for whitespace (tabs, spaces, new lines).
+
+    class LocalBlend:
+        
+        def get_mask(self, maps, alpha, use_pool): # alpha is a word map
+            k = 1
+            maps = (maps * alpha).sum(-1).mean(2) # [2, 80, 1, 16, 16, 77], [2, 1, 1, 1, 1, 77]
+            if use_pool:
+                maps = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
+            mask = nnf.interpolate(maps, size=(x_t.shape[3:]))
+            mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
+            mask = mask.gt(self.th[1-int(use_pool)])
+            mask = mask[:1] + mask
+            return mask
+        
+        def __call__(self, x_t, attention_store, step):
+            self.counter += 1
+            if self.counter > self.start_blend:
+                # attention_store["down_cross"]: 4, attention_store["up_cross"]:6, attention_store["down_cross"][0]: torch.Size([32, 1024, 77])
+                maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
+                # maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
+                maps = [item.reshape(self.alpha_layers.shape[0], -1, 8, 16, 16, MAX_NUM_WORDS) for item in maps]
+                maps = torch.cat(maps, dim=2)
+                # self.alpha_layers: torch.Size([2, 1, 1, 1, 1, 77])
+                mask = self.get_mask(maps, self.alpha_layers, True)
+                if self.substruct_layers is not None:
+                    maps_sub = ~self.get_mask(maps, self.substruct_layers, False)
+                    mask = mask * maps_sub
+                mask = mask.float()
+                mask = mask.reshape(-1, 1, mask.shape[-3], mask.shape[-2], mask.shape[-1])
+
+                x_t = x_t[:1] + mask * (x_t - x_t[:1]) # line13 algorithm
+            return x_t
+        
+        def __init__(self, prompts: List[str], words: [List[List[str]]], substruct_words=None, start_blend=0.2, th=(.3, .3)):
+            alpha_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
+            for i, (prompt, words_) in enumerate(zip(prompts, words)):
+                if type(words_) is str:
+                    words_ = [words_]
+                for word in words_:
+                    ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
+                    alpha_layers[i, :, :, :, :, ind] = 1
+            
+            if substruct_words is not None:
+                substruct_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
+                for i, (prompt, words_) in enumerate(zip(prompts, substruct_words)):
+                    if type(words_) is str:
+                        words_ = [words_]
+                    for word in words_:
+                        ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
+                        substruct_layers[i, :, :, :, :, ind] = 1
+                self.substruct_layers = substruct_layers.to(device)
+            else:
+                self.substruct_layers = None
+            self.alpha_layers = alpha_layers.to(device)
+            self.start_blend = int(start_blend * NUM_DDIM_STEPS)
+            self.counter = 0 
+            self.th=th
+
+
+            
+            
+    class EmptyControl:
+        
+        
+        def step_callback(self, x_t):
+            return x_t
+        
+        def between_steps(self):
+            return
+        
+        def __call__(self, attn, is_cross: bool, place_in_unet: str):
+            return attn
+
+        
+    class AttentionControl(abc.ABC):
+        
+        def step_callback(self, x_t):
+            return x_t
+        
+        def between_steps(self):
+            return
+        
+        @property
+        def num_uncond_att_layers(self):
+            return self.num_att_layers if LOW_RESOURCE else 0
+        
+        @abc.abstractmethod
+        def forward (self, attn, is_cross: bool, place_in_unet: str):
+            raise NotImplementedError
+
+        def __call__(self, attn, is_cross: bool, place_in_unet: str):
+            if self.cur_att_layer >= self.num_uncond_att_layers:
+                if LOW_RESOURCE:
+                    attn = self.forward(attn, is_cross, place_in_unet)
+                else:
+                    h = attn.shape[0]
+                    attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
+            self.cur_att_layer += 1
+            if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
+                self.cur_att_layer = 0
+                self.cur_step += 1
+                self.between_steps()
+            return attn
+        
+        def reset(self):
+            self.cur_step = 0
+            self.cur_att_layer = 0
+
+        def __init__(self):
+            self.cur_step = 0
+            self.num_att_layers = -1
+            self.cur_att_layer = 0
+
+    class SpatialReplace(EmptyControl):
+        
+        def step_callback(self, x_t):
+            if self.cur_step < self.stop_inject:
+                b = x_t.shape[0]
+                x_t = x_t[:1].expand(b, *x_t.shape[1:])
+            return x_t
+
+        def __init__(self, stop_inject: float):
+            super(SpatialReplace, self).__init__()
+            self.stop_inject = int((1 - stop_inject) * NUM_DDIM_STEPS)
+            
+
+    class AttentionStore(AttentionControl):
+
+        @staticmethod
+        def get_empty_store():
+            return {"down_cross": [], "mid_cross": [], "up_cross": [],
+                    "down_self": [],  "mid_self": [],  "up_self": []}
+
+        def forward(self, attn, is_cross: bool, place_in_unet: str):
+            key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
+            if attn.shape[1] <= 32 ** 2:  # avoid memory overhead
+                self.step_store[key].append(attn) # 'down_self' torch.Size([32768, 8, 8])
+            return attn
+
+        def between_steps(self):
+            if len(self.attention_store) == 0:
+                self.attention_store = self.step_store
+            else:
+                for key in self.attention_store:
+                    for i in range(len(self.attention_store[key])):
+                        self.attention_store[key][i] += self.step_store[key][i]
+            self.step_store = self.get_empty_store()
+
+        def get_average_attention(self):
+            average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
+            return average_attention
+
+
+        def reset(self):
+            super(AttentionStore, self).reset()
+            self.step_store = self.get_empty_store()
+            self.attention_store = {}
+
+        def __init__(self):
+            super(AttentionStore, self).__init__()
+            self.step_store = self.get_empty_store()
+            self.attention_store = {}
+
+            
+    class AttentionControlEdit(AttentionStore, abc.ABC):
+        
+        def step_callback(self, x_t):
+            if self.local_blend is not None:
+                x_t = self.local_blend(x_t, self.attention_store, self.cur_step)
+            return x_t
+            
+        def replace_self_attention(self, attn_base, att_replace, place_in_unet):
+            if att_replace.shape[2] <= 32 ** 2:
+                attn_base = attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
+                return attn_base
+            else:
+                return att_replace
+        
+        @abc.abstractmethod
+        def replace_cross_attention(self, attn_base, att_replace):
+            raise NotImplementedError
+        
+        def forward(self, attn, is_cross: bool, place_in_unet: str):
+            super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
+            if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
+                h = attn.shape[0] // (self.batch_size)
+                attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
+                attn_base, attn_repalce = attn[0], attn[1:]
+                if is_cross:
+                    alpha_words = self.cross_replace_alpha[self.cur_step]
+                    attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
+                    attn[1:] = attn_repalce_new
+                else:
+                    attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
+                attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
+            return attn
+        
+        def __init__(self, prompts, num_steps: int,
+                    cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
+                    self_replace_steps: Union[float, Tuple[float, float]],
+                    local_blend: Optional[LocalBlend]):
+            super(AttentionControlEdit, self).__init__()
+            self.batch_size = len(prompts)
+            self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
+            if type(self_replace_steps) is float:
+                self_replace_steps = 0, self_replace_steps
+            self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
+            self.local_blend = local_blend
+
+    class AttentionReplace(AttentionControlEdit):
+
+        def replace_cross_attention(self, attn_base, att_replace):
+            return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
+        
+        def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
+                    local_blend: Optional[LocalBlend] = None):
+            super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
+            self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
+            
+
+    class AttentionRefine(AttentionControlEdit):
+
+        def replace_cross_attention(self, attn_base, att_replace):
+            attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
+            attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
+            return attn_replace
+
+        def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
+                    local_blend: Optional[LocalBlend] = None):
+            super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
+            self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
+            self.mapper, alphas = self.mapper.to(device), alphas.to(device)
+            self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
+
+
+    class AttentionReweight(AttentionControlEdit):
+
+        def replace_cross_attention(self, attn_base, att_replace):
+            if self.prev_controller is not None:
+                attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
+            attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
+            return attn_replace
+
+        def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
+                    local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
+            super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
+            self.equalizer = equalizer.to(device)
+            self.prev_controller = controller
+
+
+    def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
+                    Tuple[float, ...]]):
+        if type(word_select) is int or type(word_select) is str:
+            word_select = (word_select,)
+        equalizer = torch.ones(1, 77)
+        
+        for word, val in zip(word_select, values):
+            inds = ptp_utils.get_word_inds(text, word, tokenizer)
+            equalizer[:, inds] = val
+        return equalizer
+
+    def aggregate_attention(attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int):
+        out = []
+        attention_maps = attention_store.get_average_attention()
+        num_pixels = res ** 2
+        for location in from_where:
+            for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
+                if item.shape[1] == num_pixels: # torch.Size([64, 256, 77]) all can pass
+                    cross_maps = item.reshape(8, 8, res, res, item.shape[-1])
+                    out.append(cross_maps)
+        out = torch.cat(out, dim=1)
+        out = out.sum(1) / out.shape[1]
+        return out.cpu()
+
+
+    def make_controller(prompts: List[str], is_replace_controller: bool, cross_replace_steps: Dict[str, float], self_replace_steps: float, blend_words=None, equilizer_params=None, mask_th=(.3,.3)) -> AttentionControlEdit:
+        if blend_words is None:
+            lb = None
+        else:
+            lb = LocalBlend(prompts, blend_word, th=mask_th)
+        if is_replace_controller:
+            controller = AttentionReplace(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
+        else:
+            controller = AttentionRefine(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
+        if equilizer_params is not None:
+            eq = get_equalizer(prompts[1], equilizer_params["words"], equilizer_params["values"])
+            controller = AttentionReweight(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
+                                        self_replace_steps=self_replace_steps, equalizer=eq, local_blend=lb, controller=controller)
+        return controller
+
+
+    def load_512_seq(image_path, left=0, right=0, top=0, bottom=0, n_sample_frame=video_len, sampling_rate=1):
+        images = []
+        for file in sorted(os.listdir(image_path)):
+            images.append(file)
+        n_images = len(images)
+        sequence_length = (n_sample_frame - 1) * sampling_rate + 1
+        if n_images < sequence_length:
+            raise ValueError
+        frames = []
+        for index in range(n_sample_frame):
+            p = os.path.join(image_path, images[index])
+            image = np.array(Image.open(p).convert("RGB"))
+            h, w, c = image.shape
+            left = min(left, w-1)
+            right = min(right, w - left - 1)
+            top = min(top, h - left - 1)
+            bottom = min(bottom, h - top - 1)
+            image = image[top:h-bottom, left:w-right]
+            h, w, c = image.shape
+            if h < w:
+                offset = (w - h) // 2
+                image = image[:, offset:offset + h]
+            elif w < h:
+                offset = (h - w) // 2
+                image = image[offset:offset + w]
+            image = np.array(Image.fromarray(image).resize((512, 512)))
+            frames.append(image)
+        return np.stack(frames)
+
+
+    class NullInversion:
+        
+        def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
+            prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
+            beta_prod_t = 1 - alpha_prod_t
+            pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
+            pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
+            prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
+            return prev_sample
+        
+        def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]): # doing inversion (math)
+            timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
+            alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
+            next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
+            next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
+            return next_sample
+        
+        def get_noise_pred_single(self, latents, t, context): # latents: torch.Size([1, 4, 64, 64]); t: tensor(1); context: torch.Size([1, 77, 768])
+            # formats are correct for video unet input; Tune-A-Video also predicts the residual
+            noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"] # easy to out of mem
+            return noise_pred
+
+        def get_noise_pred(self, latents, t, is_forward=True, context=None):
+            latents_input = torch.cat([latents] * 2)
+            if context is None:
+                context = self.context
+            guidance_scale = 1 if is_forward else GUIDANCE_SCALE
+            noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
+            noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
+            if is_forward:
+                latents = self.next_step(noise_pred, t, latents)
+            else:
+                latents = self.prev_step(noise_pred, t, latents)
+            return latents
+
+        @torch.no_grad()
+        def latent2image(self, latents, return_type='np'):
+            latents = 1 / 0.18215 * latents.detach()
+            image = self.model.vae.decode(latents)['sample']
+            if return_type == 'np':
+                image = (image / 2 + 0.5).clamp(0, 1)
+                image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+                image = (image * 255).astype(np.uint8)
+            return image
+
+        @torch.no_grad()
+        def latent2image_video(self, latents, return_type='np'):
+            latents = 1 / 0.18215 * latents.detach()
+            latents = latents[0].permute(1, 0, 2, 3)
+            image = self.model.vae.decode(latents)['sample']
+            if return_type == 'np':
+                image = (image / 2 + 0.5).clamp(0, 1)
+                image = image.cpu().permute(0, 2, 3, 1).numpy()
+                image = (image * 255).astype(np.uint8)
+            return image
+
+        @torch.no_grad()
+        def image2latent(self, image):
+            with torch.no_grad():
+                if type(image) is Image:
+                    image = np.array(image)
+                if type(image) is torch.Tensor and image.dim() == 4:
+                    latents = image
+                else:
+                    image = torch.from_numpy(image).float() / 127.5 - 1
+                    image = image.permute(2, 0, 1).unsqueeze(0).to(device)
+                    latents = self.model.vae.encode(image)['latent_dist'].mean
+                    latents = latents * 0.18215
+            return latents
+
+        @torch.no_grad()
+        def image2latent_video(self, image):
+            with torch.no_grad():
+                image = torch.from_numpy(image).float() / 127.5 - 1
+                image = image.permute(0, 3, 1, 2).to(device)
+                latents = self.model.vae.encode(image)['latent_dist'].mean
+                latents = rearrange(latents, "(b f) c h w -> b c f h w", b=1)
+                latents = latents * 0.18215
+            return latents
+
+        @torch.no_grad()
+        def init_prompt(self, prompt: str):
+            uncond_input = self.model.tokenizer(
+                [""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
+                return_tensors="pt"
+            )
+            uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0] # len=2, uncond_embeddings
+            text_input = self.model.tokenizer(
+                [prompt],
+                padding="max_length",
+                max_length=self.model.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
+            self.context = torch.cat([uncond_embeddings, text_embeddings])
+            self.prompt = prompt
+
+        @torch.no_grad()
+        def ddim_loop(self, latent):
+            uncond_embeddings, cond_embeddings = self.context.chunk(2)
+            all_latent = [latent]
+            latent = latent.clone().detach()
+            for i in range(NUM_DDIM_STEPS):
+                t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
+                # latent: torch.Size([1, 4, 8, 16, 16])
+                # cond_embeddings: torch.Size([1, 77, 768])
+                #  noise_pred: torch.Size([1, 4, 8, 16, 16])
+                noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings) # use a unet
+                latent = self.next_step(noise_pred, t, latent)
+                all_latent.append(latent)
+            return all_latent
+
+        @property
+        def scheduler(self):
+            return self.model.scheduler
+
+        @torch.no_grad()
+        def ddim_inversion(self, image):
+            latent = self.image2latent_video(image)
+            image_rec = self.latent2image_video(latent) # image: (512, 512, 3); latent: torch.Size([1, 4, 64, 64])
+            ddim_latents = self.ddim_loop(latent)
+            return image_rec, ddim_latents
+
+        def null_optimization(self, latents, num_inner_steps, epsilon): # uncond_embeddings is what we what
+            uncond_embeddings, cond_embeddings = self.context.chunk(2)
+            uncond_embeddings_list = []
+            latent_cur = latents[-1]
+            bar = tqdm(total=num_inner_steps * NUM_DDIM_STEPS)
+            for i in range(NUM_DDIM_STEPS):
+                uncond_embeddings = uncond_embeddings.clone().detach()
+                uncond_embeddings.requires_grad = True
+                optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.))
+                latent_prev = latents[len(latents) - i - 2] # GT
+                t = self.model.scheduler.timesteps[i]
+                with torch.no_grad():
+                    noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
+                for j in range(num_inner_steps):
+                    noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
+                    noise_pred = noise_pred_uncond + GUIDANCE_SCALE * (noise_pred_cond - noise_pred_uncond)
+                    latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
+                    loss = nnf.mse_loss(latents_prev_rec, latent_prev)
+                    optimizer.zero_grad()
+                    loss.backward()
+                    optimizer.step()
+                    loss_item = loss.item()
+                    bar.update()
+                    if loss_item < epsilon + i * 2e-5:
+                        break
+                for j in range(j + 1, num_inner_steps):
+                    bar.update()
+                uncond_embeddings_list.append(uncond_embeddings[:1].detach())
+                with torch.no_grad():
+                    context = torch.cat([uncond_embeddings, cond_embeddings])
+                    latent_cur = self.get_noise_pred(latent_cur, t, False, context)
+            bar.close()
+            return uncond_embeddings_list
+        
+        def invert(self, image_path: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False):
+            self.init_prompt(prompt)
+            ptp_utils.register_attention_control(self.model, None)
+            image_gt = load_512_seq(image_path, *offsets)
+            if verbose:
+                print("DDIM inversion...")
+            image_rec, ddim_latents = self.ddim_inversion(image_gt) # ddim_latents is a list, like the link in Figure 3
+            # image_rec refers to vq-autoencoder reconstruction
+            if verbose:
+                print("Null-text optimization...")
+            uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon) # ddim_latents serve as GT; easy to out of mem
+            return (image_gt, image_rec), ddim_latents[-1], uncond_embeddings
+
+        def invert_(self, image_path: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False):
+            self.init_prompt(prompt)
+            ptp_utils.register_attention_control(self.model, None)
+            image_gt = load_512_seq(image_path, *offsets)
+            if verbose:
+                print("DDIM inversion...")
+            image_rec, ddim_latents = self.ddim_inversion(image_gt) # ddim_latents is a list, like the link in Figure 3
+            # image_rec refers to vq-autoencoder reconstruction
+            if verbose:
+                print("Null-text optimization...")
+            return (image_gt, image_rec), ddim_latents[-1], None
+        
+        def __init__(self, model):
+            scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
+                                    set_alpha_to_one=False)
+            self.model = model
+            self.tokenizer = self.model.tokenizer
+            self.model.scheduler.set_timesteps(NUM_DDIM_STEPS)
+            self.prompt = None
+            self.context = None
+
+    null_inversion = NullInversion(ldm_stable)
+
+
+    @torch.no_grad()
+    def text2image_ldm_stable(
+        model,
+        prompt:  List[str],
+        controller,
+        num_inference_steps: int = 50,
+        guidance_scale: Optional[float] = 7.5,
+        generator: Optional[torch.Generator] = None,
+        latent: Optional[torch.FloatTensor] = None,
+        uncond_embeddings=None,
+        start_time=50,
+        return_type='image'
+    ):
+        batch_size = len(prompt)
+        ptp_utils.register_attention_control(model, controller)
+        height = width = 512
+        
+        text_input = model.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=model.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
+
+        max_length = text_input.input_ids.shape[-1]
+        if uncond_embeddings is None:
+            uncond_input = model.tokenizer(
+                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+            )
+            uncond_embeddings_ = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
+        else:
+            uncond_embeddings_ = None
+
+        model.scheduler.set_timesteps(num_inference_steps)
+        for i, t in enumerate(tqdm(model.scheduler.timesteps[-start_time:])):
+            if uncond_embeddings_ is None:
+                context = torch.cat([uncond_embeddings[i].expand(*text_embeddings.shape), text_embeddings])
+            else:
+                context = torch.cat([uncond_embeddings_, text_embeddings])
+            latents = latent
+            latents = ptp_utils.diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False)
+        if return_type == 'image':
+            image = ptp_utils.latent2image_video(model.vae, latents)
+        else:
+            image = latents
+        return image, latent
+
+
+    ###############
+    # Custom APIs:
+
+    ldm_stable.enable_xformers_memory_efficient_attention()
+
+    (image_gt, image_enc), x_t, uncond_embeddings = null_inversion.invert_(image_path, prompt, offsets=(0,0,0,0), verbose=True)
+
+    ##### load uncond #####
+    # uncond_embeddings_load = np.load(uncond_embeddings_path)
+    # uncond_embeddings = []
+    # for i in range(uncond_embeddings_load.shape[0]):
+    #     uncond_embeddings.append(torch.from_numpy(uncond_embeddings_load[i]).to(device))
+    #######################
+
+    ##### save uncond #####
+    # uncond_embeddings = torch.cat(uncond_embeddings)
+    # uncond_embeddings = uncond_embeddings.cpu().numpy()
+    #######################
+
+    print("Start Video-P2P!")
+
+    controller = make_controller(prompts, IRC, cross_replace_steps, self_replace_steps, blend_word, eq_params, mask_th=mask_th)
+    ptp_utils.register_attention_control(ldm_stable, controller)
+    generator = torch.Generator(device=device)
+    with torch.no_grad():
+        sequence = ldm_stable(
+            prompts,
+            generator=generator,
+            latents=x_t,
+            uncond_embeddings_pre=uncond_embeddings,
+            controller = controller,
+            video_length=video_len,
+            simple=True,
+        ).videos
+    sequence1 = rearrange(sequence[0], "c t h w -> t h w c")
+    sequence2 = rearrange(sequence[1], "c t h w -> t h w c")
+    inversion = []
+    videop2p = []
+    for i in range(sequence1.shape[0]):
+        inversion.append( Image.fromarray((sequence1[i] * 255).numpy().astype(np.uint8)) )
+        videop2p.append( Image.fromarray((sequence2[i] * 255).numpy().astype(np.uint8)) )
+
+    inversion[0].save(gif_name_1.replace('name', 'inversion_simple_debug'), save_all=True, append_images=inversion[1:], optimize=False, loop=0, duration=250)
+    videop2p[0].save(gif_name_2.replace('name', 'p2p_simple_debug'), save_all=True, append_images=videop2p[1:], optimize=False, loop=0, duration=250)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./configs/videop2p.yaml")
+    args = parser.parse_args()
+
+    main(**OmegaConf.load(args.config))

script.sh

@@ -0,0 +1,5 @@
+# python run_tuning.py  --config="configs/rabbit-jump.yaml"
+
+python run_videop2p.py --config="configs/rabbit-jump-p2p.yaml"
+
+# python run_tuning.py  --config="configs/man-motor.yaml"

seq_aligner.py

@@ -0,0 +1,196 @@
+# Copyright 2022 Google LLC
+#
+# 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 torch
+import numpy as np
+
+
+class ScoreParams:
+
+    def __init__(self, gap, match, mismatch):
+        self.gap = gap
+        self.match = match
+        self.mismatch = mismatch
+
+    def mis_match_char(self, x, y):
+        if x != y:
+            return self.mismatch
+        else:
+            return self.match
+        
+    
+def get_matrix(size_x, size_y, gap):
+    matrix = []
+    for i in range(len(size_x) + 1):
+        sub_matrix = []
+        for j in range(len(size_y) + 1):
+            sub_matrix.append(0)
+        matrix.append(sub_matrix)
+    for j in range(1, len(size_y) + 1):
+        matrix[0][j] = j*gap
+    for i in range(1, len(size_x) + 1):
+        matrix[i][0] = i*gap
+    return matrix
+
+
+def get_matrix(size_x, size_y, gap):
+    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
+    matrix[0, 1:] = (np.arange(size_y) + 1) * gap
+    matrix[1:, 0] = (np.arange(size_x) + 1) * gap
+    return matrix
+
+
+def get_traceback_matrix(size_x, size_y):
+    matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
+    matrix[0, 1:] = 1
+    matrix[1:, 0] = 2
+    matrix[0, 0] = 4
+    return matrix
+
+
+def global_align(x, y, score):
+    matrix = get_matrix(len(x), len(y), score.gap)
+    trace_back = get_traceback_matrix(len(x), len(y))
+    for i in range(1, len(x) + 1):
+        for j in range(1, len(y) + 1):
+            left = matrix[i, j - 1] + score.gap
+            up = matrix[i - 1, j] + score.gap
+            diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
+            matrix[i, j] = max(left, up, diag)
+            if matrix[i, j] == left:
+                trace_back[i, j] = 1
+            elif matrix[i, j] == up:
+                trace_back[i, j] = 2
+            else:
+                trace_back[i, j] = 3
+    return matrix, trace_back
+
+
+def get_aligned_sequences(x, y, trace_back):
+    x_seq = []
+    y_seq = []
+    i = len(x)
+    j = len(y)
+    mapper_y_to_x = []
+    while i > 0 or j > 0:
+        if trace_back[i, j] == 3:
+            x_seq.append(x[i-1])
+            y_seq.append(y[j-1])
+            i = i-1
+            j = j-1
+            mapper_y_to_x.append((j, i))
+        elif trace_back[i][j] == 1:
+            x_seq.append('-')
+            y_seq.append(y[j-1])
+            j = j-1
+            mapper_y_to_x.append((j, -1))
+        elif trace_back[i][j] == 2:
+            x_seq.append(x[i-1])
+            y_seq.append('-')
+            i = i-1
+        elif trace_back[i][j] == 4:
+            break
+    mapper_y_to_x.reverse()
+    return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
+
+
+def get_mapper(x: str, y: str, tokenizer, max_len=77):
+    x_seq = tokenizer.encode(x)
+    y_seq = tokenizer.encode(y)
+    score = ScoreParams(0, 1, -1)
+    matrix, trace_back = global_align(x_seq, y_seq, score)
+    mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
+    alphas = torch.ones(max_len)
+    alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
+    mapper = torch.zeros(max_len, dtype=torch.int64)
+    mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
+    mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
+    return mapper, alphas
+
+
+def get_refinement_mapper(prompts, tokenizer, max_len=77):
+    x_seq = prompts[0]
+    mappers, alphas = [], []
+    for i in range(1, len(prompts)):
+        mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
+        mappers.append(mapper)
+        alphas.append(alpha)
+    return torch.stack(mappers), torch.stack(alphas)
+
+
+def get_word_inds(text: str, word_place: int, tokenizer):
+    split_text = text.split(" ")
+    if type(word_place) is str:
+        word_place = [i for i, word in enumerate(split_text) if word_place == word]
+    elif type(word_place) is int:
+        word_place = [word_place]
+    out = []
+    if len(word_place) > 0:
+        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
+        cur_len, ptr = 0, 0
+
+        for i in range(len(words_encode)):
+            cur_len += len(words_encode[i])
+            if ptr in word_place:
+                out.append(i + 1)
+            if cur_len >= len(split_text[ptr]):
+                ptr += 1
+                cur_len = 0
+    return np.array(out)
+
+
+def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
+    words_x = x.split(' ')
+    words_y = y.split(' ')
+    if len(words_x) != len(words_y):
+        raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
+                         f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
+    inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
+    inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
+    inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
+    mapper = np.zeros((max_len, max_len))
+    i = j = 0
+    cur_inds = 0
+    while i < max_len and j < max_len:
+        if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
+            inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
+            if len(inds_source_) == len(inds_target_):
+                mapper[inds_source_, inds_target_] = 1
+            else:
+                ratio = 1 / len(inds_target_)
+                for i_t in inds_target_:
+                    mapper[inds_source_, i_t] = ratio
+            cur_inds += 1
+            i += len(inds_source_)
+            j += len(inds_target_)
+        elif cur_inds < len(inds_source):
+            mapper[i, j] = 1
+            i += 1
+            j += 1
+        else:
+            mapper[j, j] = 1
+            i += 1
+            j += 1
+
+    return torch.from_numpy(mapper).float()
+
+
+
+def get_replacement_mapper(prompts, tokenizer, max_len=77):
+    x_seq = prompts[0]
+    mappers = []
+    for i in range(1, len(prompts)):
+        mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
+        mappers.append(mapper)
+    return torch.stack(mappers)
+

tuneavideo/data/dataset.py

@@ -0,0 +1,57 @@
+import decord
+decord.bridge.set_bridge('torch')
+
+from torch.utils.data import Dataset
+from einops import rearrange
+import os
+from PIL import Image
+import numpy as np
+
+class TuneAVideoDataset(Dataset):
+    def __init__(
+            self,
+            video_path: str,
+            prompt: str,
+            width: int = 512,
+            height: int = 512,
+            n_sample_frames: int = 8,
+            sample_start_idx: int = 0,
+            sample_frame_rate: int = 1,
+    ):
+        self.video_path = video_path
+        self.prompt = prompt
+        self.prompt_ids = None
+        self.uncond_prompt_ids = None
+
+        self.width = width
+        self.height = height
+        self.n_sample_frames = n_sample_frames
+        self.sample_start_idx = sample_start_idx
+        self.sample_frame_rate = sample_frame_rate
+
+        if 'mp4' not in self.video_path:
+            self.images = []
+            for file in sorted(os.listdir(self.video_path), key=lambda x: int(x[:-4])):
+                if file.endswith('jpg'):
+                    self.images.append(np.asarray(Image.open(os.path.join(self.video_path, file)).convert('RGB').resize((self.width, self.height))))
+        self.images = np.stack(self.images)
+
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, index):
+        # load and sample video frames
+        if 'mp4' in self.video_path:
+            vr = decord.VideoReader(self.video_path, width=self.width, height=self.height)
+            sample_index = list(range(self.sample_start_idx, len(vr), self.sample_frame_rate))[:self.n_sample_frames]
+            video = vr.get_batch(sample_index)
+        else:
+            video = self.images[:self.n_sample_frames]
+        video = rearrange(video, "f h w c -> f c h w")
+
+        example = {
+            "pixel_values": (video / 127.5 - 1.0),
+            "prompt_ids": self.prompt_ids,
+        }
+
+        return example

tuneavideo/models/attention.py

@@ -0,0 +1,388 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm
+
+from einops import rearrange, repeat
+
+
+@dataclass
+class Transformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+class Transformer3DModel(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # Define input layers
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        if use_linear_projection:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+
+        # Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        if use_linear_projection:
+            self.proj_out = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
+        # Input
+        assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+        encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
+
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        if not self.use_linear_projection:
+            hidden_states = self.proj_in(hidden_states)
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+        else:
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+            hidden_states = self.proj_in(hidden_states)
+
+        # Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                timestep=timestep,
+                video_length=video_length
+            )
+
+        # Output
+        if not self.use_linear_projection:
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+            hidden_states = self.proj_out(hidden_states)
+        else:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+
+        output = hidden_states + residual
+
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+        if not return_dict:
+            return (output,)
+
+        return Transformer3DModelOutput(sample=output)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm = num_embeds_ada_norm is not None
+
+        # SC-Attn
+        self.attn1 = FrameAttention(
+        # self.attn1 = SparseCausalAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+        self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+        # Cross-Attn
+        if cross_attention_dim is not None:
+            self.attn2 = CrossAttention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            self.attn2 = None
+
+        if cross_attention_dim is not None:
+            self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+        else:
+            self.norm2 = None
+
+        # Feed-forward
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.norm3 = nn.LayerNorm(dim)
+
+        # Temp-Attn
+        self.attn_temp = CrossAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            upcast_attention=upcast_attention,
+        )
+        nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
+        self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
+        if not is_xformers_available():
+            print("Here is how to install it")
+            raise ModuleNotFoundError(
+                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                " xformers",
+                name="xformers",
+            )
+        elif not torch.cuda.is_available():
+            raise ValueError(
+                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
+                " available for GPU "
+            )
+        else:
+            try:
+                # Make sure we can run the memory efficient attention
+                _ = xformers.ops.memory_efficient_attention(
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                )
+            except Exception as e:
+                raise e
+            self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            if self.attn2 is not None:
+                self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
+        # SparseCausal-Attention
+        norm_hidden_states = (
+            self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
+        )
+
+        if self.only_cross_attention:
+            hidden_states = (
+                self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
+            )
+        else:
+            hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
+
+        if self.attn2 is not None:
+            # Cross-Attention
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+            hidden_states = (
+                self.attn2(
+                    norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+                )
+                + hidden_states
+            )
+
+        # Feed-forward
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+        # Temporal-Attention
+        d = hidden_states.shape[1]
+        hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+        norm_hidden_states = (
+            self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
+        )
+        hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
+        hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states
+
+
+class SparseCausalAttention(CrossAttention):
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        encoder_hidden_states = encoder_hidden_states
+
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = self.to_q(hidden_states)
+        dim = query.shape[-1]
+        query = self.reshape_heads_to_batch_dim(query)
+
+        if self.added_kv_proj_dim is not None:
+            raise NotImplementedError
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        former_frame_index = torch.arange(video_length) - 1
+        former_frame_index[0] = 0
+
+        key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
+        key = torch.cat([key[:, [0] * video_length], key[:, former_frame_index]], dim=2)
+        key = rearrange(key, "b f d c -> (b f) d c")
+
+        value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
+        value = torch.cat([value[:, [0] * video_length], value[:, former_frame_index]], dim=2)
+        value = rearrange(value, "b f d c -> (b f) d c")
+
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+        # attention, what we cannot get enough of
+        if self._use_memory_efficient_attention_xformers:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+            hidden_states = hidden_states.to(query.dtype)
+        else:
+            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+                hidden_states = self._attention(query, key, value, attention_mask)
+            else:
+                hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+        return hidden_states
+
+
+class FrameAttention(CrossAttention):
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        encoder_hidden_states = encoder_hidden_states
+
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = self.to_q(hidden_states)
+        dim = query.shape[-1]
+        query = self.reshape_heads_to_batch_dim(query)
+
+        if self.added_kv_proj_dim is not None:
+            raise NotImplementedError
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        former_frame_index = torch.arange(video_length) - 1
+        former_frame_index[0] = 0
+
+        key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
+        key = key[:, [0] * video_length]
+        key = rearrange(key, "b f d c -> (b f) d c")
+
+        value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
+        value = value[:, [0] * video_length]
+        value = rearrange(value, "b f d c -> (b f) d c")
+
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+        # attention, what we cannot get enough of
+        if self._use_memory_efficient_attention_xformers:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+            hidden_states = hidden_states.to(query.dtype)
+        else:
+            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+                hidden_states = self._attention(query, key, value, attention_mask)
+            else:
+                hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+        return hidden_states

tuneavideo/models/resnet.py

@@ -0,0 +1,209 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from einops import rearrange
+
+
+class InflatedConv3d(nn.Conv2d):
+    def forward(self, x):
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+
+class Upsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+
+        conv = None
+        if use_conv_transpose:
+            raise NotImplementedError
+        elif use_conv:
+            conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
+
+        if name == "conv":
+            self.conv = conv
+        else:
+            self.Conv2d_0 = conv
+
+    def forward(self, hidden_states, output_size=None):
+        assert hidden_states.shape[1] == self.channels
+
+        if self.use_conv_transpose:
+            raise NotImplementedError
+
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # if `output_size` is passed we force the interpolation output
+        # size and do not make use of `scale_factor=2`
+        if output_size is None:
+            hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
+        else:
+            hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        if self.use_conv:
+            if self.name == "conv":
+                hidden_states = self.conv(hidden_states)
+            else:
+                hidden_states = self.Conv2d_0(hidden_states)
+
+        return hidden_states
+
+
+class Downsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        stride = 2
+        self.name = name
+
+        if use_conv:
+            conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            raise NotImplementedError
+
+        if name == "conv":
+            self.Conv2d_0 = conv
+            self.conv = conv
+        elif name == "Conv2d_0":
+            self.conv = conv
+        else:
+            self.conv = conv
+
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
+        if self.use_conv and self.padding == 0:
+            raise NotImplementedError
+
+        assert hidden_states.shape[1] == self.channels
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlock3D(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        temb_channels=512,
+        groups=32,
+        groups_out=None,
+        pre_norm=True,
+        eps=1e-6,
+        non_linearity="swish",
+        time_embedding_norm="default",
+        output_scale_factor=1.0,
+        use_in_shortcut=None,
+    ):
+        super().__init__()
+        self.pre_norm = pre_norm
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+        self.time_embedding_norm = time_embedding_norm
+        self.output_scale_factor = output_scale_factor
+
+        if groups_out is None:
+            groups_out = groups
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if temb_channels is not None:
+            if self.time_embedding_norm == "default":
+                time_emb_proj_out_channels = out_channels
+            elif self.time_embedding_norm == "scale_shift":
+                time_emb_proj_out_channels = out_channels * 2
+            else:
+                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
+
+            self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
+        else:
+            self.time_emb_proj = None
+
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if non_linearity == "swish":
+            self.nonlinearity = lambda x: F.silu(x)
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, input_tensor, temb):
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states)
+
+        if temb is not None:
+            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+
+        if temb is not None and self.time_embedding_norm == "default":
+            hidden_states = hidden_states + temb
+
+        hidden_states = self.norm2(hidden_states)
+
+        if temb is not None and self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
+class Mish(torch.nn.Module):
+    def forward(self, hidden_states):
+        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))

tuneavideo/models/unet.py

@@ -0,0 +1,450 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import os
+import json
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from .unet_blocks import (
+    CrossAttnDownBlock3D,
+    CrossAttnUpBlock3D,
+    DownBlock3D,
+    UNetMidBlock3DCrossAttn,
+    UpBlock3D,
+    get_down_block,
+    get_up_block,
+)
+from .resnet import InflatedConv3d
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+
+        # down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+        )
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None):
+        if subfolder is not None:
+            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
+
+        config_file = os.path.join(pretrained_model_path, 'config.json')
+        if not os.path.isfile(config_file):
+            raise RuntimeError(f"{config_file} does not exist")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        config["_class_name"] = cls.__name__
+        config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D"
+        ]
+        config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ]
+
+        from diffusers.utils import WEIGHTS_NAME
+        model = cls.from_config(config)
+        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        if not os.path.isfile(model_file):
+            raise RuntimeError(f"{model_file} does not exist")
+        state_dict = torch.load(model_file, map_location="cpu")
+        for k, v in model.state_dict().items():
+            if '_temp.' in k:
+                state_dict.update({k: v})
+        model.load_state_dict(state_dict)
+
+        return model

tuneavideo/models/unet_blocks.py

@@ -0,0 +1,588 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+
+import torch
+from torch import nn
+
+from .attention import Transformer3DModel
+from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+):
+    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
+    if down_block_type == "DownBlock3D":
+        return DownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "CrossAttnDownBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
+        return CrossAttnDownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+):
+    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    if up_block_type == "UpBlock3D":
+        return UpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "CrossAttnUpBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
+        return CrossAttnUpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlock3DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    in_channels // attn_num_head_channels,
+                    in_channels=in_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    upcast_attention=upcast_attention,
+                )
+            )
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnDownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                )
+            )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        output_states = ()
+
+        for resnet, attn in zip(self.resnets, self.attentions):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class DownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_downsample=True,
+        downsample_padding=1,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None):
+        output_states = ()
+
+        for resnet in self.resnets:
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class CrossAttnUpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        res_hidden_states_tuple,
+        temb=None,
+        encoder_hidden_states=None,
+        upsample_size=None,
+        attention_mask=None,
+    ):
+        for resnet, attn in zip(self.resnets, self.attentions):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
+
+
+class UpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_output_channel: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_upsample=True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+        for resnet in self.resnets:
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states

tuneavideo/pipelines/pipeline_tuneavideo.py

@@ -0,0 +1,449 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+
+import inspect
+from typing import Callable, List, Optional, Union
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import deprecate, logging, BaseOutput
+
+from einops import rearrange, repeat
+
+from ..models.unet import UNet3DConditionModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class TuneAVideoPipelineOutput(BaseOutput):
+    videos: Union[torch.Tensor, np.ndarray]
+
+
+class TuneAVideoPipeline(DiffusionPipeline):
+    _optional_components = []
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet3DConditionModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led 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 `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` 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 `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        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 your 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,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+    def enable_vae_slicing(self):
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        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]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+
+    @property
+    def _execution_device(self):
+        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
+
+    def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
+        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="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and 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_videos_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_videos_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_videos_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_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
+
+    def decode_latents(self, latents):
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        bs = 4
+        video_list = []
+        for i in range(max(latents.shape[0]//bs, 1)):
+            video = self.vae.decode(latents[i*bs:min((i+1)*bs, latents.shape[0])]).sample
+            video = (video / 2 + 0.5).clamp(0, 1)
+            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+            video = video.cpu().float().numpy()
+            video_list.append(video)
+        if len(video_list) > 1:
+            video = np.concatenate(video_list, axis=0)
+        else:
+            video = video_list[0]
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        return video
+
+    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, height, width, 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 height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        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)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, video_length, 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:
+            rand_device = "cpu" if device.type == "mps" else device
+
+            if isinstance(generator, list):
+                shape = (1,) + shape[1:]
+                latents = [
+                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
+                    for i in range(batch_size)
+                ]
+                latents = torch.cat(latents, dim=0).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
+        else:
+            if latents.shape != shape:
+                # raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+                latents = latents.expand(shape)
+            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
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        video_length: Optional[int],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        uncond_embeddings_pre=None,
+        controller=None,
+        uncond2=False,
+        multi=False,
+        region=False,
+        simple=False,
+        **kwargs,
+    ):
+        # 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
+
+        # Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 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
+
+        # Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+        if multi:
+            text_embeddings = repeat(text_embeddings, 'b n c -> (b f) n c', f=video_length)
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            video_length,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+        latents_dtype = latents.dtype
+
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 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) # torch.Size([2, 4, 8, 64, 64])
+
+                if uncond_embeddings_pre is not None:
+                    if multi:
+                        text_embeddings[:video_length] = uncond_embeddings_pre[i]
+                    else:
+                        text_embeddings[0] = uncond_embeddings_pre[i] # text_embeddings: torch.Size([2, 77, 768])
+                        if region:
+                            text_embeddings[2] = uncond_embeddings_pre[i] # text_embeddings: torch.Size([2, 77, 768])
+                    if uncond2:
+                        if multi:
+                            text_embeddings[video_length: video_length*2] = uncond_embeddings_pre[i]
+                        else:
+                            text_embeddings[1] = uncond_embeddings_pre[i] # text_embeddings: torch.Size([2, 77, 768])
+
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype)
+                if region:
+                    mask = controller.get_mask(latents[:-1])[1:2]
+                    noise_pred[1] = noise_pred[2]*(1-mask) + noise_pred[1]*mask
+                    # noise_pred[1] = noise_pred[2]
+                
+                # 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)
+                    if simple:
+                        noise_pred[0] = noise_pred_text[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                if controller is not None:
+                    latents = controller.step_callback(latents)
+
+                # 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)
+
+        if region:
+            latents = latents[:2]
+
+        # Post-processing
+        video = self.decode_latents(latents)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            video = torch.from_numpy(video)
+
+        if not return_dict:
+            return video
+
+        return TuneAVideoPipelineOutput(videos=video)

tuneavideo/util.py

@@ -0,0 +1,84 @@
+import os
+import imageio
+import numpy as np
+from typing import Union
+
+import torch
+import torchvision
+
+from tqdm import tqdm
+from einops import rearrange
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    outputs = []
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        outputs.append(x)
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    imageio.mimsave(path, outputs, fps=fps)
+
+
+# DDIM Inversion
+@torch.no_grad()
+def init_prompt(prompt, pipeline):
+    uncond_input = pipeline.tokenizer(
+        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
+        return_tensors="pt"
+    )
+    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
+    text_input = pipeline.tokenizer(
+        [prompt],
+        padding="max_length",
+        max_length=pipeline.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
+    context = torch.cat([uncond_embeddings, text_embeddings])
+
+    return context
+
+
+def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
+              sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
+    timestep, next_timestep = min(
+        timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
+    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
+    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
+    beta_prod_t = 1 - alpha_prod_t
+    next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
+    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
+    next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
+    return next_sample
+
+
+def get_noise_pred_single(latents, t, context, unet):
+    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
+    return noise_pred
+
+
+@torch.no_grad()
+def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
+    context = init_prompt(prompt, pipeline)
+    uncond_embeddings, cond_embeddings = context.chunk(2)
+    all_latent = [latent]
+    latent = latent.clone().detach()
+    for i in tqdm(range(num_inv_steps)):
+        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
+        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
+        latent = next_step(noise_pred, t, latent, ddim_scheduler)
+        all_latent.append(latent)
+    return all_latent
+
+
+@torch.no_grad()
+def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
+    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
+    return ddim_latents