debug

README.md

@@ -1,27 +1,12 @@
 ---
-title: Video-P2P Demo
-emoji: 🐶
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-app_file: app.py
+title: Tune-A-Video Training UI
+emoji: ⚡
+colorFrom: red
+colorTo: purple
+sdk: docker
 pinned: false
+license: mit
+duplicated_from: lora-library/LoRA-DreamBooth-Training-UI
 ---
 
-# 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
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Video-P2P-Beta

@@ -0,0 +1 @@
+Subproject commit 7a8fa7a8b8d81bbba367865f47b7894cdc4efafb

Video-P2P/README.md

@@ -1,27 +0,0 @@
----
-title: Video-P2P Demo
-emoji: 🐶
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# 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

Video-P2P/configs/man-motor-tune.yaml

@@ -1,41 +0,0 @@
-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

Video-P2P/configs/man-surfing.yaml

@@ -1,41 +0,0 @@
-pretrained_model_path: "./checkpoints/stable-diffusion-v1-4"
-output_dir: "./outputs/man-surfing"
-
-train_data:
-  video_path: "data/man-surfing.mp4"
-  prompt: "a man is surfing"
-  n_sample_frames: 24
-  width: 512
-  height: 512
-  sample_start_idx: 0
-  sample_frame_rate: 1
-
-validation_data:
-  prompts:
-    - "a panda is surfing"
-    - "a boy, wearing a birthday hat, is surfing"
-    - "a raccoon is surfing, cartoon style"
-    - "Iron Man is surfing in the desert"
-  video_length: 24
-  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

Video-P2P/configs/rabbit-jump-p2p.yaml

@@ -1,16 +0,0 @@
-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

Video-P2P/configs/rabbit-jump-tune.yaml

@@ -1,41 +0,0 @@
-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

Video-P2P/ptp_utils.py

@@ -1,309 +0,0 @@
-# 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

Video-P2P/requirements.txt

@@ -1,15 +0,0 @@
-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

Video-P2P/run_tuning.py

@@ -1,367 +0,0 @@
-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))

Video-P2P/run_videop2p.py

@@ -1,707 +0,0 @@
-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'), save_all=True, append_images=inversion[1:], optimize=False, loop=0, duration=250)
-    videop2p[0].save(gif_name_2.replace('name', 'p2p'), 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))

Video-P2P/script.sh

@@ -1,5 +0,0 @@
-# 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"

Video-P2P/seq_aligner.py

@@ -1,196 +0,0 @@
-# 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)
-

Video-P2P/tuneavideo/data/dataset.py

@@ -1,57 +0,0 @@
-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

Video-P2P/tuneavideo/models/attention.py

@@ -1,388 +0,0 @@
-# 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

Video-P2P/tuneavideo/models/resnet.py

@@ -1,209 +0,0 @@
-# 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))

Video-P2P/tuneavideo/models/unet.py

@@ -1,450 +0,0 @@
-# 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

Video-P2P/tuneavideo/models/unet_blocks.py

@@ -1,588 +0,0 @@
-# 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

Video-P2P/tuneavideo/pipelines/pipeline_tuneavideo.py

@@ -1,449 +0,0 @@
-# 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)

Video-P2P/tuneavideo/util.py

@@ -1,84 +0,0 @@
-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

app.py

@@ -14,9 +14,9 @@ from app_upload import create_upload_demo
 from inference import InferencePipeline
 from trainer import Trainer
 
-TITLE = '# [Video-P2P](https://video-p2p.github.io/) UI'
+TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) UI'
 
-ORIGINAL_SPACE_ID = 'Shaldon/Video-P2P-Demo'
+ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
 SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
 GPU_DATA = getoutput('nvidia-smi')
 SHARED_UI_WARNING = f'''## Attention - Training doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.

app_training.py

@@ -23,7 +23,7 @@ def create_training_demo(trainer: Trainer,
                     training_prompt = gr.Textbox(
                         label='Training prompt',
                         max_lines=1,
-                        placeholder='A rabbit is jumping on the grass')
+                        placeholder='A man is surfing')
                     gr.Markdown('''
                         - Upload a video and write a `Training Prompt` that describes the video.
                         ''')
@@ -34,7 +34,7 @@ def create_training_demo(trainer: Trainer,
                     with gr.Row():
                         base_model = gr.Text(
                             label='Base Model',
-                            value='CompVis/stable-diffusion-v1-5',
+                            value='CompVis/stable-diffusion-v1-4',
                             max_lines=1)
                         resolution = gr.Dropdown(choices=['512', '768'],
                                                  value='512',

inference.py

@@ -13,7 +13,7 @@ from diffusers.utils.import_utils import is_xformers_available
 from einops import rearrange
 from huggingface_hub import ModelCard
 
-sys.path.append('Video-P2P')
+sys.path.append('Tune-A-Video')
 
 from tuneavideo.models.unet import UNet3DConditionModel
 from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline

requirements.txt

@@ -17,6 +17,3 @@ torchvision==0.14.1
 transformers==4.26.0
 triton==2.0.0.dev20221202
 xformers==0.0.16
-modelcards
-opencv-python
-ipywidgets

trainer.py

@@ -17,10 +17,10 @@ from omegaconf import OmegaConf
 from app_upload import ModelUploader
 from utils import save_model_card
 
-sys.path.append('Video-P2P')
+sys.path.append('Tune-A-Video')
 
-# URL_TO_JOIN_MODEL_LIBRARY_ORG = 'https://huggingface.co/organizations/Tune-A-Video-library/share/YjTcaNJmKyeHFpMBioHhzBcTzCYddVErEk'
-ORIGINAL_SPACE_ID = 'Shaldon/Video-P2P-Demo'
+URL_TO_JOIN_MODEL_LIBRARY_ORG = 'https://huggingface.co/organizations/Tune-A-Video-library/share/YjTcaNJmKyeHFpMBioHhzBcTzCYddVErEk'
+ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
 SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
 
 
@@ -43,11 +43,11 @@ class Trainer:
                            cwd=org_dir)
         return model_dir.as_posix()
 
-    # def join_model_library_org(self, token: str) -> None:
-    #     subprocess.run(
-    #         shlex.split(
-    #             f'curl -X POST -H "Authorization: Bearer {token}" -H "Content-Type: application/json" {URL_TO_JOIN_MODEL_LIBRARY_ORG}'
-    #         ))
+    def join_model_library_org(self, token: str) -> None:
+        subprocess.run(
+            shlex.split(
+                f'curl -X POST -H "Authorization: Bearer {token}" -H "Content-Type: application/json" {URL_TO_JOIN_MODEL_LIBRARY_ORG}'
+            ))
 
     def run(
         self,
@@ -90,7 +90,7 @@ class Trainer:
 
         if not output_model_name:
             timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
-            output_model_name = f'video-p2p-{timestamp}'
+            output_model_name = f'tune-a-video-{timestamp}'
         output_model_name = slugify.slugify(output_model_name)
 
         repo_dir = pathlib.Path(__file__).parent
@@ -99,11 +99,11 @@ class Trainer:
             shutil.rmtree(output_dir, ignore_errors=True)
         output_dir.mkdir(parents=True)
 
-        # if upload_to_hub:
-        #     self.join_model_library_org(
-        #         self.hf_token if self.hf_token else input_token)
+        if upload_to_hub:
+            self.join_model_library_org(
+                self.hf_token if self.hf_token else input_token)
 
-        config = OmegaConf.load('Video-P2P/configs/man-surfing.yaml')
+        config = OmegaConf.load('Tune-A-Video/configs/man-surfing.yaml')
         config.pretrained_model_path = self.download_base_model(base_model)
         config.output_dir = output_dir.as_posix()
         config.train_data.video_path = training_video.name  # type: ignore
@@ -133,7 +133,7 @@ class Trainer:
         with open(config_path, 'w') as f:
             OmegaConf.save(config, f)
 
-        command = f'accelerate launch Video-P2P/run_tuning.py --config {config_path}'
+        command = f'accelerate launch Tune-A-Video/train_tuneavideo.py --config {config_path}'
         subprocess.run(shlex.split(command))
         save_model_card(save_dir=output_dir,
                         base_model=base_model,