Source code for mmagic.models.editors.animatediff.animatediff
# Copyright (c) OpenMMLab. All rights reserved.
import inspect
from copy import deepcopy
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from einops import rearrange
from mmengine import print_log
from mmengine.logging import MMLogger
from mmengine.model import BaseModel
from safetensors import safe_open
from tqdm import tqdm
from mmagic.models.archs import TokenizerWrapper, set_lora
from mmagic.models.utils import build_module, set_tomesd, set_xformers
from mmagic.registry import DIFFUSION_SCHEDULERS, MODELS
from mmagic.structures import DataSample
from mmagic.utils.typing import SampleList
from .animatediff_utils import (convert_ldm_clip_checkpoint,
convert_ldm_unet_checkpoint,
convert_ldm_vae_checkpoint)
@MODELS.register_module('animatediff')
@MODELS.register_module()
[docs]class AnimateDiff(BaseModel):
"""Implementation of `AnimateDiff.
<https://arxiv.org/abs/2307.04725>`_ (AnimateDiff).
Args:
vae (Union[dict, nn.Module]): The config or module for VAE model.
text_encoder (Union[dict, nn.Module]): The config or module for text
encoder.
tokenizer (str): The **name** for CLIP tokenizer.
unet (Union[dict, nn.Module]): The config or module for Unet model.
schedule (Union[dict, nn.Module]): The config or module for diffusion
scheduler.
test_scheduler (Union[dict, nn.Module], optional): The config or
module for diffusion scheduler in test stage (`self.infer`). If not
passed, will use the same scheduler as `schedule`. Defaults to
None.
lora_config (dict, optional): The config for LoRA finetuning. Defaults
to None.
val_prompts (Union[str, List[str]], optional): The prompts for
validation. Defaults to None.
class_prior_prompt (str, optional): The prompt for class prior loss.
num_class_images (int, optional): The number of images for class prior.
Defaults to 3.
prior_loss_weight (float, optional): The weight for class prior loss.
Defaults to 0.
fine_tune_text_encoder (bool, optional): Whether to fine-tune text
encoder. Defaults to False.
dtype (str, optional): The dtype for the model. Defaults to 'fp16'.
enable_xformers (bool, optional): Whether to use xformers.
Defaults to True.
noise_offset_weight (bool, optional): The weight of noise offset
introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa
Defaults to 0.
tomesd_cfg (dict, optional): The config for TOMESD. Please refers to
https://github.com/dbolya/tomesd and
https://github.com/open-mmlab/mmagic/blob/main/mmagic/models/utils/tome_utils.py for detail. # noqa
Defaults to None.
data_preprocessor (dict, optional): The pre-process config of
:class:`BaseDataPreprocessor`. Defaults to
dict(type='DataPreprocessor').
init_cfg (dict, optional): The weight initialized config for
:class:`BaseModule`. Defaults to None/
"""
def __init__(
self,
vae: ModelType,
text_encoder: ModelType,
tokenizer: str,
unet: ModelType,
scheduler: ModelType,
test_scheduler: Optional[ModelType] = None,
dtype: str = 'fp32',
enable_xformers: bool = True,
noise_offset_weight: float = 0,
tomesd_cfg: Optional[dict] = None,
data_preprocessor=dict(type='DataPreprocessor'),
motion_module_cfg: Optional[dict] = None,
dream_booth_lora_cfg: Optional[dict] = None,
):
super().__init__(data_preprocessor)
default_args = dict()
if dtype is not None:
default_args['dtype'] = dtype
self.dtype = torch.float32
if dtype in ['float16', 'fp16', 'half']:
self.dtype = torch.float16
elif dtype == 'bf16':
self.dtype = torch.bfloat16
else:
assert dtype in [
'fp32', None
], ('dtype must be one of \'fp32\', \'fp16\', \'bf16\' or None.')
self.vae = build_module(vae, MODELS, default_args=default_args)
self.unet = build_module(unet, MODELS) # NOTE: initialize unet as fp32
self._unet_ori_dtype = next(self.unet.parameters()).dtype
print_log(f'Set UNet dtype to \'{self._unet_ori_dtype}\'.', 'current')
self.init_motion_module(motion_module_cfg)
self.scheduler = build_module(scheduler, DIFFUSION_SCHEDULERS)
if test_scheduler is None:
self.test_scheduler = deepcopy(self.scheduler)
else:
self.test_scheduler = build_module(test_scheduler,
DIFFUSION_SCHEDULERS)
self.text_encoder = build_module(text_encoder, MODELS)
if not isinstance(tokenizer, str):
self.tokenizer = tokenizer
else:
# NOTE: here we assume tokenizer is an string
self.tokenizer = TokenizerWrapper(tokenizer, subfolder='tokenizer')
self.unet_sample_size = self.unet.sample_size
self.vae_scale_factor = 2**(len(self.vae.block_out_channels) - 1)
self.enable_noise_offset = noise_offset_weight > 0
self.noise_offset_weight = noise_offset_weight
self.enable_xformers = enable_xformers
self.unet.set_use_memory_efficient_attention_xformers(True)
self.tomesd_cfg = tomesd_cfg
self.set_tomesd()
self.init_dreambooth_lora(dream_booth_lora_cfg)
self.prepare_model()
[docs] def set_xformers(self, module: Optional[nn.Module] = None) -> nn.Module:
"""Set xformers for the model.
Returns:
nn.Module: The model with xformers.
"""
if self.enable_xformers:
if module is None:
set_xformers(self)
else:
set_xformers(module)
[docs] def set_tomesd(self) -> nn.Module:
"""Set ToMe for the stable diffusion model.
Returns:
nn.Module: The model with ToMe.
"""
if self.tomesd_cfg is not None:
set_tomesd(self, **self.tomesd_cfg)
@property
[docs] def init_motion_module(self, motion_module_cfg):
if motion_module_cfg is not None:
if 'path' in motion_module_cfg.keys():
motion_module_state_dict = torch.load(
motion_module_cfg['path'], map_location='cpu')
# if "global_step" in motion_module_state_dict:
# func_args.update({"global_step":
# motion_module_state_dict["global_step"]})
missing, unexpected = self.unet.load_state_dict(
motion_module_state_dict, strict=False)
assert len(unexpected) == 0
[docs] def init_dreambooth_lora(self, dream_booth_lora_cfg):
# TODO: finish
if dream_booth_lora_cfg is not None:
if 'path' in dream_booth_lora_cfg.keys():
state_dict = {}
with safe_open(
dream_booth_lora_cfg['path'], framework='pt',
device='cpu') as f:
for key in f.keys():
state_dict[key] = f.get_tensor(key)
# vae
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
state_dict, self.vae.config)
self.vae.load_state_dict(converted_vae_checkpoint)
# unet
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
state_dict, self.unet.config)
self.unet.load_state_dict(
converted_unet_checkpoint, strict=False)
# text_model
self.text_encoder = convert_ldm_clip_checkpoint(state_dict)
# self.convert_lora(state_dict)
[docs] def _encode_prompt(self, prompt, device, num_videos_per_prompt,
do_classifier_free_guidance, negative_prompt):
"""Encodes the prompt into text encoder hidden states."""
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 '
f'because CLIP can only handle sequences up to'
f' {self.tokenizer.model_max_length} tokens: {removed_text}')
text_encoder = self.text_encoder.module if hasattr(
self.text_encoder, 'module') else self.text_encoder
if hasattr(text_encoder.config, 'use_attention_mask'
) and 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 '
f'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 '
f'batch size {len(negative_prompt)}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please '
f'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(text_encoder.config, 'use_attention_mask'
) and 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
[docs] def decode_latents(self, latents):
"""latents decoder."""
video_length = latents.shape[2]
latents = 1 / 0.18215 * latents
latents = rearrange(latents, 'b c f h w -> (b f) c h w')
# video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0])):
video.append(
self.vae.decode(latents[frame_idx:frame_idx + 1]).sample)
video = torch.cat(video)
video = rearrange(video, '(b f) c h w -> b c f h w', f=video_length)
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()
return video
[docs] 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."""
# 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
[docs] def check_inputs(self, prompt, height, width):
"""Check inputs.
Raise error if not correct
"""
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f'`prompt` has to be of type `str`'
f' or `list` but is {type(prompt)}')
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible'
f' 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)}."
# )
[docs] def convert_lora(self,
state_dict,
LORA_PREFIX_UNET='lora_unet',
LORA_PREFIX_TEXT_ENCODER='lora_te',
alpha=0.6):
""" Convert lora for unet and text_encoder
TODO: use this function to convert lora
Args:
state_dict (_type_): _description_
LORA_PREFIX_UNET (str, optional):
_description_. Defaults to 'lora_unet'.
LORA_PREFIX_TEXT_ENCODER (str, optional):
_description_. Defaults to 'lora_te'.
alpha (float, optional): _description_. Defaults to 0.6.
Returns:
TODO: check each output type
_type_: unet && text_encoder
"""
# load base model
# pipeline = StableDiffusionPipeline.from_pretrained(base_model_path,
# torch_dtype=torch.float32)
# load LoRA weight from .safetensors
# state_dict = load_file(checkpoint_path)
visited = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually
# will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if '.alpha' in key or key in visited:
continue
if 'text' in key:
layer_infos = key.split('.')[0].split(
LORA_PREFIX_TEXT_ENCODER + '_')[-1].split('_')
curr_layer = self.text_encoder
else:
layer_infos = key.split('.')[0].split(LORA_PREFIX_UNET +
'_')[-1].split('_')
curr_layer = self.unet
# find the target layer
temp_name = layer_infos.pop(0)
while len(layer_infos) > -1:
try:
curr_layer = curr_layer.__getattr__(temp_name)
if len(layer_infos) > 0:
temp_name = layer_infos.pop(0)
elif len(layer_infos) == 0:
break
except Exception:
if len(temp_name) > 0:
temp_name += '_' + layer_infos.pop(0)
else:
temp_name = layer_infos.pop(0)
pair_keys = []
if 'lora_down' in key:
pair_keys.append(key.replace('lora_down', 'lora_up'))
pair_keys.append(key)
else:
pair_keys.append(key)
pair_keys.append(key.replace('lora_up', 'lora_down'))
# update weight
if len(state_dict[pair_keys[0]].shape) == 4:
weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(
torch.float32)
weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(
2).to(torch.float32)
curr_layer.weight.data += alpha * torch.mm(
weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(
curr_layer.weight.data.device)
else:
weight_up = state_dict[pair_keys[0]].to(torch.float32)
weight_down = state_dict[pair_keys[1]].to(torch.float32)
curr_layer.weight.data += alpha * torch.mm(
weight_up, weight_down).to(curr_layer.weight.data.device)
# update visited list
for item in pair_keys:
visited.append(item)
return self.unet, self.text_encoder
[docs] def prepare_latents(self,
batch_size,
num_channels_latents,
video_length,
height,
width,
dtype,
device,
generator,
latents=None):
"""Prepare latent variables."""
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 '
f'{len(generator)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the'
f' length of the generators.')
if latents is None:
rand_device = 'cpu' if device.type == 'mps' else device
if isinstance(generator, list):
shape = shape
# 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 '
f'{latents.shape}, expected {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
[docs] def prepare_model(self):
"""Prepare model for training.
Move model to target dtype and disable gradient for some models.
"""
self.vae.requires_grad_(False)
print_log('Set VAE untrainable.', 'current')
self.vae.to(self.dtype)
print_log(f'Move VAE to {self.dtype}.', 'current')
# if not self.finetune_text_encoder or self.lora_config:
if 1:
self.text_encoder.requires_grad_(False)
print_log('Set Text Encoder untrainable.', 'current')
self.text_encoder.to(self.dtype)
print_log(f'Move Text Encoder to {self.dtype}.', 'current')
# if self.lora_config:
if 1:
self.unet.requires_grad_(False)
print_log('Set Unet untrainable.', 'current')
[docs] def set_lora(self):
"""Set LORA for model."""
if self.lora_config:
set_lora(self.unet, self.lora_config)
@torch.no_grad()
[docs] def val_step(self, data: dict) -> SampleList:
"""Gets the generated image of given data. Calls
``self.data_preprocessor`` and ``self.infer`` in order. Return the
generated results which will be passed to evaluator or visualizer.
Args:
data (dict or tuple or list): Data sampled from dataset.
Returns:
SampleList: Generated image or image dict.
"""
data = self.data_preprocessor(data)
data_samples = data['data_samples']
if self.val_prompts is None:
prompt = data_samples.prompt
else:
prompt = self.val_prompts
# construct a fake data_sample for destruct
data_samples.split() * len(prompt)
data_samples = DataSample.stack(data_samples.split() * len(prompt))
output = self.infer(prompt, return_type='tensor')
samples = output['samples']
samples = self.data_preprocessor.destruct(samples, data_samples)
out_data_sample = DataSample(fake_img=samples, prompt=prompt)
data_sample_list = out_data_sample.split()
return data_sample_list
@torch.no_grad()
[docs] def test_step(self, data: dict) -> SampleList:
"""Gets the generated image of given data. Calls
``self.data_preprocessor`` and ``self.infer`` in order. Return the
generated results which will be passed to evaluator or visualizer.
Args:
data (dict or tuple or list): Data sampled from dataset.
Returns:
SampleList: Generated image or image dict.
"""
if self.val_prompts is None:
data = self.data_preprocessor(data)
data_samples = data['data_samples']
prompt = data_samples.prompt
else:
prompt = self.val_prompts
# construct a fake data_sample for destruct
data_samples = DataSample.stack(data['data_samples'] * len(prompt))
output = self.infer(prompt, return_type='tensor')
samples = output['samples']
samples = self.data_preprocessor.destruct(samples, data_samples)
out_data_sample = DataSample(fake_img=samples, prompt=prompt)
data_sample_list = out_data_sample.split()
return data_sample_list
@torch.no_grad()
[docs] def infer(self,
prompt: Union[str, List[str]],
video_length: Optional[int] = 16,
height: Optional[int] = None,
width: Optional[int] = None,
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,
return_type: Optional[str] = 'tensor',
show_progress: bool = True,
seed: Optional[int] = 1007):
"""Function invoked when calling the pipeline for generation.
Args:
prompt (str or List[str]): The prompt or prompts to guide
the video generation.
video_length (int, Option): The number of frames of the
generated video. Defaults to 16.
height (int, Optional): The height in pixels of the generated
image. If not passed, the height will be
`self.unet_sample_size * self.vae_scale_factor` Defaults
to None.
width (int, Optional): The width in pixels of the generated image.
If not passed, the width will be
`self.unet_sample_size * self.vae_scale_factor` Defaults
to None.
num_inference_steps (int): The number of denoising steps.
More denoising steps usually lead to a higher quality video at
the expense of slower inference. Defaults to 50.
guidance_scale (float): Guidance scale as defined in Classifier-
Free Diffusion Guidance (https://arxiv.org/abs/2207.12598).
Defaults to 7.5
negative_prompt (str or List[str], optional): The prompt or
prompts not to guide the video generation. Ignored when not
using guidance (i.e., ignored if `guidance_scale` is less
than 1). Defaults to None.
num_videos_per_prompt (int): The number of videos to generate
per prompt. Defaults to 1.
eta (float): Corresponds to parameter eta (η) in the DDIM paper:
https://arxiv.org/abs/2010.02502. Only applies to
DDIMScheduler, will be ignored for others. Defaults to 0.0.
generator (torch.Generator, optional): A torch generator to make
generation deterministic. Defaults to None.
latents (torch.FloatTensor, optional): Pre-generated noisy latents,
sampled from a Gaussian distribution, to be used as inputs for
video generation. Can be used to tweak the same generation with
different prompts. If not provided, a latents tensor will be
generated by sampling using the supplied random `generator`.
Defaults to None.
return_type (str): The return type of the inference results.
Supported types are 'video', 'numpy', 'tensor'. If 'video'
is passed, a list of PIL images will be returned. If 'numpy'
is passed, a numpy array with shape [N, C, H, W] will be
returned, and the value range will be same as decoder's
output range. If 'tensor' is passed, the decoder's output
will be returned. Defaults to 'image'.
#TODO
Returns:
dict: A dict containing the generated video
"""
assert return_type in ['image', 'tensor', 'numpy']
# 0. Default height and width to unet
height = height or self.unet_sample_size * self.vae_scale_factor
width = width or self.unet_sample_size * self.vae_scale_factor
if seed != -1:
torch.manual_seed(seed)
print_log(f'current seed: {torch.initial_seed()}')
print_log(f'sampling {prompt} ...')
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height,
width) # NOTE: aligned with origin repo
# 2. Define call parameters
batch_size = 1
if latents is not None:
batch_size = latents.shape[0]
if isinstance(prompt, list):
batch_size = len(prompt)
device = self.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
video_dtype = self.vae.module.dtype if hasattr(self.vae, 'module') \
else self.vae.dtype
# 3. Encode input prompt
prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
if negative_prompt is not None:
negative_prompt = negative_prompt if isinstance(
negative_prompt, list) else [negative_prompt] * batch_size
text_embeddings = self._encode_prompt(
prompt, device, num_videos_per_prompt, do_classifier_free_guidance,
negative_prompt) # NOTE aligned with origin repo
# 4. Prepare timesteps
# self.scheduler.set_timesteps(num_inference_steps, device=device)
self.test_scheduler.set_timesteps(num_inference_steps)
timesteps = self.test_scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
video_length,
height,
width,
text_embeddings.dtype,
device,
generator,
latents,
) # NOTE aligned with origin repo
latents_dtype = latents.dtype
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
if show_progress:
timesteps = tqdm(timesteps)
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.test_scheduler.scale_model_input(
latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
)['sample'].to(dtype=latents_dtype)
# 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)
# compute the previous noisy sample x_t -> x_t-1
latents = self.test_scheduler.step(
noise_pred, t, latents,
**extra_step_kwargs)['prev_sample'] # FIXME: not aligned
# FIXME: revise config thresholding=False
# scheduler pred_original_sample not aligned
# fixed clip_sample=False
# 8. Post-processing
video = self.decode_latents(latents.to(video_dtype))
if return_type == 'tensor':
video = torch.from_numpy(video)
return {'samples': video}
[docs] def forward(self,
inputs: torch.Tensor,
data_samples: Optional[list] = None,
mode: str = 'tensor') -> Union[Dict[str, torch.Tensor], list]:
"""forward is not implemented now."""
raise NotImplementedError(
'Forward is not implemented now, please use infer.')