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lcm SVD as experimental option

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kijai
2024-07-02 21:13:56 +03:00
parent 180b6eff71
commit e90ac6dd2e
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lcm_scheduler.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput, logging
from diffusers.utils.torch_utils import randn_tensor
from diffusers.schedulers.scheduling_utils import SchedulerMixin
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimateLCMSVDStochasticIterativeSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: torch.FloatTensor
class AnimateLCMSVDStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
"""
Multistep and onestep sampling for consistency models.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 40):
The number of diffusion steps to train the model.
sigma_min (`float`, defaults to 0.002):
Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation.
sigma_max (`float`, defaults to 80.0):
Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation.
sigma_data (`float`, defaults to 0.5):
The standard deviation of the data distribution from the EDM
[paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation.
s_noise (`float`, defaults to 1.0):
The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,
1.011]. Defaults to 1.0 from the original implementation.
rho (`float`, defaults to 7.0):
The parameter for calculating the Karras sigma schedule from the EDM
[paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation.
clip_denoised (`bool`, defaults to `True`):
Whether to clip the denoised outputs to `(-1, 1)`.
timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*):
An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in
increasing order.
"""
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 40,
sigma_min: float = 0.002,
sigma_max: float = 80.0,
sigma_data: float = 0.5,
s_noise: float = 1.0,
rho: float = 7.0,
clip_denoised: bool = True,
):
# standard deviation of the initial noise distribution
self.init_noise_sigma = (sigma_max**2 + 1) ** 0.5
# self.init_noise_sigma = sigma_max
ramp = np.linspace(0, 1, num_train_timesteps)
sigmas = self._convert_to_karras(ramp)
sigmas = np.concatenate([sigmas, np.array([0])])
timesteps = self.sigma_to_t(sigmas)
# setable values
self.num_inference_steps = None
self.sigmas = torch.from_numpy(sigmas)
self.timesteps = torch.from_numpy(timesteps)
self.custom_timesteps = False
self.is_scale_input_called = False
self._step_index = None
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
indices = (schedule_timesteps == timestep).nonzero()
return indices.item()
@property
def step_index(self):
"""
The index counter for current timestep. It will increae 1 after each scheduler step.
"""
return self._step_index
def scale_model_input(
self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
) -> torch.FloatTensor:
"""
Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`.
Args:
sample (`torch.FloatTensor`):
The input sample.
timestep (`float` or `torch.FloatTensor`):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
A scaled input sample.
"""
# Get sigma corresponding to timestep
if self.step_index is None:
self._init_step_index(timestep)
sigma = self.sigmas[self.step_index]
sample = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
self.is_scale_input_called = True
return sample
# def _sigma_to_t(self, sigma, log_sigmas):
# # get log sigma
# log_sigma = np.log(np.maximum(sigma, 1e-10))
# # get distribution
# dists = log_sigma - log_sigmas[:, np.newaxis]
# # get sigmas range
# low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
# high_idx = low_idx + 1
# low = log_sigmas[low_idx]
# high = log_sigmas[high_idx]
# # interpolate sigmas
# w = (low - log_sigma) / (low - high)
# w = np.clip(w, 0, 1)
# # transform interpolation to time range
# t = (1 - w) * low_idx + w * high_idx
# t = t.reshape(sigma.shape)
# return t
def sigma_to_t(self, sigmas: Union[float, np.ndarray]):
"""
Gets scaled timesteps from the Karras sigmas for input to the consistency model.
Args:
sigmas (`float` or `np.ndarray`):
A single Karras sigma or an array of Karras sigmas.
Returns:
`float` or `np.ndarray`:
A scaled input timestep or scaled input timestep array.
"""
if not isinstance(sigmas, np.ndarray):
sigmas = np.array(sigmas, dtype=np.float64)
timesteps = 0.25 * np.log(sigmas + 1e-44)
return timesteps
def set_timesteps(
self,
num_inference_steps: Optional[int] = None,
device: Union[str, torch.device] = None,
timesteps: Optional[List[int]] = None,
):
"""
Sets the timesteps used for the diffusion chain (to be run before inference).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed,
`num_inference_steps` must be `None`.
"""
if num_inference_steps is None and timesteps is None:
raise ValueError(
"Exactly one of `num_inference_steps` or `timesteps` must be supplied."
)
if num_inference_steps is not None and timesteps is not None:
raise ValueError(
"Can only pass one of `num_inference_steps` or `timesteps`."
)
# Follow DDPMScheduler custom timesteps logic
if timesteps is not None:
for i in range(1, len(timesteps)):
if timesteps[i] >= timesteps[i - 1]:
raise ValueError("`timesteps` must be in descending order.")
if timesteps[0] >= self.config.num_train_timesteps:
raise ValueError(
f"`timesteps` must start before `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps}."
)
timesteps = np.array(timesteps, dtype=np.int64)
self.custom_timesteps = True
else:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps."
)
self.num_inference_steps = num_inference_steps
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
timesteps = (
(np.arange(0, num_inference_steps) * step_ratio)
.round()[::-1]
.copy()
.astype(np.int64)
)
self.custom_timesteps = False
# Map timesteps to Karras sigmas directly for multistep sampling
# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
num_train_timesteps = self.config.num_train_timesteps
ramp = timesteps[::-1].copy()
ramp = ramp / (num_train_timesteps - 1)
sigmas = self._convert_to_karras(ramp)
timesteps = self.sigma_to_t(sigmas)
sigmas = np.concatenate([sigmas, [0]]).astype(np.float32)
self.sigmas = torch.from_numpy(sigmas).to(device=device)
if str(device).startswith("mps"):
# mps does not support float64
self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
else:
self.timesteps = torch.from_numpy(timesteps).to(device=device)
self._step_index = None
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
# Modified _convert_to_karras implementation that takes in ramp as argument
def _convert_to_karras(self, ramp):
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min: float = self.config.sigma_min
sigma_max: float = self.config.sigma_max
rho = self.config.rho
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
def get_scalings(self, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
return c_skip, c_out
def get_scalings_for_boundary_condition(self, sigma):
"""
Gets the scalings used in the consistency model parameterization (from Appendix C of the
[paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition.
<Tip>
`epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`.
</Tip>
Args:
sigma (`torch.FloatTensor`):
The current sigma in the Karras sigma schedule.
Returns:
`tuple`:
A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out`
(which weights the consistency model output) is the second element.
"""
sigma_min = self.config.sigma_min
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / ((sigma) ** 2 + sigma_data**2)
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
return c_skip, c_out
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
def _init_step_index(self, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
index_candidates = (self.timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(index_candidates) > 1:
step_index = index_candidates[1]
else:
step_index = index_candidates[0]
self._step_index = step_index.item()
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[AnimateLCMSVDStochasticIterativeSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
The direct output from the learned diffusion model.
timestep (`float`):
The current timestep in the diffusion chain.
sample (`torch.FloatTensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`:
If return_dict is `True`,
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] is returned,
otherwise a tuple is returned where the first element is the sample tensor.
"""
if (
isinstance(timestep, int)
or isinstance(timestep, torch.IntTensor)
or isinstance(timestep, torch.LongTensor)
):
raise ValueError(
(
"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
f" `{self.__class__}.step()` is not supported. Make sure to pass"
" one of the `scheduler.timesteps` as a timestep."
),
)
if not self.is_scale_input_called:
logger.warning(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
sigma_min = self.config.sigma_min
sigma_max = self.config.sigma_max
if self.step_index is None:
self._init_step_index(timestep)
# sigma_next corresponds to next_t in original implementation
sigma = self.sigmas[self.step_index]
if self.step_index + 1 < self.config.num_train_timesteps:
sigma_next = self.sigmas[self.step_index + 1]
else:
# Set sigma_next to sigma_min
sigma_next = self.sigmas[-1]
# Get scalings for boundary conditions
c_skip, c_out = self.get_scalings_for_boundary_condition(sigma)
# 1. Denoise model output using boundary conditions
denoised = c_out * model_output + c_skip * sample
if self.config.clip_denoised:
denoised = denoised.clamp(-1, 1)
# 2. Sample z ~ N(0, s_noise^2 * I)
# Noise is not used for onestep sampling.
if len(self.timesteps) > 1:
noise = randn_tensor(
model_output.shape,
dtype=model_output.dtype,
device=model_output.device,
generator=generator,
)
else:
noise = torch.zeros_like(model_output)
z = noise * self.config.s_noise
sigma_hat = sigma_next.clamp(min=0, max=sigma_max)
print("denoise currently")
print(sigma_hat)
# origin
prev_sample = denoised + z * sigma_hat
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return AnimateLCMSVDStochasticIterativeSchedulerOutput(prev_sample=prev_sample)
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.FloatTensor,
) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(
device=original_samples.device, dtype=original_samples.dtype
)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(
original_samples.device, dtype=torch.float32
)
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(original_samples.device)
timesteps = timesteps.to(original_samples.device)
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
sigma = sigma.unsqueeze(-1)
noisy_samples = original_samples + noise * sigma
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
nodes.py
+37 -11
View File
@@ -19,15 +19,18 @@ from mimicmotion.pipelines.pipeline_mimicmotion import MimicMotionPipeline
from mimicmotion.modules.unet import UNetSpatioTemporalConditionModel
from mimicmotion.modules.pose_net import PoseNet
from lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
class MimicMotionModel(torch.nn.Module):
def __init__(self, base_model_path):
def __init__(self, base_model_path, lcm=False):
"""construnct base model components and load pretrained svd model except pose-net
Args:
base_model_path (str): pretrained svd model path
"""
super().__init__()
unet_subfolder = "unet_lcm" if lcm else "unet"
self.unet = UNetSpatioTemporalConditionModel.from_config(
UNetSpatioTemporalConditionModel.load_config(base_model_path, subfolder="unet", variant="fp16"))
UNetSpatioTemporalConditionModel.load_config(base_model_path, subfolder=unet_subfolder, variant="fp16"))
self.vae = AutoencoderKLTemporalDecoder.from_pretrained(
base_model_path, subfolder="vae", variant="fp16")
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
@@ -55,6 +58,7 @@ class DownloadAndLoadMimicMotionModel:
], {
"default": 'fp16'
}),
"lcm": ("BOOLEAN", {"default": False}),
},
}
@@ -64,7 +68,7 @@ class DownloadAndLoadMimicMotionModel:
FUNCTION = "loadmodel"
CATEGORY = "MimicMotionWrapper"
def loadmodel(self, precision, model):
def loadmodel(self, precision, model, lcm):
device = mm.get_torch_device()
mm.soft_empty_cache()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
@@ -86,9 +90,17 @@ class DownloadAndLoadMimicMotionModel:
pbar.update(1)
svd_path = os.path.join(folder_paths.models_dir, "diffusers", "stable-video-diffusion-img2vid-xt-1-1")
svd_lcm_path = os.path.join(folder_paths.models_dir, "diffusers", "stable-video-diffusion-img2vid-xt-1-1-lcm", "unet_lcm")
if lcm and not os.path.exists(svd_lcm_path):
print(f"Downloading AnimateLCM SVD model to: {model_path}")
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Kijai/AnimateLCM-SVD-Comfy",
allow_patterns=[f"*.json", "*diffusion_pytorch_model.fp16.safetensors*"],
local_dir=svd_path,
local_dir_use_symlinks=False)
else:
if not os.path.exists(svd_path):
#raise ValueError(f"Please download stable-video-diffusion-img2vid-xt-1-1 to {svd_path}")
print(f"Downloading SVD model to: {model_path}")
from huggingface_hub import snapshot_download
snapshot_download(repo_id="vdo/stable-video-diffusion-img2vid-xt-1-1",
@@ -97,16 +109,30 @@ class DownloadAndLoadMimicMotionModel:
local_dir_use_symlinks=False)
pbar.update(1)
mimicmotion_models = MimicMotionModel(svd_path).to(device=device).eval()
mimicmotion_models = MimicMotionModel(svd_path, lcm=lcm).to(device=device).eval()
mimicmotion_models.load_state_dict(comfy.utils.load_torch_file(model_path), strict=False)
if lcm:
lcm_noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
num_train_timesteps=40,
sigma_min=0.002,
sigma_max=700.0,
sigma_data=1.0,
s_noise=1.0,
rho=7,
clip_denoised=False,
)
scheduler = lcm_noise_scheduler
else:
scheduler = mimicmotion_models.noise_scheduler
pipeline = MimicMotionPipeline(
vae=mimicmotion_models.vae,
image_encoder=mimicmotion_models.image_encoder,
unet=mimicmotion_models.unet,
scheduler=mimicmotion_models.noise_scheduler,
feature_extractor=mimicmotion_models.feature_extractor,
pose_net=mimicmotion_models.pose_net,
vae = mimicmotion_models.vae,
image_encoder = mimicmotion_models.image_encoder,
unet = mimicmotion_models.unet,
scheduler = scheduler,
feature_extractor = mimicmotion_models.feature_extractor,
pose_net = mimicmotion_models.pose_net,
)
pipeline.unet.to(dtype)