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Unverified Commit d2fc5ebb authored by Dhruv Nair's avatar Dhruv Nair Committed by GitHub
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[Refactor] FreeInit for AnimateDiff based pipelines (#6874) 

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src/diffusers/pipelines/animatediff/pipeline_animatediff.py
View file @ d2fc5ebb
......@@ -13,12 +13,10 @@
# limitations under the License.
import inspect
import math
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import torch
import torch.fft as fft
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...image_processor import PipelineImageInput, VaeImageProcessor
......@@ -43,6 +41,7 @@ from ...utils import (
unscale_lora_layers,
)
from ...utils.torch_utils import randn_tensor
from ..free_init_utils import FreeInitMixin
from ..pipeline_utils import DiffusionPipeline
from .pipeline_output import AnimateDiffPipelineOutput
......@@ -87,72 +86,9 @@ def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type:
return outputs
def _get_freeinit_freq_filter(
shape: Tuple[int, ...],
device: Union[str, torch.dtype],
filter_type: str,
order: float,
spatial_stop_frequency: float,
temporal_stop_frequency: float,
) -> torch.Tensor:
r"""Returns the FreeInit filter based on filter type and other input conditions."""
T, H, W = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if spatial_stop_frequency == 0 or temporal_stop_frequency == 0:
return mask
if filter_type == "butterworth":
def retrieve_mask(x):
return 1 / (1 + (x / spatial_stop_frequency**2) ** order)
elif filter_type == "gaussian":
def retrieve_mask(x):
return math.exp(-1 / (2 * spatial_stop_frequency**2) * x)
elif filter_type == "ideal":
def retrieve_mask(x):
return 1 if x <= spatial_stop_frequency * 2 else 0
else:
raise NotImplementedError("`filter_type` must be one of gaussian, butterworth or ideal")
for t in range(T):
for h in range(H):
for w in range(W):
d_square = (
((spatial_stop_frequency / temporal_stop_frequency) * (2 * t / T - 1)) ** 2
+ (2 * h / H - 1) ** 2
+ (2 * w / W - 1) ** 2
)
mask[..., t, h, w] = retrieve_mask(d_square)
return mask.to(device)
def _freq_mix_3d(x: torch.Tensor, noise: torch.Tensor, LPF: torch.Tensor) -> torch.Tensor:
r"""Noise reinitialization."""
# FFT
x_freq = fft.fftn(x, dim=(-3, -2, -1))
x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))
noise_freq = fft.fftn(noise, dim=(-3, -2, -1))
noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))
# frequency mix
HPF = 1 - LPF
x_freq_low = x_freq * LPF
noise_freq_high = noise_freq * HPF
x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
# IFFT
x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))
x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real
return x_mixed
class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
class AnimateDiffPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FreeInitMixin
):
r"""
Pipeline for text-to-video generation.
......@@ -182,7 +118,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
"""
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder"]
_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
......@@ -204,6 +140,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if isinstance(unet, UNet2DConditionModel):
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
self.register_modules(
......@@ -530,63 +467,10 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
raise ValueError("The pipeline must have `unet` for using FreeU.")
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism if enabled."""
self.unet.disable_freeu()
@property
def free_init_enabled(self):
return hasattr(self, "_free_init_num_iters") and self._free_init_num_iters is not None
def enable_free_init(
self,
num_iters: int = 3,
use_fast_sampling: bool = False,
method: str = "butterworth",
order: int = 4,
spatial_stop_frequency: float = 0.25,
temporal_stop_frequency: float = 0.25,
generator: torch.Generator = None,
):
"""Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537.
This implementation has been adapted from the [official repository](https://github.com/TianxingWu/FreeInit).
Args:
num_iters (`int`, *optional*, defaults to `3`):
Number of FreeInit noise re-initialization iterations.
use_fast_sampling (`bool`, *optional*, defaults to `False`):
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables
the "Coarse-to-Fine Sampling" strategy, as mentioned in the paper, if set to `True`.
method (`str`, *optional*, defaults to `butterworth`):
Must be one of `butterworth`, `ideal` or `gaussian` to use as the filtering method for the
FreeInit low pass filter.
order (`int`, *optional*, defaults to `4`):
Order of the filter used in `butterworth` method. Larger values lead to `ideal` method behaviour
whereas lower values lead to `gaussian` method behaviour.
spatial_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as `d_s` in
the original implementation.
temporal_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as `d_t` in
the original implementation.
generator (`torch.Generator`, *optional*, defaults to `0.25`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
FreeInit generation deterministic.
"""
self._free_init_num_iters = num_iters
self._free_init_use_fast_sampling = use_fast_sampling
self._free_init_method = method
self._free_init_order = order
self._free_init_spatial_stop_frequency = spatial_stop_frequency
self._free_init_temporal_stop_frequency = temporal_stop_frequency
self._free_init_generator = generator
def disable_free_init(self):
"""Disables the FreeInit mechanism if enabled."""
self._free_init_num_iters = None
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
......@@ -691,158 +575,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
latents = latents * self.scheduler.init_noise_sigma
return latents
def _denoise_loop(
self,
timesteps,
num_inference_steps,
do_classifier_free_guidance,
guidance_scale,
num_warmup_steps,
prompt_embeds,
negative_prompt_embeds,
latents,
cross_attention_kwargs,
added_cond_kwargs,
extra_step_kwargs,
callback,
callback_steps,
callback_on_step_end,
callback_on_step_end_tensor_inputs,
):
"""Denoising loop for AnimateDiff."""
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)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample
# 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.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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)
return latents
def _free_init_loop(
self,
height,
width,
num_frames,
num_channels_latents,
batch_size,
num_videos_per_prompt,
denoise_args,
device,
):
"""Denoising loop for AnimateDiff using FreeInit noise reinitialization technique."""
latents = denoise_args.get("latents")
prompt_embeds = denoise_args.get("prompt_embeds")
timesteps = denoise_args.get("timesteps")
num_inference_steps = denoise_args.get("num_inference_steps")
latent_shape = (
batch_size * num_videos_per_prompt,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
free_init_filter_shape = (
1,
num_channels_latents,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
free_init_freq_filter = _get_freeinit_freq_filter(
shape=free_init_filter_shape,
device=device,
filter_type=self._free_init_method,
order=self._free_init_order,
spatial_stop_frequency=self._free_init_spatial_stop_frequency,
temporal_stop_frequency=self._free_init_temporal_stop_frequency,
)
with self.progress_bar(total=self._free_init_num_iters) as free_init_progress_bar:
for i in range(self._free_init_num_iters):
# For the first FreeInit iteration, the original latent is used without modification.
# Subsequent iterations apply the noise reinitialization technique.
if i == 0:
initial_noise = latents.detach().clone()
else:
current_diffuse_timestep = (
self.scheduler.config.num_train_timesteps - 1
) # diffuse to t=999 noise level
diffuse_timesteps = torch.full((batch_size,), current_diffuse_timestep).long()
z_T = self.scheduler.add_noise(
original_samples=latents, noise=initial_noise, timesteps=diffuse_timesteps.to(device)
).to(dtype=torch.float32)
z_rand = randn_tensor(
shape=latent_shape,
generator=self._free_init_generator,
device=device,
dtype=torch.float32,
)
latents = _freq_mix_3d(z_T, z_rand, LPF=free_init_freq_filter)
latents = latents.to(prompt_embeds.dtype)
# Coarse-to-Fine Sampling for faster inference (can lead to lower quality)
if self._free_init_use_fast_sampling:
current_num_inference_steps = int(num_inference_steps / self._free_init_num_iters * (i + 1))
self.scheduler.set_timesteps(current_num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
denoise_args.update({"timesteps": timesteps, "num_inference_steps": current_num_inference_steps})
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
denoise_args.update({"latents": latents, "num_warmup_steps": num_warmup_steps})
latents = self._denoise_loop(**denoise_args)
free_init_progress_bar.update()
return latents
def _retrieve_video_frames(self, latents, output_type, return_dict):
"""Helper function to handle latents to output conversion."""
if output_type == "latent":
return AnimateDiffPipelineOutput(frames=latents)
video_tensor = self.decode_latents(latents)
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
if not return_dict:
return (video,)
return AnimateDiffPipelineOutput(frames=video)
@property
def guidance_scale(self):
return self._guidance_scale
......@@ -1046,7 +778,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
self._num_timesteps = len(timesteps)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
......@@ -1068,43 +799,64 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
# 7. Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
denoise_args = {
"timesteps": timesteps,
"num_inference_steps": num_inference_steps,
"do_classifier_free_guidance": self.do_classifier_free_guidance,
"guidance_scale": guidance_scale,
"num_warmup_steps": num_warmup_steps,
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"latents": latents,
"cross_attention_kwargs": self.cross_attention_kwargs,
"added_cond_kwargs": added_cond_kwargs,
"extra_step_kwargs": extra_step_kwargs,
"callback": callback,
"callback_steps": callback_steps,
"callback_on_step_end": callback_on_step_end,
"callback_on_step_end_tensor_inputs": callback_on_step_end_tensor_inputs,
}
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
for free_init_iter in range(num_free_init_iters):
if self.free_init_enabled:
latents = self._free_init_loop(
height=height,
width=width,
num_frames=num_frames,
num_channels_latents=num_channels_latents,
batch_size=batch_size,
num_videos_per_prompt=num_videos_per_prompt,
denoise_args=denoise_args,
device=device,
latents, timesteps = self._apply_free_init(
latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
)
else:
latents = self._denoise_loop(**denoise_args)
video = self._retrieve_video_frames(latents, output_type, return_dict)
self._num_timesteps = len(timesteps)
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 self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample
# perform guidance
if self.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.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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 output_type == "latent":
return AnimateDiffPipelineOutput(frames=latents)
video_tensor = self.decode_latents(latents)
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
# 9. Offload all models
self.maybe_free_model_hooks()
return video
if not return_dict:
return (video,)
return AnimateDiffPipelineOutput(frames=video)
src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py
View file @ d2fc5ebb
......@@ -34,6 +34,7 @@ from ...schedulers import (
)
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import randn_tensor
from ..free_init_utils import FreeInitMixin
from ..pipeline_utils import DiffusionPipeline
from .pipeline_output import AnimateDiffPipelineOutput
......@@ -163,7 +164,9 @@ def retrieve_timesteps(
return timesteps, num_inference_steps
class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
class AnimateDiffVideoToVideoPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FreeInitMixin
):
r"""
Pipeline for video-to-video generation.
......@@ -193,7 +196,7 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
"""
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder"]
_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
......@@ -215,6 +218,7 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if isinstance(unet, UNet2DConditionModel):
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
self.register_modules(
......@@ -584,12 +588,12 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
if video is not None and latents is not None:
raise ValueError("Only one of `video` or `latents` should be provided")
def get_timesteps(self, num_inference_steps, strength, device):
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
timesteps = timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
......@@ -876,9 +880,8 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
self._num_timesteps = len(timesteps)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
......@@ -901,8 +904,19 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
# 7. Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 8. Denoising loop
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
for free_init_iter in range(num_free_init_iters):
if self.free_init_enabled:
latents, timesteps = self._apply_free_init(
latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
)
num_inference_steps = len(timesteps)
# make sure to readjust timesteps based on strength
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
self._num_timesteps = len(timesteps)
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
# 8. Denoising loop
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
......@@ -936,6 +950,8 @@ class AnimateDiffVideoToVideoPipeline(DiffusionPipeline, TextualInversionLoaderM
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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 output_type == "latent":
......
src/diffusers/pipelines/free_init_utils.py 0 → 100644
View file @ d2fc5ebb
# Copyright 2024 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.
import math
from typing import Tuple, Union
import torch
import torch.fft as fft
from ..utils.torch_utils import randn_tensor
class FreeInitMixin:
r"""Mixin class for FreeInit."""
def enable_free_init(
self,
num_iters: int = 3,
use_fast_sampling: bool = False,
method: str = "butterworth",
order: int = 4,
spatial_stop_frequency: float = 0.25,
temporal_stop_frequency: float = 0.25,
):
"""Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537.
This implementation has been adapted from the [official repository](https://github.com/TianxingWu/FreeInit).
Args:
num_iters (`int`, *optional*, defaults to `3`):
Number of FreeInit noise re-initialization iterations.
use_fast_sampling (`bool`, *optional*, defaults to `False`):
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables
the "Coarse-to-Fine Sampling" strategy, as mentioned in the paper, if set to `True`.
method (`str`, *optional*, defaults to `butterworth`):
Must be one of `butterworth`, `ideal` or `gaussian` to use as the filtering method for the
FreeInit low pass filter.
order (`int`, *optional*, defaults to `4`):
Order of the filter used in `butterworth` method. Larger values lead to `ideal` method behaviour
whereas lower values lead to `gaussian` method behaviour.
spatial_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as `d_s` in
the original implementation.
temporal_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as `d_t` in
the original implementation.
"""
self._free_init_num_iters = num_iters
self._free_init_use_fast_sampling = use_fast_sampling
self._free_init_method = method
self._free_init_order = order
self._free_init_spatial_stop_frequency = spatial_stop_frequency
self._free_init_temporal_stop_frequency = temporal_stop_frequency
def disable_free_init(self):
"""Disables the FreeInit mechanism if enabled."""
self._free_init_num_iters = None
@property
def free_init_enabled(self):
return hasattr(self, "_free_init_num_iters") and self._free_init_num_iters is not None
def _get_free_init_freq_filter(
self,
shape: Tuple[int, ...],
device: Union[str, torch.dtype],
filter_type: str,
order: float,
spatial_stop_frequency: float,
temporal_stop_frequency: float,
) -> torch.Tensor:
r"""Returns the FreeInit filter based on filter type and other input conditions."""
time, height, width = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if spatial_stop_frequency == 0 or temporal_stop_frequency == 0:
return mask
if filter_type == "butterworth":
def retrieve_mask(x):
return 1 / (1 + (x / spatial_stop_frequency**2) ** order)
elif filter_type == "gaussian":
def retrieve_mask(x):
return math.exp(-1 / (2 * spatial_stop_frequency**2) * x)
elif filter_type == "ideal":
def retrieve_mask(x):
return 1 if x <= spatial_stop_frequency * 2 else 0
else:
raise NotImplementedError("`filter_type` must be one of gaussian, butterworth or ideal")
for t in range(time):
for h in range(height):
for w in range(width):
d_square = (
((spatial_stop_frequency / temporal_stop_frequency) * (2 * t / time - 1)) ** 2
+ (2 * h / height - 1) ** 2
+ (2 * w / width - 1) ** 2
)
mask[..., t, h, w] = retrieve_mask(d_square)
return mask.to(device)
def _apply_freq_filter(self, x: torch.Tensor, noise: torch.Tensor, low_pass_filter: torch.Tensor) -> torch.Tensor:
r"""Noise reinitialization."""
# FFT
x_freq = fft.fftn(x, dim=(-3, -2, -1))
x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))
noise_freq = fft.fftn(noise, dim=(-3, -2, -1))
noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))
# frequency mix
high_pass_filter = 1 - low_pass_filter
x_freq_low = x_freq * low_pass_filter
noise_freq_high = noise_freq * high_pass_filter
x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
# IFFT
x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))
x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real
return x_mixed
def _apply_free_init(
self,
latents: torch.Tensor,
free_init_iteration: int,
num_inference_steps: int,
device: torch.device,
dtype: torch.dtype,
generator: torch.Generator,
):
if free_init_iteration == 0:
self._free_init_initial_noise = latents.detach().clone()
return latents, self.scheduler.timesteps
latent_shape = latents.shape
free_init_filter_shape = (1, *latent_shape[1:])
free_init_freq_filter = self._get_free_init_freq_filter(
shape=free_init_filter_shape,
device=device,
filter_type=self._free_init_method,
order=self._free_init_order,
spatial_stop_frequency=self._free_init_spatial_stop_frequency,
temporal_stop_frequency=self._free_init_temporal_stop_frequency,
)
current_diffuse_timestep = self.scheduler.config.num_train_timesteps - 1
diffuse_timesteps = torch.full((latent_shape[0],), current_diffuse_timestep).long()
z_t = self.scheduler.add_noise(
original_samples=latents, noise=self._free_init_initial_noise, timesteps=diffuse_timesteps.to(device)
).to(dtype=torch.float32)
z_rand = randn_tensor(
shape=latent_shape,
generator=generator,
device=device,
dtype=torch.float32,
)
latents = self._apply_freq_filter(z_t, z_rand, low_pass_filter=free_init_freq_filter)
latents = latents.to(dtype)
# Coarse-to-Fine Sampling for faster inference (can lead to lower quality)
if self._free_init_use_fast_sampling:
num_inference_steps = int(num_inference_steps / self._free_init_num_iters * (free_init_iteration + 1))
self.scheduler.set_timesteps(num_inference_steps, device=device)
return latents, self.scheduler.timesteps
src/diffusers/pipelines/pia/pipeline_pia.py
View file @ d2fc5ebb
......@@ -45,6 +45,7 @@ from ...utils import (
unscale_lora_layers,
)
from ...utils.torch_utils import randn_tensor
from ..free_init_utils import FreeInitMixin
from ..pipeline_utils import DiffusionPipeline
......@@ -210,7 +211,7 @@ class PIAPipelineOutput(BaseOutput):
class PIAPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin, FreeInitMixin
):
r"""
Pipeline for text-to-video generation.
......@@ -560,58 +561,6 @@ class PIAPipeline(
"""Disables the FreeU mechanism if enabled."""
self.unet.disable_freeu()
@property
def free_init_enabled(self):
return hasattr(self, "_free_init_num_iters") and self._free_init_num_iters is not None
def enable_free_init(
self,
num_iters: int = 3,
use_fast_sampling: bool = False,
method: str = "butterworth",
order: int = 4,
spatial_stop_frequency: float = 0.25,
temporal_stop_frequency: float = 0.25,
generator: Optional[torch.Generator] = None,
):
"""Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537.
This implementation has been adapted from the [official repository](https://github.com/TianxingWu/FreeInit).
Args:
num_iters (`int`, *optional*, defaults to `3`):
Number of FreeInit noise re-initialization iterations.
use_fast_sampling (`bool`, *optional*, defaults to `False`):
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables
the "Coarse-to-Fine Sampling" strategy, as mentioned in the paper, if set to `True`.
method (`str`, *optional*, defaults to `butterworth`):
Must be one of `butterworth`, `ideal` or `gaussian` to use as the filtering method for the
FreeInit low pass filter.
order (`int`, *optional*, defaults to `4`):
Order of the filter used in `butterworth` method. Larger values lead to `ideal` method behaviour
whereas lower values lead to `gaussian` method behaviour.
spatial_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as `d_s` in
the original implementation.
temporal_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as `d_t` in
the original implementation.
generator (`torch.Generator`, *optional*, defaults to `0.25`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
FreeInit generation deterministic.
"""
self._free_init_num_iters = num_iters
self._free_init_use_fast_sampling = use_fast_sampling
self._free_init_method = method
self._free_init_order = order
self._free_init_spatial_stop_frequency = spatial_stop_frequency
self._free_init_temporal_stop_frequency = temporal_stop_frequency
self._free_init_generator = generator
def disable_free_init(self):
"""Disables the FreeInit mechanism if enabled."""
self._free_init_num_iters = None
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
......@@ -795,143 +744,6 @@ class PIAPipeline(
return mask, masked_image
def _denoise_loop(
self,
timesteps,
num_inference_steps,
do_classifier_free_guidance,
guidance_scale,
num_warmup_steps,
prompt_embeds,
negative_prompt_embeds,
latents,
mask,
masked_image,
cross_attention_kwargs,
added_cond_kwargs,
extra_step_kwargs,
callback_on_step_end,
callback_on_step_end_tensor_inputs,
):
"""Denoising loop for PIA."""
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)
latent_model_input = torch.cat([latent_model_input, mask, masked_image], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample
# 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.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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()
return latents
def _free_init_loop(
self,
height,
width,
num_frames,
batch_size,
num_videos_per_prompt,
denoise_args,
device,
):
"""Denoising loop for PIA using FreeInit noise reinitialization technique."""
latents = denoise_args.get("latents")
prompt_embeds = denoise_args.get("prompt_embeds")
timesteps = denoise_args.get("timesteps")
num_inference_steps = denoise_args.get("num_inference_steps")
latent_shape = (
batch_size * num_videos_per_prompt,
4,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
free_init_filter_shape = (
1,
4,
num_frames,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
free_init_freq_filter = _get_freeinit_freq_filter(
shape=free_init_filter_shape,
device=device,
filter_type=self._free_init_method,
order=self._free_init_order,
spatial_stop_frequency=self._free_init_spatial_stop_frequency,
temporal_stop_frequency=self._free_init_temporal_stop_frequency,
)
with self.progress_bar(total=self._free_init_num_iters) as free_init_progress_bar:
for i in range(self._free_init_num_iters):
# For the first FreeInit iteration, the original latent is used without modification.
# Subsequent iterations apply the noise reinitialization technique.
if i == 0:
initial_noise = latents.detach().clone()
else:
current_diffuse_timestep = (
self.scheduler.config.num_train_timesteps - 1
) # diffuse to t=999 noise level
diffuse_timesteps = torch.full((batch_size,), current_diffuse_timestep).long()
z_T = self.scheduler.add_noise(
original_samples=latents, noise=initial_noise, timesteps=diffuse_timesteps.to(device)
).to(dtype=torch.float32)
z_rand = randn_tensor(
shape=latent_shape,
generator=self._free_init_generator,
device=device,
dtype=torch.float32,
)
latents = _freq_mix_3d(z_T, z_rand, LPF=free_init_freq_filter)
latents = latents.to(prompt_embeds.dtype)
# Coarse-to-Fine Sampling for faster inference (can lead to lower quality)
if self._free_init_use_fast_sampling:
current_num_inference_steps = int(num_inference_steps / self._free_init_num_iters * (i + 1))
self.scheduler.set_timesteps(current_num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
denoise_args.update({"timesteps": timesteps, "num_inference_steps": current_num_inference_steps})
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
denoise_args.update({"latents": latents, "num_warmup_steps": num_warmup_steps})
latents = self._denoise_loop(**denoise_args)
free_init_progress_bar.update()
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
......@@ -944,19 +756,6 @@ class PIAPipeline(
return timesteps, num_inference_steps - t_start
def _retrieve_video_frames(self, latents, output_type, return_dict):
"""Helper function to handle latents to output conversion."""
if output_type == "latent":
return PIAPipelineOutput(frames=latents)
video_tensor = self.decode_latents(latents)
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
if not return_dict:
return (video,)
return PIAPipelineOutput(frames=video)
@property
def guidance_scale(self):
return self._guidance_scale
......@@ -1191,41 +990,62 @@ class PIAPipeline(
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
denoise_args = {
"timesteps": timesteps,
"num_inference_steps": num_inference_steps,
"do_classifier_free_guidance": self.do_classifier_free_guidance,
"guidance_scale": guidance_scale,
"num_warmup_steps": num_warmup_steps,
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"latents": latents,
"mask": mask,
"masked_image": masked_image,
"cross_attention_kwargs": self.cross_attention_kwargs,
"added_cond_kwargs": added_cond_kwargs,
"extra_step_kwargs": extra_step_kwargs,
"callback_on_step_end": callback_on_step_end,
"callback_on_step_end_tensor_inputs": callback_on_step_end_tensor_inputs,
}
num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
for free_init_iter in range(num_free_init_iters):
if self.free_init_enabled:
latents = self._free_init_loop(
height=height,
width=width,
num_frames=num_frames,
batch_size=batch_size,
num_videos_per_prompt=num_videos_per_prompt,
denoise_args=denoise_args,
device=device,
latents, timesteps = self._apply_free_init(
latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
)
else:
latents = self._denoise_loop(**denoise_args)
video = self._retrieve_video_frames(latents, output_type, return_dict)
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 self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = torch.cat([latent_model_input, mask, masked_image], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample
# perform guidance
if self.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.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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 output_type == "latent":
return PIAPipelineOutput(frames=latents)
video_tensor = self.decode_latents(latents)
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
# 9. Offload all models
self.maybe_free_model_hooks()
return video
if not return_dict:
return (video,)
return PIAPipelineOutput(frames=video)
tests/pipelines/animatediff/test_animatediff.py
View file @ d2fc5ebb
......@@ -242,7 +242,6 @@ class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
inputs_normal = self.get_dummy_inputs(torch_device)
frames_normal = pipe(**inputs_normal).frames[0]
free_init_generator = torch.Generator(device=torch_device).manual_seed(0)
pipe.enable_free_init(
num_iters=2,
use_fast_sampling=True,
......@@ -250,7 +249,6 @@ class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
order=4,
spatial_stop_frequency=0.25,
temporal_stop_frequency=0.25,
generator=free_init_generator,
)
inputs_enable_free_init = self.get_dummy_inputs(torch_device)
frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]
......
tests/pipelines/animatediff/test_animatediff_video2video.py
View file @ d2fc5ebb
......@@ -267,3 +267,38 @@ class AnimateDiffVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.Tes
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
def test_free_init(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
pipe.to(torch_device)
inputs_normal = self.get_dummy_inputs(torch_device)
frames_normal = pipe(**inputs_normal).frames[0]
pipe.enable_free_init(
num_iters=2,
use_fast_sampling=True,
method="butterworth",
order=4,
spatial_stop_frequency=0.25,
temporal_stop_frequency=0.25,
)
inputs_enable_free_init = self.get_dummy_inputs(torch_device)
frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]
pipe.disable_free_init()
inputs_disable_free_init = self.get_dummy_inputs(torch_device)
frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]
sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()
max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()
self.assertGreater(
sum_enabled, 1e1, "Enabling of FreeInit should lead to results different from the default pipeline results"
)
self.assertLess(
max_diff_disabled,
1e-4,
"Disabling of FreeInit should lead to results similar to the default pipeline results",
)
tests/pipelines/pia/test_pia.py
View file @ d2fc5ebb
......@@ -255,7 +255,6 @@ class PIAPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
inputs_normal = self.get_dummy_inputs(torch_device)
frames_normal = pipe(**inputs_normal).frames[0]
free_init_generator = torch.Generator(device=torch_device).manual_seed(0)
pipe.enable_free_init(
num_iters=2,
use_fast_sampling=True,
......@@ -263,7 +262,6 @@ class PIAPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
order=4,
spatial_stop_frequency=0.25,
temporal_stop_frequency=0.25,
generator=free_init_generator,
)
inputs_enable_free_init = self.get_dummy_inputs(torch_device)
frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]
......
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