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Unverified Commit 978dec90 authored by Aryan V S's avatar Aryan V S Committed by GitHub
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[Community] AnimateDiff + Controlnet Pipeline (#5928) 



* begin work on animatediff + controlnet pipeline

* complete todos, uncomment multicontrolnet, input checks
Co-Authored-By: default avatarEdoardoBotta <botta.edoardo@gmail.com>

* update
Co-Authored-By: default avatarEdoardoBotta <botta.edoardo@gmail.com>

* add example

* update community README

* Update examples/community/README.md

---------
Co-authored-by: default avatarEdoardoBotta <botta.edoardo@gmail.com>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
parent 79a7ab92
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examples/community/README.md
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......@@ -50,6 +50,7 @@ prompt-to-prompt | change parts of a prompt and retain image structure (see [pap
| Latent Consistency Interpolation Pipeline | Interpolate the latent space of Latent Consistency Models with multiple prompts | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
| Regional Prompting Pipeline | Assign multiple prompts for different regions | [Regional Prompting Pipeline](#regional-prompting-pipeline) | - | [hako-mikan](https://github.com/hako-mikan) |
| LDM3D-sr (LDM3D upscaler) | Upscale low resolution RGB and depth inputs to high resolution | [StableDiffusionUpscaleLDM3D Pipeline](https://github.com/estelleafl/diffusers/tree/ldm3d_upscaler_community/examples/community#stablediffusionupscaleldm3d-pipeline) | - | [Estelle Aflalo](https://github.com/estelleafl) |
| AnimateDiff ControlNet Pipeline | Combines AnimateDiff with precise motion control using ControlNets | [AnimateDiff ControlNet Pipeline](#animatediff-controlnet-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SKboYeGjEQmQPWoFC0aLYpBlYdHXkvAu?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) and [Edoardo Botta](https://github.com/EdoardoBotta) |
| DemoFusion Pipeline | Implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973) | [DemoFusion Pipeline](#DemoFusion) | - | [Ruoyi Du](https://github.com/RuoyiDu) |
To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
......@@ -2839,6 +2840,70 @@ The Pipeline supports `compel` syntax. Input prompts using the `compel` structur
* Reconstructed image:
* ![dps_generated_image](https://github.com/tongdaxu/Images/assets/22267548/b74f084d-93f4-4845-83d8-44c0fa758a5f)
### AnimateDiff ControlNet Pipeline
This pipeline combines AnimateDiff and ControlNet. Enjoy precise motion control for your videos! Refer to [this](https://github.com/huggingface/diffusers/issues/5866) issue for more details.
```py
import torch
from diffusers import AutoencoderKL, ControlNetModel, MotionAdapter
from diffusers.pipelines import DiffusionPipeline
from diffusers.schedulers import DPMSolverMultistepScheduler
from PIL import Image
motion_id = "guoyww/animatediff-motion-adapter-v1-5-2"
adapter = MotionAdapter.from_pretrained(motion_id)
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = DiffusionPipeline.from_pretrained(
model_id,
motion_adapter=adapter,
controlnet=controlnet,
vae=vae,
custom_pipeline="pipeline_animatediff_controlnet",
).to(device="cuda", dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(
model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
)
pipe.enable_vae_slicing()
conditioning_frames = []
for i in range(1, 16 + 1):
conditioning_frames.append(Image.open(f"frame_{i}.png"))
prompt = "astronaut in space, dancing"
negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
result = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=512,
height=768,
conditioning_frames=conditioning_frames,
num_inference_steps=12,
).frames[0]
from diffusers.utils import export_to_gif
export_to_gif(result.frames[0], "result.gif")
```
<table>
<tr><td colspan="2" align=center><b>Conditioning Frames</b></td></tr>
<tr align=center>
<td align=center><img src="https://user-images.githubusercontent.com/7365912/265043418-23291941-864d-495a-8ba8-d02e05756396.gif" alt="input-frames"></td>
</tr>
<tr><td colspan="2" align=center><b>AnimateDiff model: SG161222/Realistic_Vision_V5.1_noVAE</b></td></tr>
<tr>
<td align=center><img src="https://github.com/huggingface/diffusers/assets/72266394/baf301e2-d03c-4129-bd84-203a1de2b2be" alt="gif-1"></td>
<td align=center><img src="https://github.com/huggingface/diffusers/assets/72266394/9f923475-ecaf-452b-92c8-4e42171182d8" alt="gif-2"></td>
</tr>
<tr><td colspan="2" align=center><b>AnimateDiff model: CardosAnime</b></td></tr>
<tr>
<td align=center><img src="https://github.com/huggingface/diffusers/assets/72266394/b2c41028-38a0-45d6-86ed-fec7446b87f7" alt="gif-1"></td>
<td align=center><img src="https://github.com/huggingface/diffusers/assets/72266394/eb7d2952-72e4-44fa-b664-077c79b4fc70" alt="gif-2"></td>
</tr>
</table>
### DemoFusion
This pipeline is the official implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973).
The original repo can be found at [repo](https://github.com/PRIS-CV/DemoFusion).
......
examples/community/pipeline_animatediff_controlnet.py 0 → 100644
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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.
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.models.unet_motion_model import MotionAdapter
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import AutoencoderKL, ControlNetModel, MotionAdapter
>>> from diffusers.pipelines import DiffusionPipeline
>>> from diffusers.schedulers import DPMSolverMultistepScheduler
>>> from PIL import Image
>>> motion_id = "guoyww/animatediff-motion-adapter-v1-5-2"
>>> adapter = MotionAdapter.from_pretrained(motion_id)
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16)
>>> vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
>>> model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
>>> pipe = DiffusionPipeline.from_pretrained(
... model_id,
... motion_adapter=adapter,
... controlnet=controlnet,
... vae=vae,
... custom_pipeline="pipeline_animatediff_controlnet",
... ).to(device="cuda", dtype=torch.float16)
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(
... model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
... )
>>> pipe.enable_vae_slicing()
>>> conditioning_frames = []
>>> for i in range(1, 16 + 1):
... conditioning_frames.append(Image.open(f"frame_{i}.png"))
>>> prompt = "astronaut in space, dancing"
>>> negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
>>> result = pipe(
... prompt=prompt,
... negative_prompt=negative_prompt,
... width=512,
... height=768,
... conditioning_frames=conditioning_frames,
... num_inference_steps=12,
... ).frames[0]
>>> from diffusers.utils import export_to_gif
>>> export_to_gif(result.frames[0], "result.gif")
```
"""
def tensor2vid(video: torch.Tensor, processor, output_type="np"):
# Based on:
# https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
batch_size, channels, num_frames, height, width = video.shape
outputs = []
for batch_idx in range(batch_size):
batch_vid = video[batch_idx].permute(1, 0, 2, 3)
batch_output = processor.postprocess(batch_vid, output_type)
outputs.append(batch_output)
return outputs
@dataclass
class AnimateDiffControlNetPipelineOutput(BaseOutput):
frames: Union[torch.Tensor, np.ndarray]
class AnimateDiffControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
r"""
Pipeline for text-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
motion_adapter ([`MotionAdapter`]):
A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
motion_adapter: MotionAdapter,
controlnet: Union[ControlNetModel, MultiControlNetModel],
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
feature_extractor: Optional[CLIPImageProcessor] = None,
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
):
super().__init__()
unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
motion_adapter=motion_adapter,
controlnet=controlnet,
scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and 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
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.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
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = (
image[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ image.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
if not hasattr(self, "unet"):
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()
# 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
# 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
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
image=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
control_guidance_end=1.0,
):
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 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)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (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 negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
" prompts. The conditionings will be fixed across the prompts."
)
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if isinstance(image, list):
for image_ in image:
self.check_image(image_, prompt, prompt_embeds)
else:
self.check_image(image, prompt, prompt_embeds)
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if not isinstance(image, list):
raise TypeError("For multiple controlnets: `image` must be type `list`")
# When `image` is a nested list:
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
elif any(isinstance(i, list) for i in image):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif len(image) != len(self.controlnet.nets):
raise ValueError(
f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
)
for control_ in image:
for image_ in control_:
self.check_image(image_, prompt, prompt_embeds)
else:
assert False
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if isinstance(controlnet_conditioning_scale, list):
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
):
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False
if not isinstance(control_guidance_start, (tuple, list)):
control_guidance_start = [control_guidance_start]
if not isinstance(control_guidance_end, (tuple, list)):
control_guidance_end = [control_guidance_end]
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
)
if isinstance(self.controlnet, MultiControlNetModel):
if len(control_guidance_start) != len(self.controlnet.nets):
raise ValueError(
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
)
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
if end > 1.0:
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
)
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
num_channels_latents,
num_frames,
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:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
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
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
def prepare_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
image_batch_size = image.shape[0]
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
@property
def guidance_scale(self):
return self._guidance_scale
@property
def clip_skip(self):
return self._clip_skip
# 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.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
num_frames: 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,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
conditioning_frames: Optional[List[PipelineImageInput]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated video.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated video.
num_frames (`int`, *optional*, defaults to 16):
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
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 is generated by sampling using the supplied random `generator`. Latents should be of shape
`(batch_size, num_channel, num_frames, height, width)`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image (`PipelineImageInput`, *optional*):
Optional image input to work with IP Adapters.
conditioning_frames (`List[PipelineImageInput]`, *optional*):
The ControlNet input condition to provide guidance to the `unet` for generation. If multiple ControlNets
are specified, images must be passed as a list such that each element of the list can be correctly
batched for input to a single ControlNet.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
`np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
of a plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
the corresponding scale as a list.
guess_mode (`bool`, *optional*, defaults to `False`):
The ControlNet encoder tries to recognize the content of the input image even if you remove all
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the ControlNet stops applying.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
allback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeine class.
Examples:
Returns:
[`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
control_guidance_start, control_guidance_end = (
mult * [control_guidance_start],
mult * [control_guidance_end],
)
# 0. 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
num_videos_per_prompt = 1
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
height=height,
width=width,
callback_steps=callback_steps,
negative_prompt=negative_prompt,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image=conditioning_frames,
controlnet_conditioning_scale=controlnet_conditioning_scale,
control_guidance_start=control_guidance_start,
control_guidance_end=control_guidance_end,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(controlnet, ControlNetModel)
else controlnet.nets[0].config.global_pool_conditions
)
guess_mode = guess_mode or global_pool_conditions
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_videos_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
)
# 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
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_videos_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
if isinstance(controlnet, ControlNetModel):
conditioning_frames = self.prepare_image(
image=conditioning_frames,
width=width,
height=height,
batch_size=batch_size * num_videos_per_prompt * num_frames,
num_images_per_prompt=num_videos_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
elif isinstance(controlnet, MultiControlNetModel):
cond_prepared_frames = []
for frame_ in conditioning_frames:
prepared_frame = self.prepare_image(
image=frame_,
width=width,
height=height,
batch_size=batch_size * num_videos_per_prompt * num_frames,
num_images_per_prompt=num_videos_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
cond_prepared_frames.append(prepared_frame)
conditioning_frames = cond_prepared_frames
else:
assert False
# 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
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
num_frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
# 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 self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
if guess_mode and self.do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(num_frames, dim=0)
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
control_model_input = torch.transpose(control_model_input, 1, 2)
control_model_input = control_model_input.reshape(
(-1, control_model_input.shape[2], control_model_input.shape[3], control_model_input.shape[4])
)
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=conditioning_frames,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
).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 AnimateDiffControlNetPipelineOutput(frames=latents)
# Post-processing
video_tensor = self.decode_latents(latents)
if output_type == "pt":
video = video_tensor
else:
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return AnimateDiffControlNetPipelineOutput(frames=video)
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