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# 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.
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from gmflow.gmflow import GMFlow
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from diffusers.models.attention_processor import Attention, AttnProcessor
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import BaseOutput, deprecate, logging
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def coords_grid(b, h, w, homogeneous=False, device=None):
y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]
stacks = [x, y]
if homogeneous:
ones = torch.ones_like(x) # [H, W]
stacks.append(ones)
grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]
grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]
if device is not None:
grid = grid.to(device)
return grid
def bilinear_sample(img, sample_coords, mode="bilinear", padding_mode="zeros", return_mask=False):
# img: [B, C, H, W]
# sample_coords: [B, 2, H, W] in image scale
if sample_coords.size(1) != 2: # [B, H, W, 2]
sample_coords = sample_coords.permute(0, 3, 1, 2)
b, _, h, w = sample_coords.shape
# Normalize to [-1, 1]
x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1
grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]
img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)
if return_mask:
mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W]
return img, mask
return img
def flow_warp(feature, flow, mask=False, mode="bilinear", padding_mode="zeros"):
b, c, h, w = feature.size()
assert flow.size(1) == 2
grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]
grid = grid.to(feature.dtype)
return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask)
def forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5):
# fwd_flow, bwd_flow: [B, 2, H, W]
# alpha and beta values are following UnFlow
# (https://arxiv.org/abs/1711.07837)
assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4
assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2
flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W]
warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W]
warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W]
diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W]
diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)
threshold = alpha * flow_mag + beta
fwd_occ = (diff_fwd > threshold).float() # [B, H, W]
bwd_occ = (diff_bwd > threshold).float()
return fwd_occ, bwd_occ
@torch.no_grad()
def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False, device=None):
if image3 is None:
image3 = image1
padder = InputPadder(image1.shape, padding_factor=8)
image1, image2 = padder.pad(image1[None].to(device), image2[None].to(device))
results_dict = flow_model(
image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True
)
flow_pr = results_dict["flow_preds"][-1] # [B, 2, H, W]
fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W]
bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W]
fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float
if pixel_consistency:
warped_image1 = flow_warp(image1, bwd_flow)
bwd_occ = torch.clamp(
bwd_occ + (abs(image2 - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1
).unsqueeze(0)
warped_results = flow_warp(image3, bwd_flow)
return warped_results, bwd_occ, bwd_flow
blur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18))
@dataclass
class TextToVideoSDPipelineOutput(BaseOutput):
"""
Output class for text-to-video pipelines.
Args:
frames (`List[np.ndarray]` or `torch.FloatTensor`)
List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as
a `torch` tensor. The length of the list denotes the video length (the number of frames).
"""
frames: Union[List[np.ndarray], torch.FloatTensor]
@torch.no_grad()
def find_flat_region(mask):
device = mask.device
kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device)
kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device)
mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode="replicate")
grad_x = torch.nn.functional.conv2d(mask_, kernel_x)
grad_y = torch.nn.functional.conv2d(mask_, kernel_y)
return ((abs(grad_x) + abs(grad_y)) == 0).float()[0]
class AttnState:
STORE = 0
LOAD = 1
LOAD_AND_STORE_PREV = 2
def __init__(self):
self.reset()
@property
def state(self):
return self.__state
@property
def timestep(self):
return self.__timestep
def set_timestep(self, t):
self.__timestep = t
def reset(self):
self.__state = AttnState.STORE
self.__timestep = 0
def to_load(self):
self.__state = AttnState.LOAD
def to_load_and_store_prev(self):
self.__state = AttnState.LOAD_AND_STORE_PREV
class CrossFrameAttnProcessor(AttnProcessor):
"""
Cross frame attention processor. Each frame attends the first frame and previous frame.
Args:
attn_state: Whether the model is processing the first frame or an intermediate frame
"""
def __init__(self, attn_state: AttnState):
super().__init__()
self.attn_state = attn_state
self.first_maps = {}
self.prev_maps = {}
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
# Is self attention
if encoder_hidden_states is None:
t = self.attn_state.timestep
if self.attn_state.state == AttnState.STORE:
self.first_maps[t] = hidden_states.detach()
self.prev_maps[t] = hidden_states.detach()
res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)
else:
if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
tmp = hidden_states.detach()
cross_map = torch.cat((self.first_maps[t], self.prev_maps[t]), dim=1)
res = super().__call__(attn, hidden_states, cross_map, attention_mask, temb)
if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
self.prev_maps[t] = tmp
else:
res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)
return res
def prepare_image(image):
if isinstance(image, torch.Tensor):
# Batch single image
if image.ndim == 3:
image = image.unsqueeze(0)
image = image.to(dtype=torch.float32)
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
return image
class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
r"""
Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
as a list, the outputs from each ControlNet are added together to create one combined additional
conditioning.
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`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder=None,
requires_safety_checker: bool = True,
device=None,
):
super().__init__(
vae,
text_encoder,
tokenizer,
unet,
controlnet,
scheduler,
safety_checker,
feature_extractor,
image_encoder,
requires_safety_checker,
)
self.to(device)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if isinstance(controlnet, (list, tuple)):
controlnet = MultiControlNetModel(controlnet)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
self.attn_state = AttnState()
attn_processor_dict = {}
for k in unet.attn_processors.keys():
if k.startswith("up"):
attn_processor_dict[k] = CrossFrameAttnProcessor(self.attn_state)
else:
attn_processor_dict[k] = AttnProcessor()
self.unet.set_attn_processor(attn_processor_dict)
flow_model = GMFlow(
feature_channels=128,
num_scales=1,
upsample_factor=8,
num_head=1,
attention_type="swin",
ffn_dim_expansion=4,
num_transformer_layers=6,
).to(self.device)
checkpoint = torch.utils.model_zoo.load_url(
"https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth",
map_location=lambda storage, loc: storage,
)
weights = checkpoint["model"] if "model" in checkpoint else checkpoint
flow_model.load_state_dict(weights, strict=False)
flow_model.eval()
self.flow_model = flow_model
# Modified from src/diffusers/pipelines/controlnet/pipeline_controlnet.StableDiffusionControlNetImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
control_guidance_end=1.0,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
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."
)
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
# 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 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.prepare_image
def prepare_control_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
# 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
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 :]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
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."
)
elif isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
frames: Union[List[np.ndarray], torch.FloatTensor] = None,
control_frames: Union[List[np.ndarray], torch.FloatTensor] = None,
strength: float = 0.8,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
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,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
warp_start: Union[float, List[float]] = 0.0,
warp_end: Union[float, List[float]] = 0.3,
mask_start: Union[float, List[float]] = 0.5,
mask_end: Union[float, List[float]] = 0.8,
smooth_boundary: bool = True,
mask_strength: Union[float, List[float]] = 0.5,
inner_strength: Union[float, List[float]] = 0.9,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
frames (`List[np.ndarray]` or `torch.FloatTensor`): The input images to be used as the starting point for the image generation process.
control_frames (`List[np.ndarray]` or `torch.FloatTensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.
strength ('float'): SDEdit strength.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
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`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](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 image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
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.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_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. Note that by default, we use a smaller conditioning scale for inpainting
than for [`~StableDiffusionControlNetPipeline.__call__`].
guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
you remove all prompts. The `guidance_scale` 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.
warp_start (`float`): Shape-aware fusion start timestep.
warp_end (`float`): Shape-aware fusion end timestep.
mask_start (`float`): Pixel-aware fusion start timestep.
mask_end (`float`):Pixel-aware fusion end timestep.
smooth_boundary (`bool`): Smooth fusion boundary. Set `True` to prevent artifacts at boundary.
mask_strength (`float`): Pixel-aware fusion strength.
inner_strength (`float`): Pixel-aware fusion detail level.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
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],
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
)
# 2. Define call parameters
# Currently we only support 1 prompt
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
assert False
else:
assert False
num_images_per_prompt = 1
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
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 = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Process the first frame
height, width = None, None
output_frames = []
self.attn_state.reset()
# 4.1 prepare frames
image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32)
first_image = image[0] # C, H, W
# 4.2 Prepare controlnet_conditioning_image
# Currently we only support single control
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_frames[0],
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
else:
assert False
# 4.3 Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
# 4.4 Prepare latent variables
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 4.5 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)
# 4.6 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)
first_x0_list = []
# 4.7 Denoising loop
num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order
with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
self.attn_state.set_timestep(t.item())
# 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)
# controlnet(s) inference
if guess_mode and 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
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]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=control_image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
# 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)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
first_x0 = pred_x0.detach()
first_x0_list.append(first_x0)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# 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 not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
first_result = image
prev_result = image
do_denormalize = [True] * image.shape[0]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
output_frames.append(image[0])
# 5. Process each frame
for idx in range(1, len(frames)):
image = frames[idx]
prev_image = frames[idx - 1]
control_image = control_frames[idx]
# 5.1 prepare frames
image = self.image_processor.preprocess(image).to(dtype=torch.float32)
prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)
warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
self.flow_model, first_image, image[0], first_result, False, self.device
)
blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))
blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)
warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(
self.flow_model, prev_image[0], image[0], prev_result, False, self.device
)
blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))
blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)
warp_mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8)
warp_flow = F.interpolate(bwd_flow_0 / 8.0, scale_factor=1.0 / 8, mode="bilinear")
# 5.2 Prepare controlnet_conditioning_image
# Currently we only support single control
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_image,
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
else:
assert False
# 5.3 Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
skip_t = int(num_inference_steps * (1 - strength))
warp_start_t = int(warp_start * num_inference_steps)
warp_end_t = int(warp_end * num_inference_steps)
mask_start_t = int(mask_start * num_inference_steps)
mask_end_t = int(mask_end * num_inference_steps)
# 5.4 Prepare latent variables
init_latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 5.5 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)
# 5.6 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)
# 5.7 Denoising loop
num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order
def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None):
dir_xt = 0
latents_dtype = latents.dtype
with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
self.attn_state.set_timestep(t.item())
if i + skip_t >= mask_start_t and i + skip_t <= mask_end_t and xtrg is not None:
rescale = torch.maximum(1.0 - mask, (1 - mask**2) ** 0.5 * inner_strength)
if noise_rescale is not None:
rescale = (1.0 - mask) * (1 - noise_rescale) + rescale * noise_rescale
noise = randn_tensor(xtrg.shape, generator=generator, device=device, dtype=xtrg.dtype)
latents_ref = self.scheduler.add_noise(xtrg, noise, t)
latents = latents_ref * mask + (1.0 - mask) * (latents - dir_xt) + rescale * dir_xt
latents = latents.to(latents_dtype)
# 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)
# controlnet(s) inference
if guess_mode and 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
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]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=control_image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [
torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples
]
mid_block_res_sample = torch.cat(
[torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
# 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)
# Get pred_x0 from scheduler
alpha_prod_t = self.scheduler.alphas_cumprod[t]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
if i + skip_t >= warp_start_t and i + skip_t <= warp_end_t:
# warp x_0
pred_x0 = (
flow_warp(first_x0_list[i], warp_flow, mode="nearest") * warp_mask
+ (1 - warp_mask) * pred_x0
)
# get x_t from x_0
latents = self.scheduler.add_noise(pred_x0, noise_pred, t).to(latents_dtype)
prev_t = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
if i == len(timesteps) - 1:
alpha_t_prev = 1.0
else:
alpha_t_prev = self.scheduler.alphas_cumprod[prev_t]
dir_xt = (1.0 - alpha_t_prev) ** 0.5 * noise_pred
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[
0
]
# 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
if mask_start_t <= mask_end_t:
self.attn_state.to_load()
else:
self.attn_state.to_load_and_store_prev()
latents = denoising_loop(init_latents)
if mask_start_t <= mask_end_t:
direct_result = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
blend_results = (1 - blend_mask_pre) * warped_pre + blend_mask_pre * direct_result
blend_results = (1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results
bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1)
blend_mask = blur(F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4))
blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1)
blend_results = blend_results.to(latents.dtype)
xtrg = self.vae.encode(blend_results).latent_dist.sample(generator)
xtrg = self.vae.config.scaling_factor * xtrg
blend_results_rec = self.vae.decode(xtrg / self.vae.config.scaling_factor, return_dict=False)[0]
xtrg_rec = self.vae.encode(blend_results_rec).latent_dist.sample(generator)
xtrg_rec = self.vae.config.scaling_factor * xtrg_rec
xtrg_ = xtrg + (xtrg - xtrg_rec)
blend_results_rec_new = self.vae.decode(xtrg_ / self.vae.config.scaling_factor, return_dict=False)[0]
tmp = (abs(blend_results_rec_new - blend_results).mean(dim=1, keepdims=True) > 0.25).float()
mask_x = F.max_pool2d(
(F.interpolate(tmp, scale_factor=1 / 8.0, mode="bilinear") > 0).float(),
kernel_size=3,
stride=1,
padding=1,
)
mask = 1 - F.max_pool2d(1 - blend_mask, kernel_size=8) # * (1-mask_x)
if smooth_boundary:
noise_rescale = find_flat_region(mask)
else:
noise_rescale = torch.ones_like(mask)
xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask
xtrg = xtrg.to(latents.dtype)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
self.attn_state.to_load_and_store_prev()
latents = denoising_loop(init_latents, mask * mask_strength, xtrg, noise_rescale)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
prev_result = image
do_denormalize = [True] * image.shape[0]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
output_frames.append(image[0])
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return output_frames
return TextToVideoSDPipelineOutput(frames=output_frames)
class InputPadder:
"""Pads images such that dimensions are divisible by 8"""
def __init__(self, dims, mode="sintel", padding_factor=8):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // padding_factor) + 1) * padding_factor - self.ht) % padding_factor
pad_wd = (((self.wd // padding_factor) + 1) * padding_factor - self.wd) % padding_factor
if mode == "sintel":
self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]
else:
self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, 0, pad_ht]
def pad(self, *inputs):
return [F.pad(x, self._pad, mode="replicate") for x in inputs]
def unpad(self, x):
ht, wd = x.shape[-2:]
c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
return x[..., c[0] : c[1], c[2] : c[3]]
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