# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import inspect
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 torch.utils.model_zoo
from einops import rearrange, repeat
from gmflow.gmflow import GMFlow
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
from diffusers.models.attention_processor import AttnProcessor2_0
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
USE_PEFT_BACKEND,
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
def clear_cache():
gc.collect()
torch.cuda.empty_cache()
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
class Dilate:
def __init__(self, kernel_size=7, channels=1, device="cpu"):
self.kernel_size = kernel_size
self.channels = channels
gaussian_kernel = torch.ones(1, 1, self.kernel_size, self.kernel_size)
gaussian_kernel = gaussian_kernel.repeat(self.channels, 1, 1, 1)
self.mean = (self.kernel_size - 1) // 2
gaussian_kernel = gaussian_kernel.to(device)
self.gaussian_filter = gaussian_kernel
def __call__(self, x):
x = F.pad(x, (self.mean, self.mean, self.mean, self.mean), "replicate")
return torch.clamp(F.conv2d(x, self.gaussian_filter, bias=None), 0, 1)
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
def numpy2tensor(img):
x0 = torch.from_numpy(img.copy()).float().cuda() / 255.0 * 2.0 - 1.0
x0 = torch.stack([x0], dim=0)
# einops.rearrange(x0, 'b h w c -> b c h w').clone()
return x0.permute(0, 3, 1, 2)
def calc_mean_std(feat, eps=1e-5, chunk=1):
size = feat.size()
assert len(size) == 4
if chunk == 2:
feat = torch.cat(feat.chunk(2), dim=3)
N, C = size[:2]
feat_var = feat.view(N // chunk, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N // chunk, C, -1).mean(dim=2).view(N // chunk, C, 1, 1)
return feat_mean.repeat(chunk, 1, 1, 1), feat_std.repeat(chunk, 1, 1, 1)
def adaptive_instance_normalization(content_feat, style_feat, chunk=1):
assert content_feat.size()[:2] == style_feat.size()[:2]
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat, chunk)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
def optimize_feature(
sample, flows, occs, correlation_matrix=[], intra_weight=1e2, iters=20, unet_chunk_size=2, optimize_temporal=True
):
"""
FRESO-guided latent feature optimization
* optimize spatial correspondence (match correlation_matrix)
* optimize temporal correspondence (match warped_image)
"""
if (flows is None or occs is None or (not optimize_temporal)) and (
intra_weight == 0 or len(correlation_matrix) == 0
):
return sample
# flows=[fwd_flows, bwd_flows]: (N-1)*2*H1*W1
# occs=[fwd_occs, bwd_occs]: (N-1)*H1*W1
# sample: 2N*C*H*W
torch.cuda.empty_cache()
video_length = sample.shape[0] // unet_chunk_size
latent = rearrange(sample.to(torch.float32), "(b f) c h w -> b f c h w", f=video_length)
cs = torch.nn.Parameter((latent.detach().clone()))
optimizer = torch.optim.Adam([cs], lr=0.2)
# unify resolution
if flows is not None and occs is not None:
scale = sample.shape[2] * 1.0 / flows[0].shape[2]
kernel = int(1 / scale)
bwd_flow_ = F.interpolate(flows[1] * scale, scale_factor=scale, mode="bilinear").repeat(
unet_chunk_size, 1, 1, 1
)
bwd_occ_ = F.max_pool2d(occs[1].unsqueeze(1), kernel_size=kernel).repeat(
unet_chunk_size, 1, 1, 1
) # 2(N-1)*1*H1*W1
fwd_flow_ = F.interpolate(flows[0] * scale, scale_factor=scale, mode="bilinear").repeat(
unet_chunk_size, 1, 1, 1
)
fwd_occ_ = F.max_pool2d(occs[0].unsqueeze(1), kernel_size=kernel).repeat(
unet_chunk_size, 1, 1, 1
) # 2(N-1)*1*H1*W1
# match frame 0,1,2,3 and frame 1,2,3,0
reshuffle_list = list(range(1, video_length)) + [0]
# attention_probs is the GRAM matrix of the normalized feature
attention_probs = None
for tmp in correlation_matrix:
if sample.shape[2] * sample.shape[3] == tmp.shape[1]:
attention_probs = tmp # 2N*HW*HW
break
n_iter = [0]
while n_iter[0] < iters:
def closure():
optimizer.zero_grad()
loss = 0
# temporal consistency loss
if optimize_temporal and flows is not None and occs is not None:
c1 = rearrange(cs[:, :], "b f c h w -> (b f) c h w")
c2 = rearrange(cs[:, reshuffle_list], "b f c h w -> (b f) c h w")
warped_image1 = flow_warp(c1, bwd_flow_)
warped_image2 = flow_warp(c2, fwd_flow_)
loss = (
abs((c2 - warped_image1) * (1 - bwd_occ_)) + abs((c1 - warped_image2) * (1 - fwd_occ_))
).mean() * 2
# spatial consistency loss
if attention_probs is not None and intra_weight > 0:
cs_vector = rearrange(cs, "b f c h w -> (b f) (h w) c")
# attention_scores = torch.bmm(cs_vector, cs_vector.transpose(-1, -2))
# cs_attention_probs = attention_scores.softmax(dim=-1)
cs_vector = cs_vector / ((cs_vector**2).sum(dim=2, keepdims=True) ** 0.5)
cs_attention_probs = torch.bmm(cs_vector, cs_vector.transpose(-1, -2))
tmp = F.l1_loss(cs_attention_probs, attention_probs) * intra_weight
loss = tmp + loss
loss.backward()
n_iter[0] += 1
return loss
optimizer.step(closure)
torch.cuda.empty_cache()
return adaptive_instance_normalization(rearrange(cs.data.to(sample.dtype), "b f c h w -> (b f) c h w"), sample)
@torch.no_grad()
def warp_tensor(sample, flows, occs, saliency, unet_chunk_size):
"""
Warp images or features based on optical flow
Fuse the warped imges or features based on occusion masks and saliency map
"""
scale = sample.shape[2] * 1.0 / flows[0].shape[2]
kernel = int(1 / scale)
bwd_flow_ = F.interpolate(flows[1] * scale, scale_factor=scale, mode="bilinear")
bwd_occ_ = F.max_pool2d(occs[1].unsqueeze(1), kernel_size=kernel) # (N-1)*1*H1*W1
if scale == 1:
bwd_occ_ = Dilate(kernel_size=13, device=sample.device)(bwd_occ_)
fwd_flow_ = F.interpolate(flows[0] * scale, scale_factor=scale, mode="bilinear")
fwd_occ_ = F.max_pool2d(occs[0].unsqueeze(1), kernel_size=kernel) # (N-1)*1*H1*W1
if scale == 1:
fwd_occ_ = Dilate(kernel_size=13, device=sample.device)(fwd_occ_)
scale2 = sample.shape[2] * 1.0 / saliency.shape[2]
saliency = F.interpolate(saliency, scale_factor=scale2, mode="bilinear")
latent = sample.to(torch.float32)
video_length = sample.shape[0] // unet_chunk_size
warp_saliency = flow_warp(saliency, bwd_flow_)
warp_saliency_ = flow_warp(saliency[0:1], fwd_flow_[video_length - 1 : video_length])
for j in range(unet_chunk_size):
for ii in range(video_length - 1):
i = video_length * j + ii
warped_image = flow_warp(latent[i : i + 1], bwd_flow_[ii : ii + 1])
mask = (1 - bwd_occ_[ii : ii + 1]) * saliency[ii + 1 : ii + 2] * warp_saliency[ii : ii + 1]
latent[i + 1 : i + 2] = latent[i + 1 : i + 2] * (1 - mask) + warped_image * mask
i = video_length * j
ii = video_length - 1
warped_image = flow_warp(latent[i : i + 1], fwd_flow_[ii : ii + 1])
mask = (1 - fwd_occ_[ii : ii + 1]) * saliency[ii : ii + 1] * warp_saliency_
latent[ii + i : ii + i + 1] = latent[ii + i : ii + i + 1] * (1 - mask) + warped_image * mask
return latent.to(sample.dtype)
def my_forward(
self,
steps=[],
layers=[0, 1, 2, 3],
flows=None,
occs=None,
correlation_matrix=[],
intra_weight=1e2,
iters=20,
optimize_temporal=True,
saliency=None,
):
"""
Hacked pipe.unet.forward()
copied from https://github.com/huggingface/diffusers/blob/v0.19.3/src/diffusers/models/unet_2d_condition.py#L700
if you are using a new version of diffusers, please copy the source code and modify it accordingly (find [HACK] in the code)
* restore and return the decoder features
* optimize the decoder features
* perform background smoothing
"""
def forward(
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
The [`UNet2DConditionModel`] forward method.
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, channel, height, width)`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
encoder_attention_mask (`torch.Tensor`):
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
which adds large negative values to the attention scores corresponding to "discard" tokens.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
aug_emb = None
if self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
# `Timesteps` does not contain any weights and will always return f32 tensors
# there might be better ways to encapsulate this.
class_labels = class_labels.to(dtype=sample.dtype)
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
if self.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
if self.config.addition_embed_type == "text":
aug_emb = self.add_embedding(encoder_hidden_states)
elif self.config.addition_embed_type == "text_image":
# Kandinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
aug_emb = self.add_embedding(text_embs, image_embs)
elif self.config.addition_embed_type == "text_time":
# SDXL - style
if "text_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
)
text_embeds = added_cond_kwargs.get("text_embeds")
if "time_ids" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
)
time_ids = added_cond_kwargs.get("time_ids")
time_embeds = self.add_time_proj(time_ids.flatten())
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(emb.dtype)
aug_emb = self.add_embedding(add_embeds)
elif self.config.addition_embed_type == "image":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
aug_emb = self.add_embedding(image_embs)
elif self.config.addition_embed_type == "image_hint":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
hint = added_cond_kwargs.get("hint")
aug_emb, hint = self.add_embedding(image_embs, hint)
sample = torch.cat([sample, hint], dim=1)
emb = emb + aug_emb if aug_emb is not None else emb
if self.time_embed_act is not None:
emb = self.time_embed_act(emb)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
# Kadinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
# For t2i-adapter CrossAttnDownBlock2D
additional_residuals = {}
if is_adapter and len(down_block_additional_residuals) > 0:
additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
**additional_residuals,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
if is_adapter and len(down_block_additional_residuals) > 0:
sample += down_block_additional_residuals.pop(0)
down_block_res_samples += res_samples
if is_controlnet:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
if is_controlnet:
sample = sample + mid_block_additional_residual
# 5. up
"""
[HACK] restore the decoder features in up_samples
"""
up_samples = ()
# down_samples = ()
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
"""
[HACK] restore the decoder features in up_samples
[HACK] optimize the decoder features
[HACK] perform background smoothing
"""
if i in layers:
up_samples += (sample,)
if timestep in steps and i in layers:
sample = optimize_feature(
sample, flows, occs, correlation_matrix, intra_weight, iters, optimize_temporal=optimize_temporal
)
if saliency is not None:
sample = warp_tensor(sample, flows, occs, saliency, 2)
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
"""
[HACK] return the output feature as well as the decoder features
"""
if not return_dict:
return (sample,) + up_samples
return UNet2DConditionOutput(sample=sample)
return forward
@torch.no_grad()
def get_single_mapping_ind(bwd_flow, bwd_occ, imgs, scale=1.0):
"""
FLATTEN: Optical fLow-guided attention (Temoporal-guided attention)
Find the correspondence between every pixels in a pair of frames
[input]
bwd_flow: 1*2*H*W
bwd_occ: 1*H*W i.e., f2 = warp(f1, bwd_flow) * bwd_occ
imgs: 2*3*H*W i.e., [f1,f2]
[output]
mapping_ind: pixel index correspondence
unlinkedmask: indicate whether a pixel has no correspondence
i.e., f2 = f1[mapping_ind] * unlinkedmask
"""
flows = F.interpolate(bwd_flow, scale_factor=1.0 / scale, mode="bilinear")[0][[1, 0]] / scale # 2*H*W
_, H, W = flows.shape
masks = torch.logical_not(F.interpolate(bwd_occ[None], scale_factor=1.0 / scale, mode="bilinear") > 0.5)[
0
] # 1*H*W
frames = F.interpolate(imgs, scale_factor=1.0 / scale, mode="bilinear").view(2, 3, -1) # 2*3*HW
grid = torch.stack(torch.meshgrid([torch.arange(H), torch.arange(W)]), dim=0).to(flows.device) # 2*H*W
warp_grid = torch.round(grid + flows)
mask = torch.logical_and(
torch.logical_and(
torch.logical_and(torch.logical_and(warp_grid[0] >= 0, warp_grid[0] < H), warp_grid[1] >= 0),
warp_grid[1] < W,
),
masks[0],
).view(-1) # HW
warp_grid = warp_grid.view(2, -1) # 2*HW
warp_ind = (warp_grid[0] * W + warp_grid[1]).to(torch.long) # HW
mapping_ind = torch.zeros_like(warp_ind) - 1 # HW
for f0ind, f1ind in enumerate(warp_ind):
if mask[f0ind]:
if mapping_ind[f1ind] == -1:
mapping_ind[f1ind] = f0ind
else:
targetv = frames[0, :, f1ind]
pref0ind = mapping_ind[f1ind]
prev = frames[1, :, pref0ind]
v = frames[1, :, f0ind]
if ((prev - targetv) ** 2).mean() > ((v - targetv) ** 2).mean():
mask[pref0ind] = False
mapping_ind[f1ind] = f0ind
else:
mask[f0ind] = False
unusedind = torch.arange(len(mask)).to(mask.device)[~mask]
unlinkedmask = mapping_ind == -1
mapping_ind[unlinkedmask] = unusedind
return mapping_ind, unlinkedmask
@torch.no_grad()
def get_mapping_ind(bwd_flows, bwd_occs, imgs, scale=1.0):
"""
FLATTEN: Optical fLow-guided attention (Temoporal-guided attention)
Find pixel correspondence between every consecutive frames in a batch
[input]
bwd_flow: (N-1)*2*H*W
bwd_occ: (N-1)*H*W
imgs: N*3*H*W
[output]
fwd_mappings: N*1*HW
bwd_mappings: N*1*HW
flattn_mask: HW*1*N*N
i.e., imgs[i,:,fwd_mappings[i]] corresponds to imgs[0]
i.e., imgs[i,:,fwd_mappings[i]][:,bwd_mappings[i]] restore the original imgs[i]
"""
N, H, W = imgs.shape[0], int(imgs.shape[2] // scale), int(imgs.shape[3] // scale)
iterattn_mask = torch.ones(H * W, N, N, dtype=torch.bool).to(imgs.device)
for i in range(len(imgs) - 1):
one_mask = torch.ones(N, N, dtype=torch.bool).to(imgs.device)
one_mask[: i + 1, i + 1 :] = False
one_mask[i + 1 :, : i + 1] = False
mapping_ind, unlinkedmask = get_single_mapping_ind(
bwd_flows[i : i + 1], bwd_occs[i : i + 1], imgs[i : i + 2], scale
)
if i == 0:
fwd_mapping = [torch.arange(len(mapping_ind)).to(mapping_ind.device)]
bwd_mapping = [torch.arange(len(mapping_ind)).to(mapping_ind.device)]
iterattn_mask[unlinkedmask[fwd_mapping[-1]]] = torch.logical_and(
iterattn_mask[unlinkedmask[fwd_mapping[-1]]], one_mask
)
fwd_mapping += [mapping_ind[fwd_mapping[-1]]]
bwd_mapping += [torch.sort(fwd_mapping[-1])[1]]
fwd_mappings = torch.stack(fwd_mapping, dim=0).unsqueeze(1)
bwd_mappings = torch.stack(bwd_mapping, dim=0).unsqueeze(1)
return fwd_mappings, bwd_mappings, iterattn_mask.unsqueeze(1)
def apply_FRESCO_opt(
pipe,
steps=[],
layers=[0, 1, 2, 3],
flows=None,
occs=None,
correlation_matrix=[],
intra_weight=1e2,
iters=20,
optimize_temporal=True,
saliency=None,
):
"""
Apply FRESCO-based optimization to a StableDiffusionPipeline
"""
pipe.unet.forward = my_forward(
pipe.unet, steps, layers, flows, occs, correlation_matrix, intra_weight, iters, optimize_temporal, saliency
)
@torch.no_grad()
def get_intraframe_paras(pipe, imgs, frescoProc, prompt_embeds, do_classifier_free_guidance=True, generator=None):
"""
Get parameters for spatial-guided attention and optimization
* perform one step denoising
* collect attention feature, stored in frescoProc.controller.stored_attn['decoder_attn']
* compute the gram matrix of the normalized feature for spatial consistency loss
"""
noise_scheduler = pipe.scheduler
timestep = noise_scheduler.timesteps[-1]
device = pipe._execution_device
B, C, H, W = imgs.shape
frescoProc.controller.disable_controller()
apply_FRESCO_opt(pipe)
frescoProc.controller.clear_store()
frescoProc.controller.enable_store()
latents = pipe.prepare_latents(
imgs.to(pipe.unet.dtype), timestep, B, 1, prompt_embeds.dtype, device, generator=generator, repeat_noise=False
)
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
model_output = pipe.unet(
latent_model_input,
timestep,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=None,
return_dict=False,
)
frescoProc.controller.disable_store()
# gram matrix of the normalized feature for spatial consistency loss
correlation_matrix = []
for tmp in model_output[1:]:
latent_vector = rearrange(tmp, "b c h w -> b (h w) c")
latent_vector = latent_vector / ((latent_vector**2).sum(dim=2, keepdims=True) ** 0.5)
attention_probs = torch.bmm(latent_vector, latent_vector.transpose(-1, -2))
correlation_matrix += [attention_probs.detach().clone().to(torch.float32)]
del attention_probs, latent_vector, tmp
del model_output
clear_cache()
return correlation_matrix
@torch.no_grad()
def get_flow_and_interframe_paras(flow_model, imgs):
"""
Get parameters for temporal-guided attention and optimization
* predict optical flow and occlusion mask
* compute pixel index correspondence for FLATTEN
"""
images = torch.stack([torch.from_numpy(img).permute(2, 0, 1).float() for img in imgs], dim=0).cuda()
imgs_torch = torch.cat([numpy2tensor(img) for img in imgs], dim=0)
reshuffle_list = list(range(1, len(images))) + [0]
results_dict = flow_model(
images,
images[reshuffle_list],
attn_splits_list=[2],
corr_radius_list=[-1],
prop_radius_list=[-1],
pred_bidir_flow=True,
)
flow_pr = results_dict["flow_preds"][-1] # [2*B, 2, H, W]
fwd_flows, bwd_flows = flow_pr.chunk(2) # [B, 2, H, W]
fwd_occs, bwd_occs = forward_backward_consistency_check(fwd_flows, bwd_flows) # [B, H, W]
warped_image1 = flow_warp(images, bwd_flows)
bwd_occs = torch.clamp(
bwd_occs + (abs(images[reshuffle_list] - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1
)
warped_image2 = flow_warp(images[reshuffle_list], fwd_flows)
fwd_occs = torch.clamp(fwd_occs + (abs(images - warped_image2).mean(dim=1) > 255 * 0.25).float(), 0, 1)
attn_mask = []
for scale in [8.0, 16.0, 32.0]:
bwd_occs_ = F.interpolate(bwd_occs[:-1].unsqueeze(1), scale_factor=1.0 / scale, mode="bilinear")
attn_mask += [
torch.cat((bwd_occs_[0:1].reshape(1, -1) > -1, bwd_occs_.reshape(bwd_occs_.shape[0], -1) > 0.5), dim=0)
]
fwd_mappings = []
bwd_mappings = []
interattn_masks = []
for scale in [8.0, 16.0]:
fwd_mapping, bwd_mapping, interattn_mask = get_mapping_ind(bwd_flows, bwd_occs, imgs_torch, scale=scale)
fwd_mappings += [fwd_mapping]
bwd_mappings += [bwd_mapping]
interattn_masks += [interattn_mask]
interattn_paras = {}
interattn_paras["fwd_mappings"] = fwd_mappings
interattn_paras["bwd_mappings"] = bwd_mappings
interattn_paras["interattn_masks"] = interattn_masks
clear_cache()
return [fwd_flows, bwd_flows], [fwd_occs, bwd_occs], attn_mask, interattn_paras
class AttentionControl:
"""
Control FRESCO-based attention
* enable/diable spatial-guided attention
* enable/diable temporal-guided attention
* enable/diable cross-frame attention
* collect intermediate attention feature (for spatial-guided attention)
"""
def __init__(self):
self.stored_attn = self.get_empty_store()
self.store = False
self.index = 0
self.attn_mask = None
self.interattn_paras = None
self.use_interattn = False
self.use_cfattn = False
self.use_intraattn = False
self.intraattn_bias = 0
self.intraattn_scale_factor = 0.2
self.interattn_scale_factor = 0.2
@staticmethod
def get_empty_store():
return {
"decoder_attn": [],
}
def clear_store(self):
del self.stored_attn
torch.cuda.empty_cache()
gc.collect()
self.stored_attn = self.get_empty_store()
self.disable_intraattn()
# store attention feature of the input frame for spatial-guided attention
def enable_store(self):
self.store = True
def disable_store(self):
self.store = False
# spatial-guided attention
def enable_intraattn(self):
self.index = 0
self.use_intraattn = True
self.disable_store()
if len(self.stored_attn["decoder_attn"]) == 0:
self.use_intraattn = False
def disable_intraattn(self):
self.index = 0
self.use_intraattn = False
self.disable_store()
def disable_cfattn(self):
self.use_cfattn = False
# cross frame attention
def enable_cfattn(self, attn_mask=None):
if attn_mask:
if self.attn_mask:
del self.attn_mask
torch.cuda.empty_cache()
self.attn_mask = attn_mask
self.use_cfattn = True
else:
if self.attn_mask:
self.use_cfattn = True
else:
print("Warning: no valid cross-frame attention parameters available!")
self.disable_cfattn()
def disable_interattn(self):
self.use_interattn = False
# temporal-guided attention
def enable_interattn(self, interattn_paras=None):
if interattn_paras:
if self.interattn_paras:
del self.interattn_paras
torch.cuda.empty_cache()
self.interattn_paras = interattn_paras
self.use_interattn = True
else:
if self.interattn_paras:
self.use_interattn = True
else:
print("Warning: no valid temporal-guided attention parameters available!")
self.disable_interattn()
def disable_controller(self):
self.disable_intraattn()
self.disable_interattn()
self.disable_cfattn()
def enable_controller(self, interattn_paras=None, attn_mask=None):
self.enable_intraattn()
self.enable_interattn(interattn_paras)
self.enable_cfattn(attn_mask)
def forward(self, context):
if self.store:
self.stored_attn["decoder_attn"].append(context.detach())
if self.use_intraattn and len(self.stored_attn["decoder_attn"]) > 0:
tmp = self.stored_attn["decoder_attn"][self.index]
self.index = self.index + 1
if self.index >= len(self.stored_attn["decoder_attn"]):
self.index = 0
self.disable_store()
return tmp
return context
def __call__(self, context):
context = self.forward(context)
return context
class FRESCOAttnProcessor2_0:
"""
Hack self attention to FRESCO-based attention
* adding spatial-guided attention
* adding temporal-guided attention
* adding cross-frame attention
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
Usage
frescoProc = FRESCOAttnProcessor2_0(2, attn_mask)
attnProc = AttnProcessor2_0()
attn_processor_dict = {}
for k in pipe.unet.attn_processors.keys():
if k.startswith("up_blocks.2") or k.startswith("up_blocks.3"):
attn_processor_dict[k] = frescoProc
else:
attn_processor_dict[k] = attnProc
pipe.unet.set_attn_processor(attn_processor_dict)
"""
def __init__(self, unet_chunk_size=2, controller=None):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.unet_chunk_size = unet_chunk_size
self.controller = controller
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
crossattn = False
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
if self.controller and self.controller.store:
self.controller(hidden_states.detach().clone())
else:
crossattn = True
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
# BC * HW * 8D
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query_raw, key_raw = None, None
if self.controller and self.controller.use_interattn and (not crossattn):
query_raw, key_raw = query.clone(), key.clone()
inner_dim = key.shape[-1] # 8D
head_dim = inner_dim // attn.heads # D
"""for efficient cross-frame attention"""
if self.controller and self.controller.use_cfattn and (not crossattn):
video_length = key.size()[0] // self.unet_chunk_size
former_frame_index = [0] * video_length
attn_mask = None
if self.controller.attn_mask is not None:
for m in self.controller.attn_mask:
if m.shape[1] == key.shape[1]:
attn_mask = m
# BC * HW * 8D --> B * C * HW * 8D
key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
# B * C * HW * 8D --> B * C * HW * 8D
if attn_mask is None:
key = key[:, former_frame_index]
else:
key = repeat(key[:, attn_mask], "b d c -> b f d c", f=video_length)
# B * C * HW * 8D --> BC * HW * 8D
key = rearrange(key, "b f d c -> (b f) d c").detach()
value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
if attn_mask is None:
value = value[:, former_frame_index]
else:
value = repeat(value[:, attn_mask], "b d c -> b f d c", f=video_length)
value = rearrange(value, "b f d c -> (b f) d c").detach()
# BC * HW * 8D --> BC * HW * 8 * D --> BC * 8 * HW * D
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# BC * 8 * HW2 * D
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# BC * 8 * HW2 * D2
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
"""for spatial-guided intra-frame attention"""
if self.controller and self.controller.use_intraattn and (not crossattn):
ref_hidden_states = self.controller(None)
assert ref_hidden_states.shape == encoder_hidden_states.shape
query_ = attn.to_q(ref_hidden_states)
key_ = attn.to_k(ref_hidden_states)
# BC * 8 * HW * D
query_ = query_.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_ = key_.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
query = F.scaled_dot_product_attention(
query_,
key_ * self.controller.intraattn_scale_factor,
query,
attn_mask=torch.eye(query_.size(-2), key_.size(-2), dtype=query.dtype, device=query.device)
* self.controller.intraattn_bias,
).detach()
del query_, key_
torch.cuda.empty_cache()
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
# output: BC * 8 * HW * D2
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
"""for temporal-guided inter-frame attention (FLATTEN)"""
if self.controller and self.controller.use_interattn and (not crossattn):
del query, key, value
torch.cuda.empty_cache()
bwd_mapping = None
fwd_mapping = None
for i, f in enumerate(self.controller.interattn_paras["fwd_mappings"]):
if f.shape[2] == hidden_states.shape[2]:
fwd_mapping = f
bwd_mapping = self.controller.interattn_paras["bwd_mappings"][i]
interattn_mask = self.controller.interattn_paras["interattn_masks"][i]
video_length = key_raw.size()[0] // self.unet_chunk_size
# BC * HW * 8D --> C * 8BD * HW
key = rearrange(key_raw, "(b f) d c -> f (b c) d", f=video_length)
query = rearrange(query_raw, "(b f) d c -> f (b c) d", f=video_length)
# BC * 8 * HW * D --> C * 8BD * HW
# key = rearrange(hidden_states, "(b f) h d c -> f (b h c) d", f=video_length) ########
# query = rearrange(hidden_states, "(b f) h d c -> f (b h c) d", f=video_length) #######
value = rearrange(hidden_states, "(b f) h d c -> f (b h c) d", f=video_length)
key = torch.gather(key, 2, fwd_mapping.expand(-1, key.shape[1], -1))
query = torch.gather(query, 2, fwd_mapping.expand(-1, query.shape[1], -1))
value = torch.gather(value, 2, fwd_mapping.expand(-1, value.shape[1], -1))
# C * 8BD * HW --> BHW, C, 8D
key = rearrange(key, "f (b c) d -> (b d) f c", b=self.unet_chunk_size)
query = rearrange(query, "f (b c) d -> (b d) f c", b=self.unet_chunk_size)
value = rearrange(value, "f (b c) d -> (b d) f c", b=self.unet_chunk_size)
# BHW * C * 8D --> BHW * C * 8 * D--> BHW * 8 * C * D
query = query.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()
key = key.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()
value = value.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()
hidden_states_ = F.scaled_dot_product_attention(
query,
key * self.controller.interattn_scale_factor,
value,
# .to(query.dtype)-1.0) * 1e6 -
attn_mask=(interattn_mask.repeat(self.unet_chunk_size, 1, 1, 1)),
# torch.eye(interattn_mask.shape[2]).to(query.device).to(query.dtype) * 1e4,
)
# BHW * 8 * C * D --> C * 8BD * HW
hidden_states_ = rearrange(hidden_states_, "(b d) h f c -> f (b h c) d", b=self.unet_chunk_size)
hidden_states_ = torch.gather(
hidden_states_, 2, bwd_mapping.expand(-1, hidden_states_.shape[1], -1)
).detach()
# C * 8BD * HW --> BC * 8 * HW * D
hidden_states = rearrange(
hidden_states_, "f (b h c) d -> (b f) h d c", b=self.unet_chunk_size, h=attn.heads
)
# BC * 8 * HW * D --> BC * HW * 8D
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
def apply_FRESCO_attn(pipe):
"""
Apply FRESCO-guided attention to a StableDiffusionPipeline
"""
frescoProc = FRESCOAttnProcessor2_0(2, AttentionControl())
attnProc = AttnProcessor2_0()
attn_processor_dict = {}
for k in pipe.unet.attn_processors.keys():
if k.startswith("up_blocks.2") or k.startswith("up_blocks.3"):
attn_processor_dict[k] = frescoProc
else:
attn_processor_dict[k] = attnProc
pipe.unet.set_attn_processor(attn_processor_dict)
return frescoProc
def retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
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 FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):
r"""
Pipeline for video-to-video translation using Stable Diffusion with FRESCO Algorithm.
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.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~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 ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` 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 more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True,
):
super().__init__(
vae,
text_encoder,
tokenizer,
unet,
controlnet,
scheduler,
safety_checker,
feature_extractor,
image_encoder,
requires_safety_checker,
)
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,
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, 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)
frescoProc = FRESCOAttnProcessor2_0(2, AttentionControl())
attnProc = AttnProcessor2_0()
attn_processor_dict = {}
for k in self.unet.attn_processors.keys():
if k.startswith("up_blocks.2") or k.startswith("up_blocks.3"):
attn_processor_dict[k] = frescoProc
else:
attn_processor_dict[k] = attnProc
self.unet.set_attn_processor(attn_processor_dict)
self.frescoProc = frescoProc
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
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_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,
**kwargs,
):
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_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: process 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: process 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, output_hidden_states=None):
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)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
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.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
raise ValueError(
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
)
image_embeds = []
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encode_image(
single_ip_adapter_image, device, 1, output_hidden_state
)
single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
single_negative_image_embeds = torch.stack(
[single_negative_image_embeds] * num_images_per_prompt, dim=0
)
if do_classifier_free_guidance:
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
single_image_embeds = single_image_embeds.to(device)
image_embeds.append(single_image_embeds)
else:
repeat_dims = [1]
image_embeds = []
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
single_image_embeds = single_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
)
single_negative_image_embeds = single_negative_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
else:
single_image_embeds = single_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
)
image_embeds.append(single_image_embeds)
return image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# 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
def check_inputs(
self,
prompt,
image,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
ip_adapter_image=None,
ip_adapter_image_embeds=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
control_guidance_end=1.0,
callback_on_step_end_tensor_inputs=None,
):
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)
):
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 image_ in image:
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 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.")
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
raise ValueError(
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
)
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.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], PIL.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.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 :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(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, repeat_noise, 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 = [
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encode(image), generator=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
if repeat_noise:
noise = randn_tensor((1, *shape[1:]), generator=generator, device=device, dtype=dtype)
one_tuple = (1,) * (len(shape) - 1)
noise = noise.repeat(batch_size, *one_tuple)
else:
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
@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,
frames: Union[List[np.ndarray], torch.FloatTensor] = None,
control_frames: Union[List[np.ndarray], torch.FloatTensor] = None,
height: Optional[int] = None,
width: Optional[int] = None,
strength: float = 0.8,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_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,
ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = 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]] = 0.8,
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"],
end_opt_step=15,
num_intraattn_steps=1,
step_interattn_end=350,
**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`.
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.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
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):
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`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
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 image
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`.
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.
ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
provided, embeddings are computed from the `ip_adapter_image` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.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.
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.
callback_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 pipeline class.
end_opt_step:
The feature optimization is activated from strength * num_inference_step to end_opt_step.
num_intraattn_steps:
Apply num_interattn_steps steps of spatial-guided attention.
step_interattn_end:
Apply temporal-guided attention in [step_interattn_end, 1000] steps
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
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],
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
control_frames[0],
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
# 2. Define call parameters
batch_size = len(frames)
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 = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_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,
)
prompt_embeds = prompt_embeds.repeat(batch_size, 1, 1)
negative_prompt_embeds = negative_prompt_embeds.repeat(batch_size, 1, 1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
batch_size * num_images_per_prompt,
self.do_classifier_free_guidance,
)
# 4. Prepare image
imgs_np = []
for frame in frames:
if isinstance(frame, PIL.Image.Image):
imgs_np.append(np.asarray(frame))
else:
# np.ndarray
imgs_np.append(frame)
images_pt = self.image_processor.preprocess(frames).to(dtype=torch.float32)
# 5. Prepare controlnet_conditioning_image
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_frames,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
elif isinstance(controlnet, MultiControlNetModel):
control_images = []
for control_image_ in control_frames:
control_image_ = self.prepare_control_image(
image=control_image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
control_images.append(control_image_)
control_image = control_images
else:
assert False
self.flow_model.to(device)
flows, occs, attn_mask, interattn_paras = get_flow_and_interframe_paras(self.flow_model, imgs_np)
correlation_matrix = get_intraframe_paras(self, images_pt, self.frescoProc, prompt_embeds, generator)
"""
Flexible settings for attention:
* Turn off FRESCO-guided attention: frescoProc.controller.disable_controller()
Then you can turn on one specific attention submodule
* Turn on Cross-frame attention: frescoProc.controller.enable_cfattn(attn_mask)
* Turn on Spatial-guided attention: frescoProc.controller.enable_intraattn()
* Turn on Temporal-guided attention: frescoProc.controller.enable_interattn(interattn_paras)
Flexible settings for optimization:
* Turn off Spatial-guided optimization: set optimize_temporal = False in apply_FRESCO_opt()
* Turn off Temporal-guided optimization: set correlation_matrix = [] in apply_FRESCO_opt()
* Turn off FRESCO-guided optimization: disable_FRESCO_opt(pipe)
Flexible settings for background smoothing:
* Turn off background smoothing: set saliency = None in apply_FRESCO_opt()
"""
self.frescoProc.controller.enable_controller(interattn_paras=interattn_paras, attn_mask=attn_mask)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
apply_FRESCO_opt(
self,
steps=timesteps[:end_opt_step],
flows=flows,
occs=occs,
correlation_matrix=correlation_matrix,
saliency=None,
optimize_temporal=True,
)
clear_cache()
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
self._num_timesteps = len(timesteps)
# 6. Prepare latent variables
latents = self.prepare_latents(
images_pt,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator=generator,
repeat_noise=True,
)
# 7. 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)
# 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
else None
)
# 7.2 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)
# 8. 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):
if i >= num_intraattn_steps:
self.frescoProc.controller.disable_intraattn()
if t < step_interattn_end:
self.frescoProc.controller.disable_interattn()
# 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)
# controlnet(s) inference
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
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 self.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=self.cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# 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, return_dict=False)[0]
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:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# If we do sequential model offloading, let's offload unet and controlnet
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.controlnet.to("cpu")
torch.cuda.empty_cache()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)