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major refactoring of modules

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
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Vladimir Mandic
2025-07-03 09:18:38 -04:00
parent 772a5c9ad3
commit c4d9338d2e
214 changed files with 1154 additions and 1153 deletions
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scripts/style_aligned/inversion.py
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# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Callable, TYPE_CHECKING
from diffusers import StableDiffusionXLPipeline
import torch
from tqdm import tqdm
if TYPE_CHECKING:
import numpy as np
T = torch.Tensor
TN = T
InversionCallback = Callable[[StableDiffusionXLPipeline, int, T, dict[str, T]], dict[str, T]]
def _get_text_embeddings(prompt: str, tokenizer, text_encoder, device):
# Tokenize text and get embeddings
text_inputs = tokenizer(prompt, padding='max_length', max_length=tokenizer.model_max_length, truncation=True, return_tensors='pt')
text_input_ids = text_inputs.input_ids
with torch.no_grad():
prompt_embeds = text_encoder(
text_input_ids.to(device),
output_hidden_states=True,
)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
if prompt == '':
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
return negative_prompt_embeds, negative_pooled_prompt_embeds
return prompt_embeds, pooled_prompt_embeds
def _encode_text_sdxl(model: StableDiffusionXLPipeline, prompt: str) -> tuple[dict[str, T], T]:
device = model._execution_device # pylint: disable=protected-access
prompt_embeds, pooled_prompt_embeds, = _get_text_embeddings(prompt, model.tokenizer, model.text_encoder, device) # pylint: disable=unused-variable
prompt_embeds_2, pooled_prompt_embeds2, = _get_text_embeddings( prompt, model.tokenizer_2, model.text_encoder_2, device)
prompt_embeds = torch.cat((prompt_embeds, prompt_embeds_2), dim=-1)
text_encoder_projection_dim = model.text_encoder_2.config.projection_dim
add_time_ids = model._get_add_time_ids((1024, 1024), (0, 0), (1024, 1024), model.text_encoder.dtype, # pylint: disable=protected-access
text_encoder_projection_dim).to(device)
added_cond_kwargs = {"text_embeds": pooled_prompt_embeds2, "time_ids": add_time_ids}
return added_cond_kwargs, prompt_embeds
def _encode_text_sdxl_with_negative(model: StableDiffusionXLPipeline, prompt: str) -> tuple[dict[str, T], T]:
added_cond_kwargs, prompt_embeds = _encode_text_sdxl(model, prompt)
added_cond_kwargs_uncond, prompt_embeds_uncond = _encode_text_sdxl(model, "")
prompt_embeds = torch.cat((prompt_embeds_uncond, prompt_embeds, ))
added_cond_kwargs = {"text_embeds": torch.cat((added_cond_kwargs_uncond["text_embeds"], added_cond_kwargs["text_embeds"])),
"time_ids": torch.cat((added_cond_kwargs_uncond["time_ids"], added_cond_kwargs["time_ids"])),}
return added_cond_kwargs, prompt_embeds
def _encode_image(model: StableDiffusionXLPipeline, image: np.ndarray) -> T:
image = torch.from_numpy(image).float() / 255.
image = (image * 2 - 1).permute(2, 0, 1).unsqueeze(0)
latent = model.vae.encode(image.to(model.vae.device, model.vae.dtype))['latent_dist'].mean * model.vae.config.scaling_factor
return latent
def _next_step(model: StableDiffusionXLPipeline, model_output: T, timestep: int, sample: T) -> T:
timestep, next_timestep = min(timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps, 999), timestep
alpha_prod_t = model.scheduler.alphas_cumprod[int(timestep)] if timestep >= 0 else model.scheduler.final_alpha_cumprod
alpha_prod_t_next = model.scheduler.alphas_cumprod[int(next_timestep)]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
return next_sample
def _get_noise_pred(model: StableDiffusionXLPipeline, latent: T, t: T, context: T, guidance_scale: float, added_cond_kwargs: dict[str, T]):
latents_input = torch.cat([latent] * 2)
noise_pred = model.unet(latents_input, t, encoder_hidden_states=context, added_cond_kwargs=added_cond_kwargs)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# latents = next_step(model, noise_pred, t, latent)
return noise_pred
def _ddim_loop(model: StableDiffusionXLPipeline, z0, prompt, guidance_scale) -> T:
all_latent = [z0]
added_cond_kwargs, text_embedding = _encode_text_sdxl_with_negative(model, prompt)
latent = z0.clone().detach().to(model.text_encoder.dtype)
for i in tqdm(range(model.scheduler.num_inference_steps)):
t = model.scheduler.timesteps[len(model.scheduler.timesteps) - i - 1]
noise_pred = _get_noise_pred(model, latent, t, text_embedding, guidance_scale, added_cond_kwargs)
latent = _next_step(model, noise_pred, t, latent)
all_latent.append(latent)
return torch.cat(all_latent).flip(0)
def make_inversion_callback(zts, offset: int = 0):
def callback_on_step_end(pipeline: StableDiffusionXLPipeline, i: int, t: T, callback_kwargs: dict[str, T]) -> dict[str, T]: # pylint: disable=unused-argument
latents = callback_kwargs['latents']
latents[0] = zts[max(offset + 1, i + 1)].to(latents.device, latents.dtype)
return {'latents': latents}
return zts[offset], callback_on_step_end
@torch.no_grad()
def ddim_inversion(model: StableDiffusionXLPipeline, x0: np.ndarray, prompt: str, num_inference_steps: int, guidance_scale,) -> T:
z0 = _encode_image(model, x0)
model.scheduler.set_timesteps(num_inference_steps, device=z0.device)
zs = _ddim_loop(model, z0, prompt, guidance_scale)
return zs
scripts/style_aligned/sa_handler.py
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# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from diffusers import StableDiffusionXLPipeline
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as nnf
from diffusers.models import attention_processor # pylint: disable=ungrouped-imports
import einops
T = torch.Tensor
@dataclass(frozen=True)
class StyleAlignedArgs:
share_group_norm: bool = True
share_layer_norm: bool = True
share_attention: bool = True
adain_queries: bool = True
adain_keys: bool = True
adain_values: bool = False
full_attention_share: bool = False
shared_score_scale: float = 1.
shared_score_shift: float = 0.
only_self_level: float = 0.
def expand_first(feat: T, scale=1.,) -> T:
b = feat.shape[0]
feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
if scale == 1:
feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
else:
feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
return feat_style.reshape(*feat.shape)
def concat_first(feat: T, dim=2, scale=1.) -> T:
feat_style = expand_first(feat, scale=scale)
return torch.cat((feat, feat_style), dim=dim)
def calc_mean_std(feat, eps: float = 1e-5) -> tuple[T, T]:
feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
feat_mean = feat.mean(dim=-2, keepdims=True)
return feat_mean, feat_std
def adain(feat: T) -> T:
feat_mean, feat_std = calc_mean_std(feat)
feat_style_mean = expand_first(feat_mean)
feat_style_std = expand_first(feat_std)
feat = (feat - feat_mean) / feat_std
feat = feat * feat_style_std + feat_style_mean
return feat
class DefaultAttentionProcessor(nn.Module):
def __init__(self):
super().__init__()
self.processor = attention_processor.AttnProcessor2_0()
def __call__(self, attn: attention_processor.Attention, hidden_states, encoder_hidden_states=None,
attention_mask=None, **kwargs):
return self.processor(attn, hidden_states, encoder_hidden_states, attention_mask)
class SharedAttentionProcessor(DefaultAttentionProcessor):
def shifted_scaled_dot_product_attention(self, attn: attention_processor.Attention, query: T, key: T, value: T) -> T:
logits = torch.einsum('bhqd,bhkd->bhqk', query, key) * attn.scale
logits[:, :, :, query.shape[2]:] += self.shared_score_shift
probs = logits.softmax(-1)
return torch.einsum('bhqk,bhkd->bhqd', probs, value)
def shared_call( # pylint: disable=unused-argument
self,
attn: attention_processor.Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
**kwargs
):
residual = hidden_states
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)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
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)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# if self.step >= self.start_inject:
if self.adain_queries:
query = adain(query)
if self.adain_keys:
key = adain(key)
if self.adain_values:
value = adain(value)
if self.share_attention:
key = concat_first(key, -2, scale=self.shared_score_scale)
value = concat_first(value, -2)
if self.shared_score_shift != 0:
hidden_states = self.shifted_scaled_dot_product_attention(attn, query, key, value,)
else:
hidden_states = nnf.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
else:
hidden_states = nnf.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
# hidden_states = adain(hidden_states)
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 __call__(self, attn: attention_processor.Attention, hidden_states, encoder_hidden_states=None,
attention_mask=None, **kwargs):
if self.full_attention_share:
_b, n, _d = hidden_states.shape
hidden_states = einops.rearrange(hidden_states, '(k b) n d -> k (b n) d', k=2)
hidden_states = super().__call__(attn, hidden_states, encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask, **kwargs)
hidden_states = einops.rearrange(hidden_states, 'k (b n) d -> (k b) n d', n=n)
else:
hidden_states = self.shared_call(attn, hidden_states, hidden_states, attention_mask, **kwargs)
return hidden_states
def __init__(self, style_aligned_args: StyleAlignedArgs):
super().__init__()
self.share_attention = style_aligned_args.share_attention
self.adain_queries = style_aligned_args.adain_queries
self.adain_keys = style_aligned_args.adain_keys
self.adain_values = style_aligned_args.adain_values
self.full_attention_share = style_aligned_args.full_attention_share
self.shared_score_scale = style_aligned_args.shared_score_scale
self.shared_score_shift = style_aligned_args.shared_score_shift
def _get_switch_vec(total_num_layers, level):
if level <= 0:
return torch.zeros(total_num_layers, dtype=torch.bool)
if level >= 1:
return torch.ones(total_num_layers, dtype=torch.bool)
to_flip = level > .5
if to_flip:
level = 1 - level
num_switch = int(level * total_num_layers)
vec = torch.arange(total_num_layers)
vec = vec % (total_num_layers // num_switch)
vec = vec == 0
if to_flip:
vec = ~vec
return vec
def init_attention_processors(pipeline: StableDiffusionXLPipeline, style_aligned_args: StyleAlignedArgs | None = None):
attn_procs = {}
unet = pipeline.unet
number_of_self, number_of_cross = 0, 0
num_self_layers = len([name for name in unet.attn_processors.keys() if 'attn1' in name])
if style_aligned_args is None:
only_self_vec = _get_switch_vec(num_self_layers, 1)
else:
only_self_vec = _get_switch_vec(num_self_layers, style_aligned_args.only_self_level)
for i, name in enumerate(unet.attn_processors.keys()):
is_self_attention = 'attn1' in name
if is_self_attention:
number_of_self += 1
if style_aligned_args is None or only_self_vec[i // 2]:
attn_procs[name] = DefaultAttentionProcessor()
else:
attn_procs[name] = SharedAttentionProcessor(style_aligned_args)
else:
number_of_cross += 1
attn_procs[name] = DefaultAttentionProcessor()
unet.set_attn_processor(attn_procs)
def register_shared_norm(pipeline: StableDiffusionXLPipeline,
share_group_norm: bool = True,
share_layer_norm: bool = True,
):
def register_norm_forward(norm_layer: nn.GroupNorm | nn.LayerNorm) -> nn.GroupNorm | nn.LayerNorm:
if not hasattr(norm_layer, 'orig_forward'):
setattr(norm_layer, 'orig_forward', norm_layer.forward) # noqa
orig_forward = norm_layer.orig_forward
def forward_(hidden_states: T) -> T:
n = hidden_states.shape[-2]
hidden_states = concat_first(hidden_states, dim=-2)
hidden_states = orig_forward(hidden_states)
return hidden_states[..., :n, :]
norm_layer.forward = forward_
return norm_layer
def get_norm_layers(pipeline_, norm_layers_: dict[str, list[nn.GroupNorm | nn.LayerNorm]]):
if isinstance(pipeline_, nn.LayerNorm) and share_layer_norm:
norm_layers_['layer'].append(pipeline_)
if isinstance(pipeline_, nn.GroupNorm) and share_group_norm:
norm_layers_['group'].append(pipeline_)
else:
for layer in pipeline_.children():
get_norm_layers(layer, norm_layers_)
norm_layers = {'group': [], 'layer': []}
get_norm_layers(pipeline.unet, norm_layers)
return [register_norm_forward(layer) for layer in norm_layers['group']] + [register_norm_forward(layer) for layer in
norm_layers['layer']]
class Handler:
def register(self, style_aligned_args: StyleAlignedArgs):
self.norm_layers = register_shared_norm(self.pipeline, style_aligned_args.share_group_norm,
style_aligned_args.share_layer_norm)
init_attention_processors(self.pipeline, style_aligned_args)
def remove(self):
for layer in self.norm_layers:
layer.forward = layer.orig_forward
self.norm_layers = []
init_attention_processors(self.pipeline, None)
def __init__(self, pipeline: StableDiffusionXLPipeline):
self.pipeline = pipeline
self.norm_layers = []