70af4845af
new file: inpaint/__main__.py new file: inpaint/api.py new file: inpaint/batch_processing.py new file: inpaint/benchmark.py new file: inpaint/cli.py new file: inpaint/const.py new file: inpaint/download.py new file: inpaint/file_manager/__init__.py new file: inpaint/file_manager/file_manager.py new file: inpaint/file_manager/storage_backends.py new file: inpaint/file_manager/utils.py new file: inpaint/helper.py new file: inpaint/installer.py new file: inpaint/model/__init__.py new file: inpaint/model/anytext/__init__.py new file: inpaint/model/anytext/anytext_model.py new file: inpaint/model/anytext/anytext_pipeline.py new file: inpaint/model/anytext/anytext_sd15.yaml new file: inpaint/model/anytext/cldm/__init__.py new file: inpaint/model/anytext/cldm/cldm.py new file: inpaint/model/anytext/cldm/ddim_hacked.py new file: inpaint/model/anytext/cldm/embedding_manager.py new file: inpaint/model/anytext/cldm/hack.py new file: inpaint/model/anytext/cldm/model.py new file: inpaint/model/anytext/cldm/recognizer.py new file: inpaint/model/anytext/ldm/__init__.py new file: inpaint/model/anytext/ldm/models/__init__.py new file: inpaint/model/anytext/ldm/models/autoencoder.py new file: inpaint/model/anytext/ldm/models/diffusion/__init__.py new file: inpaint/model/anytext/ldm/models/diffusion/ddim.py new file: inpaint/model/anytext/ldm/models/diffusion/ddpm.py new file: inpaint/model/anytext/ldm/models/diffusion/dpm_solver/__init__.py new file: inpaint/model/anytext/ldm/models/diffusion/dpm_solver/dpm_solver.py new file: inpaint/model/anytext/ldm/models/diffusion/dpm_solver/sampler.py new file: inpaint/model/anytext/ldm/models/diffusion/plms.py new file: inpaint/model/anytext/ldm/models/diffusion/sampling_util.py new file: inpaint/model/anytext/ldm/modules/__init__.py new file: inpaint/model/anytext/ldm/modules/attention.py new file: inpaint/model/anytext/ldm/modules/diffusionmodules/__init__.py new file: inpaint/model/anytext/ldm/modules/diffusionmodules/model.py new file: inpaint/model/anytext/ldm/modules/diffusionmodules/openaimodel.py new file: inpaint/model/anytext/ldm/modules/diffusionmodules/upscaling.py new file: inpaint/model/anytext/ldm/modules/diffusionmodules/util.py new file: inpaint/model/anytext/ldm/modules/distributions/__init__.py new file: inpaint/model/anytext/ldm/modules/distributions/distributions.py new file: inpaint/model/anytext/ldm/modules/ema.py new file: inpaint/model/anytext/ldm/modules/encoders/__init__.py new file: inpaint/model/anytext/ldm/modules/encoders/modules.py new file: inpaint/model/anytext/ldm/util.py new file: inpaint/model/anytext/main.py new file: inpaint/model/anytext/ocr_recog/RNN.py new file: inpaint/model/anytext/ocr_recog/RecCTCHead.py new file: inpaint/model/anytext/ocr_recog/RecModel.py new file: inpaint/model/anytext/ocr_recog/RecMv1_enhance.py new file: inpaint/model/anytext/ocr_recog/RecSVTR.py new file: inpaint/model/anytext/ocr_recog/__init__.py new file: inpaint/model/anytext/ocr_recog/common.py new file: inpaint/model/anytext/ocr_recog/en_dict.txt new file: inpaint/model/anytext/ocr_recog/ppocr_keys_v1.txt new file: inpaint/model/anytext/utils.py new file: inpaint/model/base.py new file: inpaint/model/brushnet/__init__.py new file: inpaint/model/brushnet/brushnet.py new file: inpaint/model/brushnet/brushnet_unet_forward.py new file: inpaint/model/brushnet/brushnet_wrapper.py new file: inpaint/model/brushnet/pipeline_brushnet.py new file: inpaint/model/brushnet/unet_2d_blocks.py new file: inpaint/model/controlnet.py new file: inpaint/model/ddim_sampler.py new file: inpaint/model/fcf.py new file: inpaint/model/helper/__init__.py new file: inpaint/model/helper/controlnet_preprocess.py new file: inpaint/model/helper/cpu_text_encoder.py new file: inpaint/model/helper/g_diffuser_bot.py new file: inpaint/model/instruct_pix2pix.py new file: inpaint/model/kandinsky.py new file: inpaint/model/lama.py new file: inpaint/model/ldm.py new file: inpaint/model/manga.py new file: inpaint/model/mat.py new file: inpaint/model/mi_gan.py new file: inpaint/model/opencv2.py new file: inpaint/model/original_sd_configs/__init__.py new file: inpaint/model/original_sd_configs/sd_xl_base.yaml new file: inpaint/model/original_sd_configs/sd_xl_refiner.yaml new file: inpaint/model/original_sd_configs/v1-inference.yaml new file: inpaint/model/original_sd_configs/v2-inference-v.yaml new file: inpaint/model/paint_by_example.py new file: inpaint/model/plms_sampler.py new file: inpaint/model/power_paint/__init__.py new file: inpaint/model/power_paint/pipeline_powerpaint.py new file: inpaint/model/power_paint/power_paint.py new file: inpaint/model/power_paint/power_paint_v2.py new file: inpaint/model/power_paint/powerpaint_tokenizer.py
343 lines
12 KiB
Python
343 lines
12 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from typing import Any, Dict, Optional, Tuple
|
|
|
|
import torch
|
|
from diffusers.utils import is_torch_version, logging
|
|
from diffusers.utils.torch_utils import apply_freeu
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
def CrossAttnDownBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
additional_residuals: Optional[torch.FloatTensor] = None,
|
|
down_block_add_samples: Optional[torch.FloatTensor] = None,
|
|
) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
|
|
output_states = ()
|
|
|
|
lora_scale = (
|
|
cross_attention_kwargs.get("scale", 1.0)
|
|
if cross_attention_kwargs is not None
|
|
else 1.0
|
|
)
|
|
|
|
blocks = list(zip(self.resnets, self.attentions))
|
|
|
|
for i, (resnet, attn) in enumerate(blocks):
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module, return_dict=None):
|
|
def custom_forward(*inputs):
|
|
if return_dict is not None:
|
|
return module(*inputs, return_dict=return_dict)
|
|
else:
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
ckpt_kwargs: Dict[str, Any] = (
|
|
{"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
|
)
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet),
|
|
hidden_states,
|
|
temb,
|
|
**ckpt_kwargs,
|
|
)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=lora_scale)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# apply additional residuals to the output of the last pair of resnet and attention blocks
|
|
if i == len(blocks) - 1 and additional_residuals is not None:
|
|
hidden_states = hidden_states + additional_residuals
|
|
|
|
if down_block_add_samples is not None:
|
|
hidden_states = hidden_states + down_block_add_samples.pop(0)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states, scale=lora_scale)
|
|
|
|
if down_block_add_samples is not None:
|
|
hidden_states = hidden_states + down_block_add_samples.pop(
|
|
0
|
|
) # todo: add before or after
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
def DownBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
scale: float = 1.0,
|
|
down_block_add_samples: Optional[torch.FloatTensor] = None,
|
|
) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
|
|
output_states = ()
|
|
|
|
for resnet in self.resnets:
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
if is_torch_version(">=", "1.11.0"):
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet),
|
|
hidden_states,
|
|
temb,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet), hidden_states, temb
|
|
)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=scale)
|
|
|
|
if down_block_add_samples is not None:
|
|
hidden_states = hidden_states + down_block_add_samples.pop(0)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states, scale=scale)
|
|
|
|
if down_block_add_samples is not None:
|
|
hidden_states = hidden_states + down_block_add_samples.pop(
|
|
0
|
|
) # todo: add before or after
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
def CrossAttnUpBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
upsample_size: Optional[int] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
return_res_samples: Optional[bool] = False,
|
|
up_block_add_samples: Optional[torch.FloatTensor] = None,
|
|
) -> torch.FloatTensor:
|
|
lora_scale = (
|
|
cross_attention_kwargs.get("scale", 1.0)
|
|
if cross_attention_kwargs is not None
|
|
else 1.0
|
|
)
|
|
is_freeu_enabled = (
|
|
getattr(self, "s1", None)
|
|
and getattr(self, "s2", None)
|
|
and getattr(self, "b1", None)
|
|
and getattr(self, "b2", None)
|
|
)
|
|
if return_res_samples:
|
|
output_states = ()
|
|
|
|
for resnet, attn in zip(self.resnets, self.attentions):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
|
|
# FreeU: Only operate on the first two stages
|
|
if is_freeu_enabled:
|
|
hidden_states, res_hidden_states = apply_freeu(
|
|
self.resolution_idx,
|
|
hidden_states,
|
|
res_hidden_states,
|
|
s1=self.s1,
|
|
s2=self.s2,
|
|
b1=self.b1,
|
|
b2=self.b2,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module, return_dict=None):
|
|
def custom_forward(*inputs):
|
|
if return_dict is not None:
|
|
return module(*inputs, return_dict=return_dict)
|
|
else:
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
ckpt_kwargs: Dict[str, Any] = (
|
|
{"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
|
)
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet),
|
|
hidden_states,
|
|
temb,
|
|
**ckpt_kwargs,
|
|
)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=lora_scale)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
if return_res_samples:
|
|
output_states = output_states + (hidden_states,)
|
|
if up_block_add_samples is not None:
|
|
hidden_states = hidden_states + up_block_add_samples.pop(0)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale)
|
|
if return_res_samples:
|
|
output_states = output_states + (hidden_states,)
|
|
if up_block_add_samples is not None:
|
|
hidden_states = hidden_states + up_block_add_samples.pop(0)
|
|
|
|
if return_res_samples:
|
|
return hidden_states, output_states
|
|
else:
|
|
return hidden_states
|
|
|
|
|
|
def UpBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
upsample_size: Optional[int] = None,
|
|
scale: float = 1.0,
|
|
return_res_samples: Optional[bool] = False,
|
|
up_block_add_samples: Optional[torch.FloatTensor] = None,
|
|
) -> torch.FloatTensor:
|
|
is_freeu_enabled = (
|
|
getattr(self, "s1", None)
|
|
and getattr(self, "s2", None)
|
|
and getattr(self, "b1", None)
|
|
and getattr(self, "b2", None)
|
|
)
|
|
if return_res_samples:
|
|
output_states = ()
|
|
|
|
for resnet in self.resnets:
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
|
|
# FreeU: Only operate on the first two stages
|
|
if is_freeu_enabled:
|
|
hidden_states, res_hidden_states = apply_freeu(
|
|
self.resolution_idx,
|
|
hidden_states,
|
|
res_hidden_states,
|
|
s1=self.s1,
|
|
s2=self.s2,
|
|
b1=self.b1,
|
|
b2=self.b2,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
if is_torch_version(">=", "1.11.0"):
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet),
|
|
hidden_states,
|
|
temb,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet), hidden_states, temb
|
|
)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=scale)
|
|
|
|
if return_res_samples:
|
|
output_states = output_states + (hidden_states,)
|
|
if up_block_add_samples is not None:
|
|
hidden_states = hidden_states + up_block_add_samples.pop(
|
|
0
|
|
) # todo: add before or after
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size, scale=scale)
|
|
|
|
if return_res_samples:
|
|
output_states = output_states + (hidden_states,)
|
|
if up_block_add_samples is not None:
|
|
hidden_states = hidden_states + up_block_add_samples.pop(
|
|
0
|
|
) # todo: add before or after
|
|
|
|
if return_res_samples:
|
|
return hidden_states, output_states
|
|
else:
|
|
return hidden_states
|