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cleanup: Some basic cleanup

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blessedcoolant 2023-09-05 11:54:28 +12:00
parent 07381e5a26
commit 65a76a086b
4 changed files with 10 additions and 12 deletions
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2
invokeai/backend/ip_adapter/__init__.py
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@ -1 +1 @@
from .ip_adapter import IPAdapter, IPAdapterXL, IPAdapterPlus
from .ip_adapter import IPAdapter, IPAdapterPlus, IPAdapterXL

6
invokeai/backend/ip_adapter/ip_adapter.py
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@ -1,13 +1,11 @@
# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
# and modified as needed
import os
from typing import List
import torch
from diffusers import StableDiffusionPipeline
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
# FIXME: Getting errors when trying to use PyTorch 2.0 versions of IPAttnProcessor and AttnProcessor
# so for now falling back to the default versions
@ -16,7 +14,7 @@ from PIL import Image
# from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
# else:
# from .attention_processor import IPAttnProcessor, AttnProcessor
from .attention_processor import IPAttnProcessor, AttnProcessor
from .attention_processor import AttnProcessor, IPAttnProcessor
from .resampler import Resampler

3
invokeai/backend/ip_adapter/resampler.py
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@ -1,6 +1,7 @@
# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
# tencent ailab comment: modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
# tencent ailab comment: modified from
# https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
import math
import torch

11
invokeai/backend/ip_adapter/utils.py
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@ -1,18 +1,16 @@
# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
# and modified as needed
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from diffusers.utils import is_compiled_module
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.models import ControlNetModel
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import is_compiled_module
def is_torch2_available():
@ -59,7 +57,8 @@ def generate(
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
`List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can