985 lines
42 KiB
Python
Executable File
985 lines
42 KiB
Python
Executable File
# https://github.com/cubiq/ComfyUI_IPAdapter_plus/blob/main/IPAdapterPlus.py from some early version
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# Then maintained by Forge to add InstanceID and many other things
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import torch
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import contextlib
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import os
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import math
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from backend import memory_management, attention, utils
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from backend.misc.image_resize import adaptive_resize
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from backend.patcher.clipvision import clip_preprocess
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from modules_forge.shared import controlnet_dir, models_path
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from torch import nn
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from PIL import Image
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import torch.nn.functional as F
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import torchvision.transforms as TT
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from lib_ipadapter.resampler import PerceiverAttention, FeedForward, Resampler
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GLOBAL_MODELS_DIR = os.path.join(models_path, "ipadapter")
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MODELS_DIR = GLOBAL_MODELS_DIR
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INSIGHTFACE_DIR = os.path.join(models_path, "insightface")
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class FacePerceiverResampler(torch.nn.Module):
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def __init__(
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self,
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*,
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dim=768,
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depth=4,
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dim_head=64,
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heads=16,
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embedding_dim=1280,
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output_dim=768,
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ff_mult=4,
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):
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super().__init__()
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self.proj_in = torch.nn.Linear(embedding_dim, dim)
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self.proj_out = torch.nn.Linear(dim, output_dim)
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self.norm_out = torch.nn.LayerNorm(output_dim)
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self.layers = torch.nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(
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torch.nn.ModuleList(
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[
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PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
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FeedForward(dim=dim, mult=ff_mult),
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]
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)
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)
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def forward(self, latents, x):
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x = self.proj_in(x)
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for attn, ff in self.layers:
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latents = attn(x, latents) + latents
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latents = ff(latents) + latents
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latents = self.proj_out(latents)
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return self.norm_out(latents)
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class MLPProjModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
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super().__init__()
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
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torch.nn.GELU(),
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torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
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torch.nn.LayerNorm(cross_attention_dim)
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)
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def forward(self, image_embeds):
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clip_extra_context_tokens = self.proj(image_embeds)
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return clip_extra_context_tokens
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class MLPProjModelFaceId(torch.nn.Module):
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def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.num_tokens = num_tokens
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
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torch.nn.GELU(),
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torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, id_embeds):
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clip_extra_context_tokens = self.proj(id_embeds)
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clip_extra_context_tokens = clip_extra_context_tokens.reshape(-1, self.num_tokens, self.cross_attention_dim)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class ProjModelFaceIdPlus(torch.nn.Module):
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def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.num_tokens = num_tokens
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
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torch.nn.GELU(),
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torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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self.perceiver_resampler = FacePerceiverResampler(
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dim=cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=cross_attention_dim // 64,
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embedding_dim=clip_embeddings_dim,
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output_dim=cross_attention_dim,
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ff_mult=4,
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)
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def forward(self, id_embeds, clip_embeds, scale=1.0, shortcut=False):
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x = self.proj(id_embeds)
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x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
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x = self.norm(x)
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out = self.perceiver_resampler(x, clip_embeds)
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if shortcut:
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out = x + scale * out
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return out
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class ImageProjModel(nn.Module):
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
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self.norm = nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class To_KV(nn.Module):
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def __init__(self, state_dict):
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super().__init__()
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self.to_kvs = nn.ModuleDict()
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for key, value in state_dict.items():
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self.to_kvs[key.replace(".weight", "").replace(".", "_")] = nn.Linear(value.shape[1], value.shape[0], bias=False)
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self.to_kvs[key.replace(".weight", "").replace(".", "_")].weight.data = value
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def set_model_patch_replace(model, patch_kwargs, key):
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to = model.model_options["transformer_options"]
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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if "attn2" not in to["patches_replace"]:
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to["patches_replace"]["attn2"] = {}
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if key not in to["patches_replace"]["attn2"]:
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patch = CrossAttentionPatch(**patch_kwargs)
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to["patches_replace"]["attn2"][key] = patch
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else:
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to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)
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def image_add_noise(image, noise):
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image = image.permute([0, 3, 1, 2])
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torch.manual_seed(0) # use a fixed random for reproducible results
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transforms = TT.Compose([
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TT.CenterCrop(min(image.shape[2], image.shape[3])),
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TT.Resize((224, 224), interpolation=TT.InterpolationMode.BICUBIC, antialias=True),
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TT.ElasticTransform(alpha=75.0, sigma=noise * 3.5), # shuffle the image
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TT.RandomVerticalFlip(p=1.0), # flip the image to change the geometry even more
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TT.RandomHorizontalFlip(p=1.0),
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])
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image = transforms(image.cpu())
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image = image.permute([0, 2, 3, 1])
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image = image + ((0.25 * (1 - noise) + 0.05) * torch.randn_like(image)) # add further random noise
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return image
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def zeroed_hidden_states(clip_vision, batch_size):
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image = torch.zeros([batch_size, 224, 224, 3])
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memory_management.load_model_gpu(clip_vision.patcher)
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pixel_values = clip_preprocess(image.to(clip_vision.load_device)).float()
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outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)
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outputs = outputs.hidden_states[-2].to(memory_management.intermediate_device())
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return outputs
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def min_(tensor_list):
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# return the element-wise min of the tensor list.
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x = torch.stack(tensor_list)
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mn = x.min(axis=0)[0]
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return torch.clamp(mn, min=0)
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def max_(tensor_list):
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# return the element-wise max of the tensor list.
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x = torch.stack(tensor_list)
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mx = x.max(axis=0)[0]
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return torch.clamp(mx, max=1)
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# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
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def contrast_adaptive_sharpening(image, amount):
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img = F.pad(image, pad=(1, 1, 1, 1)).cpu()
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a = img[..., :-2, :-2]
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b = img[..., :-2, 1:-1]
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c = img[..., :-2, 2:]
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d = img[..., 1:-1, :-2]
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e = img[..., 1:-1, 1:-1]
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f = img[..., 1:-1, 2:]
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g = img[..., 2:, :-2]
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h = img[..., 2:, 1:-1]
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i = img[..., 2:, 2:]
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# Computing contrast
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cross = (b, d, e, f, h)
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mn = min_(cross)
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mx = max_(cross)
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diag = (a, c, g, i)
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mn2 = min_(diag)
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mx2 = max_(diag)
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mx = mx + mx2
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mn = mn + mn2
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# Computing local weight
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inv_mx = torch.reciprocal(mx)
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amp = inv_mx * torch.minimum(mn, (2 - mx))
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# scaling
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amp = torch.sqrt(amp)
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w = - amp * (amount * (1 / 5 - 1 / 8) + 1 / 8)
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div = torch.reciprocal(1 + 4 * w)
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output = ((b + d + f + h) * w + e) * div
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output = output.clamp(0, 1)
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output = torch.nan_to_num(output)
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return (output)
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def tensorToNP(image):
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out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
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out = out[..., [2, 1, 0]]
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out = out.numpy()
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return out
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def NPToTensor(image):
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out = torch.from_numpy(image)
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out = torch.clamp(out.to(torch.float) / 255., 0.0, 1.0)
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out = out[..., [2, 1, 0]]
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return out
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class IPAdapter(nn.Module):
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def __init__(self, ipadapter_model, cross_attention_dim=1024, output_cross_attention_dim=1024,
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clip_embeddings_dim=1024, clip_extra_context_tokens=4,
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is_sdxl=False, is_plus=False, is_full=False,
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is_faceid=False, is_instant_id=False):
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super().__init__()
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self.clip_embeddings_dim = clip_embeddings_dim
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self.cross_attention_dim = cross_attention_dim
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self.output_cross_attention_dim = output_cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.is_sdxl = is_sdxl
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self.is_full = is_full
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self.is_plus = is_plus
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self.is_instant_id = is_instant_id
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if is_instant_id:
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self.image_proj_model = self.init_proj_instantid()
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elif is_faceid:
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self.image_proj_model = self.init_proj_faceid()
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elif is_plus:
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self.image_proj_model = self.init_proj_plus()
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else:
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self.image_proj_model = self.init_proj()
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self.image_proj_model.load_state_dict(ipadapter_model["image_proj"])
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self.ip_layers = To_KV(ipadapter_model["ip_adapter"])
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def init_proj(self):
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image_proj_model = ImageProjModel(
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cross_attention_dim=self.cross_attention_dim,
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clip_embeddings_dim=self.clip_embeddings_dim,
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clip_extra_context_tokens=self.clip_extra_context_tokens
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)
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return image_proj_model
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def init_proj_plus(self):
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if self.is_full:
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image_proj_model = MLPProjModel(
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cross_attention_dim=self.cross_attention_dim,
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clip_embeddings_dim=self.clip_embeddings_dim
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)
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else:
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image_proj_model = Resampler(
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dim=self.cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=20 if self.is_sdxl else 12,
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num_queries=self.clip_extra_context_tokens,
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embedding_dim=self.clip_embeddings_dim,
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output_dim=self.output_cross_attention_dim,
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ff_mult=4
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)
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return image_proj_model
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def init_proj_faceid(self):
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if self.is_plus:
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image_proj_model = ProjModelFaceIdPlus(
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cross_attention_dim=self.cross_attention_dim,
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id_embeddings_dim=512,
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clip_embeddings_dim=1280,
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num_tokens=4,
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)
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else:
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image_proj_model = MLPProjModelFaceId(
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cross_attention_dim=self.cross_attention_dim,
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id_embeddings_dim=512,
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num_tokens=self.clip_extra_context_tokens,
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)
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return image_proj_model
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def init_proj_instantid(self, image_emb_dim=512, num_tokens=16):
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image_proj_model = Resampler(
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dim=1280,
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depth=4,
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dim_head=64,
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heads=20,
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num_queries=num_tokens,
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embedding_dim=image_emb_dim,
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output_dim=self.cross_attention_dim,
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ff_mult=4,
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)
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return image_proj_model
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def get_image_embeds(self, clip_embed, clip_embed_zeroed):
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image_prompt_embeds = self.image_proj_model(clip_embed)
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uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)
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return image_prompt_embeds, uncond_image_prompt_embeds
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def get_image_embeds_faceid_plus(self, face_embed, clip_embed, s_scale, shortcut):
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embeds = self.image_proj_model(face_embed, clip_embed, scale=s_scale, shortcut=shortcut)
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return embeds
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def get_image_embeds_instantid(self, prompt_image_emb):
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c = self.image_proj_model(prompt_image_emb)
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uc = self.image_proj_model(torch.zeros_like(prompt_image_emb))
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return c, uc
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class CrossAttentionPatch:
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# forward for patching
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def __init__(self, weight, ipadapter, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
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self.weights = [weight]
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self.ipadapters = [ipadapter]
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self.conds = [cond]
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self.unconds = [uncond]
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self.number = number
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self.weight_type = [weight_type]
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self.masks = [mask]
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self.sigma_start = [sigma_start]
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self.sigma_end = [sigma_end]
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self.unfold_batch = [unfold_batch]
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self.k_key = str(self.number * 2 + 1) + "_to_k_ip"
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self.v_key = str(self.number * 2 + 1) + "_to_v_ip"
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def set_new_condition(self, weight, ipadapter, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
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self.weights.append(weight)
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self.ipadapters.append(ipadapter)
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self.conds.append(cond)
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self.unconds.append(uncond)
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self.masks.append(mask)
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self.weight_type.append(weight_type)
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self.sigma_start.append(sigma_start)
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self.sigma_end.append(sigma_end)
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self.unfold_batch.append(unfold_batch)
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def __call__(self, n, context_attn2, value_attn2, extra_options):
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org_dtype = n.dtype
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cond_or_uncond = extra_options["cond_or_uncond"]
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sigma = extra_options["sigmas"][0] if 'sigmas' in extra_options else None
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sigma = sigma.item() if sigma is not None else 999999999.9
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# extra options for AnimateDiff
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ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
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q = n
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k = context_attn2
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v = value_attn2
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b = q.shape[0]
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qs = q.shape[1]
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batch_prompt = b // len(cond_or_uncond)
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out = attention.attention_function(q, k, v, extra_options["n_heads"])
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_, _, lh, lw = extra_options["original_shape"]
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for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type, self.sigma_start, self.sigma_end, self.unfold_batch):
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if sigma > sigma_start or sigma < sigma_end:
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continue
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if unfold_batch and cond.shape[0] > 1:
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# Check AnimateDiff context window
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if ad_params is not None and ad_params["sub_idxs"] is not None:
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# if images length matches or exceeds full_length get sub_idx images
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if cond.shape[0] >= ad_params["full_length"]:
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cond = torch.Tensor(cond[ad_params["sub_idxs"]])
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uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
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# otherwise, need to do more to get proper sub_idxs masks
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else:
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# check if images length matches full_length - if not, make it match
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if cond.shape[0] < ad_params["full_length"]:
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cond = torch.cat((cond, cond[-1:].repeat((ad_params["full_length"] - cond.shape[0], 1, 1))), dim=0)
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uncond = torch.cat((uncond, uncond[-1:].repeat((ad_params["full_length"] - uncond.shape[0], 1, 1))), dim=0)
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# if we have too many remove the excess (should not happen, but just in case)
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if cond.shape[0] > ad_params["full_length"]:
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cond = cond[:ad_params["full_length"]]
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uncond = uncond[:ad_params["full_length"]]
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cond = cond[ad_params["sub_idxs"]]
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uncond = uncond[ad_params["sub_idxs"]]
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# if we don't have enough reference images repeat the last one until we reach the right size
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if cond.shape[0] < batch_prompt:
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cond = torch.cat((cond, cond[-1:].repeat((batch_prompt - cond.shape[0], 1, 1))), dim=0)
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uncond = torch.cat((uncond, uncond[-1:].repeat((batch_prompt - uncond.shape[0], 1, 1))), dim=0)
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# if we have too many remove the exceeding
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elif cond.shape[0] > batch_prompt:
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cond = cond[:batch_prompt]
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uncond = uncond[:batch_prompt]
|
|
|
|
k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond)
|
|
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond)
|
|
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond)
|
|
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond)
|
|
else:
|
|
k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
|
|
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
|
|
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
|
|
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
|
|
|
|
if weight_type.startswith("linear"):
|
|
ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) * weight
|
|
ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) * weight
|
|
else:
|
|
ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
|
|
ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
|
|
|
|
if weight_type.startswith("channel"):
|
|
# code by Lvmin Zhang at Stanford University as also seen on Fooocus IPAdapter implementation
|
|
# please read licensing notes https://github.com/lllyasviel/Fooocus/blob/69a23c4d60c9e627409d0cb0f8862cdb015488eb/extras/ip_adapter.py#L234
|
|
ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
|
|
ip_v_offset = ip_v - ip_v_mean
|
|
_, _, C = ip_k.shape
|
|
channel_penalty = float(C) / 1280.0
|
|
W = weight * channel_penalty
|
|
ip_k = ip_k * W
|
|
ip_v = ip_v_offset + ip_v_mean * W
|
|
|
|
out_ip = attention.attention_function(q, ip_k.to(org_dtype), ip_v.to(org_dtype), extra_options["n_heads"])
|
|
if weight_type.startswith("original"):
|
|
out_ip = out_ip * weight
|
|
|
|
if mask is not None:
|
|
# TODO: needs checking
|
|
mask_h = lh / math.sqrt(lh * lw / qs)
|
|
mask_h = int(mask_h) + int((qs % int(mask_h)) != 0)
|
|
mask_w = qs // mask_h
|
|
|
|
# check if using AnimateDiff and sliding context window
|
|
if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
|
|
# if mask length matches or exceeds full_length, just get sub_idx masks, resize, and continue
|
|
if mask.shape[0] >= ad_params["full_length"]:
|
|
mask_downsample = torch.Tensor(mask[ad_params["sub_idxs"]])
|
|
mask_downsample = F.interpolate(mask_downsample.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
|
|
# otherwise, need to do more to get proper sub_idxs masks
|
|
else:
|
|
# resize to needed attention size (to save on memory)
|
|
mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
|
|
# check if mask length matches full_length - if not, make it match
|
|
if mask_downsample.shape[0] < ad_params["full_length"]:
|
|
mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:].repeat((ad_params["full_length"] - mask_downsample.shape[0], 1, 1))), dim=0)
|
|
# if we have too many remove the excess (should not happen, but just in case)
|
|
if mask_downsample.shape[0] > ad_params["full_length"]:
|
|
mask_downsample = mask_downsample[:ad_params["full_length"]]
|
|
# now, select sub_idxs masks
|
|
mask_downsample = mask_downsample[ad_params["sub_idxs"]]
|
|
# otherwise, perform usual mask interpolation
|
|
else:
|
|
mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
|
|
|
|
# if we don't have enough masks repeat the last one until we reach the right size
|
|
if mask_downsample.shape[0] < batch_prompt:
|
|
mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt - mask_downsample.shape[0], 1, 1))), dim=0)
|
|
# if we have too many remove the exceeding
|
|
elif mask_downsample.shape[0] > batch_prompt:
|
|
mask_downsample = mask_downsample[:batch_prompt, :, :]
|
|
|
|
# repeat the masks
|
|
mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1)
|
|
mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2])
|
|
|
|
out_ip = out_ip * mask_downsample
|
|
|
|
out = out + out_ip
|
|
|
|
return out.to(dtype=org_dtype)
|
|
|
|
|
|
class IPAdapterModelLoader:
|
|
def load_ipadapter_model(self, ipadapter_file):
|
|
ckpt_path = os.path.join(controlnet_dir, "ipadapter", ipadapter_file)
|
|
|
|
model = utils.load_torch_file(ckpt_path, safe_load=True)
|
|
|
|
if ckpt_path.lower().endswith(".safetensors"):
|
|
st_model = {"image_proj": {}, "ip_adapter": {}}
|
|
for key in model.keys():
|
|
if key.startswith("image_proj."):
|
|
st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
|
|
elif key.startswith("ip_adapter."):
|
|
st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
|
|
model = st_model
|
|
|
|
if not "ip_adapter" in model.keys() or not model["ip_adapter"]:
|
|
raise Exception("invalid IPAdapter model {}".format(ckpt_path))
|
|
|
|
return (model,)
|
|
|
|
|
|
insightface_face_align = None
|
|
|
|
|
|
class InsightFaceLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"provider": (["CPU", "CUDA", "ROCM"],),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("INSIGHTFACE",)
|
|
FUNCTION = "load_insight_face"
|
|
CATEGORY = "ipadapter"
|
|
|
|
def load_insight_face(self, name="buffalo_l", provider="CPU"):
|
|
try:
|
|
from insightface.app import FaceAnalysis
|
|
except ImportError as e:
|
|
raise Exception(e)
|
|
|
|
if name == 'antelopev2':
|
|
from modules.modelloader import load_file_from_url
|
|
model_root = os.path.join(INSIGHTFACE_DIR, 'models', "antelopev2")
|
|
if not model_root:
|
|
os.makedirs(model_root, exist_ok=True)
|
|
for local_file, url in (
|
|
("1k3d68.onnx", "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/1k3d68.onnx"),
|
|
("2d106det.onnx", "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/2d106det.onnx"),
|
|
("genderage.onnx", "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/genderage.onnx"),
|
|
("glintr100.onnx", "https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/glintr100.onnx"),
|
|
("scrfd_10g_bnkps.onnx",
|
|
"https://huggingface.co/DIAMONIK7777/antelopev2/resolve/main/scrfd_10g_bnkps.onnx"),
|
|
):
|
|
local_path = os.path.join(model_root, local_file)
|
|
if not os.path.exists(local_path):
|
|
load_file_from_url(url, model_dir=model_root)
|
|
|
|
from insightface.utils import face_align
|
|
global insightface_face_align
|
|
insightface_face_align = face_align
|
|
|
|
model = FaceAnalysis(name=name, root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider', ])
|
|
model.prepare(ctx_id=0, det_size=(640, 640))
|
|
|
|
return (model,)
|
|
|
|
|
|
class IPAdapterApply:
|
|
def apply_ipadapter(self, ipadapter, model, weight, clip_vision=None, image=None, weight_type="original",
|
|
noise=None, embeds=None, attn_mask=None, start_at=0.0, end_at=1.0, unfold_batch=False,
|
|
insightface=None, faceid_v2=False, weight_v2=False, instant_id=False):
|
|
|
|
self.dtype = torch.float16 if memory_management.should_use_fp16() else torch.float32
|
|
self.device = memory_management.get_torch_device()
|
|
self.weight = weight
|
|
self.is_full = "proj.3.weight" in ipadapter["image_proj"]
|
|
self.is_portrait = "proj.2.weight" in ipadapter["image_proj"] and not "proj.3.weight" in ipadapter["image_proj"] and not "0.to_q_lora.down.weight" in ipadapter["ip_adapter"]
|
|
self.is_faceid = self.is_portrait or "0.to_q_lora.down.weight" in ipadapter["ip_adapter"]
|
|
self.is_plus = (self.is_full or "latents" in ipadapter["image_proj"] or "perceiver_resampler.proj_in.weight" in ipadapter["image_proj"])
|
|
self.is_instant_id = instant_id
|
|
|
|
if self.is_faceid and not insightface:
|
|
raise Exception('InsightFace must be provided for FaceID models.')
|
|
|
|
output_cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
|
|
self.is_sdxl = output_cross_attention_dim == 2048
|
|
cross_attention_dim = 1280 if self.is_plus and self.is_sdxl and not self.is_faceid else output_cross_attention_dim
|
|
clip_extra_context_tokens = 16 if self.is_plus or self.is_portrait else 4
|
|
|
|
if self.is_instant_id:
|
|
cross_attention_dim = output_cross_attention_dim
|
|
|
|
if embeds is not None:
|
|
embeds = torch.unbind(embeds)
|
|
clip_embed = embeds[0].cpu()
|
|
clip_embed_zeroed = embeds[1].cpu()
|
|
else:
|
|
if self.is_instant_id:
|
|
insightface.det_model.input_size = (640, 640) # reset the detection size
|
|
face_img = tensorToNP(image)
|
|
face_embed = []
|
|
|
|
for i in range(face_img.shape[0]):
|
|
for size in [(size, size) for size in range(640, 128, -64)]:
|
|
insightface.det_model.input_size = size # TODO: hacky but seems to be working
|
|
face = insightface.get(face_img[i])
|
|
if face:
|
|
face_embed.append(torch.from_numpy(face[0].embedding).unsqueeze(0))
|
|
|
|
if 640 not in size:
|
|
print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
|
|
break
|
|
else:
|
|
raise Exception('InsightFace: No face detected.')
|
|
|
|
face_embed = torch.stack(face_embed, dim=0)
|
|
clip_embed = face_embed
|
|
elif self.is_faceid:
|
|
insightface.det_model.input_size = (640, 640) # reset the detection size
|
|
face_img = tensorToNP(image)
|
|
face_embed = []
|
|
face_clipvision = []
|
|
|
|
for i in range(face_img.shape[0]):
|
|
for size in [(size, size) for size in range(640, 128, -64)]:
|
|
insightface.det_model.input_size = size # TODO: hacky but seems to be working
|
|
face = insightface.get(face_img[i])
|
|
if face:
|
|
face_embed.append(torch.from_numpy(face[0].normed_embedding).unsqueeze(0))
|
|
face_clipvision.append(NPToTensor(insightface_face_align.norm_crop(face_img[i], landmark=face[0].kps, image_size=224)))
|
|
|
|
if 640 not in size:
|
|
print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
|
|
break
|
|
else:
|
|
raise Exception('InsightFace: No face detected.')
|
|
|
|
face_embed = torch.stack(face_embed, dim=0)
|
|
image = torch.stack(face_clipvision, dim=0)
|
|
|
|
neg_image = image_add_noise(image, noise) if noise > 0 else None
|
|
|
|
if self.is_plus:
|
|
clip_embed = clip_vision.encode_image(image).penultimate_hidden_states
|
|
if noise > 0:
|
|
clip_embed_zeroed = clip_vision.encode_image(neg_image).penultimate_hidden_states
|
|
else:
|
|
clip_embed_zeroed = zeroed_hidden_states(clip_vision, image.shape[0])
|
|
|
|
# TODO: check noise to the uncods too
|
|
face_embed_zeroed = torch.zeros_like(face_embed)
|
|
else:
|
|
clip_embed = face_embed
|
|
clip_embed_zeroed = torch.zeros_like(clip_embed)
|
|
else:
|
|
if image.shape[1] != image.shape[2]:
|
|
print("\033[33mINFO: the IPAdapter reference image is not a square, CLIPImageProcessor will resize and crop it at the center. If the main focus of the picture is not in the middle the result might not be what you are expecting.\033[0m")
|
|
|
|
clip_embed = clip_vision.encode_image(image)
|
|
neg_image = image_add_noise(image, noise) if noise > 0 else None
|
|
|
|
if self.is_plus:
|
|
clip_embed = clip_embed.penultimate_hidden_states
|
|
if noise > 0:
|
|
clip_embed_zeroed = clip_vision.encode_image(neg_image).penultimate_hidden_states
|
|
else:
|
|
clip_embed_zeroed = zeroed_hidden_states(clip_vision, image.shape[0])
|
|
else:
|
|
clip_embed = clip_embed.image_embeds
|
|
if noise > 0:
|
|
clip_embed_zeroed = clip_vision.encode_image(neg_image).image_embeds
|
|
else:
|
|
clip_embed_zeroed = torch.zeros_like(clip_embed)
|
|
|
|
clip_embeddings_dim = clip_embed.shape[-1]
|
|
|
|
self.ipadapter = IPAdapter(
|
|
ipadapter,
|
|
cross_attention_dim=cross_attention_dim,
|
|
output_cross_attention_dim=output_cross_attention_dim,
|
|
clip_embeddings_dim=clip_embeddings_dim,
|
|
clip_extra_context_tokens=clip_extra_context_tokens,
|
|
is_sdxl=self.is_sdxl,
|
|
is_plus=self.is_plus,
|
|
is_full=self.is_full,
|
|
is_faceid=self.is_faceid,
|
|
is_instant_id=self.is_instant_id
|
|
)
|
|
|
|
self.ipadapter.to(self.device, dtype=self.dtype)
|
|
|
|
if self.is_instant_id:
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.ipadapter.get_image_embeds_instantid(face_embed.to(self.device, dtype=self.dtype))
|
|
elif self.is_faceid and self.is_plus:
|
|
image_prompt_embeds = self.ipadapter.get_image_embeds_faceid_plus(face_embed.to(self.device, dtype=self.dtype), clip_embed.to(self.device, dtype=self.dtype), weight_v2, faceid_v2)
|
|
uncond_image_prompt_embeds = self.ipadapter.get_image_embeds_faceid_plus(face_embed_zeroed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=self.dtype), weight_v2, faceid_v2)
|
|
else:
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.ipadapter.get_image_embeds(clip_embed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=self.dtype))
|
|
|
|
image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype)
|
|
|
|
work_model = model.clone()
|
|
|
|
if self.is_instant_id:
|
|
def modifier(cnet, x_noisy, t, cond, batched_number):
|
|
cond_mark = cond['transformer_options']['cond_mark'][:, None, None].to(cond['c_crossattn']) # cond is 0
|
|
c_crossattn = image_prompt_embeds * (1.0 - cond_mark) + uncond_image_prompt_embeds * cond_mark
|
|
cond['c_crossattn'] = c_crossattn
|
|
return x_noisy, t, cond, batched_number
|
|
|
|
work_model.add_controlnet_conditioning_modifier(modifier)
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.to(self.device)
|
|
|
|
sigma_start = model.model.predictor.percent_to_sigma(start_at)
|
|
sigma_end = model.model.predictor.percent_to_sigma(end_at)
|
|
|
|
patch_kwargs = {
|
|
"number": 0,
|
|
"weight": self.weight,
|
|
"ipadapter": self.ipadapter,
|
|
"cond": image_prompt_embeds,
|
|
"uncond": uncond_image_prompt_embeds,
|
|
"weight_type": weight_type,
|
|
"mask": attn_mask,
|
|
"sigma_start": sigma_start,
|
|
"sigma_end": sigma_end,
|
|
"unfold_batch": unfold_batch,
|
|
}
|
|
|
|
if not self.is_sdxl:
|
|
for id in [1, 2, 4, 5, 7, 8]: # id of input_blocks that have cross attention
|
|
set_model_patch_replace(work_model, patch_kwargs, ("input", id))
|
|
patch_kwargs["number"] += 1
|
|
for id in [3, 4, 5, 6, 7, 8, 9, 10, 11]: # id of output_blocks that have cross attention
|
|
set_model_patch_replace(work_model, patch_kwargs, ("output", id))
|
|
patch_kwargs["number"] += 1
|
|
set_model_patch_replace(work_model, patch_kwargs, ("middle", 0))
|
|
else:
|
|
for id in [4, 5, 7, 8]: # id of input_blocks that have cross attention
|
|
block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
|
|
for index in block_indices:
|
|
set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
|
|
patch_kwargs["number"] += 1
|
|
for id in range(6): # id of output_blocks that have cross attention
|
|
block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
|
|
for index in block_indices:
|
|
set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
|
|
patch_kwargs["number"] += 1
|
|
for index in range(10):
|
|
set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
|
|
patch_kwargs["number"] += 1
|
|
|
|
return (work_model,)
|
|
|
|
|
|
class IPAdapterApplyFaceID(IPAdapterApply):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"ipadapter": ("IPADAPTER",),
|
|
"clip_vision": ("CLIP_VISION",),
|
|
"insightface": ("INSIGHTFACE",),
|
|
"image": ("IMAGE",),
|
|
"model": ("MODEL",),
|
|
"weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
|
|
"noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"weight_type": (["original", "linear", "channel penalty"],),
|
|
"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"faceid_v2": ("BOOLEAN", {"default": False}),
|
|
"weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
|
|
"unfold_batch": ("BOOLEAN", {"default": False}),
|
|
},
|
|
"optional": {
|
|
"attn_mask": ("MASK",),
|
|
}
|
|
}
|
|
|
|
|
|
def prepImage(image, interpolation="LANCZOS", crop_position="center", size=(224, 224), sharpening=0.0, padding=0):
|
|
_, oh, ow, _ = image.shape
|
|
output = image.permute([0, 3, 1, 2])
|
|
|
|
if "pad" in crop_position:
|
|
target_length = max(oh, ow)
|
|
pad_l = (target_length - ow) // 2
|
|
pad_r = (target_length - ow) - pad_l
|
|
pad_t = (target_length - oh) // 2
|
|
pad_b = (target_length - oh) - pad_t
|
|
output = F.pad(output, (pad_l, pad_r, pad_t, pad_b), value=0, mode="constant")
|
|
else:
|
|
crop_size = min(oh, ow)
|
|
x = (ow - crop_size) // 2
|
|
y = (oh - crop_size) // 2
|
|
if "top" in crop_position:
|
|
y = 0
|
|
elif "bottom" in crop_position:
|
|
y = oh - crop_size
|
|
elif "left" in crop_position:
|
|
x = 0
|
|
elif "right" in crop_position:
|
|
x = ow - crop_size
|
|
|
|
x2 = x + crop_size
|
|
y2 = y + crop_size
|
|
|
|
# crop
|
|
output = output[:, :, y:y2, x:x2]
|
|
|
|
# resize (apparently PIL resize is better than tourchvision interpolate)
|
|
imgs = []
|
|
for i in range(output.shape[0]):
|
|
img = TT.ToPILImage()(output[i])
|
|
img = img.resize(size, resample=Image.Resampling[interpolation])
|
|
imgs.append(TT.ToTensor()(img))
|
|
output = torch.stack(imgs, dim=0)
|
|
imgs = None # zelous GC
|
|
|
|
if sharpening > 0:
|
|
output = contrast_adaptive_sharpening(output, sharpening)
|
|
|
|
if padding > 0:
|
|
output = F.pad(output, (padding, padding, padding, padding), value=255, mode="constant")
|
|
|
|
output = output.permute([0, 2, 3, 1])
|
|
|
|
return output
|
|
|
|
|
|
class PrepImageForInsightFace:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE",),
|
|
"crop_position": (["center", "top", "bottom", "left", "right"],),
|
|
"sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}),
|
|
"pad_around": ("BOOLEAN", {"default": True}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "prep_image"
|
|
|
|
CATEGORY = "ipadapter"
|
|
|
|
def prep_image(self, image, crop_position, sharpening=0.0, pad_around=True):
|
|
if pad_around:
|
|
padding = 30
|
|
size = (580, 580)
|
|
else:
|
|
padding = 0
|
|
size = (640, 640)
|
|
output = prepImage(image, "LANCZOS", crop_position, size, sharpening, padding)
|
|
|
|
return (output,)
|
|
|
|
|
|
class PrepImageForClipVision:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE",),
|
|
"interpolation": (["LANCZOS", "BICUBIC", "HAMMING", "BILINEAR", "BOX", "NEAREST"],),
|
|
"crop_position": (["top", "bottom", "left", "right", "center", "pad"],),
|
|
"sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "prep_image"
|
|
|
|
CATEGORY = "ipadapter"
|
|
|
|
def prep_image(self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0):
|
|
size = (224, 224)
|
|
output = prepImage(image, interpolation, crop_position, size, sharpening, 0)
|
|
return (output,)
|
|
|
|
|
|
class IPAdapterEncoder:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"clip_vision": ("CLIP_VISION",),
|
|
"image_1": ("IMAGE",),
|
|
"ipadapter_plus": ("BOOLEAN", {"default": False}),
|
|
"noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"weight_1": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
"optional": {
|
|
"image_2": ("IMAGE",),
|
|
"image_3": ("IMAGE",),
|
|
"image_4": ("IMAGE",),
|
|
"weight_2": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
|
|
"weight_3": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
|
|
"weight_4": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("EMBEDS",)
|
|
FUNCTION = "preprocess"
|
|
CATEGORY = "ipadapter"
|
|
|
|
def preprocess(self, clip_vision, image_1, ipadapter_plus, noise, weight_1, image_2=None, image_3=None, image_4=None, weight_2=1.0, weight_3=1.0, weight_4=1.0):
|
|
weight_1 *= (0.1 + (weight_1 - 0.1))
|
|
weight_2 *= (0.1 + (weight_2 - 0.1))
|
|
weight_3 *= (0.1 + (weight_3 - 0.1))
|
|
weight_4 *= (0.1 + (weight_4 - 0.1))
|
|
|
|
image = image_1
|
|
weight = [weight_1] * image_1.shape[0]
|
|
|
|
if image_2 is not None:
|
|
if image_1.shape[1:] != image_2.shape[1:]:
|
|
image_2 = adaptive_resize(image_2.movedim(-1, 1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1, -1)
|
|
image = torch.cat((image, image_2), dim=0)
|
|
weight += [weight_2] * image_2.shape[0]
|
|
if image_3 is not None:
|
|
if image.shape[1:] != image_3.shape[1:]:
|
|
image_3 = adaptive_resize(image_3.movedim(-1, 1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1, -1)
|
|
image = torch.cat((image, image_3), dim=0)
|
|
weight += [weight_3] * image_3.shape[0]
|
|
if image_4 is not None:
|
|
if image.shape[1:] != image_4.shape[1:]:
|
|
image_4 = adaptive_resize(image_4.movedim(-1, 1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1, -1)
|
|
image = torch.cat((image, image_4), dim=0)
|
|
weight += [weight_4] * image_4.shape[0]
|
|
|
|
clip_embed = clip_vision.encode_image(image)
|
|
neg_image = image_add_noise(image, noise) if noise > 0 else None
|
|
|
|
if ipadapter_plus:
|
|
clip_embed = clip_embed.penultimate_hidden_states
|
|
if noise > 0:
|
|
clip_embed_zeroed = clip_vision.encode_image(neg_image).penultimate_hidden_states
|
|
else:
|
|
clip_embed_zeroed = zeroed_hidden_states(clip_vision, image.shape[0])
|
|
else:
|
|
clip_embed = clip_embed.image_embeds
|
|
if noise > 0:
|
|
clip_embed_zeroed = clip_vision.encode_image(neg_image).image_embeds
|
|
else:
|
|
clip_embed_zeroed = torch.zeros_like(clip_embed)
|
|
|
|
if any(e != 1.0 for e in weight):
|
|
weight = torch.tensor(weight).unsqueeze(-1) if not ipadapter_plus else torch.tensor(weight).unsqueeze(-1).unsqueeze(-1)
|
|
clip_embed = clip_embed * weight
|
|
|
|
output = torch.stack((clip_embed, clip_embed_zeroed))
|
|
|
|
return (output,)
|