├── .gitignore ├── IPAdapterPlus.py ├── IPAdapter_workflow.json ├── LICENSE ├── README.md ├── __init__.py ├── examples ├── FaceID_Plus.json ├── FaceID_Plus_v2.json ├── FaceID_basic.json ├── FaceID_enhanced.json ├── FaceID_portrait.json ├── IPAdapter_2_masks.json ├── IPAdapter_Canny.json ├── IPAdapter_batch_images.json ├── IPAdapter_face.json ├── IPAdapter_inpaint.json ├── IPAdapter_mask.json ├── IPAdapter_prepped.json ├── IPAdapter_sdxl_vit-h.json ├── IPAdapter_weight_types.json ├── IPAdapter_weighted.json ├── batch_images.jpg ├── canny_controlnet.jpg ├── face_id_wf.jpg ├── face_swap.jpg ├── image_weighting.jpg ├── inpainting.jpg ├── ip-adapter negative prompt face id.json ├── ip-adapter negative prompt.json ├── masking.jpg ├── noise_example.jpg ├── prep_images.jpg ├── timestepping.jpg └── weight_types.jpg ├── ipadapter_workflow.png ├── models └── legacy_models_location.txt └── resampler.py /.gitignore: -------------------------------------------------------------------------------- 1 | /__pycache__/ 2 | /models/*.bin 3 | /models/*.safetensors 4 | -------------------------------------------------------------------------------- /IPAdapter_workflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 12, 3 | "last_link_id": 13, 4 | "nodes": [ 5 | { 6 | "id": 10, 7 | "type": "EmptyLatentImage", 8 | "pos": [ 9 | 650, 10 | 590 11 | ], 12 | "size": { 13 | "0": 210, 14 | "1": 110 15 | }, 16 | "flags": {}, 17 | "order": 0, 18 | "mode": 0, 19 | "outputs": [ 20 | { 21 | "name": "LATENT", 22 | "type": "LATENT", 23 | "links": [ 24 | 10 25 | ], 26 | "shape": 3, 27 | "slot_index": 0 28 | } 29 | ], 30 | "properties": { 31 | "Node name for S&R": "EmptyLatentImage" 32 | }, 33 | "widgets_values": [ 34 | 512, 35 | 512, 36 | 1 37 | ] 38 | }, 39 | { 40 | "id": 8, 41 | "type": "CLIPTextEncode", 42 | "pos": [ 43 | 650, 44 | 420 45 | ], 46 | "size": { 47 | "0": 210, 48 | "1": 120 49 | }, 50 | "flags": {}, 51 | "order": 7, 52 | "mode": 0, 53 | "inputs": [ 54 | { 55 | "name": "clip", 56 | "type": "CLIP", 57 | "link": 6 58 | } 59 | ], 60 | "outputs": [ 61 | { 62 | "name": "CONDITIONING", 63 | "type": "CONDITIONING", 64 | "links": [ 65 | 9 66 | ], 67 | "shape": 3, 68 | "slot_index": 0 69 | } 70 | ], 71 | "properties": { 72 | "Node name for S&R": "CLIPTextEncode" 73 | }, 74 | "widgets_values": [ 75 | "blurry, horror" 76 | ], 77 | "color": "#322", 78 | "bgcolor": "#533" 79 | }, 80 | { 81 | "id": 7, 82 | "type": "CLIPTextEncode", 83 | "pos": [ 84 | 650, 85 | 250 86 | ], 87 | "size": { 88 | "0": 210, 89 | "1": 120 90 | }, 91 | "flags": {}, 92 | "order": 6, 93 | "mode": 0, 94 | "inputs": [ 95 | { 96 | "name": "clip", 97 | "type": "CLIP", 98 | "link": 5 99 | } 100 | ], 101 | "outputs": [ 102 | { 103 | "name": "CONDITIONING", 104 | "type": "CONDITIONING", 105 | "links": [ 106 | 8 107 | ], 108 | "shape": 3, 109 | "slot_index": 0 110 | } 111 | ], 112 | "properties": { 113 | "Node name for S&R": "CLIPTextEncode" 114 | }, 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-------------------------------------------------------------------------------- 1 | # modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py 2 | # and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py 3 | 4 | import math 5 | 6 | import torch 7 | import torch.nn as nn 8 | from einops import rearrange 9 | from einops.layers.torch import Rearrange 10 | 11 | 12 | # FFN 13 | def FeedForward(dim, mult=4): 14 | inner_dim = int(dim * mult) 15 | return nn.Sequential( 16 | nn.LayerNorm(dim), 17 | nn.Linear(dim, inner_dim, bias=False), 18 | nn.GELU(), 19 | nn.Linear(inner_dim, dim, bias=False), 20 | ) 21 | 22 | 23 | def reshape_tensor(x, heads): 24 | bs, length, width = x.shape 25 | # (bs, length, width) --> (bs, length, n_heads, dim_per_head) 26 | x = x.view(bs, length, heads, -1) 27 | # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) 28 | x = x.transpose(1, 2) 29 | # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) 30 | x = x.reshape(bs, heads, length, -1) 31 | return x 32 | 33 | 34 | class PerceiverAttention(nn.Module): 35 | def __init__(self, *, dim, dim_head=64, heads=8): 36 | super().__init__() 37 | self.scale = dim_head**-0.5 38 | self.dim_head = dim_head 39 | self.heads = heads 40 | inner_dim = dim_head * heads 41 | 42 | self.norm1 = nn.LayerNorm(dim) 43 | self.norm2 = nn.LayerNorm(dim) 44 | 45 | self.to_q = nn.Linear(dim, inner_dim, bias=False) 46 | self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) 47 | self.to_out = nn.Linear(inner_dim, dim, bias=False) 48 | 49 | def forward(self, x, latents): 50 | """ 51 | Args: 52 | x (torch.Tensor): image features 53 | shape (b, n1, D) 54 | latent (torch.Tensor): latent features 55 | shape (b, n2, D) 56 | """ 57 | x = self.norm1(x) 58 | latents = self.norm2(latents) 59 | 60 | b, l, _ = latents.shape 61 | 62 | q = self.to_q(latents) 63 | kv_input = torch.cat((x, latents), dim=-2) 64 | k, v = self.to_kv(kv_input).chunk(2, dim=-1) 65 | 66 | q = reshape_tensor(q, self.heads) 67 | k = reshape_tensor(k, self.heads) 68 | v = reshape_tensor(v, self.heads) 69 | 70 | # attention 71 | scale = 1 / math.sqrt(math.sqrt(self.dim_head)) 72 | weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards 73 | weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) 74 | out = weight @ v 75 | 76 | out = out.permute(0, 2, 1, 3).reshape(b, l, -1) 77 | 78 | return self.to_out(out) 79 | 80 | 81 | class Resampler(nn.Module): 82 | def __init__( 83 | self, 84 | dim=1024, 85 | depth=8, 86 | dim_head=64, 87 | heads=16, 88 | num_queries=8, 89 | embedding_dim=768, 90 | output_dim=1024, 91 | ff_mult=4, 92 | max_seq_len: int = 257, # CLIP tokens + CLS token 93 | apply_pos_emb: bool = False, 94 | num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence 95 | ): 96 | super().__init__() 97 | self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None 98 | 99 | self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) 100 | 101 | self.proj_in = nn.Linear(embedding_dim, dim) 102 | 103 | self.proj_out = nn.Linear(dim, output_dim) 104 | self.norm_out = nn.LayerNorm(output_dim) 105 | 106 | self.to_latents_from_mean_pooled_seq = ( 107 | nn.Sequential( 108 | nn.LayerNorm(dim), 109 | nn.Linear(dim, dim * num_latents_mean_pooled), 110 | Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), 111 | ) 112 | if num_latents_mean_pooled > 0 113 | else None 114 | ) 115 | 116 | self.layers = nn.ModuleList([]) 117 | for _ in range(depth): 118 | self.layers.append( 119 | nn.ModuleList( 120 | [ 121 | PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), 122 | FeedForward(dim=dim, mult=ff_mult), 123 | ] 124 | ) 125 | ) 126 | 127 | def forward(self, x): 128 | if self.pos_emb is not None: 129 | n, device = x.shape[1], x.device 130 | pos_emb = self.pos_emb(torch.arange(n, device=device)) 131 | x = x + pos_emb 132 | 133 | latents = self.latents.repeat(x.size(0), 1, 1) 134 | 135 | x = self.proj_in(x) 136 | 137 | if self.to_latents_from_mean_pooled_seq: 138 | meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) 139 | meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) 140 | latents = torch.cat((meanpooled_latents, latents), dim=-2) 141 | 142 | for attn, ff in self.layers: 143 | latents = attn(x, latents) + latents 144 | latents = ff(latents) + latents 145 | 146 | latents = self.proj_out(latents) 147 | return self.norm_out(latents) 148 | 149 | 150 | def masked_mean(t, *, dim, mask=None): 151 | if mask is None: 152 | return t.mean(dim=dim) 153 | 154 | denom = mask.sum(dim=dim, keepdim=True) 155 | mask = rearrange(mask, "b n -> b n 1") 156 | masked_t = t.masked_fill(~mask, 0.0) 157 | 158 | return masked_t.sum(dim=dim) / denom.clamp(min=1e-5) 159 | 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