├── .github ├── FUNDING.yml └── workflows │ └── publish.yml ├── .gitignore ├── CrossAttentionPatch.py ├── IPAdapterPlusV2.py ├── LICENSE ├── README.md ├── __init__.py ├── change_node_name.py ├── exampleV2 ├── IPAdapterV1&V2.json ├── IPAdapterV1&V2.png ├── IPAdapterV2_menu.png ├── layer_weights_slider_example.jpg └── layer_weights_slider_example.json ├── examples ├── IPAdapter_FaceIDv2_Kolors.json ├── demo_workflow.jpg ├── ipadapter_advanced.json ├── ipadapter_clipvision_enhancer.json ├── ipadapter_combine_embeds.json ├── ipadapter_cosxl_edit.json ├── ipadapter_faceid.json ├── ipadapter_faceid_batch.json ├── ipadapter_ideal_faceid_config.json ├── ipadapter_kolors.json ├── ipadapter_negative_image.json ├── ipadapter_noise_injection.json ├── ipadapter_portrait.json ├── ipadapter_precise_composition.json ├── ipadapter_precise_weight_type.json ├── ipadapter_regional_conditioning.json ├── ipadapter_simple.json ├── ipadapter_style_composition.json ├── ipadapter_tiled.json ├── ipadapter_weight_types.json ├── ipadapter_weighted_embeds.json └── ipadapter_weights.json ├── image_proj_models.py ├── layer_weights_slider.py ├── models └── legacy_directory_do_not_use.txt ├── pyproject.toml └── utils.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: cubiq 4 | custom: ['https://www.paypal.com/paypalme/matt3o'] 5 | -------------------------------------------------------------------------------- /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | paths: 8 | - "pyproject.toml" 9 | 10 | jobs: 11 | publish-node: 12 | name: Publish Custom Node to registry 13 | runs-on: ubuntu-latest 14 | steps: 15 | - name: Check out code 16 | uses: actions/checkout@v4 17 | - name: Publish Custom Node 18 | uses: Comfy-Org/publish-node-action@main 19 | with: 20 | ## Add your own personal access token to your Github Repository secrets and reference it here. 21 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | /__pycache__/ 2 | /models/*.bin 3 | /models/*.safetensors 4 | .directory -------------------------------------------------------------------------------- /CrossAttentionPatch.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import math 3 | import torch.nn.functional as F 4 | from comfy.ldm.modules.attention import optimized_attention 5 | from .utils import tensor_to_size 6 | 7 | class Attn2Replace: 8 | def __init__(self, callback=None, **kwargs): 9 | self.callback = [callback] 10 | self.kwargs = [kwargs] 11 | 12 | def add(self, callback, **kwargs): 13 | self.callback.append(callback) 14 | self.kwargs.append(kwargs) 15 | 16 | for key, value in kwargs.items(): 17 | setattr(self, key, value) 18 | 19 | def __call__(self, q, k, v, extra_options): 20 | dtype = q.dtype 21 | out = optimized_attention(q, k, v, extra_options["n_heads"]) 22 | sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9 23 | 24 | for i, callback in enumerate(self.callback): 25 | if sigma <= self.kwargs[i]["sigma_start"] and sigma >= self.kwargs[i]["sigma_end"]: 26 | out = out + callback(out, q, k, v, extra_options, **self.kwargs[i]) 27 | 28 | return out.to(dtype=dtype) 29 | 30 | def ipadapter_attention(out, q, k, v, extra_options, module_key='', ipadapter=None, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only', **kwargs): 31 | dtype = q.dtype 32 | cond_or_uncond = extra_options["cond_or_uncond"] 33 | block_type = extra_options["block"][0] 34 | #block_id = extra_options["block"][1] 35 | t_idx = extra_options["transformer_index"] 36 | layers = 11 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 16 37 | k_key = module_key + "_to_k_ip" 38 | v_key = module_key + "_to_v_ip" 39 | 40 | # extra options for AnimateDiff 41 | ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None 42 | 43 | b = q.shape[0] 44 | seq_len = q.shape[1] 45 | batch_prompt = b // len(cond_or_uncond) 46 | _, _, oh, ow = extra_options["original_shape"] 47 | 48 | if weight_type == 'ease in': 49 | weight = weight * (0.05 + 0.95 * (1 - t_idx / layers)) 50 | elif weight_type == 'ease out': 51 | weight = weight * (0.05 + 0.95 * (t_idx / layers)) 52 | elif weight_type == 'ease in-out': 53 | weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (layers/2)) / (layers/2))) 54 | elif weight_type == 'reverse in-out': 55 | weight = weight * (0.05 + 0.95 * (abs(t_idx - (layers/2)) / (layers/2))) 56 | elif weight_type == 'weak input' and block_type == 'input': 57 | weight = weight * 0.2 58 | elif weight_type == 'weak middle' and block_type == 'middle': 59 | weight = weight * 0.2 60 | elif weight_type == 'weak output' and block_type == 'output': 61 | weight = weight * 0.2 62 | elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'): 63 | weight = weight * 0.2 64 | elif isinstance(weight, dict): 65 | if t_idx not in weight: 66 | return 0 67 | 68 | if weight_type == "style transfer precise": 69 | if layers == 11 and t_idx == 3: 70 | uncond = cond 71 | cond = cond * 0 72 | elif layers == 16 and (t_idx == 4 or t_idx == 5): 73 | uncond = cond 74 | cond = cond * 0 75 | elif weight_type == "composition precise": 76 | if layers == 11 and t_idx != 3: 77 | uncond = cond 78 | cond = cond * 0 79 | elif layers == 16 and (t_idx != 4 and t_idx != 5): 80 | uncond = cond 81 | cond = cond * 0 82 | 83 | weight = weight[t_idx] 84 | 85 | if cond_alt is not None and t_idx in cond_alt: 86 | cond = cond_alt[t_idx] 87 | del cond_alt 88 | 89 | if unfold_batch: 90 | # Check AnimateDiff context window 91 | if ad_params is not None and ad_params["sub_idxs"] is not None: 92 | if isinstance(weight, torch.Tensor): 93 | weight = tensor_to_size(weight, ad_params["full_length"]) 94 | weight = torch.Tensor(weight[ad_params["sub_idxs"]]) 95 | if torch.all(weight == 0): 96 | return 0 97 | weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond 98 | elif weight == 0: 99 | return 0 100 | 101 | # if image length matches or exceeds full_length get sub_idx images 102 | if cond.shape[0] >= ad_params["full_length"]: 103 | cond = torch.Tensor(cond[ad_params["sub_idxs"]]) 104 | uncond = torch.Tensor(uncond[ad_params["sub_idxs"]]) 105 | # otherwise get sub_idxs images 106 | else: 107 | cond = tensor_to_size(cond, ad_params["full_length"]) 108 | uncond = tensor_to_size(uncond, ad_params["full_length"]) 109 | cond = cond[ad_params["sub_idxs"]] 110 | uncond = uncond[ad_params["sub_idxs"]] 111 | else: 112 | if isinstance(weight, torch.Tensor): 113 | weight = tensor_to_size(weight, batch_prompt) 114 | if torch.all(weight == 0): 115 | return 0 116 | weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond 117 | elif weight == 0: 118 | return 0 119 | 120 | cond = tensor_to_size(cond, batch_prompt) 121 | uncond = tensor_to_size(uncond, batch_prompt) 122 | 123 | k_cond = ipadapter.ip_layers.to_kvs[k_key](cond) 124 | k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond) 125 | v_cond = ipadapter.ip_layers.to_kvs[v_key](cond) 126 | v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond) 127 | else: 128 | # TODO: should we always convert the weights to a tensor? 129 | if isinstance(weight, torch.Tensor): 130 | weight = tensor_to_size(weight, batch_prompt) 131 | if torch.all(weight == 0): 132 | return 0 133 | weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond 134 | elif weight == 0: 135 | return 0 136 | 137 | k_cond = ipadapter.ip_layers.to_kvs[k_key](cond).repeat(batch_prompt, 1, 1) 138 | k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond).repeat(batch_prompt, 1, 1) 139 | v_cond = ipadapter.ip_layers.to_kvs[v_key](cond).repeat(batch_prompt, 1, 1) 140 | v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond).repeat(batch_prompt, 1, 1) 141 | 142 | if len(cond_or_uncond) == 3: # TODO: cosxl, I need to check this 143 | ip_k = torch.cat([(k_cond, k_uncond, k_cond)[i] for i in cond_or_uncond], dim=0) 144 | ip_v = torch.cat([(v_cond, v_uncond, v_cond)[i] for i in cond_or_uncond], dim=0) 145 | else: 146 | ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) 147 | ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) 148 | 149 | if embeds_scaling == 'K+mean(V) w/ C penalty': 150 | scaling = float(ip_k.shape[2]) / 1280.0 151 | weight = weight * scaling 152 | ip_k = ip_k * weight 153 | ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True) 154 | ip_v = (ip_v - ip_v_mean) + ip_v_mean * weight 155 | out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) 156 | del ip_v_mean 157 | elif embeds_scaling == 'K+V w/ C penalty': 158 | scaling = float(ip_k.shape[2]) / 1280.0 159 | weight = weight * scaling 160 | ip_k = ip_k * weight 161 | ip_v = ip_v * weight 162 | out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) 163 | elif embeds_scaling == 'K+V': 164 | ip_k = ip_k * weight 165 | ip_v = ip_v * weight 166 | out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) 167 | else: 168 | #ip_v = ip_v * weight 169 | out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) 170 | out_ip = out_ip * weight # I'm doing this to get the same results as before 171 | 172 | if mask is not None: 173 | mask_h = oh / math.sqrt(oh * ow / seq_len) 174 | mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0) 175 | mask_w = seq_len // mask_h 176 | 177 | # check if using AnimateDiff and sliding context window 178 | if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None): 179 | # if mask length matches or exceeds full_length, get sub_idx masks 180 | if mask.shape[0] >= ad_params["full_length"]: 181 | mask = torch.Tensor(mask[ad_params["sub_idxs"]]) 182 | mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1) 183 | else: 184 | mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1) 185 | mask = tensor_to_size(mask, ad_params["full_length"]) 186 | mask = mask[ad_params["sub_idxs"]] 187 | else: 188 | mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1) 189 | mask = tensor_to_size(mask, batch_prompt) 190 | 191 | mask = mask.repeat(len(cond_or_uncond), 1, 1) 192 | mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2]) 193 | 194 | # covers cases where extreme aspect ratios can cause the mask to have a wrong size 195 | mask_len = mask_h * mask_w 196 | if mask_len < seq_len: 197 | pad_len = seq_len - mask_len 198 | pad1 = pad_len // 2 199 | pad2 = pad_len - pad1 200 | mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0) 201 | elif mask_len > seq_len: 202 | crop_start = (mask_len - seq_len) // 2 203 | mask = mask[:, crop_start:crop_start+seq_len, :] 204 | 205 | out_ip = out_ip * mask 206 | 207 | #out = out + out_ip 208 | 209 | return out_ip.to(dtype=dtype) 210 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI IPAdapter plus V2 2 | 3 | A copy of [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus), Only changed node name to coexist with ComfyUI_IPAdapter_plus v1 version. 4 | 5 | 6 | Thanks to author [Cubiq](https://github.com/cubiq)'s great work, Please support his original work. 7 | 8 | 9 | ![image](exampleV2/IPAdapterV2_menu.png) 10 | ![image](exampleV2/IPAdapterV1&V2.png) 11 | 12 | The copy of latest ComfyUI_IPAdapter_plus V1 version is saved on [GoogleDrive](https://drive.google.com/drive/folders/1DAcDzKcypm3mKH3yjyM2c07b3XqqM07P?usp=sharing) and [BaiduNetdisk](https://pan.baidu.com/s/1x_SVJIhEzBbENGrlonvtgw?pwd=86gf). 13 | 14 | 15 | ## Add node: 16 | ### IPAdapter Layer Weights Slider 17 | ![image](exampleV2/layer_weights_slider_example.jpg) 18 | ```IPAdapter Layer Weights Slider``` node is used in conjunction with the ```IPAdapter Mad Scientist``` node to visualize the layer_weights parameter. 19 | 20 | * Usage： 21 | The weight slider adjustment range is -1 to 1. 22 | If you want to exceed this range, adjust the ```multiplier``` to multiply the output slider value with it. -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | ██▓ ██▓███ ▄▄▄ ▓█████▄ ▄▄▄ ██▓███ ▄▄▄█████▓▓█████ ██▀███ 3 | ▓██▒▓██░ ██▒▒████▄ ▒██▀ ██▌▒████▄ ▓██░ ██▒▓ ██▒ ▓▒▓█ ▀ ▓██ ▒ ██▒ 4 | ▒██▒▓██░ ██▓▒▒██ ▀█▄ ░██ █▌▒██ ▀█▄ ▓██░ ██▓▒▒ ▓██░ ▒░▒███ ▓██ ░▄█ ▒ 5 | ░██░▒██▄█▓▒ ▒░██▄▄▄▄██ ░▓█▄ ▌░██▄▄▄▄██ ▒██▄█▓▒ ▒░ ▓██▓ ░ ▒▓█ ▄ ▒██▀▀█▄ 6 | ░██░▒██▒ ░ ░ ▓█ ▓██▒░▒████▓ ▓█ ▓██▒▒██▒ ░ ░ ▒██▒ ░ ░▒████▒░██▓ ▒██▒ 7 | ░▓ ▒▓▒░ ░ ░ ▒▒ ▓▒█░ ▒▒▓ ▒ ▒▒ ▓▒█░▒▓▒░ ░ ░ ▒ ░░ ░░ ▒░ ░░ ▒▓ ░▒▓░ 8 | ▒ ░░▒ ░ ▒ ▒▒ ░ ░ ▒ ▒ ▒ ▒▒ ░░▒ ░ ░ ░ ░ ░ ░▒ ░ ▒░ 9 | ▒ ░░░ ░ ▒ ░ ░ ░ ░ ▒ ░░ ░ ░ ░░ ░ 10 | ░ ░ ░ ░ ░ ░ ░ ░ ░ 11 | ░ 12 | · -—+ IPAdapter Plus Extension for ComfyUI +—- · 13 | Brought to you by Matteo "Matt3o/Cubiq" Spinelli 14 | https://github.com/cubiq/ComfyUI_IPAdapter_plus/ 15 | """ 16 | 17 | from .IPAdapterPlusV2 import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 18 | class_mappings = NODE_CLASS_MAPPINGS 19 | display_name_mappings = NODE_DISPLAY_NAME_MAPPINGS 20 | from .layer_weights_slider import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 21 | NODE_CLASS_MAPPINGS.update(class_mappings) 22 | NODE_DISPLAY_NAME_MAPPINGS.update(display_name_mappings) 23 | 24 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 25 | -------------------------------------------------------------------------------- /change_node_name.py: -------------------------------------------------------------------------------- 1 | '''Change ComfyUI_IPAdapter_plus Node Name to V2 2 | by chflame@163.com''' 3 | 4 | import os 5 | import glob 6 | 7 | # py file 8 | orig_file_name = 'IPAdapterPlus.py' 9 | dist_file_name = 'IPAdapterPlusV2.py' 10 | orig_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), orig_file_name) 11 | dist_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), dist_file_name) 12 | # json files 13 | json_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples") 14 | json_file_list = glob.glob(json_path + '/*.json') 15 | 16 | 17 | class_name_mapping_list = [ 18 | "IPAdapter", 19 | "IPAdapterSimple", 20 | "IPAdapterAdvanced", 21 | "IPAdapterBatch", 22 | "IPAdapterFaceID", 23 | "IPAAdapterFaceIDBatch", 24 | "IPAdapterTiled", 25 | "IPAdapterTiledBatch", 26 | "IPAdapterEmbeds", 27 | "IPAdapterUnifiedLoader", 28 | "IPAdapterUnifiedLoaderFaceID", 29 | "IPAdapterModelLoader", 30 | "IPAdapterInsightFaceLoader", 31 | "IPAdapterEncoder", 32 | "IPAdapterCombineEmbeds", 33 | "IPAdapterNoise", 34 | "PrepImageForClipVision", 35 | "IPAdapterSaveEmbeds", 36 | "IPAdapterLoadEmbeds", 37 | "IPAdapterUnifiedLoaderCommunity", 38 | "IPAdapterStyleComposition", 39 | "IPAdapterStyleCompositionBatch", 40 | "IPAdapterMS", 41 | "IPAdapterWeights", 42 | "IPAdapterRegionalConditioning", 43 | "IPAdapterCombineParams", 44 | "IPAdapterFromParams", 45 | "IPAdapterWeightsFromStrategy", 46 | "IPAdapterPromptScheduleFromWeightsStrategy", 47 | "IPAdapterCombineWeights", 48 | "IPAdapterEmbedsBatch", 49 | "IPAdapterPreciseStyleTransfer", 50 | "IPAdapterPreciseStyleTransferBatch", 51 | "IPAdapterPreciseComposition", 52 | "IPAdapterPreciseCompositionBatch", 53 | "IPAdapterClipVisionEnhancer", 54 | "IPAdapterClipVisionEnhancerBatch", 55 | "IPAdapterFaceIDKolors" 56 | ] 57 | 58 | display_name_mapping_list = [ 59 | "IPAdapter", 60 | "IPAdapter Advanced", 61 | "IPAdapter Batch (Adv.)", 62 | "IPAdapter FaceID", 63 | "IPAdapter FaceID Batch", 64 | "IPAdapter Tiled", 65 | "IPAdapter Tiled Batch", 66 | "IPAdapter Embeds", 67 | "IPAdapter Unified Loader", 68 | "IPAdapter Unified Loader FaceID", 69 | "IPAdapter Model Loader", 70 | "IPAdapter InsightFace Loader", 71 | "IPAdapter Encoder", 72 | "IPAdapter Combine Embeds", 73 | "IPAdapter Noise", 74 | "Prep Image For ClipVision", 75 | "IPAdapter Save Embeds", 76 | "IPAdapter Load Embeds", 77 | "IPAdapter Unified Loader Community", 78 | "IPAdapter Style & Composition SDXL", 79 | "IPAdapter Style & Composition Batch SDXL", 80 | "IPAdapter Mad Scientist", 81 | "IPAdapter Weights", 82 | "IPAdapter Regional Conditioning", 83 | "IPAdapter Combine Params", 84 | "IPAdapter from Params", 85 | "IPAdapter Weights From Strategy", 86 | "Prompt Schedule From Weights Strategy", 87 | "IPAdapter Combine Weights", 88 | "IPAdapter Embeds Batch", 89 | "IPAdapter Precise Style Transfer", 90 | "IPAdapter Precise Style Transfer Batch", 91 | "IPAdapter Precise Composition", 92 | "IPAdapter Precise Composition Batch", 93 | "IPAdapter ClipVision Enhancer", 94 | "IPAdapter ClipVision Enhancer Batch", 95 | "IPAdapter FaceID Kolors" 96 | ] 97 | 98 | def change_node_name(): 99 | if os.path.exists(orig_file_path): 100 | with open(orig_file_path, "r") as f: 101 | content = f.read() 102 | for class_name in class_name_mapping_list: 103 | content = content.replace(f'"{class_name}":', f'"{class_name}V2":') 104 | content = content.replace(f'class {class_name}:', f'class {class_name}V2:') 105 | content = content.replace(f'class {class_name}(', f'class {class_name}V2(') 106 | content = content.replace(f'": {class_name},', f'": {class_name}V2,') 107 | content = content.replace(f'({class_name}):', f'({class_name}V2):') 108 | content = content.replace(f'class IPAdapterV2(nn.Module):', f'class IPAdapter(nn.Module):') 109 | 110 | for display_name in display_name_mapping_list: 111 | content = content.replace(f': "{display_name}",', f': "{display_name} V2",') 112 | 113 | with open(dist_file_path, 'w') as f: 114 | f.write(content) 115 | 116 | os.remove(orig_file_path) 117 | 118 | def change_json_name(): 119 | for i in json_file_list: 120 | with open(i, "r") as f: 121 | content = f.read() 122 | for class_name in class_name_mapping_list: 123 | content = content.replace(f'"{class_name}"', f'"{class_name}V2"') 124 | # print(f'replace "{class_name}" to "{class_name}V2"') 125 | with open(i, 'w') as f: 126 | f.write(content) 127 | print(f"Change {i} node name to V2, done.") 128 | 129 | change_node_name() 130 | change_json_name() -------------------------------------------------------------------------------- /exampleV2/IPAdapterV1&V2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chflame163/ComfyUI_IPAdapter_plus_V2/24bda419546b0229fe290957411f74ab664fe52f/exampleV2/IPAdapterV1&V2.png -------------------------------------------------------------------------------- /exampleV2/IPAdapterV2_menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chflame163/ComfyUI_IPAdapter_plus_V2/24bda419546b0229fe290957411f74ab664fe52f/exampleV2/IPAdapterV2_menu.png -------------------------------------------------------------------------------- /exampleV2/layer_weights_slider_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chflame163/ComfyUI_IPAdapter_plus_V2/24bda419546b0229fe290957411f74ab664fe52f/exampleV2/layer_weights_slider_example.jpg -------------------------------------------------------------------------------- /exampleV2/layer_weights_slider_example.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 17, 3 | "last_link_id": 20, 4 | "nodes": [ 5 | { 6 | "id": 7, 7 | "type": "CLIPTextEncode", 8 | "pos": [ 9 | 840, 10 | 850 11 | ], 12 | "size": { 13 | "0": 425.27801513671875, 14 | "1": 180.6060791015625 15 | }, 16 | "flags": {}, 17 | "order": 8, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "clip", 22 | "type": "CLIP", 23 | "link": 5 24 | } 25 | ], 26 | "outputs": [ 27 | { 28 | "name": "CONDITIONING", 29 | "type": "CONDITIONING", 30 | "links": [ 31 | 6 32 | ], 33 | "slot_index": 0 34 | } 35 | ], 36 | "properties": { 37 | "Node name for S&R": "CLIPTextEncode" 38 | }, 39 | "widgets_values": [ 40 | "text, watermark" 41 | ] 42 | }, 43 | { 44 | "id": 5, 45 | "type": "EmptyLatentImage", 46 | "pos": [ 47 | 900, 48 | 1070 49 | ], 50 | "size": { 51 | "0": 315, 52 | "1": 106 53 | }, 54 | "flags": {}, 55 | "order": 0, 56 | "mode": 0, 57 | "outputs": [ 58 | { 59 | "name": "LATENT", 60 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"widgets_values": [ 539 | 6, 540 | "fixed" 541 | ] 542 | }, 543 | { 544 | "id": 22, 545 | "type": "IPAdapterWeightsV2", 546 | "pos": [ 547 | 761, 548 | 208 549 | ], 550 | "size": [ 551 | 299.9049990375719, 552 | 324.00000762939453 553 | ], 554 | "flags": {}, 555 | "order": 8, 556 | "mode": 0, 557 | "inputs": [ 558 | { 559 | "name": "image", 560 | "type": "IMAGE", 561 | "link": null 562 | }, 563 | { 564 | "name": "frames", 565 | "type": "INT", 566 | "link": 40, 567 | "widget": { 568 | "name": "frames" 569 | } 570 | } 571 | ], 572 | "outputs": [ 573 | { 574 | "name": "weights", 575 | "type": "FLOAT", 576 | "links": [ 577 | 38 578 | ], 579 | "shape": 3, 580 | "slot_index": 0 581 | }, 582 | { 583 | "name": "weights_invert", 584 | "type": "FLOAT", 585 | "links": null, 586 | "shape": 3 587 | }, 588 | { 589 | "name": "total_frames", 590 | "type": "INT", 591 | "links": null, 592 | "shape": 3 593 | }, 594 | { 595 | "name": "image_1", 596 | "type": "IMAGE", 597 | "links": null, 598 | "shape": 3 599 | }, 600 | { 601 | "name": "image_2", 602 | "type": "IMAGE", 603 | "links": null, 604 | "shape": 3 605 | } 606 | ], 607 | "properties": { 608 | "Node name for S&R": "IPAdapterWeightsV2" 609 | }, 610 | "widgets_values": [ 611 | "1.0, 0.0", 612 | "linear", 613 | 6, 614 | 0, 615 | 9999, 616 | 0, 617 | 0, 618 | "full batch" 619 | ] 620 | } 621 | ], 622 | "links": [ 623 | [ 624 | 2, 625 | 5, 626 | 0, 627 | 3, 628 | 3, 629 | "LATENT" 630 | ], 631 | [ 632 | 3, 633 | 4, 634 | 1, 635 | 6, 636 | 0, 637 | "CLIP" 638 | ], 639 | [ 640 | 4, 641 | 6, 642 | 0, 643 | 3, 644 | 1, 645 | "CONDITIONING" 646 | ], 647 | [ 648 | 5, 649 | 4, 650 | 1, 651 | 7, 652 | 0, 653 | "CLIP" 654 | ], 655 | [ 656 | 6, 657 | 7, 658 | 0, 659 | 3, 660 | 2, 661 | "CONDITIONING" 662 | ], 663 | [ 664 | 7, 665 | 3, 666 | 0, 667 | 8, 668 | 0, 669 | "LATENT" 670 | ], 671 | [ 672 | 8, 673 | 4, 674 | 2, 675 | 8, 676 | 1, 677 | "VAE" 678 | ], 679 | [ 680 | 9, 681 | 8, 682 | 0, 683 | 9, 684 | 0, 685 | "IMAGE" 686 | ], 687 | [ 688 | 25, 689 | 12, 690 | 0, 691 | 17, 692 | 0, 693 | "IMAGE" 694 | ], 695 | [ 696 | 29, 697 | 18, 698 | 1, 699 | 19, 700 | 1, 701 | "IPADAPTER" 702 | ], 703 | [ 704 | 30, 705 | 17, 706 | 0, 707 | 19, 708 | 2, 709 | "IMAGE" 710 | ], 711 | [ 712 | 31, 713 | 19, 714 | 0, 715 | 3, 716 | 0, 717 | "MODEL" 718 | ], 719 | [ 720 | 35, 721 | 21, 722 | 0, 723 | 5, 724 | 0, 725 | "INT" 726 | ], 727 | [ 728 | 36, 729 | 4, 730 | 0, 731 | 18, 732 | 0, 733 | "MODEL" 734 | ], 735 | [ 736 | 37, 737 | 18, 738 | 0, 739 | 19, 740 | 0, 741 | "MODEL" 742 | ], 743 | [ 744 | 38, 745 | 22, 746 | 0, 747 | 19, 748 | 6, 749 | "FLOAT" 750 | ], 751 | [ 752 | 40, 753 | 21, 754 | 0, 755 | 22, 756 | 1, 757 | "INT" 758 | ] 759 | ], 760 | "groups": [], 761 | "config": {}, 762 | "extra": {}, 763 | "version": 0.4 764 | } -------------------------------------------------------------------------------- /image_proj_models.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | from einops import rearrange 5 | from einops.layers.torch import Rearrange 6 | 7 | 8 | # FFN 9 | def FeedForward(dim, mult=4): 10 | inner_dim = int(dim * mult) 11 | return nn.Sequential( 12 | nn.LayerNorm(dim), 13 | nn.Linear(dim, inner_dim, bias=False), 14 | nn.GELU(), 15 | nn.Linear(inner_dim, dim, bias=False), 16 | ) 17 | 18 | 19 | def reshape_tensor(x, heads): 20 | bs, length, width = x.shape 21 | # (bs, length, width) --> (bs, length, n_heads, dim_per_head) 22 | x = x.view(bs, length, heads, -1) 23 | # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) 24 | x = x.transpose(1, 2) 25 | # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) 26 | x = x.reshape(bs, heads, length, -1) 27 | return x 28 | 29 | 30 | class PerceiverAttention(nn.Module): 31 | def __init__(self, *, dim, dim_head=64, heads=8): 32 | super().__init__() 33 | self.scale = dim_head**-0.5 34 | self.dim_head = dim_head 35 | self.heads = heads 36 | inner_dim = dim_head * heads 37 | 38 | self.norm1 = nn.LayerNorm(dim) 39 | self.norm2 = nn.LayerNorm(dim) 40 | 41 | self.to_q = nn.Linear(dim, inner_dim, bias=False) 42 | self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) 43 | self.to_out = nn.Linear(inner_dim, dim, bias=False) 44 | 45 | def forward(self, x, latents): 46 | """ 47 | Args: 48 | x (torch.Tensor): image features 49 | shape (b, n1, D) 50 | latent (torch.Tensor): latent features 51 | shape (b, n2, D) 52 | """ 53 | x = self.norm1(x) 54 | latents = self.norm2(latents) 55 | 56 | b, l, _ = latents.shape 57 | 58 | q = self.to_q(latents) 59 | kv_input = torch.cat((x, latents), dim=-2) 60 | k, v = self.to_kv(kv_input).chunk(2, dim=-1) 61 | 62 | q = reshape_tensor(q, self.heads) 63 | k = reshape_tensor(k, self.heads) 64 | v = reshape_tensor(v, self.heads) 65 | 66 | # attention 67 | scale = 1 / math.sqrt(math.sqrt(self.dim_head)) 68 | weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards 69 | weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) 70 | out = weight @ v 71 | 72 | out = out.permute(0, 2, 1, 3).reshape(b, l, -1) 73 | 74 | return self.to_out(out) 75 | 76 | 77 | class Resampler(nn.Module): 78 | def __init__( 79 | self, 80 | dim=1024, 81 | depth=8, 82 | dim_head=64, 83 | heads=16, 84 | num_queries=8, 85 | embedding_dim=768, 86 | output_dim=1024, 87 | ff_mult=4, 88 | max_seq_len: int = 257, # CLIP tokens + CLS token 89 | apply_pos_emb: bool = False, 90 | num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence 91 | ): 92 | super().__init__() 93 | self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None 94 | 95 | self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) 96 | 97 | self.proj_in = nn.Linear(embedding_dim, dim) 98 | 99 | self.proj_out = nn.Linear(dim, output_dim) 100 | self.norm_out = nn.LayerNorm(output_dim) 101 | 102 | self.to_latents_from_mean_pooled_seq = ( 103 | nn.Sequential( 104 | nn.LayerNorm(dim), 105 | nn.Linear(dim, dim * num_latents_mean_pooled), 106 | Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), 107 | ) 108 | if num_latents_mean_pooled > 0 109 | else None 110 | ) 111 | 112 | self.layers = nn.ModuleList([]) 113 | for _ in range(depth): 114 | self.layers.append( 115 | nn.ModuleList( 116 | [ 117 | PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), 118 | FeedForward(dim=dim, mult=ff_mult), 119 | ] 120 | ) 121 | ) 122 | 123 | def forward(self, x): 124 | if self.pos_emb is not None: 125 | n, device = x.shape[1], x.device 126 | pos_emb = self.pos_emb(torch.arange(n, device=device)) 127 | x = x + pos_emb 128 | 129 | latents = self.latents.repeat(x.size(0), 1, 1) 130 | 131 | x = self.proj_in(x) 132 | 133 | if self.to_latents_from_mean_pooled_seq: 134 | meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) 135 | meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) 136 | latents = torch.cat((meanpooled_latents, latents), dim=-2) 137 | 138 | for attn, ff in self.layers: 139 | latents = attn(x, latents) + latents 140 | latents = ff(latents) + latents 141 | 142 | latents = self.proj_out(latents) 143 | return self.norm_out(latents) 144 | 145 | 146 | def masked_mean(t, *, dim, mask=None): 147 | if mask is None: 148 | return t.mean(dim=dim) 149 | 150 | denom = mask.sum(dim=dim, keepdim=True) 151 | mask = rearrange(mask, "b n -> b n 1") 152 | masked_t = t.masked_fill(~mask, 0.0) 153 | 154 | return masked_t.sum(dim=dim) / denom.clamp(min=1e-5) 155 | 156 | 157 | class FacePerceiverResampler(nn.Module): 158 | def __init__( 159 | self, 160 | *, 161 | dim=768, 162 | depth=4, 163 | dim_head=64, 164 | heads=16, 165 | embedding_dim=1280, 166 | output_dim=768, 167 | ff_mult=4, 168 | ): 169 | super().__init__() 170 | 171 | self.proj_in = nn.Linear(embedding_dim, dim) 172 | self.proj_out = nn.Linear(dim, output_dim) 173 | self.norm_out = nn.LayerNorm(output_dim) 174 | self.layers = nn.ModuleList([]) 175 | for _ in range(depth): 176 | self.layers.append( 177 | nn.ModuleList( 178 | [ 179 | PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), 180 | FeedForward(dim=dim, mult=ff_mult), 181 | ] 182 | ) 183 | ) 184 | 185 | def forward(self, latents, x): 186 | x = self.proj_in(x) 187 | for attn, ff in self.layers: 188 | latents = attn(x, latents) + latents 189 | latents = ff(latents) + latents 190 | latents = self.proj_out(latents) 191 | return self.norm_out(latents) 192 | 193 | 194 | class MLPProjModel(nn.Module): 195 | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024): 196 | super().__init__() 197 | 198 | self.proj = nn.Sequential( 199 | nn.Linear(clip_embeddings_dim, clip_embeddings_dim), 200 | nn.GELU(), 201 | nn.Linear(clip_embeddings_dim, cross_attention_dim), 202 | nn.LayerNorm(cross_attention_dim) 203 | ) 204 | 205 | def forward(self, image_embeds): 206 | clip_extra_context_tokens = self.proj(image_embeds) 207 | return clip_extra_context_tokens 208 | 209 | class MLPProjModelFaceId(nn.Module): 210 | def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4): 211 | super().__init__() 212 | 213 | self.cross_attention_dim = cross_attention_dim 214 | self.num_tokens = num_tokens 215 | 216 | self.proj = nn.Sequential( 217 | nn.Linear(id_embeddings_dim, id_embeddings_dim*2), 218 | nn.GELU(), 219 | nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens), 220 | ) 221 | self.norm = nn.LayerNorm(cross_attention_dim) 222 | 223 | def forward(self, id_embeds): 224 | x = self.proj(id_embeds) 225 | x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) 226 | x = self.norm(x) 227 | return x 228 | 229 | class ProjModelFaceIdPlus(nn.Module): 230 | def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4): 231 | super().__init__() 232 | 233 | self.cross_attention_dim = cross_attention_dim 234 | self.num_tokens = num_tokens 235 | 236 | self.proj = nn.Sequential( 237 | nn.Linear(id_embeddings_dim, id_embeddings_dim*2), 238 | nn.GELU(), 239 | nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens), 240 | ) 241 | self.norm = nn.LayerNorm(cross_attention_dim) 242 | 243 | self.perceiver_resampler = FacePerceiverResampler( 244 | dim=cross_attention_dim, 245 | depth=4, 246 | dim_head=64, 247 | heads=cross_attention_dim // 64, 248 | embedding_dim=clip_embeddings_dim, 249 | output_dim=cross_attention_dim, 250 | ff_mult=4, 251 | ) 252 | 253 | def forward(self, id_embeds, clip_embeds, scale=1.0, shortcut=False): 254 | x = self.proj(id_embeds) 255 | x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) 256 | x = self.norm(x) 257 | out = self.perceiver_resampler(x, clip_embeds) 258 | if shortcut: 259 | out = x + scale * out 260 | return out 261 | 262 | class ImageProjModel(nn.Module): 263 | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): 264 | super().__init__() 265 | 266 | self.cross_attention_dim = cross_attention_dim 267 | self.clip_extra_context_tokens = clip_extra_context_tokens 268 | self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) 269 | self.norm = nn.LayerNorm(cross_attention_dim) 270 | 271 | def forward(self, image_embeds): 272 | embeds = image_embeds 273 | x = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) 274 | x = self.norm(x) 275 | return x 276 | -------------------------------------------------------------------------------- /layer_weights_slider.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | class IPA_LayerWeightsSlider: 4 | 5 | def __init__(self): 6 | pass 7 | 8 | @classmethod 9 | def INPUT_TYPES(self): 10 | 11 | return { 12 | "required": { 13 | "layer0": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 14 | "layer1": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 15 | "layer2": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 16 | "layer3": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 17 | "layer4": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 18 | "layer5": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 19 | "layer6": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 20 | "layer7": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 21 | "layer8": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 22 | "layer9": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 23 | "layer10": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 24 | "layer11": ("FLOAT", {"default": 0, "min": -1, "max": 1, "step": 0.01, "display": "slider"}), 25 | "multiplier": ("FLOAT", {"default": 1, "min": 0, "max": 999, "step": 0.01}), 26 | }, 27 | "optional": { 28 | } 29 | } 30 | 31 | RETURN_TYPES = ("STRING",) 32 | RETURN_NAMES = ("layer_weights",) 33 | FUNCTION = 'ipa_layer_weights_slider' 34 | CATEGORY = 'ipadapter/dev' 35 | 36 | def ipa_layer_weights_slider(self, layer0, layer1, layer2, layer3, layer4, layer5, layer6, 37 | layer7, layer8, layer9, layer10, layer11, multiplier): 38 | ret_str = (f"0:{layer0 * multiplier}, 1:{layer1 * multiplier}, 2:{layer2 * multiplier}, " 39 | f"3:{layer3 * multiplier}, 4:{layer4 * multiplier}, 5:{layer5 * multiplier}, " 40 | f"6:{layer6 * multiplier}, 7:{layer7 * multiplier}, 8:{layer8 * multiplier}, " 41 | f"9:{layer9 * multiplier}, 10:{layer10 * multiplier}, 11:{layer11 * multiplier}") 42 | 43 | return (ret_str,) 44 | 45 | NODE_CLASS_MAPPINGS = { 46 | "IPAdapterLayerWeightsSlider": IPA_LayerWeightsSlider 47 | } 48 | 49 | NODE_DISPLAY_NAME_MAPPINGS = { 50 | "IPAdapterLayerWeightsSlider": "IPAdapter Layer Weights Slider" 51 | } -------------------------------------------------------------------------------- /models/legacy_directory_do_not_use.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chflame163/ComfyUI_IPAdapter_plus_V2/24bda419546b0229fe290957411f74ab664fe52f/models/legacy_directory_do_not_use.txt -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui_ipadapter_plus_v2" 3 | description = "A copy of ComfyUI_IPAdapter_plus, Only changed node name to coexist with ComfyUI_IPAdapter_plus v1 version." 4 | version = "1.0.12" 5 | license = "GPL-3.0 license" 6 | 7 | [project.urls] 8 | Repository = "https://github.com/chflame163/ComfyUI_IPAdapter_plus_V2" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "chflame163" 13 | DisplayName = "ComfyUI_IPAdapter_plus_V2" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import re 2 | import torch 3 | import os 4 | import folder_paths 5 | from comfy.clip_vision import clip_preprocess, Output 6 | import comfy.utils 7 | import comfy.model_management as model_management 8 | try: 9 | import torchvision.transforms.v2 as T 10 | except ImportError: 11 | import torchvision.transforms as T 12 | 13 | def get_clipvision_file(preset): 14 | preset = preset.lower() 15 | clipvision_list = folder_paths.get_filename_list("clip_vision") 16 | 17 | if preset.startswith("vit-g"): 18 | pattern = r'(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl.*model)\.(bin|safetensors)' 19 | elif preset.startswith("kolors"): 20 | pattern = r'clip.vit.large.patch14.336\.(bin|safetensors)' 21 | else: 22 | pattern = r'(ViT.H.14.*s32B.b79K|ipadapter.*sd15|sd1.?5.*model)\.(bin|safetensors)' 23 | clipvision_file = [e for e in clipvision_list if re.search(pattern, e, re.IGNORECASE)] 24 | 25 | clipvision_file = folder_paths.get_full_path("clip_vision", clipvision_file[0]) if clipvision_file else None 26 | 27 | return clipvision_file 28 | 29 | def get_ipadapter_file(preset, is_sdxl): 30 | preset = preset.lower() 31 | ipadapter_list = folder_paths.get_filename_list("ipadapter") 32 | is_insightface = False 33 | lora_pattern = None 34 | 35 | if preset.startswith("light"): 36 | if is_sdxl: 37 | raise Exception("light model is not supported for SDXL") 38 | pattern = r'sd15.light.v11\.(safetensors|bin)$' 39 | # if v11 is not found, try with the old version 40 | if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]: 41 | pattern = r'sd15.light\.(safetensors|bin)$' 42 | elif preset.startswith("standard"): 43 | if is_sdxl: 44 | pattern = r'ip.adapter.sdxl.vit.h\.(safetensors|bin)$' 45 | else: 46 | pattern = r'ip.adapter.sd15\.(safetensors|bin)$' 47 | elif preset.startswith("vit-g"): 48 | if is_sdxl: 49 | pattern = r'ip.adapter.sdxl\.(safetensors|bin)$' 50 | else: 51 | pattern = r'sd15.vit.g\.(safetensors|bin)$' 52 | elif preset.startswith("plus ("): 53 | if is_sdxl: 54 | pattern = r'plus.sdxl.vit.h\.(safetensors|bin)$' 55 | else: 56 | pattern = r'ip.adapter.plus.sd15\.(safetensors|bin)$' 57 | elif preset.startswith("plus face"): 58 | if is_sdxl: 59 | pattern = r'plus.face.sdxl.vit.h\.(safetensors|bin)$' 60 | else: 61 | pattern = r'plus.face.sd15\.(safetensors|bin)$' 62 | elif preset.startswith("full"): 63 | if is_sdxl: 64 | raise Exception("full face model is not supported for SDXL") 65 | pattern = r'full.face.sd15\.(safetensors|bin)$' 66 | elif preset.startswith("faceid portrait ("): 67 | if is_sdxl: 68 | pattern = r'portrait.sdxl\.(safetensors|bin)$' 69 | else: 70 | pattern = r'portrait.v11.sd15\.(safetensors|bin)$' 71 | # if v11 is not found, try with the old version 72 | if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]: 73 | pattern = r'portrait.sd15\.(safetensors|bin)$' 74 | is_insightface = True 75 | elif preset.startswith("faceid portrait unnorm"): 76 | if is_sdxl: 77 | pattern = r'portrait.sdxl.unnorm\.(safetensors|bin)$' 78 | else: 79 | raise Exception("portrait unnorm model is not supported for SD1.5") 80 | is_insightface = True 81 | elif preset == "faceid": 82 | if is_sdxl: 83 | pattern = r'faceid.sdxl\.(safetensors|bin)$' 84 | lora_pattern = r'faceid.sdxl.lora\.safetensors$' 85 | else: 86 | pattern = r'faceid.sd15\.(safetensors|bin)$' 87 | lora_pattern = r'faceid.sd15.lora\.safetensors$' 88 | is_insightface = True 89 | elif preset.startswith("faceid plus -"): 90 | if is_sdxl: 91 | raise Exception("faceid plus model is not supported for SDXL") 92 | pattern = r'faceid.plus.sd15\.(safetensors|bin)$' 93 | lora_pattern = r'faceid.plus.sd15.lora\.safetensors$' 94 | is_insightface = True 95 | elif preset.startswith("faceid plus v2"): 96 | if is_sdxl: 97 | pattern = r'faceid.plusv2.sdxl\.(safetensors|bin)$' 98 | lora_pattern = r'faceid.plusv2.sdxl.lora\.safetensors$' 99 | else: 100 | pattern = r'faceid.plusv2.sd15\.(safetensors|bin)$' 101 | lora_pattern = r'faceid.plusv2.sd15.lora\.safetensors$' 102 | is_insightface = True 103 | # Community's models 104 | elif preset.startswith("composition"): 105 | if is_sdxl: 106 | pattern = r'plus.composition.sdxl\.safetensors$' 107 | else: 108 | pattern = r'plus.composition.sd15\.safetensors$' 109 | elif preset.startswith("kolors"): 110 | if is_sdxl: 111 | pattern = r'(ip_adapter_plus_general|kolors.ip.adapter.plus)\.(safetensors|bin)$' 112 | else: 113 | raise Exception("Only supported for Kolors model") 114 | else: 115 | raise Exception(f"invalid type '{preset}'") 116 | 117 | ipadapter_file = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)] 118 | ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file[0]) if ipadapter_file else None 119 | 120 | return ipadapter_file, is_insightface, lora_pattern 121 | 122 | def get_lora_file(pattern): 123 | lora_list = folder_paths.get_filename_list("loras") 124 | lora_file = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)] 125 | lora_file = folder_paths.get_full_path("loras", lora_file[0]) if lora_file else None 126 | 127 | return lora_file 128 | 129 | def ipadapter_model_loader(file): 130 | model = comfy.utils.load_torch_file(file, safe_load=True) 131 | 132 | if file.lower().endswith(".safetensors"): 133 | st_model = {"image_proj": {}, "ip_adapter": {}} 134 | for key in model.keys(): 135 | if key.startswith("image_proj."): 136 | st_model["image_proj"][key.replace("image_proj.", "")] = model[key] 137 | elif key.startswith("ip_adapter."): 138 | st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key] 139 | elif key.startswith("adapter_modules."): 140 | st_model["ip_adapter"][key.replace("adapter_modules.", "")] = model[key] 141 | model = st_model 142 | del st_model 143 | elif "adapter_modules" in model.keys(): 144 | model["ip_adapter"] = model.pop("adapter_modules") 145 | 146 | if not "ip_adapter" in model.keys() or not model["ip_adapter"]: 147 | raise Exception("invalid IPAdapter model {}".format(file)) 148 | 149 | if 'plusv2' in file.lower(): 150 | model["faceidplusv2"] = True 151 | 152 | if 'unnorm' in file.lower(): 153 | model["portraitunnorm"] = True 154 | 155 | return model 156 | 157 | def insightface_loader(provider, model_name='buffalo_l'): 158 | try: 159 | from insightface.app import FaceAnalysis 160 | except ImportError as e: 161 | raise Exception(e) 162 | 163 | path = os.path.join(folder_paths.models_dir, "insightface") 164 | model = FaceAnalysis(name=model_name, root=path, providers=[provider + 'ExecutionProvider',]) 165 | model.prepare(ctx_id=0, det_size=(640, 640)) 166 | return model 167 | 168 | def split_tiles(embeds, num_split): 169 | _, H, W, _ = embeds.shape 170 | out = [] 171 | for x in embeds: 172 | x = x.unsqueeze(0) 173 | h, w = H // num_split, W // num_split 174 | x_split = torch.cat([x[:, i*h:(i+1)*h, j*w:(j+1)*w, :] for i in range(num_split) for j in range(num_split)], dim=0) 175 | out.append(x_split) 176 | 177 | x_split = torch.stack(out, dim=0) 178 | 179 | return x_split 180 | 181 | def merge_hiddenstates(x, tiles): 182 | chunk_size = tiles*tiles 183 | x = x.split(chunk_size) 184 | 185 | out = [] 186 | for embeds in x: 187 | num_tiles = embeds.shape[0] 188 | tile_size = int((embeds.shape[1]-1) ** 0.5) 189 | grid_size = int(num_tiles ** 0.5) 190 | 191 | # Extract class tokens 192 | class_tokens = embeds[:, 0, :] # Save class tokens: [num_tiles, embeds[-1]] 193 | avg_class_token = class_tokens.mean(dim=0, keepdim=True).unsqueeze(0) # Average token, shape: [1, 1, embeds[-1]] 194 | 195 | patch_embeds = embeds[:, 1:, :] # Shape: [num_tiles, tile_size^2, embeds[-1]] 196 | reshaped = patch_embeds.reshape(grid_size, grid_size, tile_size, tile_size, embeds.shape[-1]) 197 | 198 | merged = torch.cat([torch.cat([reshaped[i, j] for j in range(grid_size)], dim=1) 199 | for i in range(grid_size)], dim=0) 200 | 201 | merged = merged.unsqueeze(0) # Shape: [1, grid_size*tile_size, grid_size*tile_size, embeds[-1]] 202 | 203 | # Pool to original size 204 | pooled = torch.nn.functional.adaptive_avg_pool2d(merged.permute(0, 3, 1, 2), (tile_size, tile_size)).permute(0, 2, 3, 1) 205 | flattened = pooled.reshape(1, tile_size*tile_size, embeds.shape[-1]) 206 | 207 | # Add back the class token 208 | with_class = torch.cat([avg_class_token, flattened], dim=1) # Shape: original shape 209 | out.append(with_class) 210 | 211 | out = torch.cat(out, dim=0) 212 | 213 | return out 214 | 215 | def merge_embeddings(x, tiles): # TODO: this needs so much testing that I don't even 216 | chunk_size = tiles*tiles 217 | x = x.split(chunk_size) 218 | 219 | out = [] 220 | for embeds in x: 221 | num_tiles = embeds.shape[0] 222 | grid_size = int(num_tiles ** 0.5) 223 | tile_size = int(embeds.shape[1] ** 0.5) 224 | reshaped = embeds.reshape(grid_size, grid_size, tile_size, tile_size) 225 | 226 | # Merge the tiles 227 | merged = torch.cat([torch.cat([reshaped[i, j] for j in range(grid_size)], dim=1) 228 | for i in range(grid_size)], dim=0) 229 | 230 | merged = merged.unsqueeze(0) # Shape: [1, grid_size*tile_size, grid_size*tile_size] 231 | 232 | # Pool to original size 233 | pooled = torch.nn.functional.adaptive_avg_pool2d(merged, (tile_size, tile_size)) # pool to [1, tile_size, tile_size] 234 | pooled = pooled.flatten(1) # flatten to [1, tile_size^2] 235 | out.append(pooled) 236 | out = torch.cat(out, dim=0) 237 | 238 | return out 239 | 240 | def encode_image_masked(clip_vision, image, mask=None, batch_size=0, tiles=1, ratio=1.0, clipvision_size=224): 241 | # full image embeds 242 | embeds = encode_image_masked_(clip_vision, image, mask, batch_size, clipvision_size=clipvision_size) 243 | tiles = min(tiles, 16) 244 | 245 | if tiles > 1: 246 | # split in tiles 247 | image_split = split_tiles(image, tiles) 248 | 249 | # get the embeds for each tile 250 | embeds_split = Output() 251 | for i in image_split: 252 | encoded = encode_image_masked_(clip_vision, i, mask, batch_size, clipvision_size=clipvision_size) 253 | if not hasattr(embeds_split, "image_embeds"): 254 | #embeds_split["last_hidden_state"] = encoded["last_hidden_state"] 255 | embeds_split["image_embeds"] = encoded["image_embeds"] 256 | embeds_split["penultimate_hidden_states"] = encoded["penultimate_hidden_states"] 257 | else: 258 | #embeds_split["last_hidden_state"] = torch.cat((embeds_split["last_hidden_state"], encoded["last_hidden_state"]), dim=0) 259 | embeds_split["image_embeds"] = torch.cat((embeds_split["image_embeds"], encoded["image_embeds"]), dim=0) 260 | embeds_split["penultimate_hidden_states"] = torch.cat((embeds_split["penultimate_hidden_states"], encoded["penultimate_hidden_states"]), dim=0) 261 | 262 | #embeds_split['last_hidden_state'] = merge_hiddenstates(embeds_split['last_hidden_state']) 263 | embeds_split["image_embeds"] = merge_embeddings(embeds_split["image_embeds"], tiles) 264 | embeds_split["penultimate_hidden_states"] = merge_hiddenstates(embeds_split["penultimate_hidden_states"], tiles) 265 | 266 | #embeds['last_hidden_state'] = torch.cat([embeds_split['last_hidden_state'], embeds['last_hidden_state']]) 267 | if embeds['image_embeds'].shape[0] > 1: # if we have more than one image we need to average the embeddings for consistency 268 | embeds['image_embeds'] = embeds['image_embeds']*ratio + embeds_split['image_embeds']*(1-ratio) 269 | embeds['penultimate_hidden_states'] = embeds['penultimate_hidden_states']*ratio + embeds_split['penultimate_hidden_states']*(1-ratio) 270 | #embeds['image_embeds'] = (embeds['image_embeds']*ratio + embeds_split['image_embeds']) / 2 271 | #embeds['penultimate_hidden_states'] = (embeds['penultimate_hidden_states']*ratio + embeds_split['penultimate_hidden_states']) / 2 272 | else: # otherwise we can concatenate them, they can be averaged later 273 | embeds['image_embeds'] = torch.cat([embeds['image_embeds']*ratio, embeds_split['image_embeds']]) 274 | embeds['penultimate_hidden_states'] = torch.cat([embeds['penultimate_hidden_states']*ratio, embeds_split['penultimate_hidden_states']]) 275 | 276 | #del embeds_split 277 | 278 | return embeds 279 | 280 | def encode_image_masked_(clip_vision, image, mask=None, batch_size=0, clipvision_size=224): 281 | model_management.load_model_gpu(clip_vision.patcher) 282 | outputs = Output() 283 | 284 | if batch_size == 0: 285 | batch_size = image.shape[0] 286 | elif batch_size > image.shape[0]: 287 | batch_size = image.shape[0] 288 | 289 | image_batch = torch.split(image, batch_size, dim=0) 290 | 291 | for img in image_batch: 292 | img = img.to(clip_vision.load_device) 293 | pixel_values = clip_preprocess(img, size=clipvision_size).float() 294 | 295 | # TODO: support for multiple masks 296 | if mask is not None: 297 | pixel_values = pixel_values * mask.to(clip_vision.load_device) 298 | 299 | out = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2) 300 | 301 | if not hasattr(outputs, "last_hidden_state"): 302 | outputs["last_hidden_state"] = out[0].to(model_management.intermediate_device()) 303 | outputs["image_embeds"] = out[2].to(model_management.intermediate_device()) 304 | outputs["penultimate_hidden_states"] = out[1].to(model_management.intermediate_device()) 305 | else: 306 | outputs["last_hidden_state"] = torch.cat((outputs["last_hidden_state"], out[0].to(model_management.intermediate_device())), dim=0) 307 | outputs["image_embeds"] = torch.cat((outputs["image_embeds"], out[2].to(model_management.intermediate_device())), dim=0) 308 | outputs["penultimate_hidden_states"] = torch.cat((outputs["penultimate_hidden_states"], out[1].to(model_management.intermediate_device())), dim=0) 309 | 310 | del img, pixel_values, out 311 | torch.cuda.empty_cache() 312 | 313 | return outputs 314 | 315 | def tensor_to_size(source, dest_size): 316 | if isinstance(dest_size, torch.Tensor): 317 | dest_size = dest_size.shape[0] 318 | source_size = source.shape[0] 319 | 320 | if source_size < dest_size: 321 | shape = [dest_size - source_size] + [1]*(source.dim()-1) 322 | source = torch.cat((source, source[-1:].repeat(shape)), dim=0) 323 | elif source_size > dest_size: 324 | source = source[:dest_size] 325 | 326 | return source 327 | 328 | def min_(tensor_list): 329 | # return the element-wise min of the tensor list. 330 | x = torch.stack(tensor_list) 331 | mn = x.min(axis=0)[0] 332 | return torch.clamp(mn, min=0) 333 | 334 | def max_(tensor_list): 335 | # return the element-wise max of the tensor list. 336 | x = torch.stack(tensor_list) 337 | mx = x.max(axis=0)[0] 338 | return torch.clamp(mx, max=1) 339 | 340 | # From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/ 341 | def contrast_adaptive_sharpening(image, amount): 342 | img = T.functional.pad(image, (1, 1, 1, 1)).cpu() 343 | 344 | a = img[..., :-2, :-2] 345 | b = img[..., :-2, 1:-1] 346 | c = img[..., :-2, 2:] 347 | d = img[..., 1:-1, :-2] 348 | e = img[..., 1:-1, 1:-1] 349 | f = img[..., 1:-1, 2:] 350 | g = img[..., 2:, :-2] 351 | h = img[..., 2:, 1:-1] 352 | i = img[..., 2:, 2:] 353 | 354 | # Computing contrast 355 | cross = (b, d, e, f, h) 356 | mn = min_(cross) 357 | mx = max_(cross) 358 | 359 | diag = (a, c, g, i) 360 | mn2 = min_(diag) 361 | mx2 = max_(diag) 362 | mx = mx + mx2 363 | mn = mn + mn2 364 | 365 | # Computing local weight 366 | inv_mx = torch.reciprocal(mx) 367 | amp = inv_mx * torch.minimum(mn, (2 - mx)) 368 | 369 | # scaling 370 | amp = torch.sqrt(amp) 371 | w = - amp * (amount * (1/5 - 1/8) + 1/8) 372 | div = torch.reciprocal(1 + 4*w) 373 | 374 | output = ((b + d + f + h)*w + e) * div 375 | output = torch.nan_to_num(output) 376 | output = output.clamp(0, 1) 377 | 378 | return output 379 | 380 | def tensor_to_image(tensor): 381 | image = tensor.mul(255).clamp(0, 255).byte().cpu() 382 | image = image[..., [2, 1, 0]].numpy() 383 | return image 384 | 385 | def image_to_tensor(image): 386 | tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1) 387 | tensor = tensor[..., [2, 1, 0]] 388 | return tensor 389 | --------------------------------------------------------------------------------