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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/.github/FUNDING.yml

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+# These are supported funding model platforms
+
+github: cubiq
+custom: ['https://www.paypal.com/paypalme/matt3o']

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/.github/workflows/publish.yml

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+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/.gitignore

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+/__pycache__/
+/models/*.bin
+/models/*.safetensors
+.directory

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/CrossAttentionPatch.py

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+import torch
+import math
+import torch.nn.functional as F
+from comfy.ldm.modules.attention import optimized_attention
+from .utils import tensor_to_size
+
+class Attn2Replace:
+    def __init__(self, callback=None, **kwargs):
+        self.callback = [callback]
+        self.kwargs = [kwargs]
+
+    def add(self, callback, **kwargs):
+        self.callback.append(callback)
+        self.kwargs.append(kwargs)
+
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def __call__(self, q, k, v, extra_options):
+        dtype = q.dtype
+        out = optimized_attention(q, k, v, extra_options["n_heads"])
+        sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
+
+        for i, callback in enumerate(self.callback):
+            if sigma <= self.kwargs[i]["sigma_start"] and sigma >= self.kwargs[i]["sigma_end"]:
+                out = out + callback(out, q, k, v, extra_options, **self.kwargs[i])
+
+        return out.to(dtype=dtype)
+
+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):
+    dtype = q.dtype
+    cond_or_uncond = extra_options["cond_or_uncond"]
+    block_type = extra_options["block"][0]
+    #block_id = extra_options["block"][1]
+    t_idx = extra_options["transformer_index"]
+    layers = 11 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 16
+    k_key = module_key + "_to_k_ip"
+    v_key = module_key + "_to_v_ip"
+
+    # extra options for AnimateDiff
+    ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
+
+    b = q.shape[0]
+    seq_len = q.shape[1]
+    batch_prompt = b // len(cond_or_uncond)
+    _, _, oh, ow = extra_options["original_shape"]
+
+    if weight_type == 'ease in':
+        weight = weight * (0.05 + 0.95 * (1 - t_idx / layers))
+    elif weight_type == 'ease out':
+        weight = weight * (0.05 + 0.95 * (t_idx / layers))
+    elif weight_type == 'ease in-out':
+        weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (layers/2)) / (layers/2)))
+    elif weight_type == 'reverse in-out':
+        weight = weight * (0.05 + 0.95 * (abs(t_idx - (layers/2)) / (layers/2)))
+    elif weight_type == 'weak input' and block_type == 'input':
+        weight = weight * 0.2
+    elif weight_type == 'weak middle' and block_type == 'middle':
+        weight = weight * 0.2
+    elif weight_type == 'weak output' and block_type == 'output':
+        weight = weight * 0.2
+    elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'):
+        weight = weight * 0.2
+    elif isinstance(weight, dict):
+        if t_idx not in weight:
+            return 0
+
+        if weight_type == "style transfer precise":
+            if layers == 11 and t_idx == 3:
+                uncond = cond
+                cond = cond * 0
+            elif layers == 16 and (t_idx == 4 or t_idx == 5):
+                uncond = cond
+                cond = cond * 0
+        elif weight_type == "composition precise":
+            if layers == 11 and t_idx != 3:
+                uncond = cond
+                cond = cond * 0
+            elif layers == 16 and (t_idx != 4 and t_idx != 5):
+                uncond = cond
+                cond = cond * 0
+
+        weight = weight[t_idx]
+
+        if cond_alt is not None and t_idx in cond_alt:
+            cond = cond_alt[t_idx]
+            del cond_alt
+
+    if unfold_batch:
+        # Check AnimateDiff context window
+        if ad_params is not None and ad_params["sub_idxs"] is not None:
+            if isinstance(weight, torch.Tensor):
+                weight = tensor_to_size(weight, ad_params["full_length"])
+                weight = torch.Tensor(weight[ad_params["sub_idxs"]])
+                if torch.all(weight == 0):
+                    return 0
+                weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
+            elif weight == 0:
+                return 0
+
+            # if image length matches or exceeds full_length get sub_idx images
+            if cond.shape[0] >= ad_params["full_length"]:
+                cond = torch.Tensor(cond[ad_params["sub_idxs"]])
+                uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
+            # otherwise get sub_idxs images
+            else:
+                cond = tensor_to_size(cond, ad_params["full_length"])
+                uncond = tensor_to_size(uncond, ad_params["full_length"])
+                cond = cond[ad_params["sub_idxs"]]
+                uncond = uncond[ad_params["sub_idxs"]]
+        else:
+            if isinstance(weight, torch.Tensor):
+                weight = tensor_to_size(weight, batch_prompt)
+                if torch.all(weight == 0):
+                    return 0
+                weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
+            elif weight == 0:
+                return 0
+
+            cond = tensor_to_size(cond, batch_prompt)
+            uncond = tensor_to_size(uncond, batch_prompt)
+
+        k_cond = ipadapter.ip_layers.to_kvs[k_key](cond)
+        k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond)
+        v_cond = ipadapter.ip_layers.to_kvs[v_key](cond)
+        v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond)
+    else:
+        # TODO: should we always convert the weights to a tensor?
+        if isinstance(weight, torch.Tensor):
+            weight = tensor_to_size(weight, batch_prompt)
+            if torch.all(weight == 0):
+                return 0
+            weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
+        elif weight == 0:
+            return 0
+
+        k_cond = ipadapter.ip_layers.to_kvs[k_key](cond).repeat(batch_prompt, 1, 1)
+        k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond).repeat(batch_prompt, 1, 1)
+        v_cond = ipadapter.ip_layers.to_kvs[v_key](cond).repeat(batch_prompt, 1, 1)
+        v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond).repeat(batch_prompt, 1, 1)
+
+    if len(cond_or_uncond) == 3: # TODO: conxl, I need to check this
+        ip_k = torch.cat([(k_cond, k_uncond, k_cond)[i] for i in cond_or_uncond], dim=0)
+        ip_v = torch.cat([(v_cond, v_uncond, v_cond)[i] for i in cond_or_uncond], dim=0)
+    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 embeds_scaling == 'K+mean(V) w/ C penalty':
+        scaling = float(ip_k.shape[2]) / 1280.0
+        weight = weight * scaling
+        ip_k = ip_k * weight
+        ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
+        ip_v = (ip_v - ip_v_mean) + ip_v_mean * weight
+        out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
+        del ip_v_mean
+    elif embeds_scaling == 'K+V w/ C penalty':
+        scaling = float(ip_k.shape[2]) / 1280.0
+        weight = weight * scaling
+        ip_k = ip_k * weight
+        ip_v = ip_v * weight
+        out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
+    elif embeds_scaling == 'K+V':
+        ip_k = ip_k * weight
+        ip_v = ip_v * weight
+        out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
+    else:
+        #ip_v = ip_v * weight
+        out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
+        out_ip = out_ip * weight # I'm doing this to get the same results as before
+
+    if mask is not None:
+        mask_h = oh / math.sqrt(oh * ow / seq_len)
+        mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0)
+        mask_w = seq_len // 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, get sub_idx masks
+            if mask.shape[0] >= ad_params["full_length"]:
+                mask = torch.Tensor(mask[ad_params["sub_idxs"]])
+                mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
+            else:
+                mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
+                mask = tensor_to_size(mask, ad_params["full_length"])
+                mask = mask[ad_params["sub_idxs"]]
+        else:
+            mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
+            mask = tensor_to_size(mask, batch_prompt)
+
+        mask = mask.repeat(len(cond_or_uncond), 1, 1)
+        mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2])
+
+        # covers cases where extreme aspect ratios can cause the mask to have a wrong size
+        mask_len = mask_h * mask_w
+        if mask_len < seq_len:
+            pad_len = seq_len - mask_len
+            pad1 = pad_len // 2
+            pad2 = pad_len - pad1
+            mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0)
+        elif mask_len > seq_len:
+            crop_start = (mask_len - seq_len) // 2
+            mask = mask[:, crop_start:crop_start+seq_len, :]
+
+        out_ip = out_ip * mask
+
+    #out = out + out_ip
+
+    return out_ip.to(dtype=dtype)

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/IPAdapterPlus.py

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+import torch
+import os
+import math
+import folder_paths
+
+import comfy.model_management as model_management
+from node_helpers import conditioning_set_values
+from comfy.clip_vision import load as load_clip_vision
+from comfy.sd import load_lora_for_models
+import comfy.utils
+
+import torch.nn as nn
+from PIL import Image
+try:
+    import torchvision.transforms.v2 as T
+except ImportError:
+    import torchvision.transforms as T
+
+from .image_proj_models import MLPProjModel, MLPProjModelFaceId, ProjModelFaceIdPlus, Resampler, ImageProjModel
+from .CrossAttentionPatch import Attn2Replace, ipadapter_attention
+from .utils import (
+    encode_image_masked,
+    tensor_to_size,
+    contrast_adaptive_sharpening,
+    tensor_to_image,
+    image_to_tensor,
+    ipadapter_model_loader,
+    insightface_loader,
+    get_clipvision_file,
+    get_ipadapter_file,
+    get_lora_file,
+)
+
+# set the models directory
+if "ipadapter" not in folder_paths.folder_names_and_paths:
+    current_paths = [os.path.join(folder_paths.models_dir, "ipadapter")]
+else:
+    current_paths, _ = folder_paths.folder_names_and_paths["ipadapter"]
+folder_paths.folder_names_and_paths["ipadapter"] = (current_paths, folder_paths.supported_pt_extensions)
+
+WEIGHT_TYPES = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output', 'weak middle', 'strong middle', 'style transfer', 'composition', 'strong style transfer', 'style and composition', 'style transfer precise', 'composition precise']
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Main IPAdapter Class
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+"""
+class IPAdapter(nn.Module):
+    def __init__(self, ipadapter_model, cross_attention_dim=1024, output_cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4, is_sdxl=False, is_plus=False, is_full=False, is_faceid=False, is_portrait_unnorm=False, is_kwai_kolors=False):
+        super().__init__()
+
+        self.clip_embeddings_dim = clip_embeddings_dim
+        self.cross_attention_dim = cross_attention_dim
+        self.output_cross_attention_dim = output_cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.is_sdxl = is_sdxl
+        self.is_full = is_full
+        self.is_plus = is_plus
+        self.is_portrait_unnorm = is_portrait_unnorm
+        self.is_kwai_kolors = is_kwai_kolors
+
+        if is_faceid and not is_portrait_unnorm:
+            self.image_proj_model = self.init_proj_faceid()
+        elif is_full:
+            self.image_proj_model = self.init_proj_full()
+        elif is_plus or is_portrait_unnorm:
+            self.image_proj_model = self.init_proj_plus()
+        else:
+            self.image_proj_model = self.init_proj()
+
+        self.image_proj_model.load_state_dict(ipadapter_model["image_proj"])
+        self.ip_layers = To_KV(ipadapter_model["ip_adapter"])
+
+    def init_proj(self):
+        image_proj_model = ImageProjModel(
+            cross_attention_dim=self.cross_attention_dim,
+            clip_embeddings_dim=self.clip_embeddings_dim,
+            clip_extra_context_tokens=self.clip_extra_context_tokens
+        )
+        return image_proj_model
+
+    def init_proj_plus(self):
+        image_proj_model = Resampler(
+            dim=self.cross_attention_dim,
+            depth=4,
+            dim_head=64,
+            heads=20 if self.is_sdxl and not self.is_kwai_kolors else 12,
+            num_queries=self.clip_extra_context_tokens,
+            embedding_dim=self.clip_embeddings_dim,
+            output_dim=self.output_cross_attention_dim,
+            ff_mult=4
+        )
+        return image_proj_model
+
+    def init_proj_full(self):
+        image_proj_model = MLPProjModel(
+            cross_attention_dim=self.cross_attention_dim,
+            clip_embeddings_dim=self.clip_embeddings_dim
+        )
+        return image_proj_model
+
+    def init_proj_faceid(self):
+        if self.is_plus:
+            image_proj_model = ProjModelFaceIdPlus(
+                cross_attention_dim=self.cross_attention_dim,
+                id_embeddings_dim=512,
+                clip_embeddings_dim=self.clip_embeddings_dim, # 1280,
+                num_tokens=self.clip_extra_context_tokens, # 4,
+            )
+        else:
+            image_proj_model = MLPProjModelFaceId(
+                cross_attention_dim=self.cross_attention_dim,
+                id_embeddings_dim=512,
+                num_tokens=self.clip_extra_context_tokens,
+            )
+        return image_proj_model
+
+    @torch.inference_mode()
+    def get_image_embeds(self, clip_embed, clip_embed_zeroed, batch_size):
+        torch_device = model_management.get_torch_device()
+        intermediate_device = model_management.intermediate_device()
+
+        if batch_size == 0:
+            batch_size = clip_embed.shape[0]
+            intermediate_device = torch_device
+        elif batch_size > clip_embed.shape[0]:
+            batch_size = clip_embed.shape[0]
+
+        clip_embed = torch.split(clip_embed, batch_size, dim=0)
+        clip_embed_zeroed = torch.split(clip_embed_zeroed, batch_size, dim=0)
+        
+        image_prompt_embeds = []
+        uncond_image_prompt_embeds = []
+
+        for ce, cez in zip(clip_embed, clip_embed_zeroed):
+            image_prompt_embeds.append(self.image_proj_model(ce.to(torch_device)).to(intermediate_device))
+            uncond_image_prompt_embeds.append(self.image_proj_model(cez.to(torch_device)).to(intermediate_device))
+
+        del clip_embed, clip_embed_zeroed
+
+        image_prompt_embeds = torch.cat(image_prompt_embeds, dim=0)
+        uncond_image_prompt_embeds = torch.cat(uncond_image_prompt_embeds, dim=0)
+
+        torch.cuda.empty_cache()
+
+        #image_prompt_embeds = self.image_proj_model(clip_embed)
+        #uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+    @torch.inference_mode()
+    def get_image_embeds_faceid_plus(self, face_embed, clip_embed, s_scale, shortcut, batch_size):
+        torch_device = model_management.get_torch_device()
+        intermediate_device = model_management.intermediate_device()
+
+        if batch_size == 0:
+            batch_size = clip_embed.shape[0]
+            intermediate_device = torch_device
+        elif batch_size > clip_embed.shape[0]:
+            batch_size = clip_embed.shape[0]
+
+        face_embed_batch = torch.split(face_embed, batch_size, dim=0)
+        clip_embed_batch = torch.split(clip_embed, batch_size, dim=0)
+
+        embeds = []
+        for face_embed, clip_embed in zip(face_embed_batch, clip_embed_batch):
+            embeds.append(self.image_proj_model(face_embed.to(torch_device), clip_embed.to(torch_device), scale=s_scale, shortcut=shortcut).to(intermediate_device))
+
+        del face_embed_batch, clip_embed_batch
+
+        embeds = torch.cat(embeds, dim=0)
+        torch.cuda.empty_cache()
+        #embeds = self.image_proj_model(face_embed, clip_embed, scale=s_scale, shortcut=shortcut)
+        return embeds
+
+class To_KV(nn.Module):
+    def __init__(self, state_dict):
+        super().__init__()
+
+        self.to_kvs = nn.ModuleDict()
+        for key, value in state_dict.items():
+            self.to_kvs[key.replace(".weight", "").replace(".", "_")] = nn.Linear(value.shape[1], value.shape[0], bias=False)
+            self.to_kvs[key.replace(".weight", "").replace(".", "_")].weight.data = value
+
+def set_model_patch_replace(model, patch_kwargs, key):
+    to = model.model_options["transformer_options"].copy()
+    if "patches_replace" not in to:
+        to["patches_replace"] = {}
+    else:
+        to["patches_replace"] = to["patches_replace"].copy()
+
+    if "attn2" not in to["patches_replace"]:
+        to["patches_replace"]["attn2"] = {}
+    else:
+        to["patches_replace"]["attn2"] = to["patches_replace"]["attn2"].copy()
+
+    if key not in to["patches_replace"]["attn2"]:
+        to["patches_replace"]["attn2"][key] = Attn2Replace(ipadapter_attention, **patch_kwargs)
+        model.model_options["transformer_options"] = to
+    else:
+        to["patches_replace"]["attn2"][key].add(ipadapter_attention, **patch_kwargs)
+
+def ipadapter_execute(model,
+                      ipadapter,
+                      clipvision,
+                      insightface=None,
+                      image=None,
+                      image_composition=None,
+                      image_negative=None,
+                      weight=1.0,
+                      weight_composition=1.0,
+                      weight_faceidv2=None,
+                      weight_type="linear",
+                      combine_embeds="concat",
+                      start_at=0.0,
+                      end_at=1.0,
+                      attn_mask=None,
+                      pos_embed=None,
+                      neg_embed=None,
+                      unfold_batch=False,
+                      embeds_scaling='V only',
+                      layer_weights=None,
+                      encode_batch_size=0,
+                      style_boost=None,
+                      composition_boost=None,
+                      enhance_tiles=1,
+                      enhance_ratio=1.0,):
+    device = model_management.get_torch_device()
+    dtype = model_management.unet_dtype()
+    if dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+        dtype = torch.float16 if model_management.should_use_fp16() else torch.float32
+
+    is_full = "proj.3.weight" in ipadapter["image_proj"]
+    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"]
+    is_portrait_unnorm = "portraitunnorm" in ipadapter
+    is_faceid = is_portrait or "0.to_q_lora.down.weight" in ipadapter["ip_adapter"] or is_portrait_unnorm
+    is_plus = (is_full or "latents" in ipadapter["image_proj"] or "perceiver_resampler.proj_in.weight" in ipadapter["image_proj"]) and not is_portrait_unnorm
+    is_faceidv2 = "faceidplusv2" in ipadapter
+    output_cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
+    is_sdxl = output_cross_attention_dim == 2048
+    is_kwai_kolors = is_sdxl and "layers.0.0.to_out.weight" in ipadapter["image_proj"] and ipadapter["image_proj"]["layers.0.0.to_out.weight"].shape[0] == 2048
+
+    if is_faceid and not insightface:
+        raise Exception("insightface model is required for FaceID models")
+
+    if is_faceidv2:
+        weight_faceidv2 = weight_faceidv2 if weight_faceidv2 is not None else weight*2
+
+    cross_attention_dim = 1280 if (is_plus and is_sdxl and not is_faceid and not is_kwai_kolors) or is_portrait_unnorm else output_cross_attention_dim
+    clip_extra_context_tokens = 16 if (is_plus and not is_faceid) or is_portrait or is_portrait_unnorm else 4
+
+    if image is not None and 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")
+
+    if isinstance(weight, list):
+        weight = torch.tensor(weight).unsqueeze(-1).unsqueeze(-1).to(device, dtype=dtype) if unfold_batch else weight[0]
+
+    if style_boost is not None:
+        weight_type = "style transfer precise"
+    elif composition_boost is not None:
+        weight_type = "composition precise"
+
+    # special weight types
+    if layer_weights is not None and layer_weights != '':
+        weight = { int(k): float(v)*weight for k, v in [x.split(":") for x in layer_weights.split(",")] }
+        weight_type = weight_type if weight_type == "style transfer precise" or weight_type == "composition precise" else "linear"
+    elif weight_type == "style transfer":
+        weight = { 6:weight } if is_sdxl else { 0:weight, 1:weight, 2:weight, 3:weight, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+    elif weight_type == "composition":
+        weight = { 3:weight } if is_sdxl else { 4:weight*0.25, 5:weight }
+    elif weight_type == "strong style transfer":
+        if is_sdxl:
+            weight = { 0:weight, 1:weight, 2:weight, 4:weight, 5:weight, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight }
+        else:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+    elif weight_type == "style and composition":
+        if is_sdxl:
+            weight = { 3:weight_composition, 6:weight }
+        else:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 4:weight_composition*0.25, 5:weight_composition, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+    elif weight_type == "strong style and composition":
+        if is_sdxl:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight_composition, 4:weight, 5:weight, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight }
+        else:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 4:weight_composition, 5:weight_composition, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+    elif weight_type == "style transfer precise":
+        weight_composition = style_boost if style_boost is not None else weight
+        if is_sdxl:
+            weight = { 3:weight_composition, 6:weight }
+        else:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 4:weight_composition*0.25, 5:weight_composition, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+    elif weight_type == "composition precise":
+        weight_composition = weight
+        weight = composition_boost if composition_boost is not None else weight
+        if is_sdxl:
+            weight = { 0:weight*.1, 1:weight*.1, 2:weight*.1, 3:weight_composition, 4:weight*.1, 5:weight*.1, 6:weight, 7:weight*.1, 8:weight*.1, 9:weight*.1, 10:weight*.1 }
+        else:
+            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 4:weight_composition*0.25, 5:weight_composition, 6:weight*.1, 7:weight*.1, 8:weight*.1, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+
+    clipvision_size=224 if not is_kwai_kolors else 336
+
+    img_comp_cond_embeds = None
+    face_cond_embeds = None
+    if is_faceid:
+        if insightface is None:
+            raise Exception("Insightface model is required for FaceID models")
+
+        from insightface.utils import face_align
+
+        insightface.det_model.input_size = (640,640) # reset the detection size
+        image_iface = tensor_to_image(image)
+        face_cond_embeds = []
+        image = []
+
+        for i in range(image_iface.shape[0]):
+            for size in [(size, size) for size in range(640, 256, -64)]:
+                insightface.det_model.input_size = size # TODO: hacky but seems to be working
+                face = insightface.get(image_iface[i])
+                if face:
+                    if not is_portrait_unnorm:
+                        face_cond_embeds.append(torch.from_numpy(face[0].normed_embedding).unsqueeze(0))
+                    else:
+                        face_cond_embeds.append(torch.from_numpy(face[0].embedding).unsqueeze(0))
+                    image.append(image_to_tensor(face_align.norm_crop(image_iface[i], landmark=face[0].kps, image_size=256 if is_sdxl else 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_cond_embeds = torch.stack(face_cond_embeds).to(device, dtype=dtype)
+        image = torch.stack(image)
+        del image_iface, face
+    
+
+    if image is not None:
+        img_cond_embeds = encode_image_masked(clipvision, image, batch_size=encode_batch_size, tiles=enhance_tiles, ratio=enhance_ratio, clipvision_size=clipvision_size)
+        if image_composition is not None:
+            img_comp_cond_embeds = encode_image_masked(clipvision, image_composition, batch_size=encode_batch_size, tiles=enhance_tiles, ratio=enhance_ratio, clipvision_size=clipvision_size)
+
+        if is_plus:
+            img_cond_embeds = img_cond_embeds.penultimate_hidden_states
+            image_negative = image_negative if image_negative is not None else torch.zeros([1, clipvision_size, clipvision_size, 3])
+            img_uncond_embeds = encode_image_masked(clipvision, image_negative, batch_size=encode_batch_size, clipvision_size=clipvision_size).penultimate_hidden_states
+            if image_composition is not None:
+                img_comp_cond_embeds = img_comp_cond_embeds.penultimate_hidden_states
+        else:
+            img_cond_embeds = img_cond_embeds.image_embeds if not is_faceid else face_cond_embeds
+            if image_negative is not None and not is_faceid:
+                img_uncond_embeds = encode_image_masked(clipvision, image_negative, batch_size=encode_batch_size, clipvision_size=clipvision_size).image_embeds
+            else:
+                img_uncond_embeds = torch.zeros_like(img_cond_embeds)
+            if image_composition is not None:
+                img_comp_cond_embeds = img_comp_cond_embeds.image_embeds
+        del image_negative, image_composition
+
+        image = None if not is_faceid else image # if it's face_id we need the cropped face for later
+    elif pos_embed is not None:
+        img_cond_embeds = pos_embed
+
+        if neg_embed is not None:
+            img_uncond_embeds = neg_embed
+        else:
+            if is_plus:
+                img_uncond_embeds = encode_image_masked(clipvision, torch.zeros([1, clipvision_size, clipvision_size, 3]), clipvision_size=clipvision_size).penultimate_hidden_states
+            else:
+                img_uncond_embeds = torch.zeros_like(img_cond_embeds)
+        del pos_embed, neg_embed
+    else:
+        raise Exception("Images or Embeds are required")
+
+    # ensure that cond and uncond have the same batch size
+    img_uncond_embeds = tensor_to_size(img_uncond_embeds, img_cond_embeds.shape[0])
+
+    img_cond_embeds = img_cond_embeds.to(device, dtype=dtype)
+    img_uncond_embeds = img_uncond_embeds.to(device, dtype=dtype)
+    if img_comp_cond_embeds is not None:
+        img_comp_cond_embeds = img_comp_cond_embeds.to(device, dtype=dtype)
+
+    # combine the embeddings if needed
+    if combine_embeds != "concat" and img_cond_embeds.shape[0] > 1 and not unfold_batch:
+        if combine_embeds == "add":
+            img_cond_embeds = torch.sum(img_cond_embeds, dim=0).unsqueeze(0)
+            if face_cond_embeds is not None:
+                face_cond_embeds = torch.sum(face_cond_embeds, dim=0).unsqueeze(0)
+            if img_comp_cond_embeds is not None:
+                img_comp_cond_embeds = torch.sum(img_comp_cond_embeds, dim=0).unsqueeze(0)
+        elif combine_embeds == "subtract":
+            img_cond_embeds = img_cond_embeds[0] - torch.mean(img_cond_embeds[1:], dim=0)
+            img_cond_embeds = img_cond_embeds.unsqueeze(0)
+            if face_cond_embeds is not None:
+                face_cond_embeds = face_cond_embeds[0] - torch.mean(face_cond_embeds[1:], dim=0)
+                face_cond_embeds = face_cond_embeds.unsqueeze(0)
+            if img_comp_cond_embeds is not None:
+                img_comp_cond_embeds = img_comp_cond_embeds[0] - torch.mean(img_comp_cond_embeds[1:], dim=0)
+                img_comp_cond_embeds = img_comp_cond_embeds.unsqueeze(0)
+        elif combine_embeds == "average":
+            img_cond_embeds = torch.mean(img_cond_embeds, dim=0).unsqueeze(0)
+            if face_cond_embeds is not None:
+                face_cond_embeds = torch.mean(face_cond_embeds, dim=0).unsqueeze(0)
+            if img_comp_cond_embeds is not None:
+                img_comp_cond_embeds = torch.mean(img_comp_cond_embeds, dim=0).unsqueeze(0)
+        elif combine_embeds == "norm average":
+            img_cond_embeds = torch.mean(img_cond_embeds / torch.norm(img_cond_embeds, dim=0, keepdim=True), dim=0).unsqueeze(0)
+            if face_cond_embeds is not None:
+                face_cond_embeds = torch.mean(face_cond_embeds / torch.norm(face_cond_embeds, dim=0, keepdim=True), dim=0).unsqueeze(0)
+            if img_comp_cond_embeds is not None:
+                img_comp_cond_embeds = torch.mean(img_comp_cond_embeds / torch.norm(img_comp_cond_embeds, dim=0, keepdim=True), dim=0).unsqueeze(0)
+        img_uncond_embeds = img_uncond_embeds[0].unsqueeze(0) # TODO: better strategy for uncond could be to average them
+
+    if attn_mask is not None:
+        attn_mask = attn_mask.to(device, dtype=dtype)
+
+    ipa = IPAdapter(
+        ipadapter,
+        cross_attention_dim=cross_attention_dim,
+        output_cross_attention_dim=output_cross_attention_dim,
+        clip_embeddings_dim=img_cond_embeds.shape[-1],
+        clip_extra_context_tokens=clip_extra_context_tokens,
+        is_sdxl=is_sdxl,
+        is_plus=is_plus,
+        is_full=is_full,
+        is_faceid=is_faceid,
+        is_portrait_unnorm=is_portrait_unnorm,
+        is_kwai_kolors=is_kwai_kolors,
+    ).to(device, dtype=dtype)
+
+    if is_faceid and is_plus:
+        cond = ipa.get_image_embeds_faceid_plus(face_cond_embeds, img_cond_embeds, weight_faceidv2, is_faceidv2, encode_batch_size)
+        # TODO: check if noise helps with the uncond face embeds
+        uncond = ipa.get_image_embeds_faceid_plus(torch.zeros_like(face_cond_embeds), img_uncond_embeds, weight_faceidv2, is_faceidv2, encode_batch_size)
+    else:
+        cond, uncond = ipa.get_image_embeds(img_cond_embeds, img_uncond_embeds, encode_batch_size)
+        if img_comp_cond_embeds is not None:
+            cond_comp = ipa.get_image_embeds(img_comp_cond_embeds, img_uncond_embeds, encode_batch_size)[0]
+
+    cond = cond.to(device, dtype=dtype)
+    uncond = uncond.to(device, dtype=dtype)
+
+    cond_alt = None
+    if img_comp_cond_embeds is not None:
+        cond_alt = { 3: cond_comp.to(device, dtype=dtype) }
+
+    del img_cond_embeds, img_uncond_embeds, img_comp_cond_embeds, face_cond_embeds
+
+    sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at)
+    sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at)
+
+    patch_kwargs = {
+        "ipadapter": ipa,
+        "weight": weight,
+        "cond": cond,
+        "cond_alt": cond_alt,
+        "uncond": uncond,
+        "weight_type": weight_type,
+        "mask": attn_mask,
+        "sigma_start": sigma_start,
+        "sigma_end": sigma_end,
+        "unfold_batch": unfold_batch,
+        "embeds_scaling": embeds_scaling,
+    }
+
+    number = 0
+    if not is_sdxl:
+        for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
+            patch_kwargs["module_key"] = str(number*2+1)
+            set_model_patch_replace(model, patch_kwargs, ("input", id))
+            number += 1
+        for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention
+            patch_kwargs["module_key"] = str(number*2+1)
+            set_model_patch_replace(model, patch_kwargs, ("output", id))
+            number += 1
+        patch_kwargs["module_key"] = str(number*2+1)
+        set_model_patch_replace(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:
+                patch_kwargs["module_key"] = str(number*2+1)
+                set_model_patch_replace(model, patch_kwargs, ("input", id, index))
+                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:
+                patch_kwargs["module_key"] = str(number*2+1)
+                set_model_patch_replace(model, patch_kwargs, ("output", id, index))
+                number += 1
+        for index in range(10):
+            patch_kwargs["module_key"] = str(number*2+1)
+            set_model_patch_replace(model, patch_kwargs, ("middle", 0, index))
+            number += 1
+
+    return (model, image)
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Loaders
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+"""
+class IPAdapterUnifiedLoader:
+    def __init__(self):
+        self.lora = None
+        self.clipvision = { "file": None, "model": None }
+        self.ipadapter = { "file": None, "model": None }
+        self.insightface = { "provider": None, "model": None }
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL", ),
+            "preset": (['LIGHT - SD1.5 only (low strength)', 'STANDARD (medium strength)', 'VIT-G (medium strength)', 'PLUS (high strength)', 'PLUS FACE (portraits)', 'FULL FACE - SD1.5 only (portraits stronger)'], ),
+        },
+        "optional": {
+            "ipadapter": ("IPADAPTER", ),
+        }}
+
+    RETURN_TYPES = ("MODEL", "IPADAPTER", )
+    RETURN_NAMES = ("model", "ipadapter", )
+    FUNCTION = "load_models"
+    CATEGORY = "ipadapter"
+
+    def load_models(self, model, preset, lora_strength=0.0, provider="CPU", ipadapter=None):
+        pipeline = { "clipvision": { 'file': None, 'model': None }, "ipadapter": { 'file': None, 'model': None }, "insightface": { 'provider': None, 'model': None } }
+        if ipadapter is not None:
+            pipeline = ipadapter
+
+        # 1. Load the clipvision model
+        clipvision_file = get_clipvision_file(preset)
+        if clipvision_file is None:
+            raise Exception("ClipVision model not found.")
+
+        if clipvision_file != self.clipvision['file']:
+            if clipvision_file != pipeline['clipvision']['file']:
+                self.clipvision['file'] = clipvision_file
+                self.clipvision['model'] = load_clip_vision(clipvision_file)
+                print(f"\033[33mINFO: Clip Vision model loaded from {clipvision_file}\033[0m")
+            else:
+                self.clipvision = pipeline['clipvision']
+
+        # 2. Load the ipadapter model
+        is_sdxl = isinstance(model.model, (comfy.model_base.SDXL, comfy.model_base.SDXLRefiner, comfy.model_base.SDXL_instructpix2pix))
+        ipadapter_file, is_insightface, lora_pattern = get_ipadapter_file(preset, is_sdxl)
+        if ipadapter_file is None:
+            raise Exception("IPAdapter model not found.")
+
+        if ipadapter_file != self.ipadapter['file']:
+            if pipeline['ipadapter']['file'] != ipadapter_file:
+                self.ipadapter['file'] = ipadapter_file
+                self.ipadapter['model'] = ipadapter_model_loader(ipadapter_file)
+                print(f"\033[33mINFO: IPAdapter model loaded from {ipadapter_file}\033[0m")
+            else:
+                self.ipadapter = pipeline['ipadapter']
+
+        # 3. Load the lora model if needed
+        if lora_pattern is not None:
+            lora_file = get_lora_file(lora_pattern)
+            lora_model = None
+            if lora_file is None:
+                raise Exception("LoRA model not found.")
+
+            if self.lora is not None:
+                if lora_file == self.lora['file']:
+                    lora_model = self.lora['model']
+                else:
+                    self.lora = None
+                    torch.cuda.empty_cache()
+
+            if lora_model is None:
+                lora_model = comfy.utils.load_torch_file(lora_file, safe_load=True)
+                self.lora = { 'file': lora_file, 'model': lora_model }
+                print(f"\033[33mINFO: LoRA model loaded from {lora_file}\033[0m")
+
+            if lora_strength > 0:
+                model, _ = load_lora_for_models(model, None, lora_model, lora_strength, 0)
+
+        # 4. Load the insightface model if needed
+        if is_insightface:
+            if provider != self.insightface['provider']:
+                if pipeline['insightface']['provider'] != provider:
+                    self.insightface['provider'] = provider
+                    self.insightface['model'] = insightface_loader(provider)
+                    print(f"\033[33mINFO: InsightFace model loaded with {provider} provider\033[0m")
+                else:
+                    self.insightface = pipeline['insightface']
+
+        return (model, { 'clipvision': self.clipvision, 'ipadapter': self.ipadapter, 'insightface': self.insightface }, )
+
+class IPAdapterUnifiedLoaderFaceID(IPAdapterUnifiedLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL", ),
+            "preset": (['FACEID', 'FACEID PLUS - SD1.5 only', 'FACEID PLUS V2', 'FACEID PORTRAIT (style transfer)', 'FACEID PORTRAIT UNNORM - SDXL only (strong)'], ),
+            "lora_strength": ("FLOAT", { "default": 0.6, "min": 0, "max": 1, "step": 0.01 }),
+            "provider": (["CPU", "CUDA", "ROCM", "DirectML", "OpenVINO", "CoreML"], ),
+        },
+        "optional": {
+            "ipadapter": ("IPADAPTER", ),
+        }}
+
+    RETURN_NAMES = ("MODEL", "ipadapter", )
+    CATEGORY = "ipadapter/faceid"
+
+class IPAdapterUnifiedLoaderCommunity(IPAdapterUnifiedLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL", ),
+            "preset": (['Composition', 'Kolors'], ),
+        },
+        "optional": {
+            "ipadapter": ("IPADAPTER", ),
+        }}
+
+    CATEGORY = "ipadapter/loaders"
+
+class IPAdapterModelLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ipadapter_file": (folder_paths.get_filename_list("ipadapter"), )}}
+
+    RETURN_TYPES = ("IPADAPTER",)
+    FUNCTION = "load_ipadapter_model"
+    CATEGORY = "ipadapter/loaders"
+
+    def load_ipadapter_model(self, ipadapter_file):
+        ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file)
+        return (ipadapter_model_loader(ipadapter_file),)
+
+class IPAdapterInsightFaceLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "provider": (["CPU", "CUDA", "ROCM"], ),
+            },
+        }
+
+    RETURN_TYPES = ("INSIGHTFACE",)
+    FUNCTION = "load_insightface"
+    CATEGORY = "ipadapter/loaders"
+
+    def load_insightface(self, provider):
+        return (insightface_loader(provider),)
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Main Apply Nodes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+"""
+class IPAdapterSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
+                "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 }),
+                "weight_type": (['standard', 'prompt is more important', 'style transfer'], ),
+            },
+            "optional": {
+                "attn_mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_ipadapter"
+    CATEGORY = "ipadapter"
+
+    def apply_ipadapter(self, model, ipadapter, image, weight, start_at, end_at, weight_type, attn_mask=None):
+        if weight_type.startswith("style"):
+            weight_type = "style transfer"
+        elif weight_type == "prompt is more important":
+            weight_type = "ease out"
+        else:
+            weight_type = "linear"
+
+        ipa_args = {
+            "image": image,
+            "weight": weight,
+            "start_at": start_at,
+            "end_at": end_at,
+            "attn_mask": attn_mask,
+            "weight_type": weight_type,
+            "insightface": ipadapter['insightface']['model'] if 'insightface' in ipadapter else None,
+        }
+
+        if 'ipadapter' not in ipadapter:
+            raise Exception("IPAdapter model not present in the pipeline. Please load the models with the IPAdapterUnifiedLoader node.")
+        if 'clipvision' not in ipadapter:
+            raise Exception("CLIPVision model not present in the pipeline. Please load the models with the IPAdapterUnifiedLoader node.")
+
+        return ipadapter_execute(model.clone(), ipadapter['ipadapter']['model'], ipadapter['clipvision']['model'], **ipa_args)
+
+class IPAdapterAdvanced:
+    def __init__(self):
+        self.unfold_batch = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_ipadapter"
+    CATEGORY = "ipadapter"
+
+    def apply_ipadapter(self, model, ipadapter, start_at=0.0, end_at=1.0, weight=1.0, weight_style=1.0, weight_composition=1.0, expand_style=False, weight_type="linear", combine_embeds="concat", weight_faceidv2=None, image=None, image_style=None, image_composition=None, image_negative=None, clip_vision=None, attn_mask=None, insightface=None, embeds_scaling='V only', layer_weights=None, ipadapter_params=None, encode_batch_size=0, style_boost=None, composition_boost=None, enhance_tiles=1, enhance_ratio=1.0):
+        is_sdxl = isinstance(model.model, (comfy.model_base.SDXL, comfy.model_base.SDXLRefiner, comfy.model_base.SDXL_instructpix2pix))
+
+        if 'ipadapter' in ipadapter:
+            ipadapter_model = ipadapter['ipadapter']['model']
+            clip_vision = clip_vision if clip_vision is not None else ipadapter['clipvision']['model']
+        else:
+            ipadapter_model = ipadapter
+
+        if clip_vision is None:
+            raise Exception("Missing CLIPVision model.")
+
+        if image_style is not None: # we are doing style + composition transfer
+            if not is_sdxl:
+                raise Exception("Style + Composition transfer is only available for SDXL models at the moment.") # TODO: check feasibility for SD1.5 models
+
+            image = image_style
+            weight = weight_style
+            if image_composition is None:
+                image_composition = image_style
+
+            weight_type = "strong style and composition" if expand_style else "style and composition"
+        if ipadapter_params is not None: # we are doing batch processing
+            image = ipadapter_params['image']
+            attn_mask = ipadapter_params['attn_mask']
+            weight = ipadapter_params['weight']
+            weight_type = ipadapter_params['weight_type']
+            start_at = ipadapter_params['start_at']
+            end_at = ipadapter_params['end_at']
+        else:
+            # at this point weight can be a list from the batch-weight or a single float
+            weight = [weight]
+
+        image = image if isinstance(image, list) else [image]
+
+        work_model = model.clone()
+
+        for i in range(len(image)):
+            if image[i] is None:
+                continue
+
+            ipa_args = {
+                "image": image[i],
+                "image_composition": image_composition,
+                "image_negative": image_negative,
+                "weight": weight[i],
+                "weight_composition": weight_composition,
+                "weight_faceidv2": weight_faceidv2,
+                "weight_type": weight_type if not isinstance(weight_type, list) else weight_type[i],
+                "combine_embeds": combine_embeds,
+                "start_at": start_at if not isinstance(start_at, list) else start_at[i],
+                "end_at": end_at if not isinstance(end_at, list) else end_at[i],
+                "attn_mask": attn_mask if not isinstance(attn_mask, list) else attn_mask[i],
+                "unfold_batch": self.unfold_batch,
+                "embeds_scaling": embeds_scaling,
+                "insightface": insightface if insightface is not None else ipadapter['insightface']['model'] if 'insightface' in ipadapter else None,
+                "layer_weights": layer_weights,
+                "encode_batch_size": encode_batch_size,
+                "style_boost": style_boost,
+                "composition_boost": composition_boost,
+                "enhance_tiles": enhance_tiles,
+                "enhance_ratio": enhance_ratio,
+            }
+
+            work_model, face_image = ipadapter_execute(work_model, ipadapter_model, clip_vision, **ipa_args)
+
+        del ipadapter
+        return (work_model, face_image, )
+
+class IPAdapterBatch(IPAdapterAdvanced):
+    def __init__(self):
+        self.unfold_batch = True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+                "encode_batch_size": ("INT", { "default": 0, "min": 0, "max": 4096 }),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterStyleComposition(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image_style": ("IMAGE",),
+                "image_composition": ("IMAGE",),
+                "weight_style": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_composition": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "expand_style": ("BOOLEAN", { "default": False }),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"], {"default": "average"}),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    CATEGORY = "ipadapter/style_composition"
+
+class IPAdapterStyleCompositionBatch(IPAdapterStyleComposition):
+    def __init__(self):
+        self.unfold_batch = True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image_style": ("IMAGE",),
+                "image_composition": ("IMAGE",),
+                "weight_style": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_composition": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "expand_style": ("BOOLEAN", { "default": False }),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterFaceID(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
+                "weight_faceidv2": ("FLOAT", { "default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+                "insightface": ("INSIGHTFACE",),
+            }
+        }
+
+    CATEGORY = "ipadapter/faceid"
+    RETURN_TYPES = ("MODEL","IMAGE",)
+    RETURN_NAMES = ("MODEL", "face_image", )
+
+class IPAAdapterFaceIDBatch(IPAdapterFaceID):
+    def __init__(self):
+        self.unfold_batch = True
+
+class IPAdapterTiled:
+    def __init__(self):
+        self.unfold_batch = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "sharpening": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "IMAGE", "MASK", )
+    RETURN_NAMES = ("MODEL", "tiles", "masks", )
+    FUNCTION = "apply_tiled"
+    CATEGORY = "ipadapter/tiled"
+
+    def apply_tiled(self, model, ipadapter, image, weight, weight_type, start_at, end_at, sharpening, combine_embeds="concat", image_negative=None, attn_mask=None, clip_vision=None, embeds_scaling='V only', encode_batch_size=0):
+        # 1. Select the models
+        if 'ipadapter' in ipadapter:
+            ipadapter_model = ipadapter['ipadapter']['model']
+            clip_vision = clip_vision if clip_vision is not None else ipadapter['clipvision']['model']
+        else:
+            ipadapter_model = ipadapter
+            clip_vision = clip_vision
+
+        if clip_vision is None:
+            raise Exception("Missing CLIPVision model.")
+
+        del ipadapter
+
+        # 2. Extract the tiles
+        tile_size = 256     # I'm using 256 instead of 224 as it is more likely divisible by the latent size, it will be downscaled to 224 by the clip vision encoder
+        _, oh, ow, _ = image.shape
+        if attn_mask is None:
+            attn_mask = torch.ones([1, oh, ow], dtype=image.dtype, device=image.device)
+
+        image = image.permute([0,3,1,2])
+        attn_mask = attn_mask.unsqueeze(1)
+        # the mask should have the same proportions as the reference image and the latent
+        attn_mask = T.Resize((oh, ow), interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
+
+        # if the image is almost a square, we crop it to a square
+        if oh / ow > 0.75 and oh / ow < 1.33:
+            # crop the image to a square
+            image = T.CenterCrop(min(oh, ow))(image)
+            resize = (tile_size*2, tile_size*2)
+
+            attn_mask = T.CenterCrop(min(oh, ow))(attn_mask)
+        # otherwise resize the smallest side and the other proportionally
+        else:
+            resize = (int(tile_size * ow / oh), tile_size) if oh < ow else (tile_size, int(tile_size * oh / ow))
+
+         # using PIL for better results
+        imgs = []
+        for img in image:
+            img = T.ToPILImage()(img)
+            img = img.resize(resize, resample=Image.Resampling['LANCZOS'])
+            imgs.append(T.ToTensor()(img))
+        image = torch.stack(imgs)
+        del imgs, img
+
+        # we don't need a high quality resize for the mask
+        attn_mask = T.Resize(resize[::-1], interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
+
+        # we allow a maximum of 4 tiles
+        if oh / ow > 4 or oh / ow < 0.25:
+            crop = (tile_size, tile_size*4) if oh < ow else (tile_size*4, tile_size)
+            image = T.CenterCrop(crop)(image)
+            attn_mask = T.CenterCrop(crop)(attn_mask)
+
+        attn_mask = attn_mask.squeeze(1)
+
+        if sharpening > 0:
+            image = contrast_adaptive_sharpening(image, sharpening)
+
+        image = image.permute([0,2,3,1])
+
+        _, oh, ow, _ = image.shape
+
+        # find the number of tiles for each side
+        tiles_x = math.ceil(ow / tile_size)
+        tiles_y = math.ceil(oh / tile_size)
+        overlap_x = max(0, (tiles_x * tile_size - ow) / (tiles_x - 1 if tiles_x > 1 else 1))
+        overlap_y = max(0, (tiles_y * tile_size - oh) / (tiles_y - 1 if tiles_y > 1 else 1))
+
+        base_mask = torch.zeros([attn_mask.shape[0], oh, ow], dtype=image.dtype, device=image.device)
+
+        # extract all the tiles from the image and create the masks
+        tiles = []
+        masks = []
+        for y in range(tiles_y):
+            for x in range(tiles_x):
+                start_x = int(x * (tile_size - overlap_x))
+                start_y = int(y * (tile_size - overlap_y))
+                tiles.append(image[:, start_y:start_y+tile_size, start_x:start_x+tile_size, :])
+                mask = base_mask.clone()
+                mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size] = attn_mask[:, start_y:start_y+tile_size, start_x:start_x+tile_size]
+                masks.append(mask)
+        del mask
+
+        # 3. Apply the ipadapter to each group of tiles
+        model = model.clone()
+        for i in range(len(tiles)):
+            ipa_args = {
+                "image": tiles[i],
+                "image_negative": image_negative,
+                "weight": weight,
+                "weight_type": weight_type,
+                "combine_embeds": combine_embeds,
+                "start_at": start_at,
+                "end_at": end_at,
+                "attn_mask": masks[i],
+                "unfold_batch": self.unfold_batch,
+                "embeds_scaling": embeds_scaling,
+                "encode_batch_size": encode_batch_size,
+            }
+            # apply the ipadapter to the model without cloning it
+            model, _ = ipadapter_execute(model, ipadapter_model, clip_vision, **ipa_args)
+
+        return (model, torch.cat(tiles), torch.cat(masks), )
+
+class IPAdapterTiledBatch(IPAdapterTiled):
+    def __init__(self):
+        self.unfold_batch = True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "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 }),
+                "sharpening": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+                "encode_batch_size": ("INT", { "default": 0, "min": 0, "max": 4096 }),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterEmbeds:
+    def __init__(self):
+        self.unfold_batch = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "pos_embed": ("EMBEDS",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "neg_embed": ("EMBEDS",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_ipadapter"
+    CATEGORY = "ipadapter/embeds"
+
+    def apply_ipadapter(self, model, ipadapter, pos_embed, weight, weight_type, start_at, end_at, neg_embed=None, attn_mask=None, clip_vision=None, embeds_scaling='V only'):
+        ipa_args = {
+            "pos_embed": pos_embed,
+            "neg_embed": neg_embed,
+            "weight": weight,
+            "weight_type": weight_type,
+            "start_at": start_at,
+            "end_at": end_at,
+            "attn_mask": attn_mask,
+            "embeds_scaling": embeds_scaling,
+            "unfold_batch": self.unfold_batch,
+        }
+
+        if 'ipadapter' in ipadapter:
+            ipadapter_model = ipadapter['ipadapter']['model']
+            clip_vision = clip_vision if clip_vision is not None else ipadapter['clipvision']['model']
+        else:
+            ipadapter_model = ipadapter
+            clip_vision = clip_vision
+
+        if clip_vision is None and neg_embed is None:
+            raise Exception("Missing CLIPVision model.")
+
+        del ipadapter
+
+        return ipadapter_execute(model.clone(), ipadapter_model, clip_vision, **ipa_args)
+
+class IPAdapterEmbedsBatch(IPAdapterEmbeds):
+    def __init__(self):
+        self.unfold_batch = True
+
+class IPAdapterMS(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_faceidv2": ("FLOAT", { "default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+                "layer_weights": ("STRING", { "default": "", "multiline": True }),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+                "insightface": ("INSIGHTFACE",),
+            }
+        }
+
+    CATEGORY = "ipadapter/dev"
+
+class IPAdapterClipVisionEnhancer(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+                "enhance_tiles": ("INT", { "default": 2, "min": 1, "max": 16 }),
+                "enhance_ratio": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    CATEGORY = "ipadapter/dev"
+
+class IPAdapterClipVisionEnhancerBatch(IPAdapterClipVisionEnhancer):
+    def __init__(self):
+        self.unfold_batch = True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "weight_type": (WEIGHT_TYPES, ),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+                "enhance_tiles": ("INT", { "default": 2, "min": 1, "max": 16 }),
+                "enhance_ratio": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05 }),
+                "encode_batch_size": ("INT", { "default": 0, "min": 0, "max": 4096 }),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterFromParams(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "ipadapter_params": ("IPADAPTER_PARAMS", ),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    CATEGORY = "ipadapter/params"
+
+class IPAdapterPreciseStyleTransfer(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "style_boost": ("FLOAT", { "default": 1.0, "min": -5, "max": 5, "step": 0.05 }),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterPreciseStyleTransferBatch(IPAdapterPreciseStyleTransfer):
+    def __init__(self):
+        self.unfold_batch = True
+
+class IPAdapterPreciseComposition(IPAdapterAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "ipadapter": ("IPADAPTER", ),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
+                "composition_boost": ("FLOAT", { "default": 0.0, "min": -5, "max": 5, "step": 0.05 }),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "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 }),
+                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
+            },
+            "optional": {
+                "image_negative": ("IMAGE",),
+                "attn_mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+class IPAdapterPreciseCompositionBatch(IPAdapterPreciseComposition):
+    def __init__(self):
+        self.unfold_batch = True
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Helpers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+"""
+class IPAdapterEncoder:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "ipadapter": ("IPADAPTER",),
+            "image": ("IMAGE",),
+            "weight": ("FLOAT", { "default": 1.0, "min": -1.0, "max": 3.0, "step": 0.01 }),
+            },
+            "optional": {
+                "mask": ("MASK",),
+                "clip_vision": ("CLIP_VISION",),
+            }
+        }
+
+    RETURN_TYPES = ("EMBEDS", "EMBEDS",)
+    RETURN_NAMES = ("pos_embed", "neg_embed",)
+    FUNCTION = "encode"
+    CATEGORY = "ipadapter/embeds"
+
+    def encode(self, ipadapter, image, weight, mask=None, clip_vision=None):
+        if 'ipadapter' in ipadapter:
+            ipadapter_model = ipadapter['ipadapter']['model']
+            clip_vision = clip_vision if clip_vision is not None else ipadapter['clipvision']['model']
+        else:
+            ipadapter_model = ipadapter
+            clip_vision = clip_vision
+
+        if clip_vision is None:
+            raise Exception("Missing CLIPVision model.")
+
+        is_plus = "proj.3.weight" in ipadapter_model["image_proj"] or "latents" in ipadapter_model["image_proj"] or "perceiver_resampler.proj_in.weight" in ipadapter_model["image_proj"]
+        is_kwai_kolors = is_plus and "layers.0.0.to_out.weight" in ipadapter_model["image_proj"] and ipadapter_model["image_proj"]["layers.0.0.to_out.weight"].shape[0] == 2048
+
+        clipvision_size = 224 if not is_kwai_kolors else 336
+
+        # resize and crop the mask to 224x224
+        if mask is not None and mask.shape[1:3] != torch.Size([clipvision_size, clipvision_size]):
+            mask = mask.unsqueeze(1)
+            transforms = T.Compose([
+                T.CenterCrop(min(mask.shape[2], mask.shape[3])),
+                T.Resize((clipvision_size, clipvision_size), interpolation=T.InterpolationMode.BICUBIC, antialias=True),
+            ])
+            mask = transforms(mask).squeeze(1)
+            #mask = T.Resize((image.shape[1], image.shape[2]), interpolation=T.InterpolationMode.BICUBIC, antialias=True)(mask.unsqueeze(1)).squeeze(1)
+
+        img_cond_embeds = encode_image_masked(clip_vision, image, mask, clipvision_size=clipvision_size)
+
+        if is_plus:
+            img_cond_embeds = img_cond_embeds.penultimate_hidden_states
+            img_uncond_embeds = encode_image_masked(clip_vision, torch.zeros([1, clipvision_size, clipvision_size, 3]), clipvision_size=clipvision_size).penultimate_hidden_states
+        else:
+            img_cond_embeds = img_cond_embeds.image_embeds
+            img_uncond_embeds = torch.zeros_like(img_cond_embeds)
+
+        if weight != 1:
+            img_cond_embeds = img_cond_embeds * weight
+
+        return (img_cond_embeds, img_uncond_embeds, )
+
+class IPAdapterCombineEmbeds:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "embed1": ("EMBEDS",),
+            "method": (["concat", "add", "subtract", "average", "norm average", "max", "min"], ),
+        },
+        "optional": {
+            "embed2": ("EMBEDS",),
+            "embed3": ("EMBEDS",),
+            "embed4": ("EMBEDS",),
+            "embed5": ("EMBEDS",),
+        }}
+
+    RETURN_TYPES = ("EMBEDS",)
+    FUNCTION = "batch"
+    CATEGORY = "ipadapter/embeds"
+
+    def batch(self, embed1, method, embed2=None, embed3=None, embed4=None, embed5=None):
+        if method=='concat' and embed2 is None and embed3 is None and embed4 is None and embed5 is None:
+            return (embed1, )
+
+        embeds = [embed1, embed2, embed3, embed4, embed5]
+        embeds = [embed for embed in embeds if embed is not None]
+        embeds = torch.cat(embeds, dim=0)
+
+        if method == "add":
+            embeds = torch.sum(embeds, dim=0).unsqueeze(0)
+        elif method == "subtract":
+            embeds = embeds[0] - torch.mean(embeds[1:], dim=0)
+            embeds = embeds.unsqueeze(0)
+        elif method == "average":
+            embeds = torch.mean(embeds, dim=0).unsqueeze(0)
+        elif method == "norm average":
+            embeds = torch.mean(embeds / torch.norm(embeds, dim=0, keepdim=True), dim=0).unsqueeze(0)
+        elif method == "max":
+            embeds = torch.max(embeds, dim=0).values.unsqueeze(0)
+        elif method == "min":
+            embeds = torch.min(embeds, dim=0).values.unsqueeze(0)
+
+        return (embeds, )
+
+class IPAdapterNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "type": (["fade", "dissolve", "gaussian", "shuffle"], ),
+                "strength": ("FLOAT", { "default": 1.0, "min": 0, "max": 1, "step": 0.05 }),
+                "blur": ("INT", { "default": 0, "min": 0, "max": 32, "step": 1 }),
+            },
+            "optional": {
+                "image_optional": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "make_noise"
+    CATEGORY = "ipadapter/utils"
+
+    def make_noise(self, type, strength, blur, image_optional=None):
+        if image_optional is None:
+            image = torch.zeros([1, 224, 224, 3])
+        else:
+            transforms = T.Compose([
+                T.CenterCrop(min(image_optional.shape[1], image_optional.shape[2])),
+                T.Resize((224, 224), interpolation=T.InterpolationMode.BICUBIC, antialias=True),
+            ])
+            image = transforms(image_optional.permute([0,3,1,2])).permute([0,2,3,1])
+
+        seed = int(torch.sum(image).item()) % 1000000007 # hash the image to get a seed, grants predictability
+        torch.manual_seed(seed)
+
+        if type == "fade":
+            noise = torch.rand_like(image)
+            noise = image * (1 - strength) + noise * strength
+        elif type == "dissolve":
+            mask = (torch.rand_like(image) < strength).float()
+            noise = torch.rand_like(image)
+            noise = image * (1-mask) + noise * mask
+        elif type == "gaussian":
+            noise = torch.randn_like(image) * strength
+            noise = image + noise
+        elif type == "shuffle":
+            transforms = T.Compose([
+                T.ElasticTransform(alpha=75.0, sigma=(1-strength)*3.5),
+                T.RandomVerticalFlip(p=1.0),
+                T.RandomHorizontalFlip(p=1.0),
+            ])
+            image = transforms(image.permute([0,3,1,2])).permute([0,2,3,1])
+            noise = torch.randn_like(image) * (strength*0.75)
+            noise = image * (1-noise) + noise
+
+        del image
+        noise = torch.clamp(noise, 0, 1)
+
+        if blur > 0:
+            if blur % 2 == 0:
+                blur += 1
+            noise = T.functional.gaussian_blur(noise.permute([0,3,1,2]), blur).permute([0,2,3,1])
+
+        return (noise, )
+
+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/utils"
+
+    def prep_image(self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0):
+        size = (224, 224)
+        _, oh, ow, _ = image.shape
+        output = image.permute([0,3,1,2])
+
+        if crop_position == "pad":
+            if oh != ow:
+                if oh > ow:
+                    pad = (oh - ow) // 2
+                    pad = (pad, 0, pad, 0)
+                elif ow > oh:
+                    pad = (ow - oh) // 2
+                    pad = (0, pad, 0, pad)
+                output = T.functional.pad(output, pad, fill=0)
+        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
+
+            output = output[:, :, y:y2, x:x2]
+
+        imgs = []
+        for img in output:
+            img = T.ToPILImage()(img) # using PIL for better results
+            img = img.resize(size, resample=Image.Resampling[interpolation])
+            imgs.append(T.ToTensor()(img))
+        output = torch.stack(imgs, dim=0)
+        del imgs, img
+
+        if sharpening > 0:
+            output = contrast_adaptive_sharpening(output, sharpening)
+
+        output = output.permute([0,2,3,1])
+
+        return (output, )
+
+class IPAdapterSaveEmbeds:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "embeds": ("EMBEDS",),
+            "filename_prefix": ("STRING", {"default": "IP_embeds"})
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "ipadapter/embeds"
+
+    def save(self, embeds, filename_prefix):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        file = f"{filename}_{counter:05}.ipadpt"
+        file = os.path.join(full_output_folder, file)
+
+        torch.save(embeds, file)
+        return (None, )
+
+class IPAdapterLoadEmbeds:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [os.path.relpath(os.path.join(root, file), input_dir) for root, dirs, files in os.walk(input_dir) for file in files if file.endswith('.ipadpt')]
+        return {"required": {"embeds": [sorted(files), ]}, }
+
+    RETURN_TYPES = ("EMBEDS", )
+    FUNCTION = "load"
+    CATEGORY = "ipadapter/embeds"
+
+    def load(self, embeds):
+        path = folder_paths.get_annotated_filepath(embeds)
+        return (torch.load(path).cpu(), )
+
+class IPAdapterWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "weights": ("STRING", {"default": '1.0, 0.0', "multiline": True }),
+            "timing": (["custom", "linear", "ease_in_out", "ease_in", "ease_out", "random"], { "default": "linear" } ),
+            "frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
+            "start_frame": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
+            "end_frame": ("INT", {"default": 9999, "min": 0, "max": 9999, "step": 1 }),
+            "add_starting_frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
+            "add_ending_frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
+            "method": (["full batch", "shift batches", "alternate batches"], { "default": "full batch" }),
+            }, "optional": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT", "FLOAT", "INT", "IMAGE", "IMAGE", "WEIGHTS_STRATEGY")
+    RETURN_NAMES = ("weights", "weights_invert", "total_frames", "image_1", "image_2", "weights_strategy")
+    FUNCTION = "weights"
+    CATEGORY = "ipadapter/weights"
+
+    def weights(self, weights='', timing='custom', frames=0, start_frame=0, end_frame=9999, add_starting_frames=0, add_ending_frames=0, method='full batch', weights_strategy=None, image=None):
+        import random
+
+        frame_count = image.shape[0] if image is not None else 0
+        if weights_strategy is not None:
+            weights = weights_strategy["weights"]
+            timing = weights_strategy["timing"]
+            frames = weights_strategy["frames"]
+            start_frame = weights_strategy["start_frame"]
+            end_frame = weights_strategy["end_frame"]
+            add_starting_frames = weights_strategy["add_starting_frames"]
+            add_ending_frames = weights_strategy["add_ending_frames"]
+            method = weights_strategy["method"]
+            frame_count = weights_strategy["frame_count"]
+        else:
+            weights_strategy = {
+                "weights": weights,
+                "timing": timing,
+                "frames": frames,
+                "start_frame": start_frame,
+                "end_frame": end_frame,
+                "add_starting_frames": add_starting_frames,
+                "add_ending_frames": add_ending_frames,
+                "method": method,
+                "frame_count": frame_count,
+            }
+
+        # convert the string to a list of floats separated by commas or newlines
+        weights = weights.replace("\n", ",")
+        weights = [float(weight) for weight in weights.split(",") if weight.strip() != ""]
+
+        if timing != "custom":
+            frames = max(frames, 2)
+            start = 0.0
+            end = 1.0
+
+            if len(weights) > 0:
+                start = weights[0]
+                end = weights[-1]
+
+            weights = []
+
+            end_frame = min(end_frame, frames)
+            duration = end_frame - start_frame
+            if start_frame > 0:
+                weights.extend([start] * start_frame)
+
+            for i in range(duration):
+                n = duration - 1
+                if timing == "linear":
+                    weights.append(start + (end - start) * i / n)
+                elif timing == "ease_in_out":
+                    weights.append(start + (end - start) * (1 - math.cos(i / n * math.pi)) / 2)
+                elif timing == "ease_in":
+                    weights.append(start + (end - start) * math.sin(i / n * math.pi / 2))
+                elif timing == "ease_out":
+                    weights.append(start + (end - start) * (1 - math.cos(i / n * math.pi / 2)))
+                elif timing == "random":
+                    weights.append(random.uniform(start, end))
+
+            weights[-1] = end if timing != "random" else weights[-1]
+            if end_frame < frames:
+                weights.extend([end] * (frames - end_frame))
+
+        if len(weights) == 0:
+            weights = [0.0]
+
+        frames = len(weights)
+
+        # repeat the images for cross fade
+        image_1 = None
+        image_2 = None
+
+        # Calculate the min and max of the weights
+        min_weight = min(weights)
+        max_weight = max(weights)
+
+        if image is not None:
+
+            if "shift" in method:
+                image_1 = image[:-1]
+                image_2 = image[1:]
+
+                weights = weights * image_1.shape[0]
+                image_1 = image_1.repeat_interleave(frames, 0)
+                image_2 = image_2.repeat_interleave(frames, 0)
+            elif "alternate" in method:
+                image_1 = image[::2].repeat_interleave(2, 0)
+                image_1 = image_1[1:]
+                image_2 = image[1::2].repeat_interleave(2, 0)
+
+                # Invert the weights relative to their own range
+                mew_weights = weights + [max_weight - (w - min_weight) for w in weights]
+
+                mew_weights = mew_weights * (image_1.shape[0] // 2)
+                if image.shape[0] % 2:
+                    image_1 = image_1[:-1]
+                else:
+                    image_2 = image_2[:-1]
+                    mew_weights = mew_weights + weights
+
+                weights = mew_weights
+                image_1 = image_1.repeat_interleave(frames, 0)
+                image_2 = image_2.repeat_interleave(frames, 0)
+            else:
+                weights = weights * image.shape[0]
+                image_1 = image.repeat_interleave(frames, 0)
+
+            # add starting and ending frames
+            if add_starting_frames > 0:
+                weights = [weights[0]] * add_starting_frames + weights
+                image_1 = torch.cat([image[:1].repeat(add_starting_frames, 1, 1, 1), image_1], dim=0)
+                if image_2 is not None:
+                    image_2 = torch.cat([image[:1].repeat(add_starting_frames, 1, 1, 1), image_2], dim=0)
+            if add_ending_frames > 0:
+                weights = weights + [weights[-1]] * add_ending_frames
+                image_1 = torch.cat([image_1, image[-1:].repeat(add_ending_frames, 1, 1, 1)], dim=0)
+                if image_2 is not None:
+                    image_2 = torch.cat([image_2, image[-1:].repeat(add_ending_frames, 1, 1, 1)], dim=0)
+
+        # reverse the weights array
+        weights_invert = weights[::-1]
+
+        frame_count = len(weights)
+
+        return (weights, weights_invert, frame_count, image_1, image_2, weights_strategy,)
+
+class IPAdapterWeightsFromStrategy(IPAdapterWeights):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "weights_strategy": ("WEIGHTS_STRATEGY",),
+            }, "optional": {
+                "image": ("IMAGE",),
+            }
+        }
+
+class IPAdapterPromptScheduleFromWeightsStrategy():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "weights_strategy": ("WEIGHTS_STRATEGY",),
+            "prompt": ("STRING", {"default": "", "multiline": True }),
+            }}
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt_schedule", )
+    FUNCTION = "prompt_schedule"
+    CATEGORY = "ipadapter/weights"
+
+    def prompt_schedule(self, weights_strategy, prompt=""):
+        frames = weights_strategy["frames"]
+        add_starting_frames = weights_strategy["add_starting_frames"]
+        add_ending_frames = weights_strategy["add_ending_frames"]
+        frame_count = weights_strategy["frame_count"]
+
+        out = ""
+
+        prompt = [p for p in prompt.split("\n") if p.strip() != ""]
+
+        if len(prompt) > 0 and frame_count > 0:
+            # prompt_pos must be the same size as the image batch
+            if len(prompt) > frame_count:
+                prompt = prompt[:frame_count]
+            elif len(prompt) < frame_count:
+                prompt += [prompt[-1]] * (frame_count - len(prompt))
+
+            if add_starting_frames > 0:
+                out += f"\"0\": \"{prompt[0]}\",\n"
+            for i in range(frame_count):
+                out += f"\"{i * frames + add_starting_frames}\": \"{prompt[i]}\",\n"
+            if add_ending_frames > 0:
+                out += f"\"{frame_count * frames + add_starting_frames}\": \"{prompt[-1]}\",\n"
+
+        return (out, )
+
+class IPAdapterCombineWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+        "required": {
+            "weights_1": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
+            "weights_2": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
+        }}
+    RETURN_TYPES = ("FLOAT", "INT")
+    RETURN_NAMES = ("weights", "count")
+    FUNCTION = "combine"
+    CATEGORY = "ipadapter/utils"
+
+    def combine(self, weights_1, weights_2):
+        if not isinstance(weights_1, list):
+            weights_1 = [weights_1]
+        if not isinstance(weights_2, list):
+            weights_2 = [weights_2]
+        weights = weights_1 + weights_2
+
+        return (weights, len(weights), )
+
+class IPAdapterRegionalConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            #"set_cond_area": (["default", "mask bounds"],),
+            "image": ("IMAGE",),
+            "image_weight": ("FLOAT", { "default": 1.0, "min": -1.0, "max": 3.0, "step": 0.05 }),
+            "prompt_weight": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.05 }),
+            "weight_type": (WEIGHT_TYPES, ),
+            "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 }),
+        }, "optional": {
+            "mask": ("MASK",),
+            "positive": ("CONDITIONING",),
+            "negative": ("CONDITIONING",),
+        }}
+
+    RETURN_TYPES = ("IPADAPTER_PARAMS", "CONDITIONING", "CONDITIONING", )
+    RETURN_NAMES = ("IPADAPTER_PARAMS", "POSITIVE", "NEGATIVE")
+    FUNCTION = "conditioning"
+
+    CATEGORY = "ipadapter/params"
+
+    def conditioning(self, image, image_weight, prompt_weight, weight_type, start_at, end_at, mask=None, positive=None, negative=None):
+        set_area_to_bounds = False #if set_cond_area == "default" else True
+
+        if mask is not None:
+            if positive is not None:
+                positive = conditioning_set_values(positive, {"mask": mask, "set_area_to_bounds": set_area_to_bounds, "mask_strength": prompt_weight})
+            if negative is not None:
+                negative = conditioning_set_values(negative, {"mask": mask, "set_area_to_bounds": set_area_to_bounds, "mask_strength": prompt_weight})
+
+        ipadapter_params = {
+            "image": [image],
+            "attn_mask": [mask],
+            "weight": [image_weight],
+            "weight_type": [weight_type],
+            "start_at": [start_at],
+            "end_at": [end_at],
+        }
+
+        return (ipadapter_params, positive, negative, )
+
+class IPAdapterCombineParams:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "params_1": ("IPADAPTER_PARAMS",),
+            "params_2": ("IPADAPTER_PARAMS",),
+        }, "optional": {
+            "params_3": ("IPADAPTER_PARAMS",),
+            "params_4": ("IPADAPTER_PARAMS",),
+            "params_5": ("IPADAPTER_PARAMS",),
+        }}
+
+    RETURN_TYPES = ("IPADAPTER_PARAMS",)
+    FUNCTION = "combine"
+    CATEGORY = "ipadapter/params"
+
+    def combine(self, params_1, params_2, params_3=None, params_4=None, params_5=None):
+        ipadapter_params = {
+            "image": params_1["image"] + params_2["image"],
+            "attn_mask": params_1["attn_mask"] + params_2["attn_mask"],
+            "weight": params_1["weight"] + params_2["weight"],
+            "weight_type": params_1["weight_type"] + params_2["weight_type"],
+            "start_at": params_1["start_at"] + params_2["start_at"],
+            "end_at": params_1["end_at"] + params_2["end_at"],
+        }
+
+        if params_3 is not None:
+            ipadapter_params["image"] += params_3["image"]
+            ipadapter_params["attn_mask"] += params_3["attn_mask"]
+            ipadapter_params["weight"] += params_3["weight"]
+            ipadapter_params["weight_type"] += params_3["weight_type"]
+            ipadapter_params["start_at"] += params_3["start_at"]
+            ipadapter_params["end_at"] += params_3["end_at"]
+        if params_4 is not None:
+            ipadapter_params["image"] += params_4["image"]
+            ipadapter_params["attn_mask"] += params_4["attn_mask"]
+            ipadapter_params["weight"] += params_4["weight"]
+            ipadapter_params["weight_type"] += params_4["weight_type"]
+            ipadapter_params["start_at"] += params_4["start_at"]
+            ipadapter_params["end_at"] += params_4["end_at"]
+        if params_5 is not None:
+            ipadapter_params["image"] += params_5["image"]
+            ipadapter_params["attn_mask"] += params_5["attn_mask"]
+            ipadapter_params["weight"] += params_5["weight"]
+            ipadapter_params["weight_type"] += params_5["weight_type"]
+            ipadapter_params["start_at"] += params_5["start_at"]
+            ipadapter_params["end_at"] += params_5["end_at"]
+
+        return (ipadapter_params, )
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ Register
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+"""
+NODE_CLASS_MAPPINGS = {
+    # Main Apply Nodes
+    "IPAdapter": IPAdapterSimple,
+    "IPAdapterAdvanced": IPAdapterAdvanced,
+    "IPAdapterBatch": IPAdapterBatch,
+    "IPAdapterFaceID": IPAdapterFaceID,
+    "IPAAdapterFaceIDBatch": IPAAdapterFaceIDBatch,
+    "IPAdapterTiled": IPAdapterTiled,
+    "IPAdapterTiledBatch": IPAdapterTiledBatch,
+    "IPAdapterEmbeds": IPAdapterEmbeds,
+    "IPAdapterEmbedsBatch": IPAdapterEmbedsBatch,
+    "IPAdapterStyleComposition": IPAdapterStyleComposition,
+    "IPAdapterStyleCompositionBatch": IPAdapterStyleCompositionBatch,
+    "IPAdapterMS": IPAdapterMS,
+    "IPAdapterClipVisionEnhancer": IPAdapterClipVisionEnhancer,
+    "IPAdapterClipVisionEnhancerBatch": IPAdapterClipVisionEnhancerBatch,
+    "IPAdapterFromParams": IPAdapterFromParams,
+    "IPAdapterPreciseStyleTransfer": IPAdapterPreciseStyleTransfer,
+    "IPAdapterPreciseStyleTransferBatch": IPAdapterPreciseStyleTransferBatch,
+    "IPAdapterPreciseComposition": IPAdapterPreciseComposition,
+    "IPAdapterPreciseCompositionBatch": IPAdapterPreciseCompositionBatch,
+
+    # Loaders
+    "IPAdapterUnifiedLoader": IPAdapterUnifiedLoader,
+    "IPAdapterUnifiedLoaderFaceID": IPAdapterUnifiedLoaderFaceID,
+    "IPAdapterModelLoader": IPAdapterModelLoader,
+    "IPAdapterInsightFaceLoader": IPAdapterInsightFaceLoader,
+    "IPAdapterUnifiedLoaderCommunity": IPAdapterUnifiedLoaderCommunity,
+
+    # Helpers
+    "IPAdapterEncoder": IPAdapterEncoder,
+    "IPAdapterCombineEmbeds": IPAdapterCombineEmbeds,
+    "IPAdapterNoise": IPAdapterNoise,
+    "PrepImageForClipVision": PrepImageForClipVision,
+    "IPAdapterSaveEmbeds": IPAdapterSaveEmbeds,
+    "IPAdapterLoadEmbeds": IPAdapterLoadEmbeds,
+    "IPAdapterWeights": IPAdapterWeights,
+    "IPAdapterCombineWeights": IPAdapterCombineWeights,
+    "IPAdapterWeightsFromStrategy": IPAdapterWeightsFromStrategy,
+    "IPAdapterPromptScheduleFromWeightsStrategy": IPAdapterPromptScheduleFromWeightsStrategy,
+    "IPAdapterRegionalConditioning": IPAdapterRegionalConditioning,
+    "IPAdapterCombineParams": IPAdapterCombineParams,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Main Apply Nodes
+    "IPAdapter": "IPAdapter",
+    "IPAdapterAdvanced": "IPAdapter Advanced",
+    "IPAdapterBatch": "IPAdapter Batch (Adv.)",
+    "IPAdapterFaceID": "IPAdapter FaceID",
+    "IPAAdapterFaceIDBatch": "IPAdapter FaceID Batch",
+    "IPAdapterTiled": "IPAdapter Tiled",
+    "IPAdapterTiledBatch": "IPAdapter Tiled Batch",
+    "IPAdapterEmbeds": "IPAdapter Embeds",
+    "IPAdapterEmbedsBatch": "IPAdapter Embeds Batch",
+    "IPAdapterStyleComposition": "IPAdapter Style & Composition SDXL",
+    "IPAdapterStyleCompositionBatch": "IPAdapter Style & Composition Batch SDXL",
+    "IPAdapterMS": "IPAdapter Mad Scientist",
+    "IPAdapterClipVisionEnhancer": "IPAdapter ClipVision Enhancer",
+    "IPAdapterClipVisionEnhancerBatch": "IPAdapter ClipVision Enhancer Batch",
+    "IPAdapterFromParams": "IPAdapter from Params",
+    "IPAdapterPreciseStyleTransfer": "IPAdapter Precise Style Transfer",
+    "IPAdapterPreciseStyleTransferBatch": "IPAdapter Precise Style Transfer Batch",
+    "IPAdapterPreciseComposition": "IPAdapter Precise Composition",
+    "IPAdapterPreciseCompositionBatch": "IPAdapter Precise Composition Batch",
+
+    # Loaders
+    "IPAdapterUnifiedLoader": "IPAdapter Unified Loader",
+    "IPAdapterUnifiedLoaderFaceID": "IPAdapter Unified Loader FaceID",
+    "IPAdapterModelLoader": "IPAdapter Model Loader",
+    "IPAdapterInsightFaceLoader": "IPAdapter InsightFace Loader",
+    "IPAdapterUnifiedLoaderCommunity": "IPAdapter Unified Loader Community",
+
+    # Helpers
+    "IPAdapterEncoder": "IPAdapter Encoder",
+    "IPAdapterCombineEmbeds": "IPAdapter Combine Embeds",
+    "IPAdapterNoise": "IPAdapter Noise",
+    "PrepImageForClipVision": "Prep Image For ClipVision",
+    "IPAdapterSaveEmbeds": "IPAdapter Save Embeds",
+    "IPAdapterLoadEmbeds": "IPAdapter Load Embeds",
+    "IPAdapterWeights": "IPAdapter Weights",
+    "IPAdapterWeightsFromStrategy": "IPAdapter Weights From Strategy",
+    "IPAdapterPromptScheduleFromWeightsStrategy": "Prompt Schedule From Weights Strategy",
+    "IPAdapterCombineWeights": "IPAdapter Combine Weights",
+    "IPAdapterRegionalConditioning": "IPAdapter Regional Conditioning",
+    "IPAdapterCombineParams": "IPAdapter Combine Params",
+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/NODES.md

@@ -0,0 +1,54 @@
+# Nodes reference
+
+Below I'm trying to document all the nodes. It's still very incomplete, be sure to check back later.
+
+## Loaders
+
+### :knot: IPAdapter Unified Loader
+
+Loads the full stack of models needed for IPAdapter to function. The returned object will contain information regarding the **ipadapter** and **clip vision models**.
+
+Multiple unified loaders should always be daisy chained through the `ipadapter` in/out. **Failing to do so will cause all models to be loaded twice.** For **the first** unified loader the `ipadapter` input **should never be connected**.
+
+#### Inputs
+- **model**, main ComfyUI model pipeline
+
+#### Optional Inputs
+- **ipadapter**, it's important to note that this is optional and used exclusively to daisy chain unified loaders. **The `ipadapter` input is never connected in the first `IPAdapter Unified Loader` of the chain.**
+
+#### Outputs
+- **model**, the model pipeline is used exclusively for configuration, the model comes out of this node untouched and it can be considered a reroute. Note that this is different  from the Unified Loader FaceID that actually alters the model with a LoRA.
+- **ipadapter**, connect this to any ipadater node. Each node will automatically detect if the `ipadapter` object contains the full stack of models or just one (like in the case [IPAdapter Model Loader](#ipadapter-model-loader)).
+
+### :knot: IPAdapter Model Loader
+
+Loads the IPAdapter model only. The returned object will be the IPAdapter model contrary to the [Unified loader](#ipadapter-unified-loader) that contains the full stack of models.
+
+#### Configuration parameters
+- **ipadapter_file**, the main IPAdapter model. It must be located into `ComfyUI/models/ipadapter` or in any path specified in the `extra_model_paths.yaml` configuration file.
+
+#### Outputs
+- **IPADAPTER**, contains the loaded model only. Note that `IPADAPTER` will have a different structure when loaded by the [Unified Loader](#ipadapter-unified-loader).
+
+## Main IPAdapter Apply Nodes
+
+### :knot: IPAdapter Advanced
+
+This node contains all the options to fine tune the IPAdapter models. It is a drop in replacement for the old `IPAdapter Apply` that is no longer available. If you have an old workflow, delete the existing `IPadapter Apply` node, add `IPAdapter Advanced` and connect all the pipes as before.
+
+#### Inputs
+- **model**, main model pipeline.
+- **ipadapter**, the IPAdapter model. It can be connected to the [IPAdapter Model Loader](#ipadapter-model-loader) or any of the Unified Loaders. If a Unified loader is used anywhere in the workflow and you don't need a different model, it's always adviced to reuse the previous `ipadapter` pipeline.
+- **image**, the reference image used to generate the positive conditioning. It should be a square image, other aspect ratios are automatically cropped in the center.
+
+#### Optional inputs
+- **image_negative**, image used to generate the negative conditioning. This is optional and normally handled by the code. It is possible to send noise or actually any image to instruct the model about what we don't want to see in the composition.
+- **attn_mask**, a mask that will be applied during the image generation. **The mask should have the same size or at least the same aspect ratio of the latent**. The mask will define the area of influence of the IPAdapter models on the final image. Black zones won't be affected, white zones will get maximum influence. It can be a grayscale mask.
+- **clip_vision**, this is optional if using any of the Unified loaders. If using the [IPAdapter Model Loader](#knot-ipadapter-model-loader) you also have to provide the clip vision model with a `Load CLIP Vision` node.
+
+#### Configuration parameters
+- **weight**, weight of the IPAdapter model. For `linear` `weight_type` (the default), a good starting point is 0.8. If you use other weight types you can experiment with higher values.
+- **weight_type**, this is how the IPAdapter is applied to the UNet block. For example `ease-in` means that the input blocks have higher weight than the output ones. `week input` means that the whole input block has lower weight. `style transfer (SDXL)` only works with SDXL and it's a very powerful tool to tranfer only the style of an image but not its content. This parameter hugely impacts how the composition reacts to the text prompting.
+- **combine_embeds**, when sending more than one reference image the embeddings can be sent one after the other (`concat`) or combined in various ways. For low spec GPUs it is adviced to `average` the embeds if you send multiple images. `subtract` subtracts the embeddings of the second image to the first; in case of 3 or more images they are averaged and subtracted to the first.
+- **start_at/end_at**, this is the timestepping. Defines at what percentage point of the generation to start applying the IPAdapter model. The initial steps are the most important so if you start later (eg: `start_at=0.3`) the generated image will have a very light conditioning.
+- **embeds_scaling**, the way the IPAdapter models are applied to the K,V. This parameter has a small impact on how the model reacts to text prompting. `K+mean(V) w/ C penalty` grants good quality at high weights (>1.0) without burning the image.

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/README.md

@@ -0,0 +1,187 @@
+# ComfyUI IPAdapter plus
+[ComfyUI](https://github.com/comfyanonymous/ComfyUI) reference implementation for [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/) models.
+
+The IPAdapter are very powerful models for image-to-image conditioning. The subject or even just the style of the reference image(s) can be easily transferred to a generation. Think of it as a 1-image lora.
+
+# Sponsorship
+
+<div align="center">
+
+**[:heart: Github Sponsor](https://github.com/sponsors/cubiq) | [:coin: Paypal](https://paypal.me/matt3o)**
+
+</div>
+
+If you like my work and wish to see updates and new features please consider sponsoring my projects.
+
+- [ComfyUI IPAdapter Plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus)
+- [ComfyUI InstantID (Native)](https://github.com/cubiq/ComfyUI_InstantID)
+- [ComfyUI Essentials](https://github.com/cubiq/ComfyUI_essentials)
+- [ComfyUI FaceAnalysis](https://github.com/cubiq/ComfyUI_FaceAnalysis)
+- [Comfy Dungeon](https://github.com/cubiq/Comfy_Dungeon)
+
+Not to mention the documentation and videos tutorials. Check my **ComfyUI Advanced Understanding** videos on YouTube for example, [part 1](https://www.youtube.com/watch?v=_C7kR2TFIX0) and [part 2](https://www.youtube.com/watch?v=ijqXnW_9gzc)
+
+The only way to keep the code open and free is by sponsoring its development. The more sponsorships the more time I can dedicate to my open source projects.
+
+Please consider a [Github Sponsorship](https://github.com/sponsors/cubiq) or [PayPal donation](https://paypal.me/matt3o) (Matteo "matt3o" Spinelli). For sponsorships of $50+, let me know if you'd like to be mentioned in this readme file, you can find me on [Discord](https://latent.vision/discord) or _matt3o :snail: gmail.com_.
+
+## Important updates
+
+**2024/07/26**: Added support for image batches and animation to the ClipVision Enhancer.
+
+**2024/07/18**: Support for Kolors.
+
+**2024/07/17**: Added experimental ClipVision Enhancer node. It was somehow inspired by the [Scaling on Scales](https://arxiv.org/pdf/2403.13043) paper but the implementation is a bit different. The new IPAdapterClipVisionEnhancer tries to catch small details by tiling the embeds (instead of the image in the pixel space), the result is a slightly higher resolution visual embedding with no cost of performance.
+
+**2024/07/11**: Added experimental Precise composition (layout) transfer. It's not as good as style. `embeds_scaling` has a huge impact. Start with strength 0.8 and boost 0.3 in SDXL and 0.6 boost 0.35 in SD1.5.
+
+**2024/06/28**: Added the `IPAdapter Precise Style Transfer` node. Increase the `style_boost` option to lower the bleeding of the composition layer. **Important:** works better in SDXL, start with a style_boost of 2; for SD1.5 try to increase the weight a little over 1.0 and set the style_boost to a value between -1 and +1, starting with 0.
+
+**2024/06/22**: Added `style transfer precise`, offers less bleeding of the embeds between the style and composition layers. It is sometimes better than the standard style transfer especially if the reference image is very different from the generated image. Works better in SDXL than SD1.5.
+
+**2024/05/21**: Improved memory allocation when `encode_batch_size`. Useful mostly for very long animations.
+
+**2024/05/02**: Add `encode_batch_size` to the Advanced batch node. This can be useful for animations with a lot of frames to reduce the VRAM usage during the image encoding. Please note that results will be slightly different based on the batch size.
+
+**2024/04/27**: Refactored the IPAdapterWeights mostly useful for AnimateDiff animations.
+
+**2024/04/21**: Added Regional Conditioning nodes to simplify attention masking and masked text conditioning.
+
+**2024/04/16**: Added support for the new SDXL portrait unnorm model (link below). It's very strong and tends to ignore the text conditioning. Lower the CFG to 3-4 or use a RescaleCFG node.
+
+**2024/04/12**: Added scheduled weights. Useful for animations.
+
+*(Older updates removed for readability)*
+
+## Example workflows
+
+The [examples directory](./examples/) has many workflows that cover all IPAdapter functionalities.
+
+![IPAdapter Example workflow](./examples/demo_workflow.jpg)
+
+## Video Tutorials
+
+<a href="https://youtu.be/_JzDcgKgghY" target="_blank">
+ <img src="https://img.youtube.com/vi/_JzDcgKgghY/hqdefault.jpg" alt="Watch the video" />
+</a>
+
+- **:star: [New IPAdapter features](https://youtu.be/_JzDcgKgghY)**
+- **:art: [IPAdapter Style and Composition](https://www.youtube.com/watch?v=czcgJnoDVd4)**
+
+The following videos are about the previous version of IPAdapter, but they still contain valuable information.
+
+:nerd_face: [Basic usage video](https://youtu.be/7m9ZZFU3HWo), :rocket: [Advanced features video](https://www.youtube.com/watch?v=mJQ62ly7jrg), :japanese_goblin: [Attention Masking video](https://www.youtube.com/watch?v=vqG1VXKteQg), :movie_camera: [Animation Features video](https://www.youtube.com/watch?v=ddYbhv3WgWw)
+
+## Installation
+
+Download or git clone this repository inside `ComfyUI/custom_nodes/` directory or use the Manager. IPAdapter always requires the latest version of ComfyUI. If something doesn't work be sure to upgrade. Beware that the automatic update of the manager sometimes doesn't work and you may need to upgrade manually.
+
+There's now a *Unified Model Loader*, for it to work you need to name the files exactly as described below. The legacy loaders work with any file name but you have to select them manually. The models can be placed into sub-directories.
+
+Remember you can also use any custom location setting an `ipadapter` entry in the `extra_model_paths.yaml` file.
+
+- `/ComfyUI/models/clip_vision`
+    - [CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors), download and rename
+    - [CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors), download and rename
+    - [clip-vit-large-patch14-336.bin](https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus/resolve/main/image_encoder/pytorch_model.bin), download and rename only for Kolors models
+- `/ComfyUI/models/ipadapter`, create it if not present
+    - [ip-adapter_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors), Basic model, average strength
+    - [ip-adapter_sd15_light_v11.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light_v11.bin), Light impact model
+    - [ip-adapter-plus_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors), Plus model, very strong
+    - [ip-adapter-plus-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors), Face model, portraits
+    - [ip-adapter-full-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors), Stronger face model, not necessarily better
+    - [ip-adapter_sd15_vit-G.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors), Base model, **requires bigG clip vision encoder**
+    - [ip-adapter_sdxl_vit-h.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors), SDXL model
+    - [ip-adapter-plus_sdxl_vit-h.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors), SDXL plus model
+    - [ip-adapter-plus-face_sdxl_vit-h.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors), SDXL face model
+    - [ip-adapter_sdxl.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors), vit-G SDXL model, **requires bigG clip vision encoder**
+    - **Deprecated** [ip-adapter_sd15_light.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors), v1.0 Light impact model
+
+**FaceID** models require `insightface`, you need to install it in your ComfyUI environment. Check [this issue](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/162) for help. Remember that most FaceID models also need a LoRA.
+
+For the Unified Loader to work the files need to be named exactly as shown in the list below.
+
+- `/ComfyUI/models/ipadapter`
+    - [ip-adapter-faceid_sd15.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin), base FaceID model
+    - [ip-adapter-faceid-plusv2_sd15.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15.bin), FaceID plus v2
+    - [ip-adapter-faceid-portrait-v11_sd15.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait-v11_sd15.bin), text prompt style transfer for portraits
+    - [ip-adapter-faceid_sdxl.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl.bin), SDXL base FaceID
+    - [ip-adapter-faceid-plusv2_sdxl.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl.bin), SDXL plus v2
+    - [ip-adapter-faceid-portrait_sdxl.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl.bin), SDXL text prompt style transfer
+    - [ip-adapter-faceid-portrait_sdxl_unnorm.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl_unnorm.bin), very strong style transfer SDXL only
+    - **Deprecated** [ip-adapter-faceid-plus_sd15.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15.bin), FaceID plus v1 
+    - **Deprecated** [ip-adapter-faceid-portrait_sd15.bin](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sd15.bin), v1 of the portrait model
+
+Most FaceID models require a LoRA. If you use the `IPAdapter Unified Loader FaceID` it will be loaded automatically if you follow the naming convention. Otherwise you have to load them manually, be careful each FaceID model has to be paired with its own specific LoRA.
+
+- `/ComfyUI/models/loras`
+    - [ip-adapter-faceid_sd15_lora.safetensors](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors)
+    - [ip-adapter-faceid-plusv2_sd15_lora.safetensors](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors)
+    - [ip-adapter-faceid_sdxl_lora.safetensors](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl_lora.safetensors), SDXL FaceID LoRA
+    - [ip-adapter-faceid-plusv2_sdxl_lora.safetensors](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl_lora.safetensors), SDXL plus v2 LoRA
+    - **Deprecated** [ip-adapter-faceid-plus_sd15_lora.safetensors](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15_lora.safetensors), LoRA for the deprecated FaceID plus v1 model
+
+All models can be found on [huggingface](https://huggingface.co/h94).
+
+### Community's models
+
+The community has baked some interesting IPAdapter models.
+
+- `/ComfyUI/models/ipadapter`
+    - [ip_plus_composition_sd15.safetensors](https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sd15.safetensors), general composition ignoring style and content, more about it [here](https://huggingface.co/ostris/ip-composition-adapter)
+    - [ip_plus_composition_sdxl.safetensors](https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sdxl.safetensors), SDXL version
+    - [Kolors-IP-Adapter-Plus.bin](https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus/resolve/main/ip_adapter_plus_general.bin?download=true), IPAdapter Plus for Kolors model
+
+if you know of other models please let me know and I will add them to the unified loader.
+
+## Generic suggestions
+
+There are many workflows included in the [examples](./examples/) directory. Please check them before asking for support.
+
+Usually it's a good idea to lower the `weight` to at least `0.8` and increase the number steps. To increase adherece to the prompt you may try to change the **weight type** in the `IPAdapter Advanced` node.
+
+## Nodes reference
+
+I'm (slowly) documenting all nodes. Please check the [Nodes reference](./NODES.md).
+
+## Troubleshooting
+
+Please check the [troubleshooting](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/108) before posting a new issue. Also remember to check the previous closed issues.
+
+## Current sponsors
+
+It's only thanks to generous sponsors that **the whole community** can enjoy open and free software. Please join me in thanking the following companies and individuals!
+
+### :trophy: Gold sponsors
+
+[![Kaiber.ai](https://f.latent.vision/imgs/kaiber.png)](https://kaiber.ai/)&nbsp; &nbsp;[![Kaiber.ai](https://f.latent.vision/imgs/replicate.png)](https://replicate.com/)
+
+### :tada: Silver sponsors
+
+[![OperArt.ai](https://f.latent.vision/imgs/openart.png?r=1)](https://openart.ai/workflows)&nbsp; &nbsp;[![OperArt.ai](https://f.latent.vision/imgs/finetuners.png)](https://www.finetuners.ai/)&nbsp; &nbsp;[![Comfy.ICU](https://f.latent.vision/imgs/comfyicu.png?r=1)](https://comfy.icu/)
+
+### Companies supporting my projects
+
+- [RunComfy](https://www.runcomfy.com/) (ComfyUI Cloud)
+
+### Esteemed individuals
+
+- [Jack Gane](https://github.com/ganeJackS)
+- [Nathan Shipley](https://www.nathanshipley.com/)
+- [Dkdnzia](https://github.com/Dkdnzia)
+
+### One-time Extraordinaires
+
+- [Eric Rollei](https://github.com/EricRollei)
+- [francaleu](https://github.com/francaleu)
+- [Neta.art](https://github.com/talesofai)
+- [Samwise Wang](https://github.com/tzwm)
+- _And all private sponsors, you know who you are!_
+
+## Credits
+
+- [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/)
+- [InstantStyle](https://github.com/InstantStyle/InstantStyle)
+- [B-Lora](https://github.com/yardenfren1996/B-LoRA/)
+- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
+- [laksjdjf](https://github.com/laksjdjf/)

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/__init__.py

@@ -0,0 +1,19 @@
+"""
+ ██▓ ██▓███   ▄▄▄      ▓█████▄  ▄▄▄       ██▓███  ▄▄▄█████▓▓█████  ██▀███
+▓██▒▓██░  ██▒▒████▄    ▒██▀ ██▌▒████▄    ▓██░  ██▒▓  ██▒ ▓▒▓█   ▀ ▓██ ▒ ██▒
+▒██▒▓██░ ██▓▒▒██  ▀█▄  ░██   █▌▒██  ▀█▄  ▓██░ ██▓▒▒ ▓██░ ▒░▒███   ▓██ ░▄█ ▒
+░██░▒██▄█▓▒ ▒░██▄▄▄▄██ ░▓█▄   ▌░██▄▄▄▄██ ▒██▄█▓▒ ▒░ ▓██▓ ░ ▒▓█  ▄ ▒██▀▀█▄
+░██░▒██▒ ░  ░ ▓█   ▓██▒░▒████▓  ▓█   ▓██▒▒██▒ ░  ░  ▒██▒ ░ ░▒████▒░██▓ ▒██▒
+░▓  ▒▓▒░ ░  ░ ▒▒   ▓▒█░ ▒▒▓  ▒  ▒▒   ▓▒█░▒▓▒░ ░  ░  ▒ ░░   ░░ ▒░ ░░ ▒▓ ░▒▓░
+ ▒ ░░▒ ░       ▒   ▒▒ ░ ░ ▒  ▒   ▒   ▒▒ ░░▒ ░         ░     ░ ░  ░  ░▒ ░ ▒░
+ ▒ ░░░         ░   ▒    ░ ░  ░   ░   ▒   ░░         ░         ░     ░░   ░
+ ░                 ░  ░   ░          ░  ░                     ░  ░   ░
+                        ░
+             · -—+ IPAdapter Plus Extension for ComfyUI +—- ·
+             Brought to you by Matteo "Matt3o/Cubiq" Spinelli
+             https://github.com/cubiq/ComfyUI_IPAdapter_plus/
+"""
+
+from .IPAdapterPlus import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_advanced.json

@@ -0,0 +1,619 @@
+{
+  "last_node_id": 17,
+  "last_link_id": 26,
+  "nodes": [
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        50,
+        730
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            20
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "sd15/realisticVisionV51_v51VAE.safetensors"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
+        1770,
+        710
+      ],
+      "size": {
+        "0": 529.7760009765625,
+        "1": 582.3048095703125
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "IPAdapter"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        690,
+        840
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "blurry, noisy, messy, lowres, jpeg, artifacts, ill, distorted, malformed"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1570,
+        700
+      ],
+      "size": {
+        "0": 140,
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+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        801,
+        1097
+      ],
+      "size": {
+        "0": 315,
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+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        690,
+        610
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "in a peaceful spring morning a woman wearing a white shirt is sitting in a park on a bench\n\nhigh quality, detailed, diffuse light"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        1210,
+        700
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 23
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        0,
+        "fixed",
+        30,
+        6.5,
+        "ddpm",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 14,
+      "type": "IPAdapterAdvanced",
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+      ],
+      "size": {
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+      "flags": {},
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "link": 21,
+          "slot_index": 1
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 26
+        },
+        {
+          "name": "image_negative",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "attn_mask",
+          "type": "MASK",
+          "link": null
+        },
+        {
+          "name": "clip_vision",
+          "type": "CLIP_VISION",
+          "link": 24,
+          "slot_index": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            23
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterAdvanced"
+      },
+      "widgets_values": [
+        0.8,
+        "linear",
+        "concat",
+        0,
+        1,
+        "V only"
+      ]
+    },
+    {
+      "id": 17,
+      "type": "PrepImageForClipVision",
+      "pos": [
+        797,
+        87
+      ],
+      "size": {
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+        "1": 106
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            26
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PrepImageForClipVision"
+      },
+      "widgets_values": [
+        "LANCZOS",
+        "top",
+        0.15
+      ]
+    },
+    {
+      "id": 15,
+      "type": "IPAdapterModelLoader",
+      "pos": [
+        308,
+        52
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IPADAPTER",
+          "type": "IPADAPTER",
+          "links": [
+            21
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterModelLoader"
+      },
+      "widgets_values": [
+        "ip-adapter-plus_sd15.safetensors"
+      ]
+    },
+    {
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+      "type": "CLIPVisionLoader",
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+        308,
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+      "size": {
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+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [
+            24
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPVisionLoader"
+      },
+      "widgets_values": [
+        "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"
+      ]
+    },
+    {
+      "id": 12,
+      "type": "LoadImage",
+      "pos": [
+        311,
+        270
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            25
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "warrior_woman.png",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
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+      0,
+      "CLIP"
+    ],
+    [
+      4,
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+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
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+      "CLIP"
+    ],
+    [
+      6,
+      7,
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+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      20,
+      4,
+      0,
+      14,
+      0,
+      "MODEL"
+    ],
+    [
+      21,
+      15,
+      0,
+      14,
+      1,
+      "IPADAPTER"
+    ],
+    [
+      23,
+      14,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      24,
+      16,
+      0,
+      14,
+      5,
+      "CLIP_VISION"
+    ],
+    [
+      25,
+      12,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      26,
+      17,
+      0,
+      14,
+      2,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_clipvision_enhancer.json

@@ -0,0 +1,918 @@
+{
+  "last_node_id": 42,
+  "last_link_id": 81,
+  "nodes": [
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -816,
+        -107
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            16
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            59
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "sdxl/RealVisXL_V4.0.safetensors"
+      ]
+    },
+    {
+      "id": 36,
+      "type": "ImageCASharpening+",
+      "pos": [
+        -160,
+        -580
+      ],
+      "size": [
+        310.79998779296875,
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+      ],
+      "flags": {},
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 67
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            68
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCASharpening+"
+      },
+      "widgets_values": [
+        0.5
+      ]
+    },
+    {
+      "id": 29,
+      "type": "ImageScale",
+      "pos": [
+        -270,
+        -440
+      ],
+      "size": [
+        231.94505310058594,
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+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 79
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "link": 51,
+          "widget": {
+            "name": "width"
+          }
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "link": 53,
+          "widget": {
+            "name": "height"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            67
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageScale"
+      },
+      "widgets_values": [
+        "lanczos",
+        224,
+        224,
+        "disabled"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "IPAdapterUnifiedLoader",
+      "pos": [
+        -296,
+        -232
+      ],
+      "size": {
+        "0": 315,
+        "1": 78
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 16
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "links": [
+            47
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "links": [
+            48
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterUnifiedLoader"
+      },
+      "widgets_values": [
+        "PLUS (high strength)"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        133,
+        72
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            76
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "blurry, glitches, artifact, distorted, malformed, dirt, eyeglasses"
+      ]
+    },
+    {
+      "id": 38,
+      "type": "EmptyLatentImage",
+      "pos": [
+        341,
+        324
+      ],
+      "size": [
+        210,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            78
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        1024,
+        1024,
+        1
+      ]
+    },
+    {
+      "id": 32,
+      "type": "KSampler",
+      "pos": [
+        722,
+        -129
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 58
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 75
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 76
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 78
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            61
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        3,
+        "fixed",
+        30,
+        5,
+        "dpmpp_2m",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 33,
+      "type": "VAEDecode",
+      "pos": [
+        1107,
+        -130
+      ],
+      "size": {
+        "0": 140,
+        "1": 46
+      },
+      "flags": {
+        "collapsed": false
+      },
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 61
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 59
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            60
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 31,
+      "type": "SimpleMath+",
+      "pos": [
+        -566,
+        -413
+      ],
+      "size": {
+        "0": 210,
+        "1": 78
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "a",
+          "type": "INT,FLOAT",
+          "link": 50
+        },
+        {
+          "name": "b",
+          "type": "INT,FLOAT",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            51,
+            53
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SimpleMath+"
+      },
+      "widgets_values": [
+        "a*224"
+      ]
+    },
+    {
+      "id": 40,
+      "type": "Note",
+      "pos": [
+        -548,
+        -578
+      ],
+      "size": [
+        263.130633831987,
+        106.42462429008373
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "Resize the image to optimal resolution. ie: 224*number_of_tiles.\n\nThe original image should be a square bigger than 224*number_of_tiles."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 39,
+      "type": "Note",
+      "pos": [
+        -18,
+        -704
+      ],
+      "size": [
+        210,
+        69.97374358070851
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "do NOT use PrepForClipVsion with the ClipVision Enhancer!"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        133,
+        -166
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            75
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "illustration of a warrior woman with long red hair wearing a full armor with purple and gold decorations, detailed, intricate, high resolution, 4k"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "LoadImage",
+      "pos": [
+        -1306,
+        -677
+      ],
+      "size": [
+        397.77813257424236,
+        475.1389358531744
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            79
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "warrior_woman.png",
+        "image"
+      ]
+    },
+    {
+      "id": 28,
+      "type": "IPAdapterClipVisionEnhancer",
+      "pos": [
+        220,
+        -620
+      ],
+      "size": [
+        315,
+        326
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 47
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "link": 48
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 68
+        },
+        {
+          "name": "image_negative",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "attn_mask",
+          "type": "MASK",
+          "link": null
+        },
+        {
+          "name": "clip_vision",
+          "type": "CLIP_VISION",
+          "link": null
+        },
+        {
+          "name": "enhance_tiles",
+          "type": "INT",
+          "link": 62,
+          "widget": {
+            "name": "enhance_tiles"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            58
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterClipVisionEnhancer"
+      },
+      "widgets_values": [
+        0.8,
+        "linear",
+        "average",
+        0,
+        1,
+        "V only",
+        2,
+        0.8
+      ]
+    },
+    {
+      "id": 34,
+      "type": "PreviewImage",
+      "pos": [
+        1323,
+        -132
+      ],
+      "size": [
+        1072.0986508932788,
+        1107.239719497359
+      ],
+      "flags": {},
+      "order": 17,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 60
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 41,
+      "type": "Note",
+      "pos": [
+        557,
+        -390
+      ],
+      "size": [
+        243.5294064400009,
+        162.67339782886762
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "if the workflow has other conditioning for the composition (like a composition IPAdapter or a controlnet) you can lower the \"enhance_ratio\" to get more details (0-0.5).\n\nIn absence of other conditioning this should be pretty high(0.5-1.0)."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 42,
+      "type": "Note",
+      "pos": [
+        -848,
+        -389
+      ],
+      "size": [
+        216.56958342785583,
+        79.05427068195002
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "If you set this to 1 it will be the same as standard IPAdapter without enhancement."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 30,
+      "type": "SimpleMath+",
+      "pos": [
+        -851,
+        -514
+      ],
+      "size": [
+        210,
+        78
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "a",
+          "type": "INT,FLOAT",
+          "link": null
+        },
+        {
+          "name": "b",
+          "type": "INT,FLOAT",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            50,
+            62
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SimpleMath+"
+      },
+      "widgets_values": [
+        "3"
+      ]
+    }
+  ],
+  "links": [
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      16,
+      4,
+      0,
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+      0,
+      "MODEL"
+    ],
+    [
+      47,
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+      0,
+      "MODEL"
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+    [
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+      "IPADAPTER"
+    ],
+    [
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+      0,
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+    ],
+    [
+      51,
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+      0,
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+      1,
+      "INT"
+    ],
+    [
+      53,
+      31,
+      0,
+      29,
+      2,
+      "INT"
+    ],
+    [
+      58,
+      28,
+      0,
+      32,
+      0,
+      "MODEL"
+    ],
+    [
+      59,
+      4,
+      2,
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+      1,
+      "VAE"
+    ],
+    [
+      60,
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+      0,
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+      0,
+      "IMAGE"
+    ],
+    [
+      61,
+      32,
+      0,
+      33,
+      0,
+      "LATENT"
+    ],
+    [
+      62,
+      30,
+      0,
+      28,
+      6,
+      "INT"
+    ],
+    [
+      67,
+      29,
+      0,
+      36,
+      0,
+      "IMAGE"
+    ],
+    [
+      68,
+      36,
+      0,
+      28,
+      2,
+      "IMAGE"
+    ],
+    [
+      75,
+      6,
+      0,
+      32,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      76,
+      7,
+      0,
+      32,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      78,
+      38,
+      0,
+      32,
+      3,
+      "LATENT"
+    ],
+    [
+      79,
+      14,
+      0,
+      29,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.3558410273836744,
+      "offset": {
+        "0": 3138.218919113904,
+        "1": 1855.039899518089
+      }
+    }
+  },
+  "version": 0.4
+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_combine_embeds.json

@@ -0,0 +1,1542 @@
+{
+  "last_node_id": 51,
+  "last_link_id": 118,
+  "nodes": [
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        801,
+        1097
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2,
+            89,
+            100,
+            111
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        1580,
+        300
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 23
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        0,
+        "fixed",
+        30,
+        6.5,
+        "dpmpp_2m",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        300,
+        700
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            20,
+            93,
+            104,
+            115
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8,
+            91,
+            102,
+            113
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "sd15/realisticVisionV51_v51VAE.safetensors"
+      ]
+    },
+    {
+      "id": 12,
+      "type": "LoadImage",
+      "pos": [
+        240,
+        -60
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            83
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "warrior_woman.png",
+        "image"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        740,
+        780
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6,
+            88,
+            99,
+            110
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "blurry, noisy, messy, lowres, jpeg, artifacts, ill, distorted, malformed"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "IPAdapterAdvanced",
+      "pos": [
+        1220,
+        260
+      ],
+      "size": {
+        "0": 315,
+        "1": 278
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "link": 21,
+          "slot_index": 1
+        },
+        {
+          "name": "image",
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_cosxl_edit.json

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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_faceid.json

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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_faceid_batch.json

@@ -0,0 +1,1023 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_ideal_faceid_config.json

@@ -0,0 +1,728 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_kolors.json

@@ -0,0 +1,680 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_negative_image.json

@@ -0,0 +1,956 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_noise_injection.json

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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_portrait.json

@@ -0,0 +1,567 @@
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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_precise_composition.json

@@ -0,0 +1,861 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_precise_weight_type.json

@@ -0,0 +1,965 @@
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ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_regional_conditioning.json

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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_simple.json

@@ -0,0 +1,546 @@
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+  "config": {},
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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_style_composition.json

@@ -0,0 +1,612 @@
+{
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+  "last_link_id": 25,
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+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_tiled.json

@@ -0,0 +1,583 @@
+{
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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_weight_types.json

@@ -0,0 +1,1738 @@
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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_weighted_embeds.json

@@ -0,0 +1,836 @@
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+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/examples/ipadapter_weights.json

@@ -0,0 +1,764 @@
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+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "IPAdapter"
+      ]
+    },
+    {
+      "id": 12,
+      "type": "LoadImage",
+      "pos": [
+        311,
+        270
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            25
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "warrior_woman.png",
+        "image"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        801,
+        1097
+      ],
+      "size": [
+        309.1109879864148,
+        82
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "batch_size",
+          "type": "INT",
+          "link": 35,
+          "widget": {
+            "name": "batch_size"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        6
+      ]
+    },
+    {
+      "id": 21,
+      "type": "PrimitiveNode",
+      "pos": [
+        340,
+        1093
+      ],
+      "size": {
+        "0": 210,
+        "1": 82
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            35,
+            40
+          ],
+          "widget": {
+            "name": "batch_size"
+          },
+          "slot_index": 0
+        }
+      ],
+      "title": "frames",
+      "properties": {
+        "Run widget replace on values": false
+      },
+      "widgets_values": [
+        6,
+        "fixed"
+      ]
+    },
+    {
+      "id": 22,
+      "type": "IPAdapterWeights",
+      "pos": [
+        761,
+        208
+      ],
+      "size": [
+        299.9049990375719,
+        324.00000762939453
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "frames",
+          "type": "INT",
+          "link": 40,
+          "widget": {
+            "name": "frames"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "weights",
+          "type": "FLOAT",
+          "links": [
+            38
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "weights_invert",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "total_frames",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "image_1",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterWeights"
+      },
+      "widgets_values": [
+        "1.0, 0.0",
+        "linear",
+        6,
+        0,
+        9999,
+        0,
+        0,
+        "full batch"
+      ]
+    }
+  ],
+  "links": [
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      25,
+      12,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      29,
+      18,
+      1,
+      19,
+      1,
+      "IPADAPTER"
+    ],
+    [
+      30,
+      17,
+      0,
+      19,
+      2,
+      "IMAGE"
+    ],
+    [
+      31,
+      19,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      35,
+      21,
+      0,
+      5,
+      0,
+      "INT"
+    ],
+    [
+      36,
+      4,
+      0,
+      18,
+      0,
+      "MODEL"
+    ],
+    [
+      37,
+      18,
+      0,
+      19,
+      0,
+      "MODEL"
+    ],
+    [
+      38,
+      22,
+      0,
+      19,
+      6,
+      "FLOAT"
+    ],
+    [
+      40,
+      21,
+      0,
+      22,
+      1,
+      "INT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/image_proj_models.py

@@ -0,0 +1,275 @@
+import math
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        max_seq_len: int = 257,  # CLIP tokens + CLS token
+        apply_pos_emb: bool = False,
+        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (
+            nn.Sequential(
+                nn.LayerNorm(dim),
+                nn.Linear(dim, dim * num_latents_mean_pooled),
+                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+            )
+            if num_latents_mean_pooled > 0
+            else None
+        )
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, "b n -> b n 1")
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
+
+
+class FacePerceiverResampler(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim=768,
+        depth=4,
+        dim_head=64,
+        heads=16,
+        embedding_dim=1280,
+        output_dim=768,
+        ff_mult=4,
+    ):
+        super().__init__()
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, latents, x):
+        x = self.proj_in(x)
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+class MLPProjModel(nn.Module):
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+        super().__init__()
+
+        self.proj = nn.Sequential(
+            nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            nn.GELU(),
+            nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            nn.LayerNorm(cross_attention_dim)
+        )
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+class MLPProjModelFaceId(nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
+        super().__init__()
+
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+
+        self.proj = nn.Sequential(
+            nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            nn.GELU(),
+            nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, id_embeds):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x
+
+class ProjModelFaceIdPlus(nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
+        super().__init__()
+
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+
+        self.proj = nn.Sequential(
+            nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            nn.GELU(),
+            nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = nn.LayerNorm(cross_attention_dim)
+
+        self.perceiver_resampler = FacePerceiverResampler(
+            dim=cross_attention_dim,
+            depth=4,
+            dim_head=64,
+            heads=cross_attention_dim // 64,
+            embedding_dim=clip_embeddings_dim,
+            output_dim=cross_attention_dim,
+            ff_mult=4,
+        )
+
+    def forward(self, id_embeds, clip_embeds, scale=1.0, shortcut=False):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        out = self.perceiver_resampler(x, clip_embeds)
+        if shortcut:
+            out = x + scale * out
+        return out
+
+class ImageProjModel(nn.Module):
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+        super().__init__()
+
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, image_embeds):
+        embeds = image_embeds
+        x = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "comfyui_ipadapter_plus"
+description = "ComfyUI reference implementation for the IPAdapter models. The IPAdapter are very powerful models for image conditioning. The style and composition of a reference can be easily transferred to the generation. Think of it as a 1-image lora."
+version = "2.0.0"
+license = "GPL-3.0 license"
+
+[project.urls]
+Repository = "https://github.com/cubiq/ComfyUI_IPAdapter_plus"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "matteo"
+DisplayName = "ComfyUI_IPAdapter_plus"
+Icon = ""

ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/utils.py

@@ -0,0 +1,384 @@
+import re
+import torch
+import os
+import folder_paths
+from comfy.clip_vision import clip_preprocess, Output
+import comfy.utils
+import comfy.model_management as model_management
+try:
+    import torchvision.transforms.v2 as T
+except ImportError:
+    import torchvision.transforms as T
+
+def get_clipvision_file(preset):
+    preset = preset.lower()
+    clipvision_list = folder_paths.get_filename_list("clip_vision")
+
+    if preset.startswith("vit-g"):
+        pattern = r'(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl.*model\.(bin|safetensors))'
+    elif preset.startswith("kolors"):
+        pattern = r'(clip.vit.large.patch14.336\.(bin|safetensors))'
+    else:
+        pattern = r'(ViT.H.14.*s32B.b79K|ipadapter.*sd15|sd1.?5.*model\.(bin|safetensors))'
+    clipvision_file = [e for e in clipvision_list if re.search(pattern, e, re.IGNORECASE)]
+
+    clipvision_file = folder_paths.get_full_path("clip_vision", clipvision_file[0]) if clipvision_file else None
+
+    return clipvision_file
+
+def get_ipadapter_file(preset, is_sdxl):
+    preset = preset.lower()
+    ipadapter_list = folder_paths.get_filename_list("ipadapter")
+    is_insightface = False
+    lora_pattern = None
+
+    if preset.startswith("light"):
+        if is_sdxl:
+            raise Exception("light model is not supported for SDXL")
+        pattern = r'sd15.light.v11\.(safetensors|bin)$'
+        # if v11 is not found, try with the old version
+        if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]:
+            pattern = r'sd15.light\.(safetensors|bin)$'
+    elif preset.startswith("standard"):
+        if is_sdxl:
+            pattern = r'ip.adapter.sdxl.vit.h\.(safetensors|bin)$'
+        else:
+            pattern = r'ip.adapter.sd15\.(safetensors|bin)$'
+    elif preset.startswith("vit-g"):
+        if is_sdxl:
+            pattern = r'ip.adapter.sdxl\.(safetensors|bin)$'
+        else:
+            pattern = r'sd15.vit.g\.(safetensors|bin)$'
+    elif preset.startswith("plus ("):
+        if is_sdxl:
+            pattern = r'plus.sdxl.vit.h\.(safetensors|bin)$'
+        else:
+            pattern = r'ip.adapter.plus.sd15\.(safetensors|bin)$'
+    elif preset.startswith("plus face"):
+        if is_sdxl:
+            pattern = r'plus.face.sdxl.vit.h\.(safetensors|bin)$'
+        else:
+            pattern = r'plus.face.sd15\.(safetensors|bin)$'
+    elif preset.startswith("full"):
+        if is_sdxl:
+            raise Exception("full face model is not supported for SDXL")
+        pattern = r'full.face.sd15\.(safetensors|bin)$'
+    elif preset.startswith("faceid portrait ("):
+        if is_sdxl:
+            pattern = r'portrait.sdxl\.(safetensors|bin)$'
+        else:
+            pattern = r'portrait.v11.sd15\.(safetensors|bin)$'
+            # if v11 is not found, try with the old version
+            if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]:
+                pattern = r'portrait.sd15\.(safetensors|bin)$'
+        is_insightface = True
+    elif preset.startswith("faceid portrait unnorm"):
+        if is_sdxl:
+            pattern = r'portrait.sdxl.unnorm\.(safetensors|bin)$'
+        else:
+            raise Exception("portrait unnorm model is not supported for SD1.5")
+        is_insightface = True
+    elif preset == "faceid":
+        if is_sdxl:
+            pattern = r'faceid.sdxl\.(safetensors|bin)$'
+            lora_pattern = r'faceid.sdxl.lora\.safetensors$'
+        else:
+            pattern = r'faceid.sd15\.(safetensors|bin)$'
+            lora_pattern = r'faceid.sd15.lora\.safetensors$'
+        is_insightface = True
+    elif preset.startswith("faceid plus -"):
+        if is_sdxl:
+            raise Exception("faceid plus model is not supported for SDXL")
+        pattern = r'faceid.plus.sd15\.(safetensors|bin)$'
+        lora_pattern = r'faceid.plus.sd15.lora\.safetensors$'
+        is_insightface = True
+    elif preset.startswith("faceid plus v2"):
+        if is_sdxl:
+            pattern = r'faceid.plusv2.sdxl\.(safetensors|bin)$'
+            lora_pattern = r'faceid.plusv2.sdxl.lora\.safetensors$'
+        else:
+            pattern = r'faceid.plusv2.sd15\.(safetensors|bin)$'
+            lora_pattern = r'faceid.plusv2.sd15.lora\.safetensors$'
+        is_insightface = True
+    # Community's models
+    elif preset.startswith("composition"):
+        if is_sdxl:
+            pattern = r'plus.composition.sdxl\.safetensors$'
+        else:
+            pattern = r'plus.composition.sd15\.safetensors$'
+    elif preset.startswith("kolors"):
+        if is_sdxl:
+            pattern = r'(ip_adapter_plus_general|kolors.ip.adapter.plus)\.(safetensors|bin)$'
+        else:
+            raise Exception("Only supported for Kolors model")
+    else:
+        raise Exception(f"invalid type '{preset}'")
+
+    ipadapter_file = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]
+    ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file[0]) if ipadapter_file else None
+
+    return ipadapter_file, is_insightface, lora_pattern
+
+def get_lora_file(pattern):
+    lora_list = folder_paths.get_filename_list("loras")
+    lora_file = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)]
+    lora_file = folder_paths.get_full_path("loras", lora_file[0]) if lora_file else None
+
+    return lora_file
+
+def ipadapter_model_loader(file):
+    model = comfy.utils.load_torch_file(file, safe_load=True)
+
+    if file.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
+        del st_model
+
+    if not "ip_adapter" in model.keys() or not model["ip_adapter"]:
+        raise Exception("invalid IPAdapter model {}".format(file))
+
+    if 'plusv2' in file.lower():
+        model["faceidplusv2"] = True
+    
+    if 'unnorm' in file.lower():
+        model["portraitunnorm"] = True
+
+    return model
+
+def insightface_loader(provider):
+    try:
+        from insightface.app import FaceAnalysis
+    except ImportError as e:
+        raise Exception(e)
+
+    path = os.path.join(folder_paths.models_dir, "insightface")
+    model = FaceAnalysis(name="buffalo_l", root=path, providers=[provider + 'ExecutionProvider',])
+    model.prepare(ctx_id=0, det_size=(640, 640))
+    return model
+
+def split_tiles(embeds, num_split):
+    _, H, W, _ = embeds.shape
+    out = []
+    for x in embeds:
+        x = x.unsqueeze(0)
+        h, w = H // num_split, W // num_split
+        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)    
+        out.append(x_split)
+    
+    x_split = torch.stack(out, dim=0)
+    
+    return x_split
+
+def merge_hiddenstates(x, tiles):
+    chunk_size = tiles*tiles
+    x = x.split(chunk_size)
+
+    out = []
+    for embeds in x:
+        num_tiles = embeds.shape[0]
+        tile_size = int((embeds.shape[1]-1) ** 0.5)
+        grid_size = int(num_tiles ** 0.5)
+
+        # Extract class tokens
+        class_tokens = embeds[:, 0, :]  # Save class tokens: [num_tiles, embeds[-1]]
+        avg_class_token = class_tokens.mean(dim=0, keepdim=True).unsqueeze(0)  # Average token, shape: [1, 1, embeds[-1]]
+
+        patch_embeds = embeds[:, 1:, :]  # Shape: [num_tiles, tile_size^2, embeds[-1]]
+        reshaped = patch_embeds.reshape(grid_size, grid_size, tile_size, tile_size, embeds.shape[-1])
+
+        merged = torch.cat([torch.cat([reshaped[i, j] for j in range(grid_size)], dim=1) 
+                            for i in range(grid_size)], dim=0)
+        
+        merged = merged.unsqueeze(0)  # Shape: [1, grid_size*tile_size, grid_size*tile_size, embeds[-1]]
+        
+        # Pool to original size
+        pooled = torch.nn.functional.adaptive_avg_pool2d(merged.permute(0, 3, 1, 2), (tile_size, tile_size)).permute(0, 2, 3, 1)
+        flattened = pooled.reshape(1, tile_size*tile_size, embeds.shape[-1])
+        
+        # Add back the class token
+        with_class = torch.cat([avg_class_token, flattened], dim=1)  # Shape: original shape
+        out.append(with_class)
+    
+    out = torch.cat(out, dim=0)
+
+    return out
+
+def merge_embeddings(x, tiles): # TODO: this needs so much testing that I don't even
+    chunk_size = tiles*tiles
+    x = x.split(chunk_size)
+
+    out = []
+    for embeds in x:
+        num_tiles = embeds.shape[0]
+        grid_size = int(num_tiles ** 0.5)
+        tile_size = int(embeds.shape[1] ** 0.5)
+        reshaped = embeds.reshape(grid_size, grid_size, tile_size, tile_size)
+        
+        # Merge the tiles
+        merged = torch.cat([torch.cat([reshaped[i, j] for j in range(grid_size)], dim=1) 
+                            for i in range(grid_size)], dim=0)
+        
+        merged = merged.unsqueeze(0)  # Shape: [1, grid_size*tile_size, grid_size*tile_size]
+        
+        # Pool to original size
+        pooled = torch.nn.functional.adaptive_avg_pool2d(merged, (tile_size, tile_size))  # pool to [1, tile_size, tile_size]
+        pooled = pooled.flatten(1)  # flatten to [1, tile_size^2]
+        out.append(pooled)
+    out = torch.cat(out, dim=0)
+    
+    return out
+
+def encode_image_masked(clip_vision, image, mask=None, batch_size=0, tiles=1, ratio=1.0, clipvision_size=224):
+    # full image embeds
+    embeds = encode_image_masked_(clip_vision, image, mask, batch_size, clipvision_size=clipvision_size)
+    tiles = min(tiles, 16)
+
+    if tiles > 1:
+        # split in tiles
+        image_split = split_tiles(image, tiles)
+
+        # get the embeds for each tile
+        embeds_split = Output()
+        for i in image_split:
+            encoded = encode_image_masked_(clip_vision, i, mask, batch_size, clipvision_size=clipvision_size)
+            if not hasattr(embeds_split, "image_embeds"):
+                #embeds_split["last_hidden_state"] = encoded["last_hidden_state"]
+                embeds_split["image_embeds"] = encoded["image_embeds"]
+                embeds_split["penultimate_hidden_states"] = encoded["penultimate_hidden_states"]
+            else:
+                #embeds_split["last_hidden_state"] = torch.cat((embeds_split["last_hidden_state"], encoded["last_hidden_state"]), dim=0)
+                embeds_split["image_embeds"] = torch.cat((embeds_split["image_embeds"], encoded["image_embeds"]), dim=0)
+                embeds_split["penultimate_hidden_states"] = torch.cat((embeds_split["penultimate_hidden_states"], encoded["penultimate_hidden_states"]), dim=0)
+
+        #embeds_split['last_hidden_state'] = merge_hiddenstates(embeds_split['last_hidden_state'])
+        embeds_split["image_embeds"] = merge_embeddings(embeds_split["image_embeds"], tiles)
+        embeds_split["penultimate_hidden_states"] = merge_hiddenstates(embeds_split["penultimate_hidden_states"], tiles)
+
+        #embeds['last_hidden_state'] = torch.cat([embeds_split['last_hidden_state'], embeds['last_hidden_state']])
+        if embeds['image_embeds'].shape[0] > 1: # if we have more than one image we need to average the embeddings for consistency
+            embeds['image_embeds'] = embeds['image_embeds']*ratio + embeds_split['image_embeds']*(1-ratio)
+            embeds['penultimate_hidden_states'] = embeds['penultimate_hidden_states']*ratio + embeds_split['penultimate_hidden_states']*(1-ratio)
+            #embeds['image_embeds'] = (embeds['image_embeds']*ratio + embeds_split['image_embeds']) / 2
+            #embeds['penultimate_hidden_states'] = (embeds['penultimate_hidden_states']*ratio + embeds_split['penultimate_hidden_states']) / 2
+        else: # otherwise we can concatenate them, they can be averaged later
+            embeds['image_embeds'] = torch.cat([embeds['image_embeds']*ratio, embeds_split['image_embeds']])
+            embeds['penultimate_hidden_states'] = torch.cat([embeds['penultimate_hidden_states']*ratio, embeds_split['penultimate_hidden_states']])
+
+    #del embeds_split
+
+    return embeds
+
+def encode_image_masked_(clip_vision, image, mask=None, batch_size=0, clipvision_size=224):
+    model_management.load_model_gpu(clip_vision.patcher)
+    outputs = Output()
+
+    if batch_size == 0:
+        batch_size = image.shape[0]
+    elif batch_size > image.shape[0]:
+        batch_size = image.shape[0]
+
+    image_batch = torch.split(image, batch_size, dim=0)
+
+    for img in image_batch:
+        img = img.to(clip_vision.load_device)
+        pixel_values = clip_preprocess(img, size=clipvision_size).float()
+
+        # TODO: support for multiple masks
+        if mask is not None:
+            pixel_values = pixel_values * mask.to(clip_vision.load_device)
+
+        out = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
+
+        if not hasattr(outputs, "last_hidden_state"):
+            outputs["last_hidden_state"] = out[0].to(model_management.intermediate_device())
+            outputs["image_embeds"] = out[2].to(model_management.intermediate_device())
+            outputs["penultimate_hidden_states"] = out[1].to(model_management.intermediate_device())
+        else:
+            outputs["last_hidden_state"] = torch.cat((outputs["last_hidden_state"], out[0].to(model_management.intermediate_device())), dim=0)
+            outputs["image_embeds"] = torch.cat((outputs["image_embeds"], out[2].to(model_management.intermediate_device())), dim=0)
+            outputs["penultimate_hidden_states"] = torch.cat((outputs["penultimate_hidden_states"], out[1].to(model_management.intermediate_device())), dim=0)
+
+    del img, pixel_values, out
+    torch.cuda.empty_cache()
+
+    return outputs
+
+def tensor_to_size(source, dest_size):
+    if isinstance(dest_size, torch.Tensor):
+        dest_size = dest_size.shape[0]
+    source_size = source.shape[0]
+
+    if source_size < dest_size:
+        shape = [dest_size - source_size] + [1]*(source.dim()-1)
+        source = torch.cat((source, source[-1:].repeat(shape)), dim=0)
+    elif source_size > dest_size:
+        source = source[:dest_size]
+
+    return source
+
+def min_(tensor_list):
+    # return the element-wise min of the tensor list.
+    x = torch.stack(tensor_list)
+    mn = x.min(axis=0)[0]
+    return torch.clamp(mn, min=0)
+
+def max_(tensor_list):
+    # return the element-wise max of the tensor list.
+    x = torch.stack(tensor_list)
+    mx = x.max(axis=0)[0]
+    return torch.clamp(mx, max=1)
+
+# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
+def contrast_adaptive_sharpening(image, amount):
+    img = T.functional.pad(image, (1, 1, 1, 1)).cpu()
+
+    a = img[..., :-2, :-2]
+    b = img[..., :-2, 1:-1]
+    c = img[..., :-2, 2:]
+    d = img[..., 1:-1, :-2]
+    e = img[..., 1:-1, 1:-1]
+    f = img[..., 1:-1, 2:]
+    g = img[..., 2:, :-2]
+    h = img[..., 2:, 1:-1]
+    i = img[..., 2:, 2:]
+
+    # Computing contrast
+    cross = (b, d, e, f, h)
+    mn = min_(cross)
+    mx = max_(cross)
+
+    diag = (a, c, g, i)
+    mn2 = min_(diag)
+    mx2 = max_(diag)
+    mx = mx + mx2
+    mn = mn + mn2
+
+    # Computing local weight
+    inv_mx = torch.reciprocal(mx)
+    amp = inv_mx * torch.minimum(mn, (2 - mx))
+
+    # scaling
+    amp = torch.sqrt(amp)
+    w = - amp * (amount * (1/5 - 1/8) + 1/8)
+    div = torch.reciprocal(1 + 4*w)
+
+    output = ((b + d + f + h)*w + e) * div
+    output = torch.nan_to_num(output)
+    output = output.clamp(0, 1)
+
+    return output
+
+def tensor_to_image(tensor):
+    image = tensor.mul(255).clamp(0, 255).byte().cpu()
+    image = image[..., [2, 1, 0]].numpy()
+    return image
+
+def image_to_tensor(image):
+    tensor = torch.clamp(torch.from_numpy(image).float() / 255., 0, 1)
+    tensor = tensor[..., [2, 1, 0]]
+    return tensor

ComfyUI/custom_nodes/comfyui_segment_anything/.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

ComfyUI/custom_nodes/comfyui_segment_anything/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

ComfyUI/custom_nodes/comfyui_segment_anything/README.md

@@ -0,0 +1,66 @@
+# ComfyUI Segment Anything
+
+This project is a ComfyUI version of
+`https://github.com/continue-revolution/sd-webui-segment-anything`. At present, only the most core functionalities have been implemented. I would like to express my gratitude to [continue-revolution](https://github.com/continue-revolution) for their preceding work on which this is based.
+
+![example](./docs/images/example.jpg)
+
+I have ensured consistency with `sd-webui-segment-anything` in terms of output when given the same input.
+
+## Requirements
+
+Please ensure that you have installed Python dependencies using the following command:
+```
+pip3 install -r requirements.txt
+```
+
+## Models
+
+The models will be automatically downloaded when used. You can also manually download them according to the table below. If the automatic download is slow, you can set the `HTTP_PROXY` and `HTTPS_PROXY` environment variables to use a proxy.
+
+### bert-base-uncased
+
+You can download the model from https://huggingface.co/bert-base-uncased/tree/main into the `models/bert-base-uncased` folder located in the root directory of ComfyUI, like this:
+
+```
+ComfyUI
+    models
+        bert-base-uncased
+            config.json
+            model.safetensors
+            tokenizer_config.json
+            tokenizer.json
+            vocab.txt
+```
+
+You can also skip this step. During the inference process, `bert-base-uncased` will be automatically downloaded through the `transformers` library, and its directory is typically `~/.cache/huggingface/hub/models--bert-base-uncased`.
+
+### GroundingDino
+
+Please directly download the models and configuration files to the `models/grounding-dino` directory under the ComfyUI root directory, without modifying the file names.
+
+| name | size | config file | model file |  
+|-|-|-|-|
+| GroundingDINO_SwinT_OGC | 694MB | [download link](https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GroundingDINO_SwinT_OGC.cfg.py) | [download link](https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth) |
+| GroundingDINO_SwinB | 938MB | [download link](https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GroundingDINO_SwinB.cfg.py) | [download link](https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth) |
+
+### SAM
+
+Please directly download the model files to the `models/sams` directory under the ComfyUI root directory, without modifying the file names.
+
+| name | size | model file |
+|-|-|-|  
+| sam_vit_h | 2.56GB |[download link](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth) |
+| sam_vit_l | 1.25GB |[download link](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth) |
+| sam_vit_b | 375MB |[download link](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth) |  
+| sam_hq_vit_h | 2.57GB |[download link](https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_h.pth) |
+| sam_hq_vit_l | 1.25GB |[download link](https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth) |
+| sam_hq_vit_b | 379MB |[download link](https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_b.pth) |
+| mobile_sam | 39MB |[download link](https://github.com/ChaoningZhang/MobileSAM/blob/master/weights/mobile_sam.pt) |
+
+
+## Contribution
+
+Thank you for considering to help out with the source code! Welcome contributions from anyone on the internet, and are grateful for even the smallest of fixes!
+
+If you'd like to contribute to this project, please fork, fix, commit and send a pull request for me to review and merge into the main code base.

ComfyUI/custom_nodes/comfyui_segment_anything/__init__.py

@@ -0,0 +1,14 @@
+from .node import *
+from .install import *
+
+NODE_CLASS_MAPPINGS = {
+    'SAMModelLoader (segment anything)': SAMModelLoader,
+    'GroundingDinoModelLoader (segment anything)': GroundingDinoModelLoader,
+    'GroundingDinoSAMSegment (segment anything)': GroundingDinoSAMSegment,
+    'InvertMask (segment anything)': InvertMask,
+    "IsMaskEmpty": IsMaskEmptyNode,
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS']
+
+

ComfyUI/custom_nodes/comfyui_segment_anything/install.py

@@ -0,0 +1,18 @@
+import sys
+import os.path
+import subprocess
+
+custom_nodes_path = os.path.dirname(os.path.abspath(__file__))
+
+def build_pip_install_cmds(args):
+    if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
+        return [sys.executable, '-s', '-m', 'pip', 'install'] + args
+    else:
+        return [sys.executable, '-m', 'pip', 'install'] + args
+
+def ensure_package():
+    cmds = build_pip_install_cmds(['-r', 'requirements.txt'])
+    subprocess.run(cmds, cwd=custom_nodes_path)
+
+if __name__ == "__main__":
+    ensure_package()

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/datasets/transforms.py

@@ -0,0 +1,311 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Transforms and data augmentation for both image + bbox.
+"""
+import os
+import random
+
+import PIL
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as F
+
+from local_groundingdino.util.box_ops import box_xyxy_to_cxcywh
+from local_groundingdino.util.misc import interpolate
+
+
+def crop(image, target, region):
+    cropped_image = F.crop(image, *region)
+
+    target = target.copy()
+    i, j, h, w = region
+
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor([h, w])
+
+    fields = ["labels", "area", "iscrowd", "positive_map"]
+
+    if "boxes" in target:
+        boxes = target["boxes"]
+        max_size = torch.as_tensor([w, h], dtype=torch.float32)
+        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
+        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
+        cropped_boxes = cropped_boxes.clamp(min=0)
+        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
+        target["boxes"] = cropped_boxes.reshape(-1, 4)
+        target["area"] = area
+        fields.append("boxes")
+
+    if "masks" in target:
+        # FIXME should we update the area here if there are no boxes?
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
+        fields.append("masks")
+
+    # remove elements for which the boxes or masks that have zero area
+    if "boxes" in target or "masks" in target:
+        # favor boxes selection when defining which elements to keep
+        # this is compatible with previous implementation
+        if "boxes" in target:
+            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
+            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
+        else:
+            keep = target["masks"].flatten(1).any(1)
+
+        for field in fields:
+            if field in target:
+                target[field] = target[field][keep]
+
+    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
+        # for debug and visualization only.
+        if "strings_positive" in target:
+            target["strings_positive"] = [
+                _i for _i, _j in zip(target["strings_positive"], keep) if _j
+            ]
+
+    return cropped_image, target
+
+
+def hflip(image, target):
+    flipped_image = F.hflip(image)
+
+    w, h = image.size
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
+            [w, 0, w, 0]
+        )
+        target["boxes"] = boxes
+
+    if "masks" in target:
+        target["masks"] = target["masks"].flip(-1)
+
+    return flipped_image, target
+
+
+def resize(image, target, size, max_size=None):
+    # size can be min_size (scalar) or (w, h) tuple
+
+    def get_size_with_aspect_ratio(image_size, size, max_size=None):
+        w, h = image_size
+        if max_size is not None:
+            min_original_size = float(min((w, h)))
+            max_original_size = float(max((w, h)))
+            if max_original_size / min_original_size * size > max_size:
+                size = int(round(max_size * min_original_size / max_original_size))
+
+        if (w <= h and w == size) or (h <= w and h == size):
+            return (h, w)
+
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+        else:
+            oh = size
+            ow = int(size * w / h)
+
+        return (oh, ow)
+
+    def get_size(image_size, size, max_size=None):
+        if isinstance(size, (list, tuple)):
+            return size[::-1]
+        else:
+            return get_size_with_aspect_ratio(image_size, size, max_size)
+
+    size = get_size(image.size, size, max_size)
+    rescaled_image = F.resize(image, size)
+
+    if target is None:
+        return rescaled_image, None
+
+    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+    ratio_width, ratio_height = ratios
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        scaled_boxes = boxes * torch.as_tensor(
+            [ratio_width, ratio_height, ratio_width, ratio_height]
+        )
+        target["boxes"] = scaled_boxes
+
+    if "area" in target:
+        area = target["area"]
+        scaled_area = area * (ratio_width * ratio_height)
+        target["area"] = scaled_area
+
+    h, w = size
+    target["size"] = torch.tensor([h, w])
+
+    if "masks" in target:
+        target["masks"] = (
+            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+        )
+
+    return rescaled_image, target
+
+
+def pad(image, target, padding):
+    # assumes that we only pad on the bottom right corners
+    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
+    if target is None:
+        return padded_image, None
+    target = target.copy()
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor(padded_image.size[::-1])
+    if "masks" in target:
+        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+    return padded_image, target
+
+
+class ResizeDebug(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        return resize(img, target, self.size)
+
+
+class RandomCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        region = T.RandomCrop.get_params(img, self.size)
+        return crop(img, target, region)
+
+
+class RandomSizeCrop(object):
+    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
+        # respect_boxes:    True to keep all boxes
+        #                   False to tolerence box filter
+        self.min_size = min_size
+        self.max_size = max_size
+        self.respect_boxes = respect_boxes
+
+    def __call__(self, img: PIL.Image.Image, target: dict):
+        init_boxes = len(target["boxes"])
+        max_patience = 10
+        for i in range(max_patience):
+            w = random.randint(self.min_size, min(img.width, self.max_size))
+            h = random.randint(self.min_size, min(img.height, self.max_size))
+            region = T.RandomCrop.get_params(img, [h, w])
+            result_img, result_target = crop(img, target, region)
+            if (
+                not self.respect_boxes
+                or len(result_target["boxes"]) == init_boxes
+                or i == max_patience - 1
+            ):
+                return result_img, result_target
+        return result_img, result_target
+
+
+class CenterCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        image_width, image_height = img.size
+        crop_height, crop_width = self.size
+        crop_top = int(round((image_height - crop_height) / 2.0))
+        crop_left = int(round((image_width - crop_width) / 2.0))
+        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return hflip(img, target)
+        return img, target
+
+
+class RandomResize(object):
+    def __init__(self, sizes, max_size=None):
+        assert isinstance(sizes, (list, tuple))
+        self.sizes = sizes
+        self.max_size = max_size
+
+    def __call__(self, img, target=None):
+        size = random.choice(self.sizes)
+        return resize(img, target, size, self.max_size)
+
+
+class RandomPad(object):
+    def __init__(self, max_pad):
+        self.max_pad = max_pad
+
+    def __call__(self, img, target):
+        pad_x = random.randint(0, self.max_pad)
+        pad_y = random.randint(0, self.max_pad)
+        return pad(img, target, (pad_x, pad_y))
+
+
+class RandomSelect(object):
+    """
+    Randomly selects between transforms1 and transforms2,
+    with probability p for transforms1 and (1 - p) for transforms2
+    """
+
+    def __init__(self, transforms1, transforms2, p=0.5):
+        self.transforms1 = transforms1
+        self.transforms2 = transforms2
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return self.transforms1(img, target)
+        return self.transforms2(img, target)
+
+
+class ToTensor(object):
+    def __call__(self, img, target):
+        return F.to_tensor(img), target
+
+
+class RandomErasing(object):
+    def __init__(self, *args, **kwargs):
+        self.eraser = T.RandomErasing(*args, **kwargs)
+
+    def __call__(self, img, target):
+        return self.eraser(img), target
+
+
+class Normalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image, target=None):
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        if target is None:
+            return image, None
+        target = target.copy()
+        h, w = image.shape[-2:]
+        if "boxes" in target:
+            boxes = target["boxes"]
+            boxes = box_xyxy_to_cxcywh(boxes)
+            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
+            target["boxes"] = boxes
+        return image, target
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(t)
+        format_string += "\n)"
+        return format_string

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/__init__.py

@@ -0,0 +1,15 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from .groundingdino import build_groundingdino

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -0,0 +1 @@
+from .backbone import build_backbone

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -0,0 +1,221 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Backbone modules.
+"""
+
+from typing import Dict, List
+
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+
+from local_groundingdino.util.misc import NestedTensor, is_main_process
+
+from .position_encoding import build_position_encoding
+from .swin_transformer import build_swin_transformer
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+    without which any other models than torchvision.models.resnet[18,34,50,101]
+    produce nans.
+    """
+
+    def __init__(self, n):
+        super(FrozenBatchNorm2d, self).__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        num_batches_tracked_key = prefix + "num_batches_tracked"
+        if num_batches_tracked_key in state_dict:
+            del state_dict[num_batches_tracked_key]
+
+        super(FrozenBatchNorm2d, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
+    def forward(self, x):
+        # move reshapes to the beginning
+        # to make it fuser-friendly
+        w = self.weight.reshape(1, -1, 1, 1)
+        b = self.bias.reshape(1, -1, 1, 1)
+        rv = self.running_var.reshape(1, -1, 1, 1)
+        rm = self.running_mean.reshape(1, -1, 1, 1)
+        eps = 1e-5
+        scale = w * (rv + eps).rsqrt()
+        bias = b - rm * scale
+        return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+    def __init__(
+        self,
+        backbone: nn.Module,
+        train_backbone: bool,
+        num_channels: int,
+        return_interm_indices: list,
+    ):
+        super().__init__()
+        for name, parameter in backbone.named_parameters():
+            if (
+                not train_backbone
+                or "layer2" not in name
+                and "layer3" not in name
+                and "layer4" not in name
+            ):
+                parameter.requires_grad_(False)
+
+        return_layers = {}
+        for idx, layer_index in enumerate(return_interm_indices):
+            return_layers.update(
+                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
+            )
+
+        # if len:
+        #     if use_stage1_feature:
+        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+        #     else:
+        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
+        # else:
+        #     return_layers = {'layer4': "0"}
+        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+        self.num_channels = num_channels
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self.body(tensor_list.tensors)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        # import ipdb; ipdb.set_trace()
+        return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+
+    def __init__(
+        self,
+        name: str,
+        train_backbone: bool,
+        dilation: bool,
+        return_interm_indices: list,
+        batch_norm=FrozenBatchNorm2d,
+    ):
+        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
+            backbone = getattr(torchvision.models, name)(
+                replace_stride_with_dilation=[False, False, dilation],
+                pretrained=is_main_process(),
+                norm_layer=batch_norm,
+            )
+        else:
+            raise NotImplementedError("Why you can get here with name {}".format(name))
+        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
+        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
+        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+        num_channels_all = [256, 512, 1024, 2048]
+        num_channels = num_channels_all[4 - len(return_interm_indices) :]
+        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in xs.items():
+            out.append(x)
+            # position encoding
+            pos.append(self[1](x).to(x.tensors.dtype))
+
+        return out, pos
+
+
+def build_backbone(args):
+    """
+    Useful args:
+        - backbone: backbone name
+        - lr_backbone:
+        - dilation
+        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
+        - backbone_freeze_keywords:
+        - use_checkpoint: for swin only for now
+
+    """
+    position_embedding = build_position_encoding(args)
+    train_backbone = True
+    if not train_backbone:
+        raise ValueError("Please set lr_backbone > 0")
+    return_interm_indices = args.return_interm_indices
+    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+    args.backbone_freeze_keywords
+    use_checkpoint = getattr(args, "use_checkpoint", False)
+
+    if args.backbone in ["resnet50", "resnet101"]:
+        backbone = Backbone(
+            args.backbone,
+            train_backbone,
+            args.dilation,
+            return_interm_indices,
+            batch_norm=FrozenBatchNorm2d,
+        )
+        bb_num_channels = backbone.num_channels
+    elif args.backbone in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]:
+        pretrain_img_size = int(args.backbone.split("_")[-2])
+        backbone = build_swin_transformer(
+            args.backbone,
+            pretrain_img_size=pretrain_img_size,
+            out_indices=tuple(return_interm_indices),
+            dilation=False,
+            use_checkpoint=use_checkpoint,
+        )
+
+        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
+    else:
+        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
+
+    assert len(bb_num_channels) == len(
+        return_interm_indices
+    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
+
+    model = Joiner(backbone, position_embedding)
+    model.num_channels = bb_num_channels
+    assert isinstance(
+        bb_num_channels, List
+    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
+    # import ipdb; ipdb.set_trace()
+    return model

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -0,0 +1,186 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+from local_groundingdino.util.misc import NestedTensor
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            # if os.environ.get("SHILONG_AMP", None) == '1':
+            #     eps = 1e-4
+            # else:
+            #     eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+
+class PositionEmbeddingSineHW(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
+    ):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperatureH = temperatureH
+        self.temperatureW = temperatureW
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+
+        # import ipdb; ipdb.set_trace()
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
+        pos_x = x_embed[:, :, :, None] / dim_tx
+
+        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
+        pos_y = y_embed[:, :, :, None] / dim_ty
+
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+        # import ipdb; ipdb.set_trace()
+
+        return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, num_pos_feats=256):
+        super().__init__()
+        self.row_embed = nn.Embedding(50, num_pos_feats)
+        self.col_embed = nn.Embedding(50, num_pos_feats)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.row_embed.weight)
+        nn.init.uniform_(self.col_embed.weight)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        h, w = x.shape[-2:]
+        i = torch.arange(w, device=x.device)
+        j = torch.arange(h, device=x.device)
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+        pos = (
+            torch.cat(
+                [
+                    x_emb.unsqueeze(0).repeat(h, 1, 1),
+                    y_emb.unsqueeze(1).repeat(1, w, 1),
+                ],
+                dim=-1,
+            )
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+            .repeat(x.shape[0], 1, 1, 1)
+        )
+        return pos
+
+
+def build_position_encoding(args):
+    N_steps = args.hidden_dim // 2
+    if args.position_embedding in ("v2", "sine"):
+        # TODO find a better way of exposing other arguments
+        position_embedding = PositionEmbeddingSineHW(
+            N_steps,
+            temperatureH=args.pe_temperatureH,
+            temperatureW=args.pe_temperatureW,
+            normalize=True,
+        )
+    elif args.position_embedding in ("v3", "learned"):
+        position_embedding = PositionEmbeddingLearned(N_steps)
+    else:
+        raise ValueError(f"not supported {args.position_embedding}")
+
+    return position_embedding

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -0,0 +1,802 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# --------------------------------------------------------
+# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from local_groundingdino.util.misc import NestedTensor
+
+
+class Mlp(nn.Module):
+    """Multilayer perceptron."""
+
+    def __init__(
+        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    """Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(
+        self,
+        dim,
+        window_size,
+        num_heads,
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        window_size=7,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=to_2tuple(self.window_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+        )
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size
+        )  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(
+            -1, self.window_size * self.window_size, C
+        )  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        dim,
+        depth,
+        num_heads,
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        downsample=None,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    shift_size=0 if (i % 2 == 0) else window_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        w_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size
+        )  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+            attn_mask == 0, float(0.0)
+        )
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
+    """
+
+    def __init__(
+        self,
+        pretrain_img_size=224,
+        patch_size=4,
+        in_chans=3,
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        norm_layer=nn.LayerNorm,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        dilation=False,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.dilation = dilation
+
+        # if use_checkpoint:
+        #     print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,
+        )
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1],
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+            )
+            trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        # prepare downsample list
+        downsamplelist = [PatchMerging for i in range(self.num_layers)]
+        downsamplelist[-1] = None
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        if self.dilation:
+            downsamplelist[-2] = None
+            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                # dim=int(embed_dim * 2 ** i_layer),
+                dim=num_features[i_layer],
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                norm_layer=norm_layer,
+                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                downsample=downsamplelist[i_layer],
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+
+        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f"norm{i_layer}"
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    # def init_weights(self, pretrained=None):
+    #     """Initialize the weights in backbone.
+    #     Args:
+    #         pretrained (str, optional): Path to pre-trained weights.
+    #             Defaults to None.
+    #     """
+
+    #     def _init_weights(m):
+    #         if isinstance(m, nn.Linear):
+    #             trunc_normal_(m.weight, std=.02)
+    #             if isinstance(m, nn.Linear) and m.bias is not None:
+    #                 nn.init.constant_(m.bias, 0)
+    #         elif isinstance(m, nn.LayerNorm):
+    #             nn.init.constant_(m.bias, 0)
+    #             nn.init.constant_(m.weight, 1.0)
+
+    #     if isinstance(pretrained, str):
+    #         self.apply(_init_weights)
+    #         logger = get_root_logger()
+    #         load_checkpoint(self, pretrained, strict=False, logger=logger)
+    #     elif pretrained is None:
+    #         self.apply(_init_weights)
+    #     else:
+    #         raise TypeError('pretrained must be a str or None')
+
+    def forward_raw(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+            # import ipdb; ipdb.set_trace()
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # outs:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+        return tuple(outs)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # out:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+
+        # collect for nesttensors
+        outs_dict = {}
+        for idx, out_i in enumerate(outs):
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
+            outs_dict[idx] = NestedTensor(out_i, mask)
+
+        return outs_dict
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+def build_swin_transformer(modelname, pretrain_img_size, **kw):
+    assert modelname in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]
+
+    model_para_dict = {
+        "swin_T_224_1k": dict(
+            embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
+        ),
+        "swin_B_224_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
+        ),
+        "swin_B_384_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
+        ),
+        "swin_L_224_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
+        ),
+        "swin_L_384_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
+        ),
+    }
+    kw_cgf = model_para_dict[modelname]
+    kw_cgf.update(kw)
+    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
+    return model
+
+
+if __name__ == "__main__":
+    model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
+    x = torch.rand(2, 3, 1024, 1024)
+    y = model.forward_raw(x)
+    import ipdb
+
+    ipdb.set_trace()
+    x = torch.rand(2, 3, 384, 384)
+    y = model.forward_raw(x)

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/bertwarper.py

@@ -0,0 +1,269 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+from torch import nn
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+
+
+class BertModelWarper(nn.Module):
+    def __init__(self, bert_model):
+        super().__init__()
+        # self.bert = bert_modelc
+
+        self.config = bert_model.config
+        self.embeddings = bert_model.embeddings
+        self.encoder = bert_model.encoder
+        self.pooler = bert_model.pooler
+
+        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
+        self.invert_attention_mask = bert_model.invert_attention_mask
+        self.get_head_mask = bert_model.get_head_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, device
+        )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class TextEncoderShell(nn.Module):
+    def __init__(self, text_encoder):
+        super().__init__()
+        self.text_encoder = text_encoder
+        self.config = self.text_encoder.config
+
+    def forward(self, **kw):
+        # feed into text encoder
+        return self.text_encoder(**kw)
+
+
+def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+
+        previous_col = col
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long)
+
+
+def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    cate_to_token_mask_list = [[] for _ in range(bs)]
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
+            c2t_maski[previous_col + 1 : col] = True
+            cate_to_token_mask_list[row].append(c2t_maski)
+        previous_col = col
+
+    cate_to_token_mask_list = [
+        torch.stack(cate_to_token_mask_listi, dim=0)
+        for cate_to_token_mask_listi in cate_to_token_mask_list
+    ]
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/fuse_modules.py

@@ -0,0 +1,297 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+
+
+class FeatureResizer(nn.Module):
+    """
+    This class takes as input a set of embeddings of dimension C1 and outputs a set of
+    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
+    """
+
+    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
+        super().__init__()
+        self.do_ln = do_ln
+        # Object feature encoding
+        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
+        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, encoder_features):
+        x = self.fc(encoder_features)
+        if self.do_ln:
+            x = self.layer_norm(x)
+        output = self.dropout(x)
+        return output
+
+
+def l1norm(X, dim, eps=1e-8):
+    """L1-normalize columns of X"""
+    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X"""
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
+    """
+    query: (n_context, queryL, d)
+    context: (n_context, sourceL, d)
+    """
+    batch_size_q, queryL = query.size(0), query.size(1)
+    batch_size, sourceL = context.size(0), context.size(1)
+
+    # Get attention
+    # --> (batch, d, queryL)
+    queryT = torch.transpose(query, 1, 2)
+
+    # (batch, sourceL, d)(batch, d, queryL)
+    # --> (batch, sourceL, queryL)
+    attn = torch.bmm(context, queryT)
+    if raw_feature_norm == "softmax":
+        # --> (batch*sourceL, queryL)
+        attn = attn.view(batch_size * sourceL, queryL)
+        attn = nn.Softmax()(attn)
+        # --> (batch, sourceL, queryL)
+        attn = attn.view(batch_size, sourceL, queryL)
+    elif raw_feature_norm == "l2norm":
+        attn = l2norm(attn, 2)
+    elif raw_feature_norm == "clipped_l2norm":
+        attn = nn.LeakyReLU(0.1)(attn)
+        attn = l2norm(attn, 2)
+    else:
+        raise ValueError("unknown first norm type:", raw_feature_norm)
+    # --> (batch, queryL, sourceL)
+    attn = torch.transpose(attn, 1, 2).contiguous()
+    # --> (batch*queryL, sourceL)
+    attn = attn.view(batch_size * queryL, sourceL)
+    attn = nn.Softmax()(attn * smooth)
+    # --> (batch, queryL, sourceL)
+    attn = attn.view(batch_size, queryL, sourceL)
+    # --> (batch, sourceL, queryL)
+    attnT = torch.transpose(attn, 1, 2).contiguous()
+
+    # --> (batch, d, sourceL)
+    contextT = torch.transpose(context, 1, 2)
+    # (batch x d x sourceL)(batch x sourceL x queryL)
+    # --> (batch, d, queryL)
+    weightedContext = torch.bmm(contextT, attnT)
+    # --> (batch, queryL, d)
+    weightedContext = torch.transpose(weightedContext, 1, 2)
+
+    return weightedContext, attnT
+
+
+class BiMultiHeadAttention(nn.Module):
+    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+        super(BiMultiHeadAttention, self).__init__()
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.v_dim = v_dim
+        self.l_dim = l_dim
+
+        assert (
+            self.head_dim * self.num_heads == self.embed_dim
+        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+        self.scale = self.head_dim ** (-0.5)
+        self.dropout = dropout
+
+        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+        self.stable_softmax_2d = True
+        self.clamp_min_for_underflow = True
+        self.clamp_max_for_overflow = True
+
+        self._reset_parameters()
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _reset_parameters(self):
+        nn.init.xavier_uniform_(self.v_proj.weight)
+        self.v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.l_proj.weight)
+        self.l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_v_proj.weight)
+        self.values_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_l_proj.weight)
+        self.values_l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_v_proj.weight)
+        self.out_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_l_proj.weight)
+        self.out_l_proj.bias.data.fill_(0)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        """_summary_
+
+        Args:
+            v (_type_): bs, n_img, dim
+            l (_type_): bs, n_text, dim
+            attention_mask_v (_type_, optional): _description_. bs, n_img
+            attention_mask_l (_type_, optional): _description_. bs, n_text
+
+        Returns:
+            _type_: _description_
+        """
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        bsz, tgt_len, _ = v.size()
+
+        query_states = self.v_proj(v) * self.scale
+        key_states = self._shape(self.l_proj(l), -1, bsz)
+        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_v_states = value_v_states.view(*proj_shape)
+        value_l_states = value_l_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
+
+        if self.stable_softmax_2d:
+            attn_weights = attn_weights - attn_weights.max()
+
+        if self.clamp_min_for_underflow:
+            attn_weights = torch.clamp(
+                attn_weights, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights = torch.clamp(
+                attn_weights, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        attn_weights_T = attn_weights.transpose(1, 2)
+        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        if self.clamp_min_for_underflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        # mask vison for language
+        if attention_mask_v is not None:
+            attention_mask_v = (
+                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+        attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+        # mask language for vision
+        if attention_mask_l is not None:
+            attention_mask_l = (
+                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+        attn_weights_v = attn_weights.softmax(dim=-1)
+
+        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+            )
+
+        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+            )
+
+        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output_v = attn_output_v.transpose(1, 2)
+        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+        attn_output_l = attn_output_l.transpose(1, 2)
+        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+        attn_output_v = self.out_v_proj(attn_output_v)
+        attn_output_l = self.out_l_proj(attn_output_l)
+
+        return attn_output_v, attn_output_l
+
+
+# Bi-Direction MHA (text->image, image->text)
+class BiAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        l_dim,
+        embed_dim,
+        num_heads,
+        dropout=0.1,
+        drop_path=0.0,
+        init_values=1e-4,
+        cfg=None,
+    ):
+        """
+        Inputs:
+            embed_dim - Dimensionality of input and attention feature vectors
+            hidden_dim - Dimensionality of hidden layer in feed-forward network
+                         (usually 2-4x larger than embed_dim)
+            num_heads - Number of heads to use in the Multi-Head Attention block
+            dropout - Amount of dropout to apply in the feed-forward network
+        """
+        super(BiAttentionBlock, self).__init__()
+
+        # pre layer norm
+        self.layer_norm_v = nn.LayerNorm(v_dim)
+        self.layer_norm_l = nn.LayerNorm(l_dim)
+        self.attn = BiMultiHeadAttention(
+            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+        )
+
+        # add layer scale for training stability
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
+        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        v = self.layer_norm_v(v)
+        l = self.layer_norm_l(l)
+        delta_v, delta_l = self.attn(
+            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+        )
+        # v, l = v + delta_v, l + delta_l
+        v = v + self.drop_path(self.gamma_v * delta_v)
+        l = l + self.drop_path(self.gamma_l * delta_l)
+        return v, l
+
+    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)

ComfyUI/custom_nodes/comfyui_segment_anything/local_groundingdino/models/GroundingDINO/groundingdino.py

@@ -0,0 +1,385 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR model and criterion classes.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------
+import copy
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from local_groundingdino.util import get_tokenlizer
+from local_groundingdino.util.misc import (
+    NestedTensor,
+    inverse_sigmoid,
+    nested_tensor_from_tensor_list,
+)
+
+from ..registry import MODULE_BUILD_FUNCS
+from .backbone import build_backbone
+from .bertwarper import (
+    BertModelWarper,
+    generate_masks_with_special_tokens_and_transfer_map,
+)
+from .transformer import build_transformer
+from .utils import MLP, ContrastiveEmbed
+
+
+class GroundingDINO(nn.Module):
+    """This is the Cross-Attention Detector module that performs object detection"""
+
+    def __init__(
+        self,
+        backbone,
+        transformer,
+        num_queries,
+        aux_loss=False,
+        iter_update=False,
+        query_dim=2,
+        num_feature_levels=1,
+        nheads=8,
+        # two stage
+        two_stage_type="no",  # ['no', 'standard']
+        dec_pred_bbox_embed_share=True,
+        two_stage_class_embed_share=True,
+        two_stage_bbox_embed_share=True,
+        num_patterns=0,
+        dn_number=100,
+        dn_box_noise_scale=0.4,
+        dn_label_noise_ratio=0.5,
+        dn_labelbook_size=100,
+        text_encoder_type="bert-base-uncased",
+        sub_sentence_present=True,
+        max_text_len=256,
+    ):
+        """Initializes the model.
+        Parameters:
+            backbone: torch module of the backbone to be used. See backbone.py
+            transformer: torch module of the transformer architecture. See transformer.py
+            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
+                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
+            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
+        """
+        super().__init__()
+        self.num_queries = num_queries
+        self.transformer = transformer
+        self.hidden_dim = hidden_dim = transformer.d_model
+        self.num_feature_levels = num_feature_levels
+        self.nheads = nheads
+        self.max_text_len = 256
+        self.sub_sentence_present = sub_sentence_present
+
+        # setting query dim
+        self.query_dim = query_dim
+        assert query_dim == 4
+
+        # for dn training
+        self.num_patterns = num_patterns
+        self.dn_number = dn_number
+        self.dn_box_noise_scale = dn_box_noise_scale
+        self.dn_label_noise_ratio = dn_label_noise_ratio
+        self.dn_labelbook_size = dn_labelbook_size
+
+        # bert
+        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
+        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
+        self.bert.pooler.dense.weight.requires_grad_(False)
+        self.bert.pooler.dense.bias.requires_grad_(False)
+        self.bert = BertModelWarper(bert_model=self.bert)
+
+        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
+        nn.init.constant_(self.feat_map.bias.data, 0)
+        nn.init.xavier_uniform_(self.feat_map.weight.data)
+        # freeze
+
+        # special tokens
+        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+
+        # prepare input projection layers
+        if num_feature_levels > 1:
+            num_backbone_outs = len(backbone.num_channels)
+            input_proj_list = []
+            for _ in range(num_backbone_outs):
+                in_channels = backbone.num_channels[_]
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+            for _ in range(num_feature_levels - num_backbone_outs):
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+                in_channels = hidden_dim
+            self.input_proj = nn.ModuleList(input_proj_list)
+        else:
+            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
+            self.input_proj = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                ]
+            )
+
+        self.backbone = backbone
+        self.aux_loss = aux_loss
+        self.box_pred_damping = box_pred_damping = None
+
+        self.iter_update = iter_update
+        assert iter_update, "Why not iter_update?"
+
+        # prepare pred layers
+        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
+        # prepare class & box embed
+        _class_embed = ContrastiveEmbed()
+
+        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
+        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
+        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
+
+        if dec_pred_bbox_embed_share:
+            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
+        else:
+            box_embed_layerlist = [
+                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
+            ]
+        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
+        self.bbox_embed = nn.ModuleList(box_embed_layerlist)
+        self.class_embed = nn.ModuleList(class_embed_layerlist)
+        self.transformer.decoder.bbox_embed = self.bbox_embed
+        self.transformer.decoder.class_embed = self.class_embed
+
+        # two stage
+        self.two_stage_type = two_stage_type
+        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        if two_stage_type != "no":
+            if two_stage_bbox_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_bbox_embed = _bbox_embed
+            else:
+                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
+
+            if two_stage_class_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_class_embed = _class_embed
+            else:
+                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
+
+            self.refpoint_embed = None
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        # init input_proj
+        for proj in self.input_proj:
+            nn.init.xavier_uniform_(proj[0].weight, gain=1)
+            nn.init.constant_(proj[0].bias, 0)
+
+    def init_ref_points(self, use_num_queries):
+        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
+
+    def forward(self, samples: NestedTensor, targets: List = None, **kw):
+        """The forward expects a NestedTensor, which consists of:
+           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
+           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
+
+        It returns a dict with the following elements:
+           - "pred_logits": the classification logits (including no-object) for all queries.
+                            Shape= [batch_size x num_queries x num_classes]
+           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
+                           (center_x, center_y, width, height). These values are normalized in [0, 1],
+                           relative to the size of each individual image (disregarding possible padding).
+                           See PostProcess for information on how to retrieve the unnormalized bounding box.
+           - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
+                            dictionnaries containing the two above keys for each decoder layer.
+        """
+        if targets is None:
+            captions = kw["captions"]
+        else:
+            captions = [t["caption"] for t in targets]
+        len(captions)
+
+        # encoder texts
+        tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
+            samples.device
+        )
+        (
+            text_self_attention_masks,
+            position_ids,
+            cate_to_token_mask_list,
+        ) = generate_masks_with_special_tokens_and_transfer_map(
+            tokenized, self.specical_tokens, self.tokenizer
+        )
+
+        if text_self_attention_masks.shape[1] > self.max_text_len:
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+            position_ids = position_ids[:, : self.max_text_len]
+            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
+            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
+            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+
+        # extract text embeddings
+        if self.sub_sentence_present:
+            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+            tokenized_for_encoder["attention_mask"] = text_self_attention_masks
+            tokenized_for_encoder["position_ids"] = position_ids
+        else:
+            # import ipdb; ipdb.set_trace()
+            tokenized_for_encoder = tokenized
+
+        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
+
+        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
+        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
+        # text_token_mask: True for nomask, False for mask
+        # text_self_attention_masks: True for nomask, False for mask
+
+        if encoded_text.shape[1] > self.max_text_len:
+            encoded_text = encoded_text[:, : self.max_text_len, :]
+            text_token_mask = text_token_mask[:, : self.max_text_len]
+            position_ids = position_ids[:, : self.max_text_len]
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+
+        text_dict = {
+            "encoded_text": encoded_text,  # bs, 195, d_model
+            "text_token_mask": text_token_mask,  # bs, 195
+            "position_ids": position_ids,  # bs, 195
+            "text_self_attention_masks": text_self_attention_masks,  # bs, 195,195
+        }
+
+        # import ipdb; ipdb.set_trace()
+
+        if isinstance(samples, (list, torch.Tensor)):
+            samples = nested_tensor_from_tensor_list(samples)
+        features, poss = self.backbone(samples)
+
+        srcs = []
+        masks = []
+        for l, feat in enumerate(features):
+            src, mask = feat.decompose()
+            srcs.append(self.input_proj[l](src))
+            masks.append(mask)
+            assert mask is not None
+        if self.num_feature_levels > len(srcs):
+            _len_srcs = len(srcs)
+            for l in range(_len_srcs, self.num_feature_levels):
+                if l == _len_srcs:
+                    src = self.input_proj[l](features[-1].tensors)
+                else:
+                    src = self.input_proj[l](srcs[-1])
+                m = samples.mask
+                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
+                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
+                srcs.append(src)
+                masks.append(mask)
+                poss.append(pos_l)
+
+        input_query_bbox = input_query_label = attn_mask = dn_meta = None
+        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
+            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
+        )
+
+        # deformable-detr-like anchor update
+        outputs_coord_list = []
+        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
+            zip(reference[:-1], self.bbox_embed, hs)
+        ):
+            layer_delta_unsig = layer_bbox_embed(layer_hs)
+            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
+            layer_outputs_unsig = layer_outputs_unsig.sigmoid()
+            outputs_coord_list.append(layer_outputs_unsig)
+        outputs_coord_list = torch.stack(outputs_coord_list)
+
+        # output
+        outputs_class = torch.stack(
+            [
+                layer_cls_embed(layer_hs, text_dict)
+                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
+            ]
+        )
+        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
+
+        # # for intermediate outputs
+        # if self.aux_loss:
+        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
+
+        # # for encoder output
+        # if hs_enc is not None:
+        #     # prepare intermediate outputs
+        #     interm_coord = ref_enc[-1]
+        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
+        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
+        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
+
+        return out
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_coord):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        return [
+            {"pred_logits": a, "pred_boxes": b}
+            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
+        ]
+
+
+@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
+def build_groundingdino(args):
+
+    backbone = build_backbone(args)
+    transformer = build_transformer(args)
+
+    dn_labelbook_size = args.dn_labelbook_size
+    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
+    sub_sentence_present = args.sub_sentence_present
+
+    model = GroundingDINO(
+        backbone,
+        transformer,
+        num_queries=args.num_queries,
+        aux_loss=True,
+        iter_update=True,
+        query_dim=4,
+        num_feature_levels=args.num_feature_levels,
+        nheads=args.nheads,
+        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
+        two_stage_type=args.two_stage_type,
+        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
+        two_stage_class_embed_share=args.two_stage_class_embed_share,
+        num_patterns=args.num_patterns,
+        dn_number=0,
+        dn_box_noise_scale=args.dn_box_noise_scale,
+        dn_label_noise_ratio=args.dn_label_noise_ratio,
+        dn_labelbook_size=dn_labelbook_size,
+        text_encoder_type=args.text_encoder_type,
+        sub_sentence_present=sub_sentence_present,
+        max_text_len=args.max_text_len,
+    )
+
+    return model