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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_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_IPAdapter_plus/.gitignore

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

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: cosxl, 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_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, encoder_hid_proj=None, weight_kolors=1.0):
+        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"], encoder_hid_proj=encoder_hid_proj, weight_kolors=weight_kolors)
+
+    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,
+                num_tokens=self.clip_extra_context_tokens,
+            )
+        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))
+
+        embeds = torch.cat(embeds, dim=0)
+        del face_embed_batch, clip_embed_batch
+        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, encoder_hid_proj=None, weight_kolors=1.0):
+        super().__init__()
+
+        if encoder_hid_proj is not None:
+            hid_proj = nn.Linear(encoder_hid_proj["weight"].shape[1], encoder_hid_proj["weight"].shape[0], bias=True)
+            hid_proj.weight.data = encoder_hid_proj["weight"] * weight_kolors
+            hid_proj.bias.data = encoder_hid_proj["bias"] * weight_kolors
+
+        self.to_kvs = nn.ModuleDict()
+        for key, value in state_dict.items():
+            if encoder_hid_proj is not None:
+                linear_proj = nn.Linear(value.shape[1], value.shape[0], bias=False)
+                linear_proj.weight.data = value
+                self.to_kvs[key.replace(".weight", "").replace(".", "_")] = nn.Sequential(hid_proj, linear_proj)
+            else:
+                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_kolors=1.0,
+                      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_unnorm = "portraitunnorm" in ipadapter
+    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
+    output_cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
+    is_sdxl = output_cross_attention_dim == 2048
+    is_kwai_kolors_faceid = "perceiver_resampler.layers.0.0.to_out.weight" in ipadapter["image_proj"] and ipadapter["image_proj"]["perceiver_resampler.layers.0.0.to_out.weight"].shape[0] == 4096
+    is_faceidv2 = "faceidplusv2" in ipadapter or is_kwai_kolors_faceid
+    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) or is_kwai_kolors_faceid
+    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"] and not is_kwai_kolors_faceid
+    is_faceid = is_portrait or "0.to_q_lora.down.weight" in ipadapter["ip_adapter"] or is_portrait_unnorm or is_kwai_kolors_faceid
+
+    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
+
+    if is_kwai_kolors_faceid:
+        cross_attention_dim = 4096
+    elif is_kwai_kolors:
+        cross_attention_dim = 2048
+    elif (is_plus and is_sdxl and not is_faceid) or is_portrait_unnorm:
+        cross_attention_dim = 1280
+    else:
+        cross_attention_dim = output_cross_attention_dim
+    
+    if is_kwai_kolors_faceid:
+        clip_extra_context_tokens = 6
+    elif (is_plus and not is_faceid) or is_portrait or is_portrait_unnorm:
+        clip_extra_context_tokens = 16
+    else:
+        clip_extra_context_tokens = 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=336 if is_kwai_kolors_faceid else 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)
+
+    encoder_hid_proj = None
+
+    if is_kwai_kolors_faceid and hasattr(model.model, "diffusion_model") and hasattr(model.model.diffusion_model, "encoder_hid_proj"):
+        encoder_hid_proj = model.model.diffusion_model.encoder_hid_proj.state_dict()
+
+    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,
+        encoder_hid_proj=encoder_hid_proj,
+        weight_kolors=weight_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", 1))
+    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", 1, 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"], ),
+                "model_name": (['buffalo_l', 'antelopev2'], )
+            },
+        }
+
+    RETURN_TYPES = ("INSIGHTFACE",)
+    FUNCTION = "load_insightface"
+    CATEGORY = "ipadapter/loaders"
+
+    def load_insightface(self, provider, model_name):
+        return (insightface_loader(provider, model_name=model_name),)
+
+"""
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ 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, weight_kolors=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,
+                "weight_kolors": weight_kolors,
+            }
+
+            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 IPAdapterFaceIDKolors(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_kolors": ("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 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,
+    "IPAdapterFaceIDKolors": IPAdapterFaceIDKolors,
+    "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",
+    "IPAdapterFaceIDKolors": "IPAdapter FaceID Kolors",
+    "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_IPAdapter_plus/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
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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_IPAdapter_plus/README.md

@@ -0,0 +1,184 @@
+# 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)
+
+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
+
+**2024/09/13**: Fixed a nasty bug in the middle block patching that we are carrying around since the beginning. Unfortunately the generated images won't be exactly the same as before. Anyway the middle block doesn't have a huge impact, so it shouldn't be a big deal. It does **not** impact Style or Composition transfer, only linear generations. I do not generally report on small bug fixes but this one may cause different results so I thought it's worth mentioning.
+
+**2024/08/02**: Support for Kolors FaceIDv2. Please check the [example workflow](./examples/IPAdapter_FaceIDv2_Kolors.json) for best practices.
+
+**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.
+
+*(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
+    - [Kolors-IP-Adapter-FaceID-Plus.bin](https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus/resolve/main/ipa-faceid-plus.bin?download=true), IPAdapter FaceIDv2 for Kolors model. **Note:** Kolors is trained on InsightFace  **antelopev2** model, you need to [manually download it](https://huggingface.co/MonsterMMORPG/tools/tree/main) and place it inside the `models/inisghtface` directory.
+
+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;[![InstaSD](https://f.latent.vision/imgs/instasd.png)](https://www.instasd.com/)
+
+### :tada: Silver sponsors
+
+[![OperArt.ai](https://f.latent.vision/imgs/openart.png?r=1)](https://openart.ai/workflows)&nbsp; &nbsp;[![Finetuners](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/)
+
+### Other companies supporting my projects
+
+- [RunComfy](https://www.runcomfy.com/) (ComfyUI Cloud)
+
+### Esteemed individuals
+
+- [Øystein Ø. Olsen](https://github.com/FireNeslo)
+- [Jack Gane](https://github.com/ganeJackS)
+- [Nathan Shipley](https://www.nathanshipley.com/)
+- [Dkdnzia](https://github.com/Dkdnzia)
+
+[And all my public and private sponsors!](https://github.com/sponsors/cubiq)
+
+## 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_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_IPAdapter_plus/examples/IPAdapter_FaceIDv2_Kolors.json

@@ -0,0 +1,852 @@
+{
+  "last_node_id": 125,
+  "last_link_id": 214,
+  "nodes": [
+    {
+      "id": 97,
+      "type": "MZ_ChatGLM3_V2",
+      "pos": [
+        1573,
+        984
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "chatglm3_model",
+          "type": "CHATGLM3MODEL",
+          "link": 142,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            153
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MZ_ChatGLM3_V2"
+      },
+      "widgets_values": [
+        "blurry"
+      ]
+    },
+    {
+      "id": 76,
+      "type": "IPAdapterModelLoader",
+      "pos": [
+        1240,
+        1630
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IPADAPTER",
+          "type": "IPADAPTER",
+          "links": [
+            183
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterModelLoader"
+      },
+      "widgets_values": [
+        "Kolors-IP-Adapter-FaceID-Plus.bin"
+      ]
+    },
+    {
+      "id": 93,
+      "type": "MZ_KolorsUNETLoaderV2",
+      "pos": [
+        1240,
+        1510
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "links": [
+            185
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MZ_KolorsUNETLoaderV2"
+      },
+      "widgets_values": [
+        "diffusion_pytorch_model.fp16.safetensors"
+      ]
+    },
+    {
+      "id": 96,
+      "type": "MZ_ChatGLM3Loader",
+      "pos": [
+        1161,
+        938
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "chatglm3_model",
+          "type": "CHATGLM3MODEL",
+          "links": [
+            141,
+            142
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MZ_ChatGLM3Loader"
+      },
+      "widgets_values": [
+        "chatglm3-8bit.safetensors"
+      ]
+    },
+    {
+      "id": 94,
+      "type": "MZ_ChatGLM3_V2",
+      "pos": [
+        1574,
+        702
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "chatglm3_model",
+          "type": "CHATGLM3MODEL",
+          "link": 141,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            152
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MZ_ChatGLM3_V2"
+      },
+      "widgets_values": [
+        "closeup photo of a beautiful woman wearing cyberpunk clothing, in background a cyberpunk city, highly detailed, 4k"
+      ]
+    },
+    {
+      "id": 78,
+      "type": "CLIPVisionLoader",
+      "pos": [
+        1239,
+        1743
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [
+            182
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPVisionLoader"
+      },
+      "widgets_values": [
+        "clip-vit-large-patch14-336.bin"
+      ]
+    },
+    {
+      "id": 70,
+      "type": "VAELoader",
+      "pos": [
+        2138,
+        1291
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            156
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "sdxl_vae.safetensors"
+      ]
+    },
+    {
+      "id": 123,
+      "type": "IPAdapterNoise",
+      "pos": [
+        1373,
+        2032
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image_optional",
+          "type": "IMAGE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            207
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterNoise"
+      },
+      "widgets_values": [
+        "gaussian",
+        0.6,
+        0
+      ]
+    },
+    {
+      "id": 99,
+      "type": "KSampler",
+      "pos": [
+        2145,
+        950
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 186
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 152
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 153
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 154
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            155
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        19,
+        "fixed",
+        30,
+        5,
+        "dpmpp_2m",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 114,
+      "type": "IPAdapterFaceIDKolors",
+      "pos": [
+        1873,
+        1716
+      ],
+      "size": {
+        "0": 262.18988037109375,
+        "1": 346
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 185
+        },
+        {
+          "name": "ipadapter",
+          "type": "IPADAPTER",
+          "link": 183
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 211
+        },
+        {
+          "name": "image_negative",
+          "type": "IMAGE",
+          "link": 207,
+          "slot_index": 3
+        },
+        {
+          "name": "attn_mask",
+          "type": "MASK",
+          "link": null
+        },
+        {
+          "name": "clip_vision",
+          "type": "CLIP_VISION",
+          "link": 182
+        },
+        {
+          "name": "insightface",
+          "type": "INSIGHTFACE",
+          "link": 181
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            186
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "face_image",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IPAdapterFaceIDKolors"
+      },
+      "widgets_values": [
+        1,
+        0.9500000000000001,
+        0.85,
+        "linear",
+        "average",
+        0,
+        1,
+        "V only"
+      ]
+    },
+    {
+      "id": 100,
+      "type": "VAEDecode",
+      "pos": [
+        2530,
+        958
+      ],
+      "size": {
+        "0": 140,
+        "1": 46
+      },
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 155
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 156
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            214
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 101,
+      "type": "PreviewImage",
+      "pos": [
+        2539,
+        1094
+      ],
+      "size": {
+        "0": 844.5427856445312,
+        "1": 850.6759643554688
+      },
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 214
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 9,
+      "type": "EmptyLatentImage",
+      "pos": [
+        1602,
+        1256
+      ],
+      "size": {
+        "0": 368.5347900390625,
+        "1": 106
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_advanced.json

@@ -0,0 +1,619 @@
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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_clipvision_enhancer.json

@@ -0,0 +1,918 @@
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+          "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,
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+    [
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+    [
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+    ],
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+      51,
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+      1,
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+    ],
+    [
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+      31,
+      0,
+      29,
+      2,
+      "INT"
+    ],
+    [
+      58,
+      28,
+      0,
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+      0,
+      "MODEL"
+    ],
+    [
+      59,
+      4,
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+      "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_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",
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ComfyUI_IPAdapter_plus/examples/ipadapter_cosxl_edit.json

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ComfyUI_IPAdapter_plus/examples/ipadapter_faceid.json

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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_faceid_batch.json

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

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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_kolors.json

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

@@ -0,0 +1,956 @@
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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_noise_injection.json

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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_portrait.json

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

ComfyUI_IPAdapter_plus/examples/ipadapter_precise_composition.json

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

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ComfyUI_IPAdapter_plus/examples/ipadapter_regional_conditioning.json

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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_simple.json

@@ -0,0 +1,546 @@
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+      "mode": 0,
+      "outputs": [
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+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
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+        "Node name for S&R": "EmptyLatentImage"
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+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI_IPAdapter_plus/examples/ipadapter_style_composition.json

@@ -0,0 +1,612 @@
+{
+  "last_node_id": 16,
+  "last_link_id": 25,
+  "nodes": [
+    {
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+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
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+      },
+      "widgets_values": [
+        "blurry, noisy, messy, lowres, jpeg, artifacts, ill, distorted, malformed"
+      ]
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+    {
+      "id": 11,
+      "type": "IPAdapterUnifiedLoader",
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+        },
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+          "type": "IPADAPTER",
+          "link": null
+        }
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+            21
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+          "slot_index": 0
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+      "properties": {
+        "Node name for S&R": "IPAdapterUnifiedLoader"
+      },
+      "widgets_values": [
+        "PLUS (high strength)"
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+          "type": "IMAGE",
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+          "link": 25
+        },
+        {
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+          "type": "IMAGE",
+          "link": null
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+          "link": null
+        },
+        {
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+          "type": "CLIP_VISION",
+          "link": null
+        }
+      ],
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+          "link": 6
+        },
+        {
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+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
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+          "type": "LATENT",
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+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
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+      "outputs": [
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+          "type": "IMAGE",
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+        "IPAdapter"
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+      ],
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+      "widgets_values": [
+        "sdxl/AlbedoBaseXL.safetensors"
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+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI_IPAdapter_plus/examples/ipadapter_tiled.json

@@ -0,0 +1,583 @@
+{
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+  "last_link_id": 32,
+  "nodes": [
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+      ],
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+}

ComfyUI_IPAdapter_plus/examples/ipadapter_weight_types.json

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

ComfyUI_IPAdapter_plus/examples/ipadapter_weighted_embeds.json

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

ComfyUI_IPAdapter_plus/examples/ipadapter_weights.json

@@ -0,0 +1,764 @@
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+      "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_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_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_IPAdapter_plus/utils.py

@@ -0,0 +1,388 @@
+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]
+            elif key.startswith("adapter_modules."):
+                st_model["ip_adapter"][key.replace("adapter_modules.", "")] = model[key]
+        model = st_model
+        del st_model
+    elif "adapter_modules" in model.keys():
+        model["ip_adapter"] = model.pop("adapter_modules")
+
+    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, model_name='buffalo_l'):
+    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=model_name, 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