import torch import contextlib import os import math import comfy.utils import comfy.model_management from comfy.clip_vision import clip_preprocess from comfy.ldm.modules.attention import optimized_attention import folder_paths from torch import nn from PIL import Image import torch.nn.functional as F import torchvision.transforms as TT from .resampler import Resampler # set the models directory backward compatible GLOBAL_MODELS_DIR = os.path.join(folder_paths.models_dir, "ipadapter") MODELS_DIR = GLOBAL_MODELS_DIR if os.path.isdir(GLOBAL_MODELS_DIR) else os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") if "ipadapter" not in folder_paths.folder_names_and_paths: folder_paths.folder_names_and_paths["ipadapter"] = ([MODELS_DIR], folder_paths.supported_pt_extensions) else: folder_paths.folder_names_and_paths["ipadapter"][1].update(folder_paths.supported_pt_extensions) class MLPProjModel(torch.nn.Module): """SD model with image prompt""" def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024): super().__init__() self.proj = torch.nn.Sequential( torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim), torch.nn.GELU(), torch.nn.Linear(clip_embeddings_dim, cross_attention_dim), torch.nn.LayerNorm(cross_attention_dim) ) def forward(self, image_embeds): clip_extra_context_tokens = self.proj(image_embeds) return clip_extra_context_tokens 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 clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) clip_extra_context_tokens = self.norm(clip_extra_context_tokens) return clip_extra_context_tokens class To_KV(nn.Module): def __init__(self, state_dict): super().__init__() self.to_kvs = nn.ModuleDict() for key, value in state_dict.items(): self.to_kvs[key.replace(".weight", "").replace(".", "_")] = nn.Linear(value.shape[1], value.shape[0], bias=False) self.to_kvs[key.replace(".weight", "").replace(".", "_")].weight.data = value def set_model_patch_replace(model, patch_kwargs, key): to = model.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if "attn2" not in to["patches_replace"]: to["patches_replace"]["attn2"] = {} if key not in to["patches_replace"]["attn2"]: patch = CrossAttentionPatch(**patch_kwargs) to["patches_replace"]["attn2"][key] = patch else: to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs) def image_add_noise(image, noise): image = image.permute([0,3,1,2]) torch.manual_seed(0) # use a fixed random for reproducible results transforms = TT.Compose([ TT.CenterCrop(min(image.shape[2], image.shape[3])), TT.Resize((224, 224), interpolation=TT.InterpolationMode.BICUBIC, antialias=True), TT.ElasticTransform(alpha=75.0, sigma=noise*3.5), # shuffle the image TT.RandomVerticalFlip(p=1.0), # flip the image to change the geometry even more TT.RandomHorizontalFlip(p=1.0), ]) image = transforms(image.cpu()) image = image.permute([0,2,3,1]) image = image + ((0.25*(1-noise)+0.05) * torch.randn_like(image) ) # add further random noise return image def zeroed_hidden_states(clip_vision, batch_size): image = torch.zeros([batch_size, 224, 224, 3]) comfy.model_management.load_model_gpu(clip_vision.patcher) pixel_values = clip_preprocess(image.to(clip_vision.load_device)) if clip_vision.dtype != torch.float32: precision_scope = torch.autocast else: precision_scope = lambda a, b: contextlib.nullcontext(a) with precision_scope(comfy.model_management.get_autocast_device(clip_vision.load_device), torch.float32): outputs = clip_vision.model(pixel_values, intermediate_output=-2) # we only need the penultimate hidden states outputs = outputs[1].to(comfy.model_management.intermediate_device()) return outputs 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 = F.pad(image, pad=(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 = output.clamp(0, 1) output = torch.nan_to_num(output) return (output) 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): 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.image_proj_model = self.init_proj() if not is_plus else self.init_proj_plus() self.image_proj_model.load_state_dict(ipadapter_model["image_proj"]) self.ip_layers = To_KV(ipadapter_model["ip_adapter"]) def init_proj(self): image_proj_model = ImageProjModel( cross_attention_dim=self.cross_attention_dim, clip_embeddings_dim=self.clip_embeddings_dim, clip_extra_context_tokens=self.clip_extra_context_tokens ) return image_proj_model def init_proj_plus(self): if self.is_full: image_proj_model = MLPProjModel( cross_attention_dim=self.cross_attention_dim, clip_embeddings_dim=self.clip_embeddings_dim ) else: image_proj_model = Resampler( dim=self.cross_attention_dim, depth=4, dim_head=64, heads=20 if self.is_sdxl 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 @torch.inference_mode() def get_image_embeds(self, clip_embed, clip_embed_zeroed): 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 class CrossAttentionPatch: # forward for patching def __init__(self, weight, ipadapter, device, dtype, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False): self.weights = [weight] self.ipadapters = [ipadapter] self.conds = [cond] self.unconds = [uncond] self.device = 'cuda' if 'cuda' in device.type else 'cpu' self.dtype = dtype if 'cuda' in self.device else torch.bfloat16 self.number = number self.weight_type = [weight_type] self.masks = [mask] self.sigma_start = [sigma_start] self.sigma_end = [sigma_end] self.unfold_batch = [unfold_batch] self.k_key = str(self.number*2+1) + "_to_k_ip" self.v_key = str(self.number*2+1) + "_to_v_ip" def set_new_condition(self, weight, ipadapter, device, dtype, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False): self.weights.append(weight) self.ipadapters.append(ipadapter) self.conds.append(cond) self.unconds.append(uncond) self.masks.append(mask) self.device = 'cuda' if 'cuda' in device.type else 'cpu' self.dtype = dtype if 'cuda' in self.device else torch.bfloat16 self.weight_type.append(weight_type) self.sigma_start.append(sigma_start) self.sigma_end.append(sigma_end) self.unfold_batch.append(unfold_batch) def __call__(self, n, context_attn2, value_attn2, extra_options): org_dtype = n.dtype cond_or_uncond = extra_options["cond_or_uncond"] sigma = extra_options["sigmas"][0].item() if 'sigmas' in extra_options else 999999999.9 # extra options for AnimateDiff ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None with torch.autocast(device_type=self.device, dtype=self.dtype): q = n k = context_attn2 v = value_attn2 b = q.shape[0] qs = q.shape[1] batch_prompt = b // len(cond_or_uncond) out = optimized_attention(q, k, v, extra_options["n_heads"]) _, _, lh, lw = extra_options["original_shape"] for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type, self.sigma_start, self.sigma_end, self.unfold_batch): if sigma > sigma_start or sigma < sigma_end: continue if unfold_batch and cond.shape[0] > 1: # Check AnimateDiff context window if ad_params is not None and ad_params["sub_idxs"] is not None: # if images 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, need to do more to get proper sub_idxs masks else: # check if images length matches full_length - if not, make it match if cond.shape[0] < ad_params["full_length"]: cond = torch.cat((cond, cond[-1:].repeat((ad_params["full_length"]-cond.shape[0], 1, 1))), dim=0) uncond = torch.cat((uncond, uncond[-1:].repeat((ad_params["full_length"]-uncond.shape[0], 1, 1))), dim=0) # if we have too many remove the excess (should not happen, but just in case) if cond.shape[0] > ad_params["full_length"]: cond = cond[:ad_params["full_length"]] uncond = uncond[:ad_params["full_length"]] cond = cond[ad_params["sub_idxs"]] uncond = uncond[ad_params["sub_idxs"]] # if we don't have enough reference images repeat the last one until we reach the right size if cond.shape[0] < batch_prompt: cond = torch.cat((cond, cond[-1:].repeat((batch_prompt-cond.shape[0], 1, 1))), dim=0) uncond = torch.cat((uncond, uncond[-1:].repeat((batch_prompt-uncond.shape[0], 1, 1))), dim=0) # if we have too many remove the exceeding elif cond.shape[0] > batch_prompt: cond = cond[:batch_prompt] uncond = uncond[:batch_prompt] k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond) k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond) v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond) v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond) else: k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1) k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1) v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1) v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1) if weight_type.startswith("linear"): ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) * weight ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) * weight else: ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) if weight_type.startswith("channel"): # code by Lvmin Zhang at Stanford University as also seen on Fooocus IPAdapter implementation # please read licensing notes https://github.com/lllyasviel/Fooocus/blob/main/fooocus_extras/ip_adapter.py#L225 ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True) ip_v_offset = ip_v - ip_v_mean _, _, C = ip_k.shape channel_penalty = float(C) / 1280.0 W = weight * channel_penalty ip_k = ip_k * W ip_v = ip_v_offset + ip_v_mean * W out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) if weight_type.startswith("original"): out_ip = out_ip * weight if mask is not None: # TODO: needs checking mask_h = max(1, round(lh / math.sqrt(lh * lw / qs))) mask_w = qs // mask_h # check if using AnimateDiff and sliding context window if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None): # if mask length matches or exceeds full_length, just get sub_idx masks, resize, and continue if mask.shape[0] >= ad_params["full_length"]: mask_downsample = torch.Tensor(mask[ad_params["sub_idxs"]]) mask_downsample = F.interpolate(mask_downsample.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1) # otherwise, need to do more to get proper sub_idxs masks else: # resize to needed attention size (to save on memory) mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1) # check if mask length matches full_length - if not, make it match if mask_downsample.shape[0] < ad_params["full_length"]: mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:].repeat((ad_params["full_length"]-mask_downsample.shape[0], 1, 1))), dim=0) # if we have too many remove the excess (should not happen, but just in case) if mask_downsample.shape[0] > ad_params["full_length"]: mask_downsample = mask_downsample[:ad_params["full_length"]] # now, select sub_idxs masks mask_downsample = mask_downsample[ad_params["sub_idxs"]] # otherwise, perform usual mask interpolation else: mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1) # if we don't have enough masks repeat the last one until we reach the right size if mask_downsample.shape[0] < batch_prompt: mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt-mask_downsample.shape[0], 1, 1))), dim=0) # if we have too many remove the exceeding elif mask_downsample.shape[0] > batch_prompt: mask_downsample = mask_downsample[:batch_prompt, :, :] # repeat the masks mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1) mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2]) out_ip = out_ip * mask_downsample out = out + out_ip return out.to(dtype=org_dtype) class IPAdapterModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ipadapter_file": (folder_paths.get_filename_list("ipadapter"), )}} RETURN_TYPES = ("IPADAPTER",) FUNCTION = "load_ipadapter_model" CATEGORY = "ipadapter" def load_ipadapter_model(self, ipadapter_file): ckpt_path = folder_paths.get_full_path("ipadapter", ipadapter_file) model = comfy.utils.load_torch_file(ckpt_path, safe_load=True) if ckpt_path.lower().endswith(".safetensors"): st_model = {"image_proj": {}, "ip_adapter": {}} for key in model.keys(): if key.startswith("image_proj."): st_model["image_proj"][key.replace("image_proj.", "")] = model[key] elif key.startswith("ip_adapter."): st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key] model = st_model if not "ip_adapter" in model.keys() or not model["ip_adapter"]: raise Exception("invalid IPAdapter model {}".format(ckpt_path)) return (model,) class IPAdapterApply: @classmethod def INPUT_TYPES(s): return { "required": { "ipadapter": ("IPADAPTER", ), "clip_vision": ("CLIP_VISION",), "image": ("IMAGE",), "model": ("MODEL", ), "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }), "noise": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01 }), "weight_type": (["original", "linear", "channel penalty"], ), "start_at": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), "end_at": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), "unfold_batch": ("BOOLEAN", { "default": False }), }, "optional": { "attn_mask": ("MASK",), } } RETURN_TYPES = ("MODEL",) FUNCTION = "apply_ipadapter" CATEGORY = "ipadapter" def apply_ipadapter(self, ipadapter, model, weight, clip_vision=None, image=None, weight_type="original", noise=None, embeds=None, attn_mask=None, start_at=0.0, end_at=1.0, unfold_batch=False): self.dtype = model.model.diffusion_model.dtype self.device = comfy.model_management.get_torch_device() self.weight = weight self.is_full = "proj.0.weight" in ipadapter["image_proj"] self.is_plus = self.is_full or "latents" in ipadapter["image_proj"] output_cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1] self.is_sdxl = output_cross_attention_dim == 2048 cross_attention_dim = 1280 if self.is_plus and self.is_sdxl else output_cross_attention_dim clip_extra_context_tokens = 16 if self.is_plus else 4 if embeds is not None: embeds = torch.unbind(embeds) clip_embed = embeds[0].cpu() clip_embed_zeroed = embeds[1].cpu() else: if image.shape[1] != image.shape[2]: print("\033[33mINFO: the IPAdapter reference image is not a square, CLIPImageProcessor will resize and crop it at the center. If the main focus of the picture is not in the middle the result might not be what you are expecting.\033[0m") clip_embed = clip_vision.encode_image(image) neg_image = image_add_noise(image, noise) if noise > 0 else None if self.is_plus: clip_embed = clip_embed.penultimate_hidden_states if noise > 0: clip_embed_zeroed = clip_vision.encode_image(neg_image).penultimate_hidden_states else: clip_embed_zeroed = zeroed_hidden_states(clip_vision, image.shape[0]) else: clip_embed = clip_embed.image_embeds if noise > 0: clip_embed_zeroed = clip_vision.encode_image(neg_image).image_embeds else: clip_embed_zeroed = torch.zeros_like(clip_embed) clip_embeddings_dim = clip_embed.shape[-1] self.ipadapter = IPAdapter( ipadapter, cross_attention_dim=cross_attention_dim, output_cross_attention_dim=output_cross_attention_dim, clip_embeddings_dim=clip_embeddings_dim, clip_extra_context_tokens=clip_extra_context_tokens, is_sdxl=self.is_sdxl, is_plus=self.is_plus, is_full=self.is_full, ) self.ipadapter.to(self.device, dtype=self.dtype) image_prompt_embeds, uncond_image_prompt_embeds = self.ipadapter.get_image_embeds(clip_embed.to(self.device, self.dtype), clip_embed_zeroed.to(self.device, self.dtype)) image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype) uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype) work_model = model.clone() if attn_mask is not None: attn_mask = attn_mask.to(self.device) sigma_start = model.model.model_sampling.percent_to_sigma(start_at) sigma_end = model.model.model_sampling.percent_to_sigma(end_at) patch_kwargs = { "number": 0, "weight": self.weight, "ipadapter": self.ipadapter, "device": self.device, "dtype": self.dtype, "cond": image_prompt_embeds, "uncond": uncond_image_prompt_embeds, "weight_type": weight_type, "mask": attn_mask, "sigma_start": sigma_start, "sigma_end": sigma_end, "unfold_batch": unfold_batch, } if not self.is_sdxl: for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention set_model_patch_replace(work_model, patch_kwargs, ("input", id)) patch_kwargs["number"] += 1 for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention set_model_patch_replace(work_model, patch_kwargs, ("output", id)) patch_kwargs["number"] += 1 set_model_patch_replace(work_model, patch_kwargs, ("middle", 0)) else: for id in [4,5,7,8]: # id of input_blocks that have cross attention block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth for index in block_indices: set_model_patch_replace(work_model, patch_kwargs, ("input", id, index)) patch_kwargs["number"] += 1 for id in range(6): # id of output_blocks that have cross attention block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth for index in block_indices: set_model_patch_replace(work_model, patch_kwargs, ("output", id, index)) patch_kwargs["number"] += 1 for index in range(10): set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index)) patch_kwargs["number"] += 1 return (work_model, ) class PrepImageForClipVision: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "interpolation": (["LANCZOS", "BICUBIC", "HAMMING", "BILINEAR", "BOX", "NEAREST"],), "crop_position": (["top", "bottom", "left", "right", "center", "pad"],), "sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "prep_image" CATEGORY = "ipadapter" def prep_image(self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0): _, oh, ow, _ = image.shape output = image.permute([0,3,1,2]) if "pad" in crop_position: target_length = max(oh, ow) pad_l = (target_length - ow) // 2 pad_r = (target_length - ow) - pad_l pad_t = (target_length - oh) // 2 pad_b = (target_length - oh) - pad_t output = F.pad(output, (pad_l, pad_r, pad_t, pad_b), value=0, mode="constant") else: crop_size = min(oh, ow) x = (ow-crop_size) // 2 y = (oh-crop_size) // 2 if "top" in crop_position: y = 0 elif "bottom" in crop_position: y = oh-crop_size elif "left" in crop_position: x = 0 elif "right" in crop_position: x = ow-crop_size x2 = x+crop_size y2 = y+crop_size # crop output = output[:, :, y:y2, x:x2] # resize (apparently PIL resize is better than tourchvision interpolate) imgs = [] for i in range(output.shape[0]): img = TT.ToPILImage()(output[i]) img = img.resize((224,224), resample=Image.Resampling[interpolation]) imgs.append(TT.ToTensor()(img)) output = torch.stack(imgs, dim=0) if sharpening > 0: output = contrast_adaptive_sharpening(output, sharpening) output = output.permute([0,2,3,1]) return (output,) class IPAdapterEncoder: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "image_1": ("IMAGE",), "ipadapter_plus": ("BOOLEAN", { "default": False }), "noise": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01 }), "weight_1": ("FLOAT", { "default": 1.0, "min": 0, "max": 1.0, "step": 0.01 }), }, "optional": { "image_2": ("IMAGE",), "image_3": ("IMAGE",), "image_4": ("IMAGE",), "weight_2": ("FLOAT", { "default": 1.0, "min": 0, "max": 1.0, "step": 0.01 }), "weight_3": ("FLOAT", { "default": 1.0, "min": 0, "max": 1.0, "step": 0.01 }), "weight_4": ("FLOAT", { "default": 1.0, "min": 0, "max": 1.0, "step": 0.01 }), } } RETURN_TYPES = ("EMBEDS",) FUNCTION = "preprocess" CATEGORY = "ipadapter" def preprocess(self, clip_vision, image_1, ipadapter_plus, noise, weight_1, image_2=None, image_3=None, image_4=None, weight_2=1.0, weight_3=1.0, weight_4=1.0): weight_1 *= (0.1 + (weight_1 - 0.1)) weight_1 = 1.19e-05 if weight_1 <= 1.19e-05 else weight_1 weight_2 *= (0.1 + (weight_2 - 0.1)) weight_2 = 1.19e-05 if weight_2 <= 1.19e-05 else weight_2 weight_3 *= (0.1 + (weight_3 - 0.1)) weight_3 = 1.19e-05 if weight_3 <= 1.19e-05 else weight_3 weight_4 *= (0.1 + (weight_4 - 0.1)) weight_5 = 1.19e-05 if weight_4 <= 1.19e-05 else weight_4 image = image_1 weight = [weight_1]*image_1.shape[0] if image_2 is not None: if image_1.shape[1:] != image_2.shape[1:]: image_2 = comfy.utils.common_upscale(image_2.movedim(-1,1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1,-1) image = torch.cat((image, image_2), dim=0) weight += [weight_2]*image_2.shape[0] if image_3 is not None: if image.shape[1:] != image_3.shape[1:]: image_3 = comfy.utils.common_upscale(image_3.movedim(-1,1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1,-1) image = torch.cat((image, image_3), dim=0) weight += [weight_3]*image_3.shape[0] if image_4 is not None: if image.shape[1:] != image_4.shape[1:]: image_4 = comfy.utils.common_upscale(image_4.movedim(-1,1), image.shape[2], image.shape[1], "bilinear", "center").movedim(1,-1) image = torch.cat((image, image_4), dim=0) weight += [weight_4]*image_4.shape[0] clip_embed = clip_vision.encode_image(image) neg_image = image_add_noise(image, noise) if noise > 0 else None if ipadapter_plus: clip_embed = clip_embed.penultimate_hidden_states if noise > 0: clip_embed_zeroed = clip_vision.encode_image(neg_image).penultimate_hidden_states else: clip_embed_zeroed = zeroed_hidden_states(clip_vision, image.shape[0]) else: clip_embed = clip_embed.image_embeds if noise > 0: clip_embed_zeroed = clip_vision.encode_image(neg_image).image_embeds else: clip_embed_zeroed = torch.zeros_like(clip_embed) if any(e != 1.0 for e in weight): weight = torch.tensor(weight).unsqueeze(-1) if not ipadapter_plus else torch.tensor(weight).unsqueeze(-1).unsqueeze(-1) clip_embed = clip_embed * weight output = torch.stack((clip_embed, clip_embed_zeroed)) return( output, ) class IPAdapterApplyEncoded(IPAdapterApply): @classmethod def INPUT_TYPES(s): return { "required": { "ipadapter": ("IPADAPTER", ), "embeds": ("EMBEDS",), "model": ("MODEL", ), "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }), "weight_type": (["original", "linear", "channel penalty"], ), "start_at": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }), "end_at": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }), "unfold_batch": ("BOOLEAN", { "default": False }), }, "optional": { "attn_mask": ("MASK",), } } 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": "embeds/IPAdapter"}) }, } RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "ipadapter" 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" def load(self, embeds): path = folder_paths.get_annotated_filepath(embeds) output = torch.load(path).cpu() return (output, ) class IPAdapterBatchEmbeds: @classmethod def INPUT_TYPES(s): return {"required": { "embed1": ("EMBEDS",), "embed2": ("EMBEDS",), }} RETURN_TYPES = ("EMBEDS",) FUNCTION = "batch" CATEGORY = "ipadapter" def batch(self, embed1, embed2): output = torch.cat((embed1, embed2), dim=1) return (output, ) NODE_CLASS_MAPPINGS = { "IPAdapterModelLoader": IPAdapterModelLoader, "IPAdapterApply": IPAdapterApply, "IPAdapterApplyEncoded": IPAdapterApplyEncoded, "PrepImageForClipVision": PrepImageForClipVision, "IPAdapterEncoder": IPAdapterEncoder, "IPAdapterSaveEmbeds": IPAdapterSaveEmbeds, "IPAdapterLoadEmbeds": IPAdapterLoadEmbeds, "IPAdapterBatchEmbeds": IPAdapterBatchEmbeds, } NODE_DISPLAY_NAME_MAPPINGS = { "IPAdapterModelLoader": "Load IPAdapter Model", "IPAdapterApply": "Apply IPAdapter", "IPAdapterApplyEncoded": "Apply IPAdapter from Encoded", "PrepImageForClipVision": "Prepare Image For Clip Vision", "IPAdapterEncoder": "Encode IPAdapter Image", "IPAdapterSaveEmbeds": "Save IPAdapter Embeds", "IPAdapterLoadEmbeds": "Load IPAdapter Embeds", "IPAdapterBatchEmbeds": "IPAdapter Batch Embeds", }