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Start work on tile IPA feature

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peteromallet
2024-03-27 23:31:50 +01:00
parent bb16d05c44
commit c807b18b9a
23 changed files with 12110 additions and 1413 deletions
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imports/ComfyUI_IPAdapter_plus/.gitignore
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/__pycache__/
/models/*.bin
/models/*.safetensors
.directory
imports/ComfyUI_IPAdapter_plus/CrossAttentionPatchImport.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 CrossAttentionPatchImport:
# forward for patching
def __init__(self, ipadapter=None, number=0, weight=1.0, cond=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights = [weight]
self.ipadapters = [ipadapter]
self.conds = [cond]
self.unconds = [uncond]
self.weight_types = [weight_type]
self.masks = [mask]
self.sigma_starts = [sigma_start]
self.sigma_ends = [sigma_end]
self.unfold_batch = [unfold_batch]
self.embeds_scaling = [embeds_scaling]
self.number = number
self.layers = 10 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 15 # TODO: check if this is a valid condition to detect all models
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, ipadapter=None, number=0, weight=1.0, cond=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights.append(weight)
self.ipadapters.append(ipadapter)
self.conds.append(cond)
self.unconds.append(uncond)
self.weight_types.append(weight_type)
self.masks.append(mask)
self.sigma_starts.append(sigma_start)
self.sigma_ends.append(sigma_end)
self.unfold_batch.append(unfold_batch)
self.embeds_scaling.append(embeds_scaling)
def __call__(self, q, k, v, extra_options):
dtype = q.dtype
cond_or_uncond = extra_options["cond_or_uncond"]
sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
block_type = extra_options["block"][0]
#block_id = extra_options["block"][1]
t_idx = extra_options["transformer_index"]
# 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)
out = optimized_attention(q, k, v, extra_options["n_heads"])
_, _, oh, ow = extra_options["original_shape"]
for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch, embeds_scaling in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_types, self.sigma_starts, self.sigma_ends, self.unfold_batch, self.embeds_scaling):
if sigma <= sigma_start and sigma >= sigma_end:
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 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"]]
cond = tensor_to_size(cond, batch_prompt)
uncond = tensor_to_size(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 == 'ease in':
weight = weight * (0.05 + 0.95 * (1 - t_idx / self.layers))
elif weight_type == 'ease out':
weight = weight * (0.05 + 0.95 * (t_idx / self.layers))
elif weight_type == 'ease in-out':
weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (self.layers/2)) / (self.layers/2)))
elif weight_type == 'reverse in-out':
weight = weight * (0.05 + 0.95 * (abs(t_idx - (self.layers/2)) / (self.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 weight_type.startswith('style transfer'):
if t_idx != 6:
weight = 0.0
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.to(dtype=dtype)
imports/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 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 MLPProjModelImport, MLPProjModelFaceIdImport, ProjModelFaceIdPlusImport, ResamplerImport, ImageProjModelImport
from .CrossAttentionPatchImport import CrossAttentionPatchImport
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 (SDXL)']
"""
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Main IPAdapter Class
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
class IPAdapterImport(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):
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
if is_faceid:
self.image_proj_model = self.init_proj_faceid()
elif is_full:
self.image_proj_model = self.init_proj_full()
elif is_plus:
self.image_proj_model = self.init_proj_plus()
else:
self.image_proj_model = self.init_proj()
self.image_proj_model.load_state_dict(ipadapter_model["image_proj"])
self.ip_layers = To_KV(ipadapter_model["ip_adapter"])
def init_proj(self):
image_proj_model = ImageProjModelImport(
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 = ResamplerImport(
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
def init_proj_full(self):
image_proj_model = MLPProjModelImport(
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 = ProjModelFaceIdPlusImport(
cross_attention_dim=self.cross_attention_dim,
id_embeddings_dim=512,
clip_embeddings_dim=self.clip_embeddings_dim, # 1280,
num_tokens=self.clip_extra_context_tokens, # 4,
)
else:
image_proj_model = MLPProjModelFaceIdImport(
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):
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):
embeds = self.image_proj_model(face_embed, clip_embed, scale=s_scale, shortcut=shortcut)
return embeds
class To_KV(nn.Module):
def __init__(self, state_dict):
super().__init__()
self.to_kvs = nn.ModuleDict()
for key, value in state_dict.items():
self.to_kvs[key.replace(".weight", "").replace(".", "_")] = nn.Linear(value.shape[1], value.shape[0], bias=False)
self.to_kvs[key.replace(".weight", "").replace(".", "_")].weight.data = value
def set_model_patch_replace(model, patch_kwargs, key):
to = model.model_options["transformer_options"]
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"]:
to["patches_replace"]["attn2"][key] = CrossAttentionPatchImport(**patch_kwargs)
else:
to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)
def ipadapter_execute(model,
ipadapter,
clipvision,
insightface=None,
image=None,
image_negative=None,
weight=1.0,
weight_faceidv2=None,
weight_type="linear",
combine_embeds="concat",
start_at=0.0,
end_at=1.0,
attn_mask=None,
pos_embed=None,
neg_embed=None,
unfold_batch=False,
embeds_scaling='V only'):
dtype = torch.float16 if model_management.should_use_fp16() else torch.bfloat16 if model_management.should_use_bf16() else torch.float32
device = model_management.get_torch_device()
is_full = "proj.3.weight" in ipadapter["image_proj"]
is_portrait = "proj.2.weight" in ipadapter["image_proj"] and not "proj.3.weight" in ipadapter["image_proj"] and not "0.to_q_lora.down.weight" in ipadapter["ip_adapter"]
is_faceid = is_portrait or "0.to_q_lora.down.weight" in ipadapter["ip_adapter"]
is_plus = is_full or "latents" in ipadapter["image_proj"] or "perceiver_resampler.proj_in.weight" in ipadapter["image_proj"]
is_faceidv2 = "faceidplusv2" in ipadapter
output_cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
is_sdxl = output_cross_attention_dim == 2048
if weight_type == "style transfer (SDXL)" and not is_sdxl:
weight_type = "linear"
print("\033[33mINFO: 'Style Transfer' weight type is only available for SDXL models, falling back to 'linear'.\033[0m")
if is_faceid and not insightface:
raise Exception("insightface model is required for FaceID models")
if is_faceidv2:
weight_faceidv2 = weight_faceidv2 if weight_faceidv2 is not None else weight*2
cross_attention_dim = 1280 if is_plus and is_sdxl and not is_faceid else output_cross_attention_dim
clip_extra_context_tokens = 16 if (is_plus and not is_faceid) or is_portrait else 4
if image is not None and image.shape[1] != image.shape[2]:
print("\033[33mINFO: the IPAdapter reference image is not a square, CLIPImageProcessor will resize and crop it at the center. If the main focus of the picture is not in the middle the result might not be what you are expecting.\033[0m")
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:
face_cond_embeds.append(torch.from_numpy(face[0].normed_embedding).unsqueeze(0))
image.append(image_to_tensor(face_align.norm_crop(image_iface[i], landmark=face[0].kps, image_size=256)))
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)
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, 224, 224, 3])
img_uncond_embeds = encode_image_masked(clipvision, image_negative).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:
img_uncond_embeds = encode_image_masked(clipvision, image_negative).image_embeds
else:
img_uncond_embeds = torch.zeros_like(img_cond_embeds)
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, 224, 224, 3])).penultimate_hidden_states
else:
img_uncond_embeds = torch.zeros_like(img_cond_embeds)
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)
# 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)
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)
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)
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)
img_uncond_embeds = img_uncond_embeds[0].unsqueeze(0) # TODO: better strategy for uncond could be to average them
if attn_mask is not None:
attn_mask = attn_mask.to(device, dtype=dtype)
ipa = IPAdapterImport(
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
).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)
# TODO: check if noise helps with the uncod face embeds
uncod = ipa.get_image_embeds_faceid_plus(torch.zeros_like(face_cond_embeds), img_uncond_embeds, weight_faceidv2, is_faceidv2)
else:
cond, uncod = ipa.get_image_embeds(img_cond_embeds, img_uncond_embeds)
cond = cond.to(device, dtype=dtype)
uncod = uncod.to(device, dtype=dtype)
del img_cond_embeds, img_uncond_embeds
sigma_start = model.model.model_sampling.percent_to_sigma(start_at)
sigma_end = model.model.model_sampling.percent_to_sigma(end_at)
patch_kwargs = {
"ipadapter": ipa,
"number": 0,
"weight": weight,
"cond": cond,
"uncond": uncod,
"weight_type": weight_type,
"mask": attn_mask,
"sigma_start": sigma_start,
"sigma_end": sigma_end,
"unfold_batch": unfold_batch,
"embeds_scaling": embeds_scaling,
}
if not is_sdxl:
for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
set_model_patch_replace(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(model, patch_kwargs, ("output", id))
patch_kwargs["number"] += 1
set_model_patch_replace(model, patch_kwargs, ("middle", 0))
else:
for id in [4,5,7,8]: # id of input_blocks that have cross attention
block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
for index in block_indices:
set_model_patch_replace(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(model, patch_kwargs, ("output", id, index))
patch_kwargs["number"] += 1
for index in range(10):
set_model_patch_replace(model, patch_kwargs, ("middle", 0, index))
patch_kwargs["number"] += 1
return model
class IPAdapterAdvancedImport:
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 }),
"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, image, weight, weight_type, start_at, end_at, combine_embeds="concat", weight_faceidv2=None, image_negative=None, clip_vision=None, attn_mask=None, insightface=None, embeds_scaling='V only'):
ipa_args = {
"image": image,
"image_negative": image_negative,
"weight": weight,
"weight_faceidv2": weight_faceidv2,
"weight_type": weight_type,
"combine_embeds": combine_embeds,
"start_at": start_at,
"end_at": end_at,
"attn_mask": attn_mask,
"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
}
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
return (ipadapter_execute(model.clone(), ipadapter_model, clip_vision, **ipa_args), )
class IPAdapterTiledImport:
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"
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'):
# 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,
}
# 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 PrepImageForClipVisionImport:
@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):
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, )
imports/ComfyUI_IPAdapter_plus/LICENSE
+674
View File
@@ -0,0 +1,674 @@
GNU GENERAL PUBLIC LICENSE
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imports/ComfyUI_IPAdapter_plus/README.md
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# ComfyUI IPAdapter plus
[ComfyUI](https://github.com/comfyanonymous/ComfyUI) reference implementation for [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/) models.
IPAdapter implementation that follows the ComfyUI way of doing things. The code is memory efficient, fast, and shouldn't break with Comfy updates.
# Open source for you but not free for me...
I started working on IPAdapter because I needed it for my work. As the project evolved I'm inevitably receiving feature requests, bug reports and support requests.
I'm an open source advocate and I'm happy to share all my code for free but maintaining the IPAdapter, the [Essentials](https://github.com/cubiq/ComfyUI_essentials), [InstantID](https://github.com/cubiq/ComfyUI_InstantID) and [Face Analysis](https://github.com/cubiq/ComfyUI_FaceAnalysis) takes time.
**I'm not expecting donations but if you are making a profit from my projects it is only fair that you give something back.** I'm talking especially to companies here, I know the struggles of being a freelancer.
Please contact me if you are interested in a sponsorship at _matt3o@gmail_ or consider a contribution via [PayPal](https://paypal.me/matt3o) (Matteo "matt3o" Spinelli, Firenze, IT). That will help maintaining the code, adding new features and working on better documentation.
And in that regard I really need to thank [Nathan Shipley](https://www.nathanshipley.com/) for his generous donation. Go check his website, he's terribly talented.
## :warning: IPAdapter V2: complete Code rewrite warning
A code cleanup was long overdue and with the occasion I also added a few new important features. The code should be faster and should take less resources but with such an important code rewrite it's inevitable to have introduced some new bugs.
**At the moment I'm releasing this completely undocumented!** I will post better documentation and video tutorials in the coming days. In the meantime you can check the `example` directory for most of the old and new features.
## Important updates
**2024/03/23**: Complete code rewrite!. **This is a breaking update!** Your previous workflows won't work and you'll need to recreate them. You've been warned! After the update, refresh your browser, delete the old IPAdapter nodes and create the new ones.
**2024/02/02**: Added experimental [tiled IPAdapter](#tiled-ipadapter). It lets you easily handle reference images that are not square. Can be useful for upscaling.
**2024/01/19**: Support for FaceID Portrait models.
**2024/01/16**: Notably increased quality of FaceID Plus/v2 models. Check the [comparison](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/195) of all face models.
*(previous updates removed for better readability)*
## What is it?
The IPAdapter are very powerful models for image-to-image conditioning. Given one or more reference images you can do variations augmented by text prompt, controlnets and masks. Think of it as a 1-image lora.
## Example workflow
The [example 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)**
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. Beware that the automatic update of the manager sometimes doesn't work and you may need to upgrade manually.
IPAdapter always requires the latest version of ComfyUI. If something doesn't work be sure to upgrade!
There's now an *Unified Model Loader*, for it to work you need to name the files exactly how it is described below.
The pre-trained models are available on [huggingface](https://huggingface.co/h94/IP-Adapter), download and place them in the `ComfyUI/models/ipadapter` directory (create it if not present). You can also use any custom location setting an `ipadapter` entry in the `extra_model_paths.yaml` file.
IPAdapter also needs the image encoders. You need the [CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) and [CLIP-ViT-bigG-14-laion2B-39B-b160k.safetensors](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) image encoders, you may already have them. If you don't, download them but **be careful because the file name is the same for both!** Rename them and place them in the `ComfyUI/models/clip_vision/` directory.
The following table shows the combination of Checkpoint and Image encoder to use for each IPAdapter Model. Any Tensor size mismatch you may get it is likely caused by a wrong combination.
| SD v. | IPadapter | Img encoder | Notes |
|---|---|---|---|
| v1.5 | [ip-adapter_sd15](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors) | ViT-H | Basic model, average strength |
| v1.5 | [ip-adapter_sd15_light](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors) | ViT-H | Light model, very light impact |
| v1.5 | [ip-adapter_sd15_light_v11](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light_v11.bin) | ViT-H | Updated light model |
| v1.5 | [ip-adapter-plus_sd15](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors) | ViT-H | Plus model, very strong |
| v1.5 | [ip-adapter-plus-face_sd15](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors) | ViT-H | Face model, use only for faces |
| v1.5 | [ip-adapter-full-face_sd15](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors) | ViT-H | Stronger face model, not necessarily better |
| v1.5 | [ip-adapter_sd15_vit-G](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors) | ViT-bigG | Base model trained with a bigG encoder |
| SDXL | [ip-adapter_sdxl](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors) | ViT-bigG | Base SDXL model, mostly deprecated |
| SDXL | [ip-adapter_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors) | ViT-H | New base SDXL model |
| SDXL | [ip-adapter-plus_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors) | ViT-H | SDXL plus model, stronger |
| SDXL | [ip-adapter-plus-face_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors) | ViT-H | SDXL face model |
**FaceID** requires `insightface`, you need to install them in your ComfyUI environment. Check [this issue](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/162) for help.
When the dependencies are satisfied you need:
| SD v. | IPadapter | Img encoder | Lora |
|---|---|---|---|
| v1.5 | [FaceID](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin) | (not used¹) | [FaceID Lora](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors) |
| v1.5 | [FaceID Plus](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15.bin) | ViT-H | [FaceID Plus Lora](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15_lora.safetensors) |
| v1.5 | [FaceID Plus v2](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15.bin) | ViT-H | [FaceID Plus v2 Lora](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors) |
| v1.5 | [FaceID Portrait](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sd15.bin) | (not used¹)| not needed |
| SDXL | [FaceID](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl.bin) | (not used¹) | [FaceID SDXL Lora](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl_lora.safetensors) |
| SDXL | [FaceID Plus v2](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl.bin) | ViT-H | [FaceID SDXL Lora](https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl_lora.safetensors) |
¹ The base FaceID model doesn't make use of a CLIP vision encoder. Remember to pair any FaceID model together with any other Face model to make it more effective.
The loras need to be placed into `ComfyUI/models/loras/` directory.
## Generic suggestions
There's a basic workflow included in this repo and a few examples in the [examples](./examples/) directory. Usually it's a good idea to lower the `weight` to at least `0.8` and increase the steps a little.
## Documentation soon to come...
Working on it!
## Troubleshooting
Please check the [troubleshooting](https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/108) before posting a new issue. Alse remember to check the previous closed issues.
## Credits
- [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/)
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
- [laksjdjf](https://github.com/laksjdjf/IPAdapter-ComfyUI/)
imports/ComfyUI_IPAdapter_plus/__init__.py
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"""
██▓ ██▓███ ▄▄▄ ▓█████▄ ▄▄▄ ██▓███ ▄▄▄█████▓▓█████ ██▀███
▓██▒▓██░ ██▒▒████▄ ▒██▀ ██▌▒████▄ ▓██░ ██▒▓ ██▒ ▓▒▓█ ▀ ▓██ ▒ ██▒
▒██▒▓██░ ██▓▒▒██ ▀█▄ ░██ █▌▒██ ▀█▄ ▓██░ ██▓▒▒ ▓██░ ▒░▒███ ▓██ ░▄█ ▒
░██░▒██▄█▓▒ ▒░██▄▄▄▄██ ░▓█▄ ▌░██▄▄▄▄██ ▒██▄█▓▒ ▒░ ▓██▓ ░ ▒▓█ ▄ ▒██▀▀█▄
░██░▒██▒ ░ ░ ▓█ ▓██▒░▒████▓ ▓█ ▓██▒▒██▒ ░ ░ ▒██▒ ░ ░▒████▒░██▓ ▒██▒
░▓ ▒▓▒░ ░ ░ ▒▒ ▓▒█░ ▒▒▓ ▒ ▒▒ ▓▒█░▒▓▒░ ░ ░ ▒ ░░ ░░ ▒░ ░░ ▒▓ ░▒▓░
▒ ░░▒ ░ ▒ ▒▒ ░ ░ ▒ ▒ ▒ ▒▒ ░░▒ ░ ░ ░ ░ ░ ░▒ ░ ▒░
▒ ░░░ ░ ▒ ░ ░ ░ ░ ▒ ░░ ░ ░ ░░ ░
░ ░ ░ ░ ░ ░ ░ ░ ░
░
·-—+ 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']
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_advanced.json
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{
"last_node_id": 17,
"last_link_id": 26,
"nodes": [
{
"id": 4,
"type": "CheckpointLoaderSimple",
"pos": [
50,
730
],
"size": {
"0": 315,
"1": 98
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
20
],
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
3,
5
],
"slot_index": 1
},
{
"name": "VAE",
"type": "VAE",
"links": [
8
],
"slot_index": 2
}
],
"properties": {
"Node name for S&R": "CheckpointLoaderSimple"
},
"widgets_values": [
"sd15/realisticVisionV51_v51VAE.safetensors"
]
},
{
"id": 9,
"type": "SaveImage",
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1770,
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],
"size": [
529.7760009765616,
582.3048192804504
],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9
}
],
"properties": {},
"widgets_values": [
"IPAdapter"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
690,
840
],
"size": {
"0": 425.27801513671875,
"1": 180.6060791015625
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 5
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
6
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"blurry, noisy, messy, lowres, jpeg, artifacts, ill, distorted, malformed"
]
},
{
"id": 8,
"type": "VAEDecode",
"pos": [
1570,
700
],
"size": [
140,
46
],
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 7
},
{
"name": "vae",
"type": "VAE",
"link": 8
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
9
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEDecode"
}
},
{
"id": 5,
"type": "EmptyLatentImage",
"pos": [
801,
1097
],
"size": {
"0": 315,
"1": 106
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
2
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "EmptyLatentImage"
},
"widgets_values": [
512,
512,
1
]
},
{
"id": 16,
"type": "CLIPVisionLoader",
"pos": [
308,
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],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_combine_embeds.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_faceid.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_faceid_batch.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_ideal_faceid_config.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_negative_image.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_noise_injection.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_simple.json
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_tiled.json
+582
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imports/ComfyUI_IPAdapter_plus/examples/ipadapter_weight_types.json
+1733
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File diff suppressed because it is too large Load Diff
imports/ComfyUI_IPAdapter_plus/examples/ipadapter_weighted_embeds.json
+836
View File
@@ -0,0 +1,836 @@
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}
],
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"type": "EMBEDS",
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imports/ComfyUI_IPAdapter_plus/image_proj_models.py
+275
View File
@@ -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 FeedForwardImport(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 PerceiverAttentionImport(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 ResamplerImport(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(
[
PerceiverAttentionImport(dim=dim, dim_head=dim_head, heads=heads),
FeedForwardImport(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 FacePerceiverResamplerImport(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(
[
PerceiverAttentionImport(dim=dim, dim_head=dim_head, heads=heads),
FeedForwardImport(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 MLPProjModelImport(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 MLPProjModelFaceIdImport(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 ProjModelFaceIdPlusImport(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 = FacePerceiverResamplerImport(
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 ImageProjModelImport(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
imports/ComfyUI_IPAdapter_plus/models/legacy_directory_do_not_use.txt
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imports/ComfyUI_IPAdapter_plus/utils.py
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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 = '(ViT.bigG.14.*39B.b160k|ipadapter.*sdxl|sdxl.*model\.(bin|safetensors))'
else:
pattern = '(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 = 'sd15.light.v11\.(safetensors|bin)$'
# if light model v11 is not found, try with the old version
if not [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]:
pattern = 'sd15.light\.(safetensors|bin)$'
elif preset.startswith("standard"):
if is_sdxl:
pattern = 'ip.adapter.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = 'ip.adapter.sd15\.(safetensors|bin)$'
elif preset.startswith("vit-g"):
if is_sdxl:
pattern = 'ip.adapter.sdxl\.(safetensors|bin)$'
else:
pattern = 'sd15.vit.g\.(safetensors|bin)$'
elif preset.startswith("plus ("):
if is_sdxl:
pattern = 'plus.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = 'ip.adapter.plus.sd15\.(safetensors|bin)$'
elif preset.startswith("plus face"):
if is_sdxl:
pattern = 'plus.face.sdxl.vit.h\.(safetensors|bin)$'
else:
pattern = 'plus.face.sd15\.(safetensors|bin)$'
elif preset.startswith("full"):
if is_sdxl:
raise Exception("full face model is not supported for SDXL")
pattern = 'full.face.sd15\.(safetensors|bin)$'
elif preset.startswith("faceid portrait"):
if is_sdxl:
raise Exception("portrait model is not supported for SDXL")
pattern = 'portrait.sd15\.(safetensors|bin)$'
is_insightface = True
elif preset == "faceid":
if is_sdxl:
pattern = 'faceid.sdxl\.(safetensors|bin)$'
lora_pattern = 'faceid.sdxl.lora\.safetensors$'
else:
pattern = 'faceid.sd15\.(safetensors|bin)$'
lora_pattern = 'faceid.sd15.lora\.safetensors$'
is_insightface = True
elif preset.startswith("faceid plus -"):
if is_sdxl:
raise Exception("faceid plus model is not supported for SDXL")
pattern = 'faceid.plus.sd15\.(safetensors|bin)$'
lora_pattern = 'faceid.plus.sd15.lora\.safetensors$'
is_insightface = True
elif preset.startswith("faceid plus v2"):
if is_sdxl:
pattern = 'faceid.plusv2.sdxl\.(safetensors|bin)$'
lora_pattern = 'faceid.plusv2.sdxl.lora\.safetensors$'
else:
pattern = 'faceid.plusv2.sd15\.(safetensors|bin)$'
lora_pattern = 'faceid.plusv2.sd15.lora\.safetensors$'
is_insightface = True
else:
raise Exception(f"invalid type '{preset}'")
ipadapter_file = [e for e in ipadapter_list if re.search(pattern, e, re.IGNORECASE)]
ipadapter_file = folder_paths.get_full_path("ipadapter", ipadapter_file[0]) if ipadapter_file else None
return ipadapter_file, is_insightface, lora_pattern
def get_lora_file(pattern):
lora_list = folder_paths.get_filename_list("loras")
lora_file = [e for e in lora_list if re.search(pattern, e, re.IGNORECASE)]
lora_file = folder_paths.get_full_path("loras", lora_file[0]) if lora_file else None
return lora_file
def ipadapter_model_loader(file):
model = comfy.utils.load_torch_file(file, safe_load=True)
if file.lower().endswith(".safetensors"):
st_model = {"image_proj": {}, "ip_adapter": {}}
for key in model.keys():
if key.startswith("image_proj."):
st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
elif key.startswith("ip_adapter."):
st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
model = st_model
del st_model
if not "ip_adapter" in model.keys() or not model["ip_adapter"]:
raise Exception("invalid IPAdapter model {}".format(file))
if 'plusv2' in file.lower():
model["faceidplusv2"] = True
return model
def insightface_loader(provider):
try:
from insightface.app import FaceAnalysis
except ImportError as e:
raise Exception(e)
path = os.path.join(folder_paths.models_dir, "insightface")
model = FaceAnalysis(name="buffalo_l", root=path, providers=[provider + 'ExecutionProvider',])
model.prepare(ctx_id=0, det_size=(640, 640))
return model
def encode_image_masked(clip_vision, image, mask=None):
model_management.load_model_gpu(clip_vision.patcher)
image = image.to(clip_vision.load_device)
pixel_values = clip_preprocess(image.to(clip_vision.load_device)).float()
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)
outputs = Output()
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())
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
imports/IPAdapterPlus.py
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