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import torch
import contextlib
import os
import math
import ldm_patched.modules.utils
import ldm_patched.modules.model_management
from ldm_patched.modules.clip_vision import clip_preprocess
from ldm_patched.ldm.modules.attention import optimized_attention
from modules_forge.shared import preprocessor_dir
from torch import nn
from PIL import Image
import torch.nn.functional as F
import torchvision.transforms as TT
from .resampler import PerceiverAttention, FeedForward, Resampler
GLOBAL_MODELS_DIR = preprocessor_dir
MODELS_DIR = preprocessor_dir
INSIGHTFACE_DIR = os.path.join(preprocessor_dir, "insightface")
os.makedirs(INSIGHTFACE_DIR, exist_ok=True)
class FacePerceiverResampler(torch.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 = torch.nn.Linear(embedding_dim, dim)
self.proj_out = torch.nn.Linear(dim, output_dim)
self.norm_out = torch.nn.LayerNorm(output_dim)
self.layers = torch.nn.ModuleList([])
for _ in range(depth):
self.layers.append(
torch.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(torch.nn.Module):
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 MLPProjModelFaceId(torch.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 = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, id_embeds):
clip_extra_context_tokens = self.proj(id_embeds)
clip_extra_context_tokens = clip_extra_context_tokens.reshape(-1, self.num_tokens, self.cross_attention_dim)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
class ProjModelFaceIdPlus(torch.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 = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
)
self.norm = torch.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
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])
ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher)
pixel_values = clip_preprocess(image.to(clip_vision.load_device)).float()
outputs = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
# we only need the penultimate hidden states
outputs = outputs[1].to(ldm_patched.modules.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)
def tensorToNP(image):
out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
out = out[..., [2, 1, 0]]
out = out.numpy()
return out
def NPToTensor(image):
out = torch.from_numpy(image)
out = torch.clamp(out.to(torch.float)/255., 0.0, 1.0)
out = out[..., [2, 1, 0]]
return out
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):
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_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 = 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
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=1280,
num_tokens=4,
)
else:
image_proj_model = MLPProjModelFaceId(
cross_attention_dim=self.cross_attention_dim,
id_embeddings_dim=512,
num_tokens=self.clip_extra_context_tokens,
)
return image_proj_model
@torch.inference_mode()
def get_image_embeds(self, clip_embed, clip_embed_zeroed):
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 CrossAttentionPatch:
# forward for patching
def __init__(self, weight, ipadapter, 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.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, 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.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
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/69a23c4d60c9e627409d0cb0f8862cdb015488eb/extras/ip_adapter.py#L234
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 = lh / math.sqrt(lh * lw / qs)
mask_h = int(mask_h) + int((qs % int(mask_h)) != 0)
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:
RETURN_TYPES = ("IPADAPTER",)
FUNCTION = "load_ipadapter_model"
CATEGORY = "ipadapter"
def load_ipadapter_model(self, ckpt_path):
model = ldm_patched.modules.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,)
insightface_face_align = None
class InsightFaceLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"provider": (["CPU", "CUDA", "ROCM"], ),
},
}
RETURN_TYPES = ("INSIGHTFACE",)
FUNCTION = "load_insight_face"
CATEGORY = "ipadapter"
def load_insight_face(self, provider):
try:
from insightface.app import FaceAnalysis
except ImportError as e:
raise Exception(e)
from insightface.utils import face_align
global insightface_face_align
insightface_face_align = face_align
model = FaceAnalysis(name="buffalo_l", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',])
model.prepare(ctx_id=0, det_size=(640, 640))
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, insightface=None, faceid_v2=False, weight_v2=False):
self.dtype = torch.float16 if ldm_patched.modules.model_management.should_use_fp16() else torch.float32
self.device = ldm_patched.modules.model_management.get_torch_device()
self.weight = weight
self.is_full = "proj.3.weight" in ipadapter["image_proj"]
self.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"]
self.is_faceid = self.is_portrait or "0.to_q_lora.down.weight" in ipadapter["ip_adapter"]
self.is_plus = (self.is_full or "latents" in ipadapter["image_proj"] or "perceiver_resampler.proj_in.weight" in ipadapter["image_proj"])
if self.is_faceid and not insightface:
raise Exception('InsightFace must be provided for FaceID models.')
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 and not self.is_faceid else output_cross_attention_dim
clip_extra_context_tokens = 16 if self.is_plus or self.is_portrait else 4
if embeds is not None:
embeds = torch.unbind(embeds)
clip_embed = embeds[0].cpu()
clip_embed_zeroed = embeds[1].cpu()
else:
if self.is_faceid:
insightface.det_model.input_size = (640,640) # reset the detection size
face_img = tensorToNP(image)
face_embed = []
face_clipvision = []
for i in range(face_img.shape[0]):
for size in [(size, size) for size in range(640, 128, -64)]:
insightface.det_model.input_size = size # TODO: hacky but seems to be working
face = insightface.get(face_img[i])
if face:
face_embed.append(torch.from_numpy(face[0].normed_embedding).unsqueeze(0))
face_clipvision.append(NPToTensor(insightface_face_align.norm_crop(face_img[i], landmark=face[0].kps, image_size=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_embed = torch.stack(face_embed, dim=0)
image = torch.stack(face_clipvision, dim=0)
neg_image = image_add_noise(image, noise) if noise > 0 else None
if self.is_plus:
clip_embed = clip_vision.encode_image(image).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])
# TODO: check noise to the uncods too
face_embed_zeroed = torch.zeros_like(face_embed)
else:
clip_embed = face_embed
clip_embed_zeroed = torch.zeros_like(clip_embed)
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,
is_faceid=self.is_faceid,
)
self.ipadapter.to(self.device, dtype=self.dtype)
if self.is_faceid and self.is_plus:
image_prompt_embeds = self.ipadapter.get_image_embeds_faceid_plus(face_embed.to(self.device, dtype=self.dtype), clip_embed.to(self.device, dtype=self.dtype), weight_v2, faceid_v2)
uncond_image_prompt_embeds = self.ipadapter.get_image_embeds_faceid_plus(face_embed_zeroed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=self.dtype), weight_v2, faceid_v2)
else:
image_prompt_embeds, uncond_image_prompt_embeds = self.ipadapter.get_image_embeds(clip_embed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=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,
"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 IPAdapterApplyFaceID(IPAdapterApply):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ipadapter": ("IPADAPTER", ),
"clip_vision": ("CLIP_VISION",),
"insightface": ("INSIGHTFACE",),
"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 }),
"faceid_v2": ("BOOLEAN", { "default": False }),
"weight_v2": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
"unfold_batch": ("BOOLEAN", { "default": False }),
},
"optional": {
"attn_mask": ("MASK",),
}
}
def prepImage(image, interpolation="LANCZOS", crop_position="center", size=(224,224), sharpening=0.0, padding=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(size, resample=Image.Resampling[interpolation])
imgs.append(TT.ToTensor()(img))
output = torch.stack(imgs, dim=0)
imgs = None # zelous GC
if sharpening > 0:
output = contrast_adaptive_sharpening(output, sharpening)
if padding > 0:
output = F.pad(output, (padding, padding, padding, padding), value=255, mode="constant")
output = output.permute([0,2,3,1])
return output
class PrepImageForInsightFace:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"crop_position": (["center", "top", "bottom", "left", "right"],),
"sharpening": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}),
"pad_around": ("BOOLEAN", { "default": True }),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "prep_image"
CATEGORY = "ipadapter"
def prep_image(self, image, crop_position, sharpening=0.0, pad_around=True):
if pad_around:
padding = 30
size = (580, 580)
else:
padding = 0
size = (640, 640)
output = prepImage(image, "LANCZOS", crop_position, size, sharpening, padding)
return (output, )
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):
size = (224, 224)
output = prepImage(image, interpolation, crop_position, size, sharpening, 0)
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_2 *= (0.1 + (weight_2 - 0.1))
weight_3 *= (0.1 + (weight_3 - 0.1))
weight_4 *= (0.1 + (weight_4 - 0.1))
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 = ldm_patched.modules.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 = ldm_patched.modules.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 = ldm_patched.modules.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 IPAdapterBatchEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"embed1": ("EMBEDS",),
"embed2": ("EMBEDS",),
}}
RETURN_TYPES = ("EMBEDS",)
FUNCTION = "batch"
CATEGORY = "ipadapter"
def batch(self, embed1, embed2):
return (torch.cat((embed1, embed2), dim=1), )

674
extensions-builtin/sd_forge_ipadapter/LICENSE Normal file
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@@ -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
of this license document, but changing it is not allowed.
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To protect your rights, we need to prevent others from denying you
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Developers that use the GNU GPL protect your rights with two steps:
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extensions-builtin/sd_forge_ipadapter/resampler.py Normal file
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# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
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)

1
extensions-builtin/sd_forge_ipadapter/thanks Normal file
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This repo is modified from https://github.com/cubiq/ComfyUI_IPAdapter_plus