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README.md
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# ComfyUI-IPAdapter-SD3
ComfyUI implementation of the [InstantX IP-Adapter for SD3.5 Large](https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter).
## Installation
Download [`ip-adapter.bin` from the original repository](https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter/blob/main/ip-adapter.bin), and place it in the `models/ipadapter` folder of your ComfyUI installation.
__init__.py
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
models/jointblock.py
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from typing import Optional
import torch
from torch import nn
from torch.nn import functional as F
from einops import rearrange
from comfy.ldm.modules.attention import optimized_attention
from comfy.ldm.modules.diffusionmodules.mmdit import (
DismantledBlock,
RMSNorm,
JointBlock,
)
class AdaLayerNorm(nn.Module):
"""
Norm layer adaptive layer norm zero (adaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, time_embedding_dim=None, mode="normal"):
super().__init__()
self.silu = nn.SiLU()
num_params_dict = dict(
zero=6,
normal=2,
)
num_params = num_params_dict[mode]
self.linear = nn.Linear(
time_embedding_dim or embedding_dim, num_params * embedding_dim, bias=True
)
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
self.mode = mode
def forward(
self,
x,
hidden_dtype=None,
emb=None,
):
emb = self.linear(self.silu(emb))
if self.mode == "normal":
shift_msa, scale_msa = emb.chunk(2, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x
elif self.mode == "zero":
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(
6, dim=1
)
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class IPAttnProcessor(nn.Module):
def __init__(
self,
hidden_size=None,
cross_attention_dim=None,
ip_hidden_states_dim=None,
ip_encoder_hidden_states_dim=None,
head_dim=None,
timesteps_emb_dim=1280,
):
super().__init__()
self.norm_ip = AdaLayerNorm(
ip_hidden_states_dim, time_embedding_dim=timesteps_emb_dim
)
self.to_k_ip = nn.Linear(ip_hidden_states_dim, hidden_size, bias=False)
self.to_v_ip = nn.Linear(ip_hidden_states_dim, hidden_size, bias=False)
self.norm_q = RMSNorm(head_dim, 1e-6)
self.norm_k = RMSNorm(head_dim, 1e-6)
self.norm_ip_k = RMSNorm(head_dim, 1e-6)
def forward(
self,
ip_hidden_states,
img_query,
img_key=None,
img_value=None,
t_emb=None,
n_heads=1,
):
if ip_hidden_states is None:
return None
if not hasattr(self, "to_k_ip") or not hasattr(self, "to_v_ip"):
return None
# norm ip input
norm_ip_hidden_states = self.norm_ip(ip_hidden_states, emb=t_emb)
# to k and v
ip_key = self.to_k_ip(norm_ip_hidden_states)
ip_value = self.to_v_ip(norm_ip_hidden_states)
# reshape
img_query = rearrange(img_query, "b l (h d) -> b h l d", h=n_heads)
img_key = rearrange(img_key, "b l (h d) -> b h l d", h=n_heads)
# note that the image is in a different shape: b l h d
# so we transpose to b h l d
# or do we have to transpose here?
img_value = torch.transpose(img_value, 1, 2)
ip_key = rearrange(ip_key, "b l (h d) -> b h l d", h=n_heads)
ip_value = rearrange(ip_value, "b l (h d) -> b h l d", h=n_heads)
# norm
img_query = self.norm_q(img_query)
img_key = self.norm_k(img_key)
ip_key = self.norm_ip_k(ip_key)
# cat img
key = torch.cat([img_key, ip_key], dim=2)
value = torch.cat([img_value, ip_value], dim=2)
#
ip_hidden_states = F.scaled_dot_product_attention(
img_query, key, value, dropout_p=0.0, is_causal=False
)
ip_hidden_states = rearrange(ip_hidden_states, "b h l d -> b l (h d)")
ip_hidden_states = ip_hidden_states.to(img_query.dtype)
return ip_hidden_states
class JointBlockIPWrapper:
"""To be used as a patch_replace with Comfy"""
def __init__(
self,
original_block: JointBlock,
adapter: IPAttnProcessor,
ip_options=None,
):
self.original_block = original_block
self.adapter = adapter
self.ip_options = ip_options
def block_mixing(self, context, x, context_block, x_block, c):
"""
Comes from mmdit.py. Modified to add ipadapter attention.
"""
context_qkv, context_intermediates = context_block.pre_attention(context, c)
if x_block.x_block_self_attn:
x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)
else:
x_qkv, x_intermediates = x_block.pre_attention(x, c)
qkv = tuple(torch.cat((context_qkv[j], x_qkv[j]), dim=1) for j in range(3))
attn = optimized_attention(
qkv[0],
qkv[1],
qkv[2],
heads=x_block.attn.num_heads,
)
context_attn, x_attn = (
attn[:, : context_qkv[0].shape[1]],
attn[:, context_qkv[0].shape[1] :],
)
# IP-Adapter
ip_attn = self.adapter(
self.ip_options["hidden_states"],
*x_qkv,
self.ip_options["t_emb"],
x_block.attn.num_heads,
)
x_attn = x_attn + ip_attn * self.ip_options["weight"]
# Everything else is unchanged
if not context_block.pre_only:
context = context_block.post_attention(context_attn, *context_intermediates)
else:
context = None
if x_block.x_block_self_attn:
attn2 = optimized_attention(
x_qkv2[0],
x_qkv2[1],
x_qkv2[2],
heads=x_block.attn2.num_heads,
)
x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
else:
x = x_block.post_attention(x_attn, *x_intermediates)
return context, x
def __call__(self, args, _):
# Code from mmdit.py:
# in this case, we're blocks_replace[("double_block", i)]
# note that although we're passed the original block,
# we can't actually get it from inside its wrapper
# (which would simplify the whole code...)
# ```
# def block_wrap(args):
# out = {}
# out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
# return out
# out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
# context = out["txt"]
# x = out["img"]
# ```
c, x = self.block_mixing(
args["txt"],
args["img"],
self.original_block.context_block,
self.original_block.x_block,
c=args["vec"],
)
return {"txt": c, "img": x}
models/resampler.py
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# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
import math
import torch
import torch.nn as nn
from diffusers.models.embeddings import Timesteps, TimestepEmbedding
def get_timestep_embedding(
timesteps: torch.Tensor,
embedding_dim: int,
flip_sin_to_cos: bool = False,
downscale_freq_shift: float = 1,
scale: float = 1,
max_period: int = 10000,
):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
embeddings. :return: an [N x dim] Tensor of positional embeddings.
"""
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
half_dim = embedding_dim // 2
exponent = -math.log(max_period) * torch.arange(
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
)
exponent = exponent / (half_dim - downscale_freq_shift)
emb = torch.exp(exponent)
emb = timesteps[:, None].float() * emb[None, :]
# scale embeddings
emb = scale * emb
# concat sine and cosine embeddings
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
# flip sine and cosine embeddings
if flip_sin_to_cos:
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
# zero pad
if embedding_dim % 2 == 1:
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
# 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, shift=None, scale=None):
"""
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)
if shift is not None and scale is not None:
latents = latents * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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,
*args,
**kwargs,
):
super().__init__()
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.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):
latents = self.latents.repeat(x.size(0), 1, 1)
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 TimeResampler(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,
timestep_in_dim=320,
timestep_flip_sin_to_cos=True,
timestep_freq_shift=0,
):
super().__init__()
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.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
# msa
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
# ff
FeedForward(dim=dim, mult=ff_mult),
# adaLN
nn.Sequential(nn.SiLU(), nn.Linear(dim, 4 * dim, bias=True)),
]
)
)
# time
self.time_proj = Timesteps(
timestep_in_dim, timestep_flip_sin_to_cos, timestep_freq_shift
)
self.time_embedding = TimestepEmbedding(timestep_in_dim, dim, act_fn="silu")
# adaLN
# self.adaLN_modulation = nn.Sequential(
# nn.SiLU(),
# nn.Linear(timestep_out_dim, 6 * timestep_out_dim, bias=True)
# )
def forward(self, x, timestep, need_temb=False):
timestep_emb = self.embedding_time(x, timestep) # bs, dim
latents = self.latents.repeat(x.size(0), 1, 1)
x = self.proj_in(x)
x = x + timestep_emb[:, None]
for attn, ff, adaLN_modulation in self.layers:
shift_msa, scale_msa, shift_mlp, scale_mlp = adaLN_modulation(
timestep_emb
).chunk(4, dim=1)
latents = attn(x, latents, shift_msa, scale_msa) + latents
res = latents
for idx_ff in range(len(ff)):
layer_ff = ff[idx_ff]
latents = layer_ff(latents)
if idx_ff == 0 and isinstance(layer_ff, nn.LayerNorm): # adaLN
latents = latents * (
1 + scale_mlp.unsqueeze(1)
) + shift_mlp.unsqueeze(1)
latents = latents + res
# latents = ff(latents) + latents
latents = self.proj_out(latents)
latents = self.norm_out(latents)
if need_temb:
return latents, timestep_emb
else:
return latents
def embedding_time(self, sample, timestep):
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, None)
return emb
if __name__ == "__main__":
model = TimeResampler(
dim=1280,
depth=4,
dim_head=64,
heads=20,
num_queries=16,
embedding_dim=512,
output_dim=2048,
ff_mult=4,
timestep_in_dim=320,
timestep_flip_sin_to_cos=True,
timestep_freq_shift=0,
in_channel_extra_emb=2048,
)
nodes.py
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import os
import logging
from functools import partial
import torch
import folder_paths
from transformers import AutoProcessor, SiglipVisionModel
from PIL import Image
import numpy as np
from .models.resampler import TimeResampler
from .models.jointblock import JointBlockIPWrapper, IPAttnProcessor
MODELS_DIR = os.path.join(folder_paths.models_dir, "ipadapter")
if "ipadapter" not in folder_paths.folder_names_and_paths:
current_paths = [MODELS_DIR]
else:
current_paths, _ = folder_paths.folder_names_and_paths["ipadapter"]
folder_paths.folder_names_and_paths["ipadapter"] = (
current_paths,
folder_paths.supported_pt_extensions,
)
def patch(patcher, ip_procs, curried_resampler, weight=1.0):
"""
Patches a model_sampler to add the ipadapter
"""
mmdit = patcher.model.diffusion_model
timestep_schedule_max = patcher.model.model_config.sampling_settings.get("timesteps", 1000)
# hook the model's forward function
# so that when it gets called, we can grab the timestep and send it to the resampler
ip_options = {"hidden_states": None, "t_emb": None, "t": None, "weight": weight}
def ddit_wrapper(forward, args):
# these will need to be accessible to the IPAdapters
# this is between 0 and 1, so the adapters can calculate start_point and end_point
ip_options["t"] = args["timestep"]
# but the resampler wants between 0 and MAX_STEPS
timestep_expanded = args["timestep"] * timestep_schedule_max
image_emb, t_emb = curried_resampler(timestep_expanded)
ip_options["hidden_states"] = image_emb
ip_options["t_emb"] = t_emb
# is this the right way to call?
return forward(args["input"], args["timestep"], **args["c"])
patcher.set_model_unet_function_wrapper(ddit_wrapper)
# patch each dit block
for i, block in enumerate(mmdit.joint_blocks):
wrapper = JointBlockIPWrapper(block, ip_procs[i], ip_options)
patcher.set_model_patch_replace(wrapper, "dit", "double_block", i)
class SD3IPAdapter:
def __init__(self, encoder_path: str, checkpoint: str, device):
self.device = device
# load image encoder
self.encoder = SiglipVisionModel.from_pretrained(encoder_path).to(
self.device, dtype=torch.float16
)
self.processor = AutoProcessor.from_pretrained(encoder_path)
# load the checkpoint right away
self.state_dict = torch.load(
os.path.join(MODELS_DIR, checkpoint),
map_location=self.device,
weights_only=True,
)
# todo: infer some of the params from the checkpoint instead of hardcoded
self.resampler = TimeResampler(
dim=1280,
depth=4,
dim_head=64,
heads=20,
num_queries=64,
embedding_dim=1152,
output_dim=2432,
ff_mult=4,
timestep_in_dim=320,
timestep_flip_sin_to_cos=True,
timestep_freq_shift=0,
)
self.resampler.eval()
self.resampler.to(self.device, dtype=torch.float16)
self.resampler.load_state_dict(self.state_dict["image_proj"])
# now we'll create the attention processors
# ip_adapter.keys looks like [0.proj, 0.to_k, ..., 1.proj, 1.to_k, ...]
n_procs = len(
set(x.split(".")[0] for x in self.state_dict["ip_adapter"].keys())
)
self.procs = torch.nn.ModuleList(
[
# this is hardcoded for SD3.5L
IPAttnProcessor(
hidden_size=2432,
cross_attention_dim=2432,
ip_hidden_states_dim=2432,
ip_encoder_hidden_states_dim=2432,
head_dim=64,
timesteps_emb_dim=1280,
).to(self.device, dtype=torch.float16)
for _ in range(n_procs)
]
)
self.procs.load_state_dict(self.state_dict["ip_adapter"])
@torch.inference_mode()
def encode(self, image):
clip_image = self.processor.image_processor(
image, return_tensors="pt", do_rescale=False
).pixel_values
clip_image_embeds = self.encoder(
clip_image.to(self.device, dtype=self.encoder.dtype),
output_hidden_states=True,
).hidden_states[-2]
clip_image_embeds = torch.cat(
[torch.zeros_like(clip_image_embeds), clip_image_embeds], dim=0
)
clip_image_embeds = clip_image_embeds.to(dtype=torch.float16)
return clip_image_embeds
class IPAdapterSD3Loader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ipadapter": (folder_paths.get_filename_list("ipadapter"),),
"clip_vision": (["google/siglip-so400m-patch14-384"],),
"provider": (["cuda", "cpu", "mps"],),
}
}
RETURN_TYPES = ("IP_ADAPTER_SD3_INSTANTX",)
RETURN_NAMES = ("ipadapter",)
FUNCTION = "load_model"
CATEGORY = "InstantXNodes"
def load_model(self, ipadapter, clip_vision, provider):
logging.info("Loading InstantX IPAdapter SD3 model.")
model = SD3IPAdapter(clip_vision, ipadapter, provider)
return (model,)
class ApplyIPAdapterSD3:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"ipadapter": ("IP_ADAPTER_SD3_INSTANTX",),
"image": ("IMAGE",),
"weight": (
"FLOAT",
{"default": 1.0, "min": -1.0, "max": 5.0, "step": 0.05},
),
"start_percent": (
"FLOAT",
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"end_percent": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
},
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply_ipadapter"
CATEGORY = "InstantXNodes"
def apply_ipadapter(
self, model, ipadapter, image, weight, start_percent, end_percent
):
# set model
new_model = model.clone()
embeds = ipadapter.encode(image)
curried_resampler = partial(ipadapter.resampler, embeds, need_temb=True)
patch(new_model, ipadapter.procs, curried_resampler, weight=weight)
return (new_model,)
NODE_CLASS_MAPPINGS = {
"IPAdapterSD3Loader": IPAdapterSD3Loader,
"ApplyIPAdapterSD3": ApplyIPAdapterSD3,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"IPAdapterSD3Loader": "Load IPAdapter SD3 Model",
"ApplyIPAdapterSD3": "Apply IPAdapter SD3 Model",
}
requirements.txt
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torch
pillow
einops==0.8.0
transformers>=4.37.2
diffusers
sentencepiece>=0.2.0
protobuf>=4.25.5