nodes/cubiq-ComfyUI_IPAdapter_plus
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streamlined workflow. added noise feature

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This commit is contained in:
matt3o
2023-09-15 15:31:08 +02:00
parent 3497984180
commit 1cbe9c829c
13 changed files with 2405 additions and 1938 deletions
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IPAdapterPlus.py
+46 -75
View File
@@ -8,6 +8,7 @@ from comfy.clip_vision import ClipVisionModel
from torch import nn
import torch.nn.functional as F
import torchvision.transforms as TT
from .resampler import Resampler
@@ -17,8 +18,6 @@ MODELS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2
SD_XL_CHANNELS = [640] * 8 + [1280] * 40 + [1280] * 60 + [640] * 12 + [1280] * 20
MAX_NOISE = 5.0
def get_filename_list(path):
return [f for f in os.listdir(path) if f.endswith('.bin')]
@@ -77,20 +76,38 @@ def attention(q, k, v, extra_options):
out = out.transpose(1, 2).reshape(b, -1, extra_options["n_heads"] * extra_options["dim_head"])
return out
def encode_zero_image(clip_vision, noise=0):
image = torch.zeros( [1, 3, 224, 224] )
# TODO: still have to find the best way to add noise to the uncod image
def image_add_noise(image, noise):
image = image.permute([0,3,1,2])
transforms = TT.Compose([
TT.CenterCrop(min(image.shape[2], image.shape[3])),
TT.Resize((28, 28), antialias=True),
#TT.Resize((224, 224), interpolation=TT.InterpolationMode.NEAREST, antialias=True),
#TT.ColorJitter(contrast=(0.75,0.75)),
#TT.ElasticTransform(alpha=75.0, sigma=noise+.25),
TT.RandomVerticalFlip(p=1.0),
TT.RandomHorizontalFlip(p=1.0),
])
torch.manual_seed(0) # use a fixed random for reproducible results
image = transforms(image)
image = image.permute([0,2,3,1])
image = image + ((1.9-1.9*noise) * torch.randn_like(image) + 0.1)
return image
def encode_neg_image(clip_vision, image=None, noise=0):
if noise > 0 and image is not None:
image = image_add_noise(image, noise)
if image is None:
image = torch.zeros( [1, 3, 224, 224] )
img = torch.clip((255. * image), 0, 255).round().int()
img = list(map(lambda a: a, img))
inputs = clip_vision.processor(images=img, return_tensors="pt")
comfy.model_management.load_model_gpu(clip_vision.patcher)
pixel_values = inputs['pixel_values'].to(clip_vision.load_device)
if noise > 0:
min_noise = -noise
max_noise = noise
torch.manual_seed(0) # fixed seed for reproducible results
pixel_values = (max_noise - min_noise) * torch.rand_like(pixel_values) + min_noise
else:
if noise == 0:
pixel_values = torch.zeros_like(pixel_values)
if clip_vision.dtype != torch.float32:
@@ -116,17 +133,16 @@ class IPAdapter(nn.Module):
def __init__(self, ipadapter_model, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
super().__init__()
self.ipadapter_model = ipadapter_model
self.clip_embeddings_dim = clip_embeddings_dim
self.cross_attention_dim = self.ipadapter_model["ip_adapter"]["1.to_k_ip.weight"].shape[1]
self.cross_attention_dim = ipadapter_model["ip_adapter"]["1.to_k_ip.weight"].shape[1]
self.clip_extra_context_tokens = clip_extra_context_tokens
self.image_proj_model = self.init_proj()
self.image_proj_model.load_state_dict(self.ipadapter_model["image_proj"])
self.image_proj_model.load_state_dict(ipadapter_model["image_proj"])
self.ip_layers = To_KV(cross_attention_dim)
self.ip_layers.load_state_dict(self.ipadapter_model["ip_adapter"])
self.ip_layers.load_state_dict(ipadapter_model["ip_adapter"])
def init_proj(self):
image_proj_model = ImageProjModel(
cross_attention_dim=self.cross_attention_dim,
@@ -192,34 +208,11 @@ class CrossAttentionPatch:
ip_v = ipadapter.ip_layers.to_kvs[self.number*2+1](uncond_cond)
ip_out = attention(q, ip_k, ip_v, extra_options)
if mask is not None:
mask_size = mask.shape[0] * mask.shape[1]
down_sample_rate = int((mask_size // 64 // out.shape[1]) ** (1/2))
mask_downsample = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), scale_factor= 1/8/down_sample_rate, mode="nearest").squeeze(0)
mask_downsample = mask_downsample.view(1,-1, 1).repeat(out.shape[0], 1, out.shape[2])
ip_out = ip_out * mask_downsample
out = out + ip_out * weight
return out.to(dtype=org_dtype)
class IPAdapterCLIPVisionEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"image": ("IMAGE",)
}}
RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
FUNCTION = "encode"
CATEGORY = "ipadapter"
def encode(self, clip_vision, image):
output = clip_vision.encode_image(image)
return ((output, clip_vision), )
class IPAdapterModelLoader:
@classmethod
def INPUT_TYPES(s):
@@ -246,63 +239,44 @@ class IPAdapterApply:
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL", ),
"ipadapter": ("IPADAPTER", ),
"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
"clip_vision": ("CLIP_VISION",),
"image": ("IMAGE",),
"model": ("MODEL", ),
"weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.1 }),
"noise": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01 })
},
"hidden": {
"noise": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("MODEL",)
RETURN_TYPES = ("MODEL", )
FUNCTION = "apply_ipadapter"
CATEGORY = "ipadapter"
uncond_hidden_states = None
def apply_ipadapter(self, model, ipadapter, clip_vision_output, weight, noise=0, mask=None):
def apply_ipadapter(self, ipadapter, clip_vision, image, model, weight, noise):
self.dtype = model.model.diffusion_model.dtype
self.device = comfy.model_management.get_torch_device()
self.weight = weight
# check if we are using the 'CLIP Vision Encode IPAdapter' node
self.is_ipadapter_encode = type(clip_vision_output) is tuple
if self.is_ipadapter_encode:
clip_embed, clip_vision_model = clip_vision_output
else:
clip_embed = clip_vision_output.image_embeds
cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
self.is_sdxl = cross_attention_dim == 2048
clip_embed = clip_vision.encode_image(image)
self.is_plus = "latents" in ipadapter["image_proj"]
if self.is_plus:
# IPAdapter Plus requires the IPAdapter Encoder
if not self.is_ipadapter_encode:
raise Exception("IPAdapter Plus requires 'CLIP Vision Encode (IPAdapter)' node")
clip_embed = clip_embed.penultimate_hidden_states
clip_embed_zeroed = encode_zero_image(clip_vision_model, noise).penultimate_hidden_states
clip_extra_context_tokens = 16
clip_embed = clip_embed.penultimate_hidden_states
clip_embed_zeroed = encode_neg_image(clip_vision, image=image, noise=noise).penultimate_hidden_states
else:
if self.is_ipadapter_encode:
clip_embed = clip_embed.image_embeds
clip_extra_context_tokens = 4
clip_embed = clip_embed.image_embeds
if noise > 0:
noise = MAX_NOISE * noise
clip_embed_zeroed = encode_zero_image(clip_vision_model, noise).image_embeds
clip_embed_zeroed = encode_neg_image(clip_vision, image=image, noise=noise).image_embeds
else:
clip_embed_zeroed = torch.zeros_like(clip_embed)
clip_extra_context_tokens = 4
clip_embeddings_dim = clip_embed.shape[-1]
cross_attention_dim = ipadapter["ip_adapter"]["1.to_k_ip.weight"].shape[1]
self.is_sdxl = cross_attention_dim == 2048
IPA = IPAdapterPlus if self.is_plus else IPAdapter
@@ -328,7 +302,6 @@ class IPAdapterApply:
"dtype": self.dtype,
"cond": image_prompt_embeds,
"uncond": uncond_image_prompt_embeds,
"mask": mask if mask is None else mask.to(self.device, dtype=self.dtype)
}
if not self.is_sdxl:
@@ -354,16 +327,14 @@ class IPAdapterApply:
set_model_patch_replace(work_model, patch_kwargs, ("midlle", 0, index))
patch_kwargs["number"] += 1
return (work_model,)
return (work_model, )
NODE_CLASS_MAPPINGS = {
"IPAdapterModelLoader": IPAdapterModelLoader,
"IPAdapterApply": IPAdapterApply,
"IPAdapterCLIPVisionEncode": IPAdapterCLIPVisionEncode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"IPAdapterModelLoader": "Load IPAdapter Model",
"IPAdapterApply": "Apply IPAdapter",
"IPAdapterCLIPVisionEncode": "CLIP Vision Encode (IPAdapter)"
}
IPAdapter_workflow-sdxl_vit-h.json
+597
View File
@@ -0,0 +1,597 @@
{
"last_node_id": 12,
"last_link_id": 13,
"nodes": [
{
"id": 8,
"type": "CLIPTextEncode",
"pos": [
650,
420
],
"size": [
210,
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],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 6
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
9
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
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"widgets_values": [
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"color": "#322",
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],
"flags": {},
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"mode": 0,
"inputs": [
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"link": 5
}
],
"outputs": [
{
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"type": "CONDITIONING",
"links": [
8
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"beautiful renaissance girl, detailed"
],
"color": "#232",
"bgcolor": "#353"
},
{
"id": 11,
"type": "VAEDecode",
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],
"size": [
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],
"flags": {},
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"inputs": [
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},
{
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"link": 12
}
],
"outputs": [
{
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"type": "IMAGE",
"links": [
13
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
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}
},
{
"id": 12,
"type": "SaveImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
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"link": 13
}
],
"properties": {},
"widgets_values": [
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]
},
{
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"type": "LoadImage",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
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"links": [
3
],
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},
{
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"type": "MASK",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"venere.jpg",
"image"
]
},
{
"id": 3,
"type": "IPAdapterModelLoader",
"pos": [
290,
60
],
"size": [
300,
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],
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "IPADAPTER",
"type": "IPADAPTER",
"links": [
1
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "IPAdapterModelLoader"
},
"widgets_values": [
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]
},
{
"id": 1,
"type": "CheckpointLoaderSimple",
"pos": [
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280
],
"size": [
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],
"flags": {},
"order": 2,
"mode": 0,
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{
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"type": "MODEL",
"links": [
4
],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
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6
],
"shape": 3,
"slot_index": 1
},
{
"name": "VAE",
"type": "VAE",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "CheckpointLoaderSimple"
},
"widgets_values": [
"sd_xl_base_1.0.safetensors"
]
},
{
"id": 2,
"type": "VAELoader",
"pos": [
940,
480
],
"size": [
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],
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
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"type": "VAE",
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],
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}
],
"properties": {
"Node name for S&R": "VAELoader"
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"widgets_values": [
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},
{
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],
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],
"flags": {},
"order": 4,
"mode": 0,
"outputs": [
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"links": [
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],
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}
],
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"Node name for S&R": "EmptyLatentImage"
},
"widgets_values": [
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]
},
{
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"type": "CLIPVisionLoader",
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],
"size": [
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],
"flags": {},
"order": 5,
"mode": 0,
"outputs": [
{
"name": "CLIP_VISION",
"type": "CLIP_VISION",
"links": [
2
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPVisionLoader"
},
"widgets_values": [
"IP-Adapter_sd15_pytorch_model.bin"
]
},
{
"id": 5,
"type": "IPAdapterApply",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "ipadapter",
"type": "IPADAPTER",
"link": 1
},
{
"name": "clip_vision",
"type": "CLIP_VISION",
"link": 2
},
{
"name": "image",
"type": "IMAGE",
"link": 3
},
{
"name": "model",
"type": "MODEL",
"link": 4
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
7
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "IPAdapterApply"
},
"widgets_values": [
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]
},
{
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"type": "KSampler",
"pos": [
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],
"size": {
"0": 315,
"1": 262
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 7
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 8
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 9
},
{
"name": "latent_image",
"type": "LATENT",
"link": 10
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
11
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KSampler"
},
"widgets_values": [
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"ddim",
"ddim_uniform",
1
]
}
],
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"IPADAPTER"
],
[
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],
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]
],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}
IPAdapter_workflow.json
+373 -392
View File
@@ -1,20 +1,166 @@
{
"last_node_id": 33,
"last_link_id": 70,
"last_node_id": 12,
"last_link_id": 13,
"nodes": [
{
"id": 4,
"type": "CLIPVisionLoader",
"pos": [
290,
170
],
"size": {
"0": 300,
"1": 60
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "CLIP_VISION",
"type": "CLIP_VISION",
"links": [
2
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPVisionLoader"
},
"widgets_values": [
"IP-Adapter_sd15_pytorch_model.bin"
]
},
{
"id": 1,
"type": "CheckpointLoaderSimple",
"pos": [
290,
280
],
"size": {
"0": 300,
"1": 100
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
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"type": "MODEL",
"links": [
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],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
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],
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},
{
"name": "VAE",
"type": "VAE",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "CheckpointLoaderSimple"
},
"widgets_values": [
"absolutereality_v181.safetensors"
]
},
{
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"pos": [
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],
"size": {
"0": 210,
"1": 110
},
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"order": 2,
"mode": 0,
"outputs": [
{
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"type": "LATENT",
"links": [
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],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "EmptyLatentImage"
},
"widgets_values": [
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]
},
{
"id": 3,
"type": "IPAdapterModelLoader",
"pos": [
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],
"size": {
"0": 300,
"1": 60
},
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "IPADAPTER",
"type": "IPADAPTER",
"links": [
1
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "IPAdapterModelLoader"
},
"widgets_values": [
"ip-adapter_sd15.bin"
]
},
{
"id": 8,
"type": "CLIPTextEncode",
"pos": [
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650,
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],
"size": {
"0": 210,
"1": 76
"1": 120
},
"flags": {},
"order": 6,
"order": 7,
"mode": 0,
"inputs": [
{
@@ -28,7 +174,7 @@
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
20
9
],
"shape": 3,
"slot_index": 0
@@ -38,30 +184,30 @@
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"a girl with fox ears"
"blurry, horror"
],
"color": "#232",
"bgcolor": "#353"
"color": "#322",
"bgcolor": "#533"
},
{
"id": 9,
"id": 7,
"type": "CLIPTextEncode",
"pos": [
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650,
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],
"size": {
"0": 210,
"1": 76
"1": 120
},
"flags": {},
"order": 7,
"order": 6,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 7
"link": 5
}
],
"outputs": [
@@ -69,7 +215,7 @@
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
21
8
],
"shape": 3,
"slot_index": 0
@@ -79,17 +225,17 @@
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"horror"
"beautiful renaissance girl, detailed"
],
"color": "#322",
"bgcolor": "#533"
"color": "#232",
"bgcolor": "#353"
},
{
"id": 12,
"id": 11,
"type": "VAEDecode",
"pos": [
1430,
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1300,
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],
"size": {
"0": 140,
@@ -102,12 +248,12 @@
{
"name": "samples",
"type": "LATENT",
"link": 12
"link": 11
},
{
"name": "vae",
"type": "VAE",
"link": 13
"link": 12
}
],
"outputs": [
@@ -115,7 +261,7 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
31
13
],
"shape": 3,
"slot_index": 0
@@ -125,26 +271,139 @@
"Node name for S&R": "VAEDecode"
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README.md
+44 -16
View File
@@ -1,10 +1,12 @@
# ComfyUI_IPAdapter_Plus
ComfyUI reference implementation for [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/tree/6fb9d3554a5c774f41e187e8fdbc7b9a1db8c2e3) models.
ComfyUI reference implementation for [IPAdapter](https://github.com/tencent-ailab/IP-Adapter/) models.
The code is mostly taken from the original IPAdapter repository and [laksjdjf's](https://github.com/laksjdjf/IPAdapter-ComfyUI/tree/main) implementation. I just made the extension closer to how ComfyUI makes things.
The difference of this extension and other IPAdapter implementations is that I try to follow as close as possible the ComfyUI way of doing things. This should result in faster, more reliable code.
## Updates
**2023/9/15**: Huge code cleanup! I streamlined the node structure for a tidier workflow. **IMPORTANT** this is a breaking update, we don't need the dedicated clip vision encoder anymore. Please check the new included workflows. Also introduced the new `noise` option, see below for details.
**2023/9/11**: The code has been rewritten to take advantage of the new ComfyUI updates regarding clip vision. Expect lower memory usage.
## Example workflow
@@ -25,40 +27,66 @@ For SD1.5 you need:
For SDXL you need:
- [ip-adapter_sdxl.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.bin)
- [ip-adapter_sdxl_vit-h.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.bin) **This model requires the use of the SD1.5 encoder despite being for SDXL**
Additionally you need the clip vision models:
Additionally you need the image encoders to be placed in the `ComfyUI/models/clip_vision/` directory:
- SD 1.5: [pytorch_model.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin)
- SDXL: [pytorch_model.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/pytorch_model.bin)
- [SD 1.5 model](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) (use this also for the SDXL **ip-adapter_sdxl_vit-h.bin** model)
- [SDXL model](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors)
You can rename them to something easier to remember (eg: `ip-adapter_sd15-image-encoder.bin`) and place them under `ComfyUI/models/clip_vision/`.
You can rename them to something easier to remember or put them into a sub-directory.
## What it is
The IPAdapter are very powerful moderls for image to image conditioning. Given a reference image you can do image variations augmented by text prompt, control net and masks.
## How to use
There's a basic workflow included in this repo and a few examples in the [example](./examples/) directory.
There's a basic workflow included in this repo and a few examples in the [example](./examples/) directory. Usually it's a good idea to lower the `weight` to at least `0.8`.
**IMPORTANT:** Remember to use the `CLIP Vision Encode (IPAdapter)` node (and not the standard one included with ComfyUI). Also be sure to **select the model of the same kind (1.5 vs XL)**, there are 3 of them: clip vision, IPApdapter and the main checkpoint.
The `noise` paramenter is an exclusive experimental exploitation of the IPAdapter models. You can set it to even just `0.01` for an arguably better result. **Please report your experience with the noise option**!
### Samplers
<details>
<summary>Nerd-explain of the noise option</summary>
Weirdly enough all **dpmpp_2m** (including _sde) samplers seem to perfom a little worse with IPAdapter and they generally need 5-10 more steps than usual. **ddmin** and **euler** seem to be pretty good.
I made quite a few tests and I was not sure if it was worth it to include this feature, but the results are interesting enough to warrant at least a test period. Let me know what you think.
### IPAdapter + Canny ControlNet
<img src="./examples/noise_example.jpg" width="100%" alt="canny controlnet" />
Basically the IPAdapter sends two pictures for the conditioning, one is the reference the other --that you don't see-- is an empty image that could be considered like a negative conditioning.
What I'm doing is to send a very noisy image instead of an empty one. The `noise` parameter determines the amount of noise. A value of `0.01` adds a lot of noise and creates an image close to the original created by the IPAdapter (a very noisy image being intelligible could be considered like an empty image); a value of `1.0` removes most of noise so the image gets conditioned more.
</details>
### KSampler configuration suggestions
Weirdly enough all `dpmpp_2m` (including `_sde`) samplers seem to perfom poorly with IPAdapter and they generally need 5-10 more steps than usual. `ddmin` and `euler` seem to be pretty good.
The model tends to burn the images a little. If needed lower the CFG scale.
It generally requires a few more steps than usual, if the result is underwhelming try to add 10+ steps.
### IPAdapter + ControlNet
The model is very effective when paired with a ControlNet. In the example below I experimented with Canny. [The workflow](./examples/IPAdapter_Canny.json) is in the examples directory.
<img src="./examples/canny_cn.jpg" width="100%" alt="canny controlnet" />
<img src="./examples/canny_controlnet.jpg" width="100%" alt="canny controlnet" />
### IPAdapter Face
IPAdapter offers an interesting model for a kind of "face swap" effect. [The workflow is provided](./examples/face.json).
IPAdapter offers an interesting model for a kind of "face swap" effect. [The workflow is provided](./examples/face.json). Set a close up face as reference image and then input your text prompt as always. The generated character should have the face of the reference. It also works with img2img given a high denoise.
<img src="./examples/face_swap.jpg" width="100%" alt="face swap" />
<img src="./examples/face_swap.jpg" width="50%" alt="face swap" />
### Masking
Masking in img2img generally works but I find inpainting to be far more effective. The [inpainting workflow](./examples/inpainting.json) uses the face model together with an inpainting checkpoint.
The most effect way to apply the IPAdapter to a region is by [inpainting workflow](./examples/inpainting.json). Remeber to use a specific checkpoint for inpainting otherwise it won't work. Even if you are inpainting a face I fince that the IPAdapter-Plus (not the face one), works best.
<img src="./examples/inpainting.jpg" width="100%" alt="inpainting" />
**Important:** when masking the *IPAdapter Apply* node be sure that the mask is of the same size of the latent.
## Credits
- [IPAdapter](ttps://github.com/tencent-ailab/IP-Adapter/)
- [laksjdjf](https://github.com/laksjdjf/IPAdapter-ComfyUI/)
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
examples/IPAdapter_Canny.json
+606 -676
View File
File diff suppressed because it is too large Load Diff
examples/face.json → examples/IPAdapter_face.json
+308 -328
View File
@@ -1,54 +1,86 @@
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@@ -61,12 +93,12 @@
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@@ -74,7 +106,7 @@
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@@ -128,7 +272,7 @@
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@@ -136,7 +280,7 @@
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@@ -146,31 +290,31 @@
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@@ -186,195 +330,39 @@
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