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add:easy ipadapterApplyEncoder and ipadapterApplyEmbeds

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yolain
2024-03-29 18:00:40 +08:00
parent 514f93c892
commit 83ca2f8ab6
4 changed files with 171 additions and 11 deletions
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README.en.md
+2
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@@ -37,6 +37,8 @@ PS: Please update [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAd
<br>
- Added `easy ipadapterApply`
- Added `easy ipadapterApplyADV`
- Added `easy ipadapterApplyEncoder`
- Added `easy ipadapterApplyEmbeds`
- Added `easy preMaskDetailerFix`
- Fixed `easy stylesSelector` is change the prompt when not select the style
README.md
+2
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@@ -41,6 +41,8 @@ PS: 请更新至最新版v2的 [ComfyUI_IPAdapter_plus](https://github.com/cubiq
<br>
- 增加 `easy ipadapterApply`
- 增加 `easy ipadapterApplyADV`
- 增加 `easy ipadapterApplyEncoder`
- 增加 `easy ipadapterApplyEmbeds`
- 增加 `easy preMaskDetailerFix`
- 修复 `easy stylesSelector` 当未选择样式时，原有提示词发生了变化
py/easyNodes.py
+143 -8
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@@ -1598,7 +1598,7 @@ def insightface_loader(provider):
class ipadapter:
def __init__(self):
self.normol_presets = [
self.normal_presets = [
'LIGHT - SD1.5 only (low strength)',
'STANDARD (medium strength)',
'VIT-G (medium strength)',
@@ -1612,7 +1612,9 @@ class ipadapter:
'FACEID PLUS V2',
'FACEID PORTRAIT (style transfer)'
]
self.presets = self.normol_presets + self.faceid_presets
self.weight_types = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output',
'weak middle', 'strong middle', 'style transfer (SDXL)']
self.presets = self.normal_presets + self.faceid_presets
def error(self):
@@ -1831,7 +1833,7 @@ class ipadapterApply(ipadapter):
FUNCTION = "apply"
def apply(self, model, image, preset, lora_strength, provider, weight, weight_faceidv2, start_at, end_at, cache_mode, use_tiled, attn_mask=None, optional_ipadapter=None):
tiles, masks = [None], [None]
tiles, masks = image, [None]
model, ipadapter = self.load_model(model, preset, lora_strength, provider, clip_vision=None, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode)
if use_tiled and preset not in self.faceid_presets:
if "IPAdapterTiled" not in ALL_NODE_CLASS_MAPPINGS:
@@ -1859,9 +1861,9 @@ class ipadapterApplyAdvanced(ipadapter):
@classmethod
def INPUT_TYPES(cls):
presets = cls().presets
WEIGHT_TYPES = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output',
'weak middle', 'strong middle', 'style transfer (SDXL)']
ipa_cls = cls()
presets = ipa_cls.presets
weight_types = ipa_cls.weight_types
return {
"required": {
"model": ("MODEL",),
@@ -1871,7 +1873,7 @@ class ipadapterApplyAdvanced(ipadapter):
"provider": (["CPU", "CUDA", "ROCM", "DirectML", "OpenVINO", "CoreML"],),
"weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
"weight_faceidv2": ("FLOAT", {"default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }),
"weight_type": (WEIGHT_TYPES,),
"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}),
@@ -1896,7 +1898,7 @@ class ipadapterApplyAdvanced(ipadapter):
FUNCTION = "apply"
def apply(self, model, image, preset, lora_strength, provider, weight, weight_faceidv2, weight_type, combine_embeds, start_at, end_at, embeds_scaling, cache_mode, use_tiled, use_batch, sharpening, image_negative=None, clip_vision=None, attn_mask=None, optional_ipadapter=None):
tiles, masks = [None], [None]
tiles, masks = image, [None]
model, ipadapter = self.load_model(model, preset, lora_strength, provider, clip_vision=clip_vision, optional_ipadapter=optional_ipadapter, cache_mode=cache_mode)
if use_tiled:
if use_batch:
@@ -1920,6 +1922,135 @@ class ipadapterApplyAdvanced(ipadapter):
model, = cls().apply_ipadapter(model, ipadapter, image, weight, weight_type, start_at, end_at, combine_embeds=combine_embeds, weight_faceidv2=weight_faceidv2, image_negative=image_negative, clip_vision=clip_vision, attn_mask=attn_mask, insightface=None, embeds_scaling=embeds_scaling)
return (model, tiles, masks, ipadapter)
class ipadapterApplyEncoder(ipadapter):
def __init__(self):
super().__init__()
pass
@classmethod
def INPUT_TYPES(cls):
ipa_cls = cls()
normal_presets = ipa_cls.normal_presets
max_embeds_num = 3
inputs = {
"required": {
"model": ("MODEL",),
"image1": ("IMAGE",),
"preset": (normal_presets,),
"num_embeds": ("INT", {"default": 2, "min": 1, "max": max_embeds_num}),
},
"optional": {}
}
for i in range(1, max_embeds_num + 1):
if i > 1:
inputs["optional"][f"image{i}"] = ("IMAGE",)
for i in range(1, max_embeds_num + 1):
inputs["optional"][f"mask{i}"] = ("MASK",)
inputs["optional"][f"weight{i}"] = ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05})
inputs["optional"]["combine_method"] = (["concat", "add", "subtract", "average", "norm average", "max", "min"],)
inputs["optional"]["optional_ipadapter"] = ("IPADAPTER",)
inputs["optional"]["pos_embeds"] = ("EMBEDS",)
inputs["optional"]["neg_embeds"] = ("EMBEDS",)
return inputs
RETURN_TYPES = ("MODEL", "IPADAPTER", "EMBEDS", "EMBEDS", )
RETURN_NAMES = ("model", "ipadapter", "pos_embed", "neg_embed", )
CATEGORY = "EasyUse/Adapter"
FUNCTION = "apply"
def batch(self, embeds, method):
if method == 'concat' and len(embeds) == 1:
return (embeds[0],)
embeds = [embed for embed in embeds if embed is not None]
embeds = torch.cat(embeds, dim=0)
if method == "add":
embeds = torch.sum(embeds, dim=0).unsqueeze(0)
elif method == "subtract":
embeds = embeds[0] - torch.mean(embeds[1:], dim=0)
embeds = embeds.unsqueeze(0)
elif method == "average":
embeds = torch.mean(embeds, dim=0).unsqueeze(0)
elif method == "norm average":
embeds = torch.mean(embeds / torch.norm(embeds, dim=0, keepdim=True), dim=0).unsqueeze(0)
elif method == "max":
embeds = torch.max(embeds, dim=0).values.unsqueeze(0)
elif method == "min":
embeds = torch.min(embeds, dim=0).values.unsqueeze(0)
return embeds
def apply(self, **kwargs):
model = kwargs['model']
preset = kwargs['preset']
if 'optional_ipadapter' in kwargs:
ipadapter = kwargs['optional_ipadapter']
else:
model, ipadapter = self.load_model(model, preset, 0, 'CPU', clip_vision=None, optional_ipadapter=None, cache_mode='none')
if "IPAdapterEncoder" not in ALL_NODE_CLASS_MAPPINGS:
self.error()
encoder_cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterEncoder"]
pos_embeds = kwargs["pos_embeds"] if "pos_embeds" in kwargs else []
neg_embeds = kwargs["neg_embeds"] if "neg_embeds" in kwargs else []
for i in range(1, kwargs['num_embeds'] + 1):
if f"image{i}" not in kwargs:
raise Exception(f"image{i} is required")
kwargs[f"mask{i}"] = kwargs[f"mask{i}"] if f"mask{i}" in kwargs else None
kwargs[f"weight{i}"] = kwargs[f"weight{i}"] if f"weight{i}" in kwargs else 1.0
pos, neg = encoder_cls().encode(ipadapter, kwargs[f"image{i}"], kwargs[f"weight{i}"], kwargs[f"mask{i}"], clip_vision=None)
pos_embeds.append(pos)
neg_embeds.append(neg)
pos_embeds = self.batch(pos_embeds, kwargs['combine_method'])
neg_embeds = self.batch(neg_embeds, kwargs['combine_method'])
return (model, ipadapter, pos_embeds, neg_embeds)
class ipadapterApplyEmbeds(ipadapter):
def __init__(self):
super().__init__()
pass
@classmethod
def INPUT_TYPES(cls):
ipa_cls = cls()
weight_types = ipa_cls.weight_types
return {
"required": {
"model": ("MODEL",),
"ipadapter": ("IPADAPTER",),
"pos_embed": ("EMBEDS",),
"weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
"weight_type": (weight_types,),
"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": {
"neg_embed": ("EMBEDS",),
"attn_mask": ("MASK",),
}
}
RETURN_TYPES = ("MODEL", "IPADAPTER",)
RETURN_NAMES = ("model", "ipadapter", )
CATEGORY = "EasyUse/Adapter"
FUNCTION = "apply"
def apply(self, model, ipadapter, pos_embed, weight, weight_type, start_at, end_at, embeds_scaling, attn_mask=None, neg_embed=None,):
if "IPAdapterEmbeds" not in ALL_NODE_CLASS_MAPPINGS:
self.error()
cls = ALL_NODE_CLASS_MAPPINGS["IPAdapterEmbeds"]
model, = cls().apply_ipadapter(model, ipadapter, pos_embed, weight, weight_type, start_at, end_at, neg_embed=neg_embed, attn_mask=attn_mask, clip_vision=None, embeds_scaling=embeds_scaling)
return (model, ipadapter)
#Apply InstantID
class instantID:
@@ -5872,6 +6003,8 @@ NODE_CLASS_MAPPINGS = {
# Adapter 适配器
"easy ipadapterApply": ipadapterApply,
"easy ipadapterApplyADV": ipadapterApplyAdvanced,
"easy ipadapterApplyEncoder": ipadapterApplyEncoder,
"easy ipadapterApplyEmbeds": ipadapterApplyEmbeds,
"easy instantIDApply": instantIDApply,
"easy instantIDApplyADV": instantIDApplyAdvanced,
# Inpaint 内补
@@ -5961,6 +6094,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
# Adapter 适配器
"easy ipadapterApply": "Easy Apply IPAdapter",
"easy ipadapterApplyADV": "Easy Apply IPAdapter (Advanced)",
"easy ipadapterApplyEncoder": "Easy Apply IPAdapter (Encoder)",
"easy ipadapterApplyEmbeds": "Easy Apply IPAdapter (Embeds)",
"easy instantIDApply": "Easy Apply InstantID",
"easy instantIDApplyADV": "Easy Apply InstantID (Advanced)",
# Inpaint 内补
web/js/easy/easyDynamicWidgets.js
+24 -3
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@@ -28,6 +28,11 @@ function toggleWidget(node, widget, show = false, suffix = "") {
node.setSize([node.size[0], height]);
}
function toggleInput(node, name, show = false) {
if(!show){
}
}
function widgetLogic(node, widget) {
if (widget.name === 'lora_name') {
if (widget.value === "None") {
@@ -227,8 +232,8 @@ function widgetLogic(node, widget) {
toggleWidget(node, findWidgetByName(node, 'provider'))
toggleWidget(node, findWidgetByName(node, 'weight_faceidv2'))
toggleWidget(node, findWidgetByName(node, 'use_tiled'), true)
let use_tiled = findWidgetByName(node, 'use_tiled').value
if(use_tiled){
let use_tiled = findWidgetByName(node, 'use_tiled')
if(use_tiled && use_tiled.value){
toggleWidget(node, findWidgetByName(node, 'sharpening'), true)
}else {
toggleWidget(node, findWidgetByName(node, 'sharpening'))
@@ -261,6 +266,21 @@ function widgetLogic(node, widget) {
toggleWidget(node, findWidgetByName(node, 'sharpening'))
updateNodeHeight(node)
}
if (widget.name === 'num_embeds') {
let number_to_show = widget.value + 1
for (let i = 0; i < number_to_show; i++) {
toggleInput(node, 'image'+i, true)
toggleInput(node, 'mask'+i, true)
toggleWidget(node, findWidgetByName(node, 'weight'+i), true)
}
for (let i = number_to_show; i < 6; i++) {
toggleInput(node, 'image'+i)
toggleInput(node, 'mask'+i)
toggleWidget(node, findWidgetByName(node, 'weight'+i))
}
updateNodeHeight(node)
}
}
function widgetLogic2(node, widget) {
@@ -541,6 +561,7 @@ app.registerExtension({
case 'easy pipeEdit':
case 'easy ipadapterApply':
case 'easy ipadapterApplyADV':
case 'easy ipadapterApplyEncoder':
getSetters(node)
break
case "easy wildcards":
@@ -977,7 +998,7 @@ const getSetWidgets = ['rescale_after_model', 'rescale',
'refiner_lora1_name', 'refiner_lora2_name', 'upscale_method',
'image_output', 'add_noise', 'info', 'sampler_name',
'ckpt_B_name', 'ckpt_C_name', 'save_model', 'refiner_ckpt_name',
'num_loras', 'mode', 'toggle', 'resolution', 'target_parameter', 'input_count', 'replace_count', 'downscale_mode', 'range_mode','text_combine_mode', 'input_mode','lora_count','ckpt_count', 'conditioning_mode', 'preset', 'use_tiled', 'use_batch']
'num_loras', 'mode', 'toggle', 'resolution', 'target_parameter', 'input_count', 'replace_count', 'downscale_mode', 'range_mode','text_combine_mode', 'input_mode','lora_count','ckpt_count', 'conditioning_mode', 'preset', 'use_tiled', 'use_batch', 'num_embeds']
function getSetters(node) {
if (node.widgets)