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refactor ipadapterweights

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This commit is contained in:
matt3o
2024-04-27 19:04:09 +02:00
parent fef59c5b7f
commit 57e1caf3df
3 changed files with 363 additions and 248 deletions
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IPAdapterPlus.py
+91 -9
View File
@@ -1334,20 +1334,26 @@ class IPAdapterWeights:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"weights": ("STRING", {"default": '1.0', "multiline": True }),
"timing": (["custom", "linear", "ease_in_out", "ease_in", "ease_out", "reverse_in_out", "random"], ),
"weights": ("STRING", {"default": '1.0, 0.0', "multiline": True }),
"timing": (["custom", "linear", "ease_in_out", "ease_in", "ease_out", "random"], ),
"frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
"start_frame": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
"end_frame": ("INT", {"default": 9999, "min": 0, "max": 9999, "step": 1 }),
},
"add_starting_frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
"add_ending_frames": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1 }),
"method": (["full batch", "shift batches", "alternate batches"], { "default": "full batch" }),
}, "optional": {
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("FLOAT",)
RETURN_TYPES = ("FLOAT", "FLOAT", "INT", "IMAGE", "IMAGE")
RETURN_NAMES = ("weights", "weights_invert", "total_frames", "image_1", "image_2")
FUNCTION = "weights"
CATEGORY = "ipadapter/utils"
def weights(self, weights, timing, frames, start_frame, end_frame):
def weights(self, weights, timing, frames, start_frame, end_frame, add_starting_frames, add_ending_frames, method, image=None):
import random
# convert the string to a list of floats separated by commas or newlines
@@ -1355,6 +1361,7 @@ class IPAdapterWeights:
weights = [float(weight) for weight in weights.split(",") if weight.strip() != ""]
if timing != "custom":
frames = max(frames, 2)
start = 0.0
end = 1.0
@@ -1379,19 +1386,92 @@ class IPAdapterWeights:
weights.append(start + (end - start) * math.sin(i / n * math.pi / 2))
elif timing == "ease_out":
weights.append(start + (end - start) * (1 - math.cos(i / n * math.pi / 2)))
elif timing == "reverse_in_out":
weights.append(start + (end - start) * (1 - math.sin((1 - i / n) * math.pi / 2)))
elif timing == "random":
weights.append(random.uniform(start, end))
weights[-1] = end if timing != "random" else weights[-1]
weights[-1] = end if timing != "random" else weights[-1]
if end_frame < frames:
weights.extend([end] * (frames - end_frame))
if len(weights) == 0:
weights = [0.0]
frames = len(weights)
# repeat the images for cross fade
image_1 = None
image_2 = None
if image is not None:
if "shift" in method:
image_1 = image[:-1]
image_2 = image[1:]
weights = weights * image_1.shape[0]
#weights_invert = [1.0 - w for w in weights]
return (weights, )
image_1 = image_1.repeat_interleave(frames, 0)
image_2 = image_2.repeat_interleave(frames, 0)
elif "alternate" in method:
image_1 = image[::2].repeat_interleave(2, 0)
image_1 = image_1[1:]
image_2 = image[1::2].repeat_interleave(2, 0)
mew_weights = weights + [1.0 - w for w in weights]
mew_weights = mew_weights * (image_1.shape[0] // 2)
if image.shape[0] % 2:
image_1 = image_1[:-1]
else:
image_2 = image_2[:-1]
mew_weights = mew_weights + weights
weights = mew_weights
#weights_invert = [1.0 - w for w in weights]
image_1 = image_1.repeat_interleave(frames, 0)
image_2 = image_2.repeat_interleave(frames, 0)
else:
weights = weights * image.shape[0]
#weights_invert = [1.0 - w for w in weights]
image_1 = image.repeat_interleave(frames, 0)
# add starting and ending frames
if add_starting_frames > 0:
weights = [weights[0]] * add_starting_frames + weights
image_1 = torch.cat([image[:1].repeat(add_starting_frames, 1, 1, 1), image_1], dim=0)
if image_2 is not None:
image_2 = torch.cat([image[:1].repeat(add_starting_frames, 1, 1, 1), image_2], dim=0)
if add_ending_frames > 0:
weights = weights + [weights[-1]] * add_ending_frames
image_1 = torch.cat([image_1, image[-1:].repeat(add_ending_frames, 1, 1, 1)], dim=0)
if image_2 is not None:
image_2 = torch.cat([image_2, image[-1:].repeat(add_ending_frames, 1, 1, 1)], dim=0)
weights_invert = [1.0 - w for w in weights]
return (weights, weights_invert, len(weights), image_1, image_2, )
class IPAdapterCombineWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weights_1": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
"weights_2": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
}}
RETURN_TYPES = ("FLOAT", "INT")
RETURN_NAMES = ("weights", "count")
FUNCTION = "combine"
CATEGORY = "ipadapter/utils"
def combine(self, weights_1, weights_2):
if not isinstance(weights_1, list):
weights_1 = [weights_1]
if not isinstance(weights_2, list):
weights_2 = [weights_2]
weights = weights_1 + weights_2
return (weights, len(weights), )
class IPAdapterRegionalConditioning:
@classmethod
@@ -1521,6 +1601,7 @@ NODE_CLASS_MAPPINGS = {
"IPAdapterSaveEmbeds": IPAdapterSaveEmbeds,
"IPAdapterLoadEmbeds": IPAdapterLoadEmbeds,
"IPAdapterWeights": IPAdapterWeights,
"IPAdapterCombineWeights": IPAdapterCombineWeights,
"IPAdapterRegionalConditioning": IPAdapterRegionalConditioning,
"IPAdapterCombineParams": IPAdapterCombineParams,
}
@@ -1555,6 +1636,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"IPAdapterSaveEmbeds": "IPAdapter Save Embeds",
"IPAdapterLoadEmbeds": "IPAdapter Load Embeds",
"IPAdapterWeights": "IPAdapter Weights",
"IPAdapterCombineWeights": "IPAdapter Combine Weights",
"IPAdapterRegionalConditioning": "IPAdapter Regional Conditioning",
"IPAdapterCombineParams": "IPAdapter Combine Params",
}
README.md
+3 -1
View File
@@ -27,6 +27,8 @@ Please consider a [Github Sponsorship](https://github.com/sponsors/cubiq) or [Pa
## Important updates
**2024/04/27**: Refactored the IPAdapterWeights mostly useful for AnimateDiff animations.
**2024/04/21**: Added Regional Conditioning nodes to simplify attention masking and masked text conditioning.
**2024/04/16**: Added support for the new SDXL portrait unnorm model (link below). It's very strong and tends to ignore the text conditioning. Lower the CFG to 3-4 or use a RescaleCFG node.
@@ -144,7 +146,7 @@ It's only thanks to generous sponsors that **the whole community** can enjoy ope
### :trophy: Gold sponsors
[![Kaiber.ai](https://f.latent.vision/imgs/kaiber.png)](https://kaiber.ai/)
[![Kaiber.ai](https://f.latent.vision/imgs/kaiber.png)](https://kaiber.ai/)&nbsp; &nbsp;[![Kaiber.ai](https://f.latent.vision/imgs/replicate.png)](https://replicate.com/)
### :tada: Silver sponsors
examples/ipadapter_weights.json
+269 -238
View File
@@ -1,6 +1,6 @@
{
"last_node_id": 21,
"last_link_id": 37,
"last_node_id": 22,
"last_link_id": 40,
"nodes": [
{
"id": 7,
@@ -14,7 +14,7 @@
"1": 180.6060791015625
},
"flags": {},
"order": 8,
"order": 5,
"mode": 0,
"inputs": [
{
@@ -80,49 +80,6 @@
"Node name for S&R": "VAEDecode"
}
},
{
"id": 5,
"type": "EmptyLatentImage",
"pos": [
801,
1097
],
"size": [
315,
106
],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "batch_size",
"type": "INT",
"link": 35,
"widget": {
"name": "batch_size"
}
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
2
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "EmptyLatentImage"
},
"widgets_values": [
512,
512,
6
]
},
{
"id": 6,
"type": "CLIPTextEncode",
@@ -135,7 +92,7 @@
"1": 164.31304931640625
},
"flags": {},
"order": 7,
"order": 4,
"mode": 0,
"inputs": [
{
@@ -220,170 +177,6 @@
1
]
},
{
"id": 12,
"type": "LoadImage",
"pos": [
311,
270
],
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
25
],
"shape": 3,
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"warrior_woman.png",
"image"
]
},
{
"id": 17,
"type": "PrepImageForClipVision",
"pos": [
797,
87
],
"size": {
"0": 315,
"1": 106
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 25
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
30
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "PrepImageForClipVision"
},
"widgets_values": [
"LANCZOS",
"top",
0.15
]
},
{
"id": 20,
"type": "IPAdapterWeights",
"pos": [
757,
318
],
"size": [
263.5047280787487,
183.75987616018006
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "frames",
"type": "INT",
"link": 34,
"widget": {
"name": "frames"
},
"slot_index": 0
}
],
"outputs": [
{
"name": "FLOAT",
"type": "FLOAT",
"links": [
32
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "IPAdapterWeights"
},
"widgets_values": [
"1.0,0.0",
"linear",
6,
0,
9999
]
},
{
"id": 21,
"type": "PrimitiveNode",
"pos": [
340,
1093
],
"size": {
"0": 210,
"1": 82
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "INT",
"type": "INT",
"links": [
34,
35
],
"widget": {
"name": "frames"
},
"slot_index": 0
}
],
"title": "frames",
"properties": {
"Run widget replace on values": false
},
"widgets_values": [
6,
"fixed"
]
},
{
"id": 19,
"type": "IPAdapterBatch",
@@ -391,10 +184,10 @@
1173,
251
],
"size": [
315,
254
],
"size": {
"0": 315,
"1": 254
},
"flags": {},
"order": 9,
"mode": 0,
@@ -432,7 +225,7 @@
{
"name": "weight",
"type": "FLOAT",
"link": 32,
"link": 38,
"widget": {
"name": "weight"
},
@@ -473,7 +266,7 @@
"1": 78
},
"flags": {},
"order": 6,
"order": 3,
"mode": 0,
"inputs": [
{
@@ -525,7 +318,7 @@
"1": 98
},
"flags": {},
"order": 2,
"order": 0,
"mode": 0,
"outputs": [
{
@@ -561,6 +354,47 @@
"sd15/realisticVisionV51_v51VAE.safetensors"
]
},
{
"id": 17,
"type": "PrepImageForClipVision",
"pos": [
788,
43
],
"size": {
"0": 315,
"1": 106
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 25
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
30
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "PrepImageForClipVision"
},
"widgets_values": [
"LANCZOS",
"top",
0.15
]
},
{
"id": 9,
"type": "SaveImage",
@@ -568,10 +402,10 @@
1770,
710
],
"size": [
556.2374508110479,
892.1895739499892
],
"size": {
"0": 556.2374267578125,
"1": 892.1895751953125
},
"flags": {},
"order": 12,
"mode": 0,
@@ -586,6 +420,203 @@
"widgets_values": [
"IPAdapter"
]
},
{
"id": 12,
"type": "LoadImage",
"pos": [
311,
270
],
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
25
],
"shape": 3,
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"warrior_woman.png",
"image"
]
},
{
"id": 5,
"type": "EmptyLatentImage",
"pos": [
801,
1097
],
"size": [
309.1109879864148,
82
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "batch_size",
"type": "INT",
"link": 35,
"widget": {
"name": "batch_size"
}
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
2
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "EmptyLatentImage"
},
"widgets_values": [
512,
512,
6
]
},
{
"id": 21,
"type": "PrimitiveNode",
"pos": [
340,
1093
],
"size": {
"0": 210,
"1": 82
},
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
"name": "INT",
"type": "INT",
"links": [
35,
40
],
"widget": {
"name": "batch_size"
},
"slot_index": 0
}
],
"title": "frames",
"properties": {
"Run widget replace on values": false
},
"widgets_values": [
6,
"fixed"
]
},
{
"id": 22,
"type": "IPAdapterWeights",
"pos": [
761,
208
],
"size": [
299.9049990375719,
324.00000762939453
],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": null
},
{
"name": "frames",
"type": "INT",
"link": 40,
"widget": {
"name": "frames"
}
}
],
"outputs": [
{
"name": "weights",
"type": "FLOAT",
"links": [
38
],
"shape": 3,
"slot_index": 0
},
{
"name": "weights_invert",
"type": "FLOAT",
"links": null,
"shape": 3
},
{
"name": "total_frames",
"type": "INT",
"links": null,
"shape": 3
},
{
"name": "image_1",
"type": "IMAGE",
"links": null,
"shape": 3
},
{
"name": "image_2",
"type": "IMAGE",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "IPAdapterWeights"
},
"widgets_values": [
"1.0, 0.0",
"linear",
6,
0,
9999,
0,
0,
"full batch"
]
}
],
"links": [
@@ -685,22 +716,6 @@
0,
"MODEL"
],
[
32,
20,
0,
19,
6,
"FLOAT"
],
[
34,
21,
0,
20,
0,
"INT"
],
[
35,
21,
@@ -724,6 +739,22 @@
19,
0,
"MODEL"
],
[
38,
22,
0,
19,
6,
"FLOAT"
],
[
40,
21,
0,
22,
1,
"INT"
]
],
"groups": [],