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+ How to Apply These Terms to Your New Programs
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+ The GNU General Public License does not permit incorporating your program
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+ <https://www.gnu.org/licenses/why-not-lgpl.html>.
README.md ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Efficiency Nodes for ComfyUI
2
+ =======
3
+ ### A collection of <a href="https://github.com/comfyanonymous/ComfyUI" >ComfyUI</a> custom nodes to help streamline workflows and reduce total node count.
4
+ ## [Direct Download Link](https://github.com/LucianoCirino/efficiency-nodes-comfyui/releases/download/v1.62/efficiency-nodes-comfyui.zip)
5
+
6
+ ## **Currently Available Nodes:**
7
+ <details><summary>Ksampler (Efficient)</summary><ul>
8
+ • A modded KSampler with the ability to preview/output images and run scripts.<br>
9
+ </ul></details>
10
+
11
+ <details><summary>Efficient Loader</summary><ul>
12
+ • A combination of common initialization nodes.
13
+ </ul></details>
14
+
15
+ <details><summary>XY Plotter Nodes</summary><ul>
16
+ • A collection of nodes that allows users to specify parameters for the KSampler (Efficient) to plot on a grid.
17
+ </ul></details>
18
+
19
+ <details><summary>Image Overlay</summary><ul>
20
+ • Node that allows for flexible image overlaying.
21
+ </ul></details>
22
+
23
+ <details><summary>SimpleEval Nodes</summary><ul>
24
+ • A collection of nodes that allows users to write simple Python expressions for a variety of data types using the "<a href="https://github.com/danthedeckie/simpleeval" >simpleeval</a>" library.
25
+
26
+ </ul></details>
27
+
28
+ ## **Examples:**
29
+
30
+ - HiResFix using the **Efficient Loader** & **Ksampler (Efficient)**
31
+
32
+ <img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/HiRes Fix (overview).png" width="720">
33
+
34
+ - 2D Plotting using the **XY Plot** & **Ksampler (Efficient)** nodes
35
+
36
+ <img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/XYplot/X-Seeds Y-Checkpoints (overview).png" width="720">
37
+
38
+ - Photobashing using the **Image Overlay** node
39
+
40
+ <img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/Image Overlay (overview).png" width="720">
41
+
42
+ ## **Install:**
43
+ To install, drop the "_**efficiency-nodes-comfyui**_" folder into the "_**...\ComfyUI\ComfyUI\custom_nodes**_" directory and restart UI.
__init__.py ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ from .efficiency_nodes import NODE_CLASS_MAPPINGS
2
+
3
+ __all__ = ['NODE_CLASS_MAPPINGS']
arial.ttf ADDED
Binary file (367 kB). View file
 
efficiency_nodes.py ADDED
The diff for this file is too large to render. See raw diff
 
node_settings.json ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "Efficient Loader": {
3
+ "model_cache": {
4
+ "ckpt": 1,
5
+ "vae": 1,
6
+ "lora": 1
7
+ }
8
+ },
9
+ "KSampler (Efficient)": {
10
+ "vae_tiled": false
11
+ },
12
+ "XY Plot": {
13
+ "model_cache": {
14
+ "ckpt": 5,
15
+ "vae": 5,
16
+ "lora": 5
17
+ },
18
+ "vae_tiled": false
19
+ }
20
+ }
21
+
requirements.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ simpleeval
tsc_sd.py ADDED
@@ -0,0 +1,308 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+
4
+ # Get the absolute path of the parent directory of the current script
5
+ my_dir = os.path.dirname(os.path.abspath(__file__))
6
+
7
+ # Add the My directory path to the sys.path list
8
+ sys.path.append(my_dir)
9
+
10
+ # Construct the absolute path to the ComfyUI directory
11
+ comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
12
+
13
+ # Add the ComfyUI directory path to the sys.path list
14
+ sys.path.append(comfy_dir)
15
+
16
+ # Import functions from ComfyUI
17
+ from comfy.sd import *
18
+ from comfy import utils
19
+
20
+ LORA_CLIP_MAP = {
21
+ "mlp.fc1": "mlp_fc1",
22
+ "mlp.fc2": "mlp_fc2",
23
+ "self_attn.k_proj": "self_attn_k_proj",
24
+ "self_attn.q_proj": "self_attn_q_proj",
25
+ "self_attn.v_proj": "self_attn_v_proj",
26
+ "self_attn.out_proj": "self_attn_out_proj",
27
+ }
28
+
29
+ LORA_UNET_MAP_ATTENTIONS = {
30
+ "proj_in": "proj_in",
31
+ "proj_out": "proj_out",
32
+ "transformer_blocks.0.attn1.to_q": "transformer_blocks_0_attn1_to_q",
33
+ "transformer_blocks.0.attn1.to_k": "transformer_blocks_0_attn1_to_k",
34
+ "transformer_blocks.0.attn1.to_v": "transformer_blocks_0_attn1_to_v",
35
+ "transformer_blocks.0.attn1.to_out.0": "transformer_blocks_0_attn1_to_out_0",
36
+ "transformer_blocks.0.attn2.to_q": "transformer_blocks_0_attn2_to_q",
37
+ "transformer_blocks.0.attn2.to_k": "transformer_blocks_0_attn2_to_k",
38
+ "transformer_blocks.0.attn2.to_v": "transformer_blocks_0_attn2_to_v",
39
+ "transformer_blocks.0.attn2.to_out.0": "transformer_blocks_0_attn2_to_out_0",
40
+ "transformer_blocks.0.ff.net.0.proj": "transformer_blocks_0_ff_net_0_proj",
41
+ "transformer_blocks.0.ff.net.2": "transformer_blocks_0_ff_net_2",
42
+ }
43
+
44
+ LORA_UNET_MAP_RESNET = {
45
+ "in_layers.2": "resnets_{}_conv1",
46
+ "emb_layers.1": "resnets_{}_time_emb_proj",
47
+ "out_layers.3": "resnets_{}_conv2",
48
+ "skip_connection": "resnets_{}_conv_shortcut"
49
+ }
50
+
51
+ def load_lora_tsc(path, to_load):
52
+ lora = utils.load_torch_file(path)
53
+ patch_dict = {}
54
+ loaded_keys = set()
55
+ for x in to_load:
56
+ alpha_name = "{}.alpha".format(x)
57
+ alpha = None
58
+ if alpha_name in lora.keys():
59
+ alpha = lora[alpha_name].item()
60
+ loaded_keys.add(alpha_name)
61
+
62
+ A_name = "{}.lora_up.weight".format(x)
63
+ B_name = "{}.lora_down.weight".format(x)
64
+ mid_name = "{}.lora_mid.weight".format(x)
65
+
66
+ if A_name in lora.keys():
67
+ mid = None
68
+ if mid_name in lora.keys():
69
+ mid = lora[mid_name]
70
+ loaded_keys.add(mid_name)
71
+ patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
72
+ loaded_keys.add(A_name)
73
+ loaded_keys.add(B_name)
74
+
75
+
76
+ ######## loha
77
+ hada_w1_a_name = "{}.hada_w1_a".format(x)
78
+ hada_w1_b_name = "{}.hada_w1_b".format(x)
79
+ hada_w2_a_name = "{}.hada_w2_a".format(x)
80
+ hada_w2_b_name = "{}.hada_w2_b".format(x)
81
+ hada_t1_name = "{}.hada_t1".format(x)
82
+ hada_t2_name = "{}.hada_t2".format(x)
83
+ if hada_w1_a_name in lora.keys():
84
+ hada_t1 = None
85
+ hada_t2 = None
86
+ if hada_t1_name in lora.keys():
87
+ hada_t1 = lora[hada_t1_name]
88
+ hada_t2 = lora[hada_t2_name]
89
+ loaded_keys.add(hada_t1_name)
90
+ loaded_keys.add(hada_t2_name)
91
+
92
+ patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
93
+ loaded_keys.add(hada_w1_a_name)
94
+ loaded_keys.add(hada_w1_b_name)
95
+ loaded_keys.add(hada_w2_a_name)
96
+ loaded_keys.add(hada_w2_b_name)
97
+
98
+
99
+ ######## lokr
100
+ lokr_w1_name = "{}.lokr_w1".format(x)
101
+ lokr_w2_name = "{}.lokr_w2".format(x)
102
+ lokr_w1_a_name = "{}.lokr_w1_a".format(x)
103
+ lokr_w1_b_name = "{}.lokr_w1_b".format(x)
104
+ lokr_t2_name = "{}.lokr_t2".format(x)
105
+ lokr_w2_a_name = "{}.lokr_w2_a".format(x)
106
+ lokr_w2_b_name = "{}.lokr_w2_b".format(x)
107
+
108
+ lokr_w1 = None
109
+ if lokr_w1_name in lora.keys():
110
+ lokr_w1 = lora[lokr_w1_name]
111
+ loaded_keys.add(lokr_w1_name)
112
+
113
+ lokr_w2 = None
114
+ if lokr_w2_name in lora.keys():
115
+ lokr_w2 = lora[lokr_w2_name]
116
+ loaded_keys.add(lokr_w2_name)
117
+
118
+ lokr_w1_a = None
119
+ if lokr_w1_a_name in lora.keys():
120
+ lokr_w1_a = lora[lokr_w1_a_name]
121
+ loaded_keys.add(lokr_w1_a_name)
122
+
123
+ lokr_w1_b = None
124
+ if lokr_w1_b_name in lora.keys():
125
+ lokr_w1_b = lora[lokr_w1_b_name]
126
+ loaded_keys.add(lokr_w1_b_name)
127
+
128
+ lokr_w2_a = None
129
+ if lokr_w2_a_name in lora.keys():
130
+ lokr_w2_a = lora[lokr_w2_a_name]
131
+ loaded_keys.add(lokr_w2_a_name)
132
+
133
+ lokr_w2_b = None
134
+ if lokr_w2_b_name in lora.keys():
135
+ lokr_w2_b = lora[lokr_w2_b_name]
136
+ loaded_keys.add(lokr_w2_b_name)
137
+
138
+ lokr_t2 = None
139
+ if lokr_t2_name in lora.keys():
140
+ lokr_t2 = lora[lokr_t2_name]
141
+ loaded_keys.add(lokr_t2_name)
142
+
143
+ if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
144
+ patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)
145
+
146
+ for x in lora.keys():
147
+ if x not in loaded_keys:
148
+ print("lora key not loaded", x)
149
+ return patch_dict
150
+
151
+ def model_lora_keys(model, key_map={}):
152
+ sdk = model.state_dict().keys()
153
+
154
+ counter = 0
155
+ for b in range(12):
156
+ tk = "diffusion_model.input_blocks.{}.1".format(b)
157
+ up_counter = 0
158
+ for c in LORA_UNET_MAP_ATTENTIONS:
159
+ k = "{}.{}.weight".format(tk, c)
160
+ if k in sdk:
161
+ lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP_ATTENTIONS[c])
162
+ key_map[lora_key] = k
163
+ up_counter += 1
164
+ if up_counter >= 4:
165
+ counter += 1
166
+ for c in LORA_UNET_MAP_ATTENTIONS:
167
+ k = "diffusion_model.middle_block.1.{}.weight".format(c)
168
+ if k in sdk:
169
+ lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP_ATTENTIONS[c])
170
+ key_map[lora_key] = k
171
+ counter = 3
172
+ for b in range(12):
173
+ tk = "diffusion_model.output_blocks.{}.1".format(b)
174
+ up_counter = 0
175
+ for c in LORA_UNET_MAP_ATTENTIONS:
176
+ k = "{}.{}.weight".format(tk, c)
177
+ if k in sdk:
178
+ lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP_ATTENTIONS[c])
179
+ key_map[lora_key] = k
180
+ up_counter += 1
181
+ if up_counter >= 4:
182
+ counter += 1
183
+ counter = 0
184
+ text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
185
+ for b in range(24):
186
+ for c in LORA_CLIP_MAP:
187
+ k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
188
+ if k in sdk:
189
+ lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
190
+ key_map[lora_key] = k
191
+
192
+
193
+ #Locon stuff
194
+ ds_counter = 0
195
+ counter = 0
196
+ for b in range(12):
197
+ tk = "diffusion_model.input_blocks.{}.0".format(b)
198
+ key_in = False
199
+ for c in LORA_UNET_MAP_RESNET:
200
+ k = "{}.{}.weight".format(tk, c)
201
+ if k in sdk:
202
+ lora_key = "lora_unet_down_blocks_{}_{}".format(counter // 2, LORA_UNET_MAP_RESNET[c].format(counter % 2))
203
+ key_map[lora_key] = k
204
+ key_in = True
205
+ for bb in range(3):
206
+ k = "{}.{}.op.weight".format(tk[:-2], bb)
207
+ if k in sdk:
208
+ lora_key = "lora_unet_down_blocks_{}_downsamplers_0_conv".format(ds_counter)
209
+ key_map[lora_key] = k
210
+ ds_counter += 1
211
+ if key_in:
212
+ counter += 1
213
+
214
+ counter = 0
215
+ for b in range(3):
216
+ tk = "diffusion_model.middle_block.{}".format(b)
217
+ key_in = False
218
+ for c in LORA_UNET_MAP_RESNET:
219
+ k = "{}.{}.weight".format(tk, c)
220
+ if k in sdk:
221
+ lora_key = "lora_unet_mid_block_{}".format(LORA_UNET_MAP_RESNET[c].format(counter))
222
+ key_map[lora_key] = k
223
+ key_in = True
224
+ if key_in:
225
+ counter += 1
226
+
227
+ counter = 0
228
+ us_counter = 0
229
+ for b in range(12):
230
+ tk = "diffusion_model.output_blocks.{}.0".format(b)
231
+ key_in = False
232
+ for c in LORA_UNET_MAP_RESNET:
233
+ k = "{}.{}.weight".format(tk, c)
234
+ if k in sdk:
235
+ lora_key = "lora_unet_up_blocks_{}_{}".format(counter // 3, LORA_UNET_MAP_RESNET[c].format(counter % 3))
236
+ key_map[lora_key] = k
237
+ key_in = True
238
+ for bb in range(3):
239
+ k = "{}.{}.conv.weight".format(tk[:-2], bb)
240
+ if k in sdk:
241
+ lora_key = "lora_unet_up_blocks_{}_upsamplers_0_conv".format(us_counter)
242
+ key_map[lora_key] = k
243
+ us_counter += 1
244
+ if key_in:
245
+ counter += 1
246
+
247
+ return key_map
248
+
249
+ def load_lora_for_models_tsc(model, clip, lora_path, strength_model, strength_clip):
250
+ key_map = model_lora_keys(model.model)
251
+ key_map = model_lora_keys(clip.cond_stage_model, key_map)
252
+ loaded = load_lora_tsc(lora_path, key_map)
253
+ new_modelpatcher = model.clone()
254
+ k = new_modelpatcher.add_patches(loaded, strength_model)
255
+ new_clip = clip.clone()
256
+ k1 = new_clip.add_patches(loaded, strength_clip)
257
+ k = set(k)
258
+ k1 = set(k1)
259
+ for x in loaded:
260
+ if (x not in k) and (x not in k1):
261
+ print("NOT LOADED", x)
262
+
263
+ return (new_modelpatcher, new_clip)
264
+
265
+ def load_checkpoint_guess_config_tsc(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
266
+ sd = utils.load_torch_file(ckpt_path)
267
+ sd_keys = sd.keys()
268
+ clip = None
269
+ clipvision = None
270
+ vae = None
271
+ model = None
272
+ clip_target = None
273
+
274
+ parameters = calculate_parameters(sd, "model.diffusion_model.")
275
+ fp16 = model_management.should_use_fp16(model_params=parameters)
276
+
277
+ class WeightsLoader(torch.nn.Module):
278
+ pass
279
+
280
+ model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16)
281
+ if model_config is None:
282
+ raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
283
+
284
+ if model_config.clip_vision_prefix is not None:
285
+ if output_clipvision:
286
+ clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
287
+
288
+ offload_device = model_management.unet_offload_device()
289
+ model = model_config.get_model(sd, "model.diffusion_model.")
290
+ model = model.to(offload_device)
291
+ model.load_model_weights(sd, "model.diffusion_model.")
292
+
293
+ if output_vae:
294
+ vae = VAE()
295
+ w = WeightsLoader()
296
+ w.first_stage_model = vae.first_stage_model
297
+ load_model_weights(w, sd)
298
+
299
+ if output_clip:
300
+ w = WeightsLoader()
301
+ clip_target = model_config.clip_target()
302
+ clip = CLIP(clip_target, embedding_directory=embedding_directory)
303
+ w.cond_stage_model = clip.cond_stage_model
304
+ sd = model_config.process_clip_state_dict(sd)
305
+ load_model_weights(w, sd)
306
+
307
+ return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision)
308
+
tsc_utils.py ADDED
@@ -0,0 +1,427 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Efficiency Nodes Utility functions
2
+
3
+ from torch import Tensor
4
+ import torch
5
+ from PIL import Image
6
+ import numpy as np
7
+
8
+ import os
9
+ import sys
10
+ import json
11
+ import folder_paths
12
+
13
+ # Get the absolute path of the parent directory of the current script
14
+ my_dir = os.path.dirname(os.path.abspath(__file__))
15
+
16
+ # Add the My directory path to the sys.path list
17
+ sys.path.append(my_dir)
18
+
19
+ # Construct the absolute path to the ComfyUI directory
20
+ comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
21
+
22
+ # Add the ComfyUI directory path to the sys.path list
23
+ sys.path.append(comfy_dir)
24
+
25
+ # Import functions from ComfyUI
26
+ import comfy.sd
27
+
28
+ # Load my version of Comfy functions
29
+ from tsc_sd import *
30
+
31
+ # Cache for Efficiency Node models
32
+ loaded_objects = {
33
+ "ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id])
34
+ "vae": [], # (vae_name, vae, [id])
35
+ "lora": [] # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id])
36
+ }
37
+
38
+ # Cache for Ksampler (Efficient) Outputs
39
+ last_helds: dict[str, list] = {
40
+ "results": [], # (results, id) # Preview Images, stored as a pil image list
41
+ "latent": [], # (latent, id) # Latent outputs, stored as a latent tensor list
42
+ "images": [], # (images, id) # Image outputs, stored as an image tensor list
43
+ "vae_decode": [], # (vae_decode, id) # Used to track wether to vae-decode or not
44
+ }
45
+
46
+ # Tensor to PIL (grabbed from WAS Suite)
47
+ def tensor2pil(image: torch.Tensor) -> Image.Image:
48
+ return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
49
+
50
+ # Convert PIL to Tensor (grabbed from WAS Suite)
51
+ def pil2tensor(image: Image.Image) -> torch.Tensor:
52
+ return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
53
+
54
+ def extract_node_info(prompt, id, indirect_key=None):
55
+ # Convert ID to string
56
+ id = str(id)
57
+ node_id = None
58
+
59
+ # If an indirect_key (like 'script') is provided, perform a two-step lookup
60
+ if indirect_key:
61
+ # Ensure the id exists in the prompt and has an 'inputs' entry with the indirect_key
62
+ if id in prompt and 'inputs' in prompt[id] and indirect_key in prompt[id]['inputs']:
63
+ # Extract the indirect_id
64
+ indirect_id = prompt[id]['inputs'][indirect_key][0]
65
+
66
+ # Ensure the indirect_id exists in the prompt
67
+ if indirect_id in prompt:
68
+ node_id = indirect_id
69
+ return prompt[indirect_id].get('class_type', None), node_id
70
+
71
+ # If indirect_key is not found within the prompt
72
+ return None, None
73
+
74
+ # If no indirect_key is provided, perform a direct lookup
75
+ return prompt.get(id, {}).get('class_type', None), node_id
76
+
77
+ def extract_node_value(prompt, id, key):
78
+ # If ID is in data, return its 'inputs' value for a given key. Otherwise, return None.
79
+ return prompt.get(str(id), {}).get('inputs', {}).get(key, None)
80
+
81
+ def print_loaded_objects_entries(id=None, prompt=None, show_id=False):
82
+ print("-" * 40) # Print an empty line followed by a separator line
83
+ if id is not None:
84
+ id = str(id) # Convert ID to string
85
+ if prompt is not None and id is not None:
86
+ node_name, _ = extract_node_info(prompt, id)
87
+ if show_id:
88
+ print(f"\033[36m{node_name} Models Cache: (node_id:{int(id)})\033[0m")
89
+ else:
90
+ print(f"\033[36m{node_name} Models Cache:\033[0m")
91
+ elif id is None:
92
+ print(f"\033[36mGlobal Models Cache:\033[0m")
93
+ else:
94
+ print(f"\033[36mModels Cache: \nnode_id:{int(id)}\033[0m")
95
+ entries_found = False
96
+ for key in ["ckpt", "vae", "lora"]:
97
+ entries_with_id = loaded_objects[key] if id is None else [entry for entry in loaded_objects[key] if id in entry[-1]]
98
+ if not entries_with_id: # If no entries with the chosen ID, print None and skip this key
99
+ continue
100
+ entries_found = True
101
+ print(f"{key.capitalize()}:")
102
+ for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1
103
+ if key == "lora":
104
+ lora_models_info = ', '.join(f"{os.path.splitext(os.path.basename(name))[0]}({round(strength_model, 2)},{round(strength_clip, 2)})" for name, strength_model, strength_clip in entry[0])
105
+ base_ckpt_name = os.path.splitext(os.path.basename(entry[1]))[0] # Split logic for base_ckpt
106
+ if id is None:
107
+ associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids
108
+ print(f" [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info} (ids: {associated_ids})")
109
+ else:
110
+ print(f" [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info}")
111
+ else:
112
+ name_without_ext = os.path.splitext(os.path.basename(entry[0]))[0]
113
+ if id is None:
114
+ associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids
115
+ print(f" [{i}] {name_without_ext} (ids: {associated_ids})")
116
+ else:
117
+ print(f" [{i}] {name_without_ext}")
118
+ if not entries_found:
119
+ print("-")
120
+
121
+
122
+ # This function cleans global variables associated with nodes that are no longer detected on UI
123
+ def globals_cleanup(prompt):
124
+ global loaded_objects
125
+ global last_helds
126
+
127
+ # Step 1: Clean up last_helds
128
+ for key in list(last_helds.keys()):
129
+ original_length = len(last_helds[key])
130
+ last_helds[key] = [(value, id) for value, id in last_helds[key] if str(id) in prompt.keys()]
131
+ ###if original_length != len(last_helds[key]):
132
+ ###print(f'Updated {key} in last_helds: {last_helds[key]}')
133
+
134
+ # Step 2: Clean up loaded_objects
135
+ for key in list(loaded_objects.keys()):
136
+ for i, tup in enumerate(list(loaded_objects[key])):
137
+ # Remove ids from id array in each tuple that don't exist in prompt
138
+ id_array = [id for id in tup[-1] if str(id) in prompt.keys()]
139
+ if len(id_array) != len(tup[-1]):
140
+ if id_array:
141
+ loaded_objects[key][i] = tup[:-1] + (id_array,)
142
+ ###print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}')
143
+ else:
144
+ # If id array becomes empty, delete the corresponding tuple
145
+ loaded_objects[key].remove(tup)
146
+ ###print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.')
147
+
148
+ def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=False):
149
+ """
150
+ Searches for tuple index that contains ckpt_name in "ckpt" array of loaded_objects.
151
+ If found, extracts the model, clip, and vae from the loaded_objects.
152
+ If not found, loads the checkpoint, extracts the model, clip, and vae.
153
+ The id parameter represents the node ID and is used for caching models for the XY Plot node.
154
+ If the cache limit is reached for a specific id, clears the cache and returns the loaded model, clip, and vae without adding a new entry.
155
+ If there is cache space, adds the id to the ids list if it's not already there.
156
+ If there is cache space and the checkpoint was not found in loaded_objects, adds a new entry to loaded_objects.
157
+
158
+ Parameters:
159
+ - ckpt_name: name of the checkpoint to load.
160
+ - id: an identifier for caching models for specific nodes.
161
+ - output_vae: boolean, if True loads the VAE too.
162
+ - cache (optional): an integer that specifies how many checkpoint entries with a given id can exist in loaded_objects. Defaults to None.
163
+ """
164
+ global loaded_objects
165
+
166
+ for entry in loaded_objects["ckpt"]:
167
+ if entry[0] == ckpt_name:
168
+ _, model, clip, vae, ids = entry
169
+ cache_full = cache and len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) >= cache
170
+
171
+ if cache_full:
172
+ clear_cache(id, cache, "ckpt")
173
+ elif id not in ids:
174
+ ids.append(id)
175
+
176
+ return model, clip, vae
177
+
178
+ ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
179
+ out = load_checkpoint_guess_config_tsc(ckpt_path, output_vae, output_clip=True,
180
+ embedding_directory=folder_paths.get_folder_paths("embeddings"))
181
+ model = out[0]
182
+ clip = out[1]
183
+ vae = out[2] # bvae
184
+
185
+ if cache:
186
+ if len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) < cache:
187
+ loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))
188
+ else:
189
+ clear_cache(id, cache, "ckpt")
190
+ if cache_overwrite:
191
+ # Find the first entry with the id, remove the id from the entry's id list
192
+ for e in loaded_objects["ckpt"]:
193
+ if id in e[-1]:
194
+ e[-1].remove(id)
195
+ # If the id list becomes empty, remove the entry from the "ckpt" list
196
+ if not e[-1]:
197
+ loaded_objects["ckpt"].remove(e)
198
+ break
199
+ loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))
200
+
201
+ return model, clip, vae
202
+
203
+ def get_bvae_by_ckpt_name(ckpt_name):
204
+ for ckpt in loaded_objects["ckpt"]:
205
+ if ckpt[0] == ckpt_name:
206
+ return ckpt[3] # return 'bvae' variable
207
+ return None # return None if no match is found
208
+
209
+ def load_vae(vae_name, id, cache=None, cache_overwrite=False):
210
+ """
211
+ Extracts the vae with a given name from the "vae" array in loaded_objects.
212
+ If the vae is not found, creates a new VAE object with the given name and adds it to the "vae" array.
213
+ Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
214
+ If the cache limit is reached for a specific id, returns the loaded vae without adding id or making a new entry in loaded_objects.
215
+ If there is cache space, and the id is not in the ids list, adds the id to the ids list.
216
+ If there is cache space, and the vae was not found in loaded_objects, adds a new entry to the loaded_objects.
217
+
218
+ Parameters:
219
+ - vae_name: name of the VAE to load.
220
+ - id (optional): an identifier for caching models for specific nodes. Defaults to None.
221
+ - cache (optional): an integer that specifies how many vae entries with a given id can exist in loaded_objects. Defaults to None.
222
+ """
223
+ global loaded_objects
224
+
225
+ for i, entry in enumerate(loaded_objects["vae"]):
226
+ if entry[0] == vae_name:
227
+ vae, ids = entry[1], entry[2]
228
+ if id not in ids:
229
+ if cache and len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) >= cache:
230
+ return vae
231
+ ids.append(id)
232
+ if cache:
233
+ clear_cache(id, cache, "vae")
234
+ return vae
235
+
236
+ vae_path = folder_paths.get_full_path("vae", vae_name)
237
+ vae = comfy.sd.VAE(ckpt_path=vae_path)
238
+
239
+ if cache:
240
+ if len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) < cache:
241
+ loaded_objects["vae"].append((vae_name, vae, [id]))
242
+ else:
243
+ clear_cache(id, cache, "vae")
244
+ if cache_overwrite:
245
+ # Find the first entry with the id, remove the id from the entry's id list
246
+ for e in loaded_objects["vae"]:
247
+ if id in e[-1]:
248
+ e[-1].remove(id)
249
+ # If the id list becomes empty, remove the entry from the "vae" list
250
+ if not e[-1]:
251
+ loaded_objects["vae"].remove(e)
252
+ break
253
+ loaded_objects["vae"].append((vae_name, vae, [id]))
254
+
255
+ return vae
256
+
257
+ def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_overwrite=False):
258
+ """
259
+ Extracts the Lora model with a given name from the "lora" array in loaded_objects.
260
+ If the Lora model is not found or strength values changed or model changed, creates a new Lora object with the given name and adds it to the "lora" array.
261
+ Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
262
+ If the cache limit is reached for a specific id, clears the cache and returns the loaded Lora model and clip without adding a new entry.
263
+ If there is cache space, adds the id to the ids list if it's not already there.
264
+ If there is cache space and the Lora model was not found in loaded_objects, adds a new entry to loaded_objects.
265
+
266
+ Parameters:
267
+ - lora_params: A list of tuples, where each tuple contains lora_name, strength_model, strength_clip.
268
+ - ckpt_name: name of the checkpoint from which the Lora model is created.
269
+ - id: an identifier for caching models for specific nodes.
270
+ - cache (optional): an integer that specifies how many Lora entries with a given id can exist in loaded_objects. Defaults to None.
271
+ """
272
+ global loaded_objects
273
+
274
+ for entry in loaded_objects["lora"]:
275
+ # Convert to sets and compare
276
+ if set(entry[0]) == set(lora_params) and entry[1] == ckpt_name:
277
+
278
+ _, _, lora_model, lora_clip, ids = entry
279
+ cache_full = cache and len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) >= cache
280
+
281
+ if cache_full:
282
+ clear_cache(id, cache, "lora")
283
+ elif id not in ids:
284
+ ids.append(id)
285
+
286
+ # Additional cache handling for 'ckpt' just like in 'load_checkpoint' function
287
+ for ckpt_entry in loaded_objects["ckpt"]:
288
+ if ckpt_entry[0] == ckpt_name:
289
+ _, _, _, _, ckpt_ids = ckpt_entry
290
+ ckpt_cache_full = ckpt_cache and len(
291
+ [ckpt_entry for ckpt_entry in loaded_objects["ckpt"] if id in ckpt_entry[-1]]) >= ckpt_cache
292
+
293
+ if ckpt_cache_full:
294
+ clear_cache(id, ckpt_cache, "ckpt")
295
+ elif id not in ckpt_ids:
296
+ ckpt_ids.append(id)
297
+
298
+ return lora_model, lora_clip
299
+
300
+ def recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths):
301
+ if len(lora_params) == 0:
302
+ return ckpt, clip
303
+
304
+ lora_name, strength_model, strength_clip = lora_params[0]
305
+ lora_path = folder_paths.get_full_path("loras", lora_name)
306
+ lora_model, lora_clip = load_lora_for_models_tsc(ckpt, clip, lora_path, strength_model, strength_clip)
307
+
308
+ # Call the function again with the new lora_model and lora_clip and the remaining tuples
309
+ return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths)
310
+
311
+ # Unpack lora parameters from the first element of the list for now
312
+ lora_name, strength_model, strength_clip = lora_params[0]
313
+ ckpt, clip, _ = load_checkpoint(ckpt_name, id, cache=ckpt_cache, cache_overwrite=cache_overwrite)
314
+
315
+ lora_model, lora_clip = recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths)
316
+
317
+ if cache:
318
+ if len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) < cache:
319
+ loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
320
+ else:
321
+ clear_cache(id, cache, "lora")
322
+ if cache_overwrite:
323
+ # Find the first entry with the id, remove the id from the entry's id list
324
+ for e in loaded_objects["lora"]:
325
+ if id in e[-1]:
326
+ e[-1].remove(id)
327
+ # If the id list becomes empty, remove the entry from the "lora" list
328
+ if not e[-1]:
329
+ loaded_objects["lora"].remove(e)
330
+ break
331
+ loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
332
+
333
+ return lora_model, lora_clip
334
+
335
+ def clear_cache(id, cache, dict_name):
336
+ """
337
+ Clear the cache for a specific id in a specific dictionary (either "ckpt" or "vae").
338
+ If the cache limit is reached for a specific id, deletes the id from the oldest entry.
339
+ If the id array of the entry becomes empty, deletes the entry.
340
+ """
341
+ # Get all entries associated with the id_element
342
+ id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
343
+ while len(id_associated_entries) > cache:
344
+ # Identify an older entry (but not necessarily the oldest) containing id
345
+ older_entry = id_associated_entries[0]
346
+ # Remove the id_element from the older entry
347
+ older_entry[-1].remove(id)
348
+ # If the id array of the older entry becomes empty after this, delete the entry
349
+ if not older_entry[-1]:
350
+ loaded_objects[dict_name].remove(older_entry)
351
+ # Update the id_associated_entries
352
+ id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
353
+
354
+ def clear_cache_by_exception(node_id, vae_dict=None, ckpt_dict=None, lora_dict=None):
355
+ global loaded_objects
356
+
357
+ dict_mapping = {
358
+ "vae_dict": "vae",
359
+ "ckpt_dict": "ckpt",
360
+ "lora_dict": "lora"
361
+ }
362
+
363
+ for arg_name, arg_val in {"vae_dict": vae_dict, "ckpt_dict": ckpt_dict, "lora_dict": lora_dict}.items():
364
+ if arg_val is None:
365
+ continue
366
+
367
+ dict_name = dict_mapping[arg_name]
368
+
369
+ for tuple_idx, tuple_item in enumerate(loaded_objects[dict_name].copy()):
370
+ if arg_name == "lora_dict":
371
+ # Iterate over the tuples (lora_params, ckpt_name) in arg_val
372
+ for lora_params, ckpt_name in arg_val:
373
+ # Compare lists of tuples considering order inside tuples, but not order of tuples
374
+ if set(lora_params) == set(tuple_item[0]) and ckpt_name == tuple_item[1]:
375
+ break
376
+ else: # If no match was found in lora_dict, remove the tuple from loaded_objects
377
+ if node_id in tuple_item[-1]:
378
+ tuple_item[-1].remove(node_id)
379
+ if not tuple_item[-1]:
380
+ loaded_objects[dict_name].remove(tuple_item)
381
+ continue
382
+ elif tuple_item[0] not in arg_val: # Only remove the tuple if it's not in arg_val
383
+ if node_id in tuple_item[-1]:
384
+ tuple_item[-1].remove(node_id)
385
+ if not tuple_item[-1]:
386
+ loaded_objects[dict_name].remove(tuple_item)
387
+
388
+
389
+ # Retrieve the cache number from 'node_settings' json file
390
+ def get_cache_numbers(node_name):
391
+ # Get the directory path of the current file
392
+ my_dir = os.path.dirname(os.path.abspath(__file__))
393
+ # Construct the file path for node_settings.json
394
+ settings_file = os.path.join(my_dir, 'node_settings.json')
395
+ # Load the settings from the JSON file
396
+ with open(settings_file, 'r') as file:
397
+ node_settings = json.load(file)
398
+ # Retrieve the cache numbers for the given node
399
+ model_cache_settings = node_settings.get(node_name, {}).get('model_cache', {})
400
+ vae_cache = int(model_cache_settings.get('vae', 1))
401
+ ckpt_cache = int(model_cache_settings.get('ckpt', 1))
402
+ lora_cache = int(model_cache_settings.get('lora', 1))
403
+ return vae_cache, ckpt_cache, lora_cache
404
+
405
+ def print_last_helds(id=None):
406
+ print("\n" + "-" * 40) # Print an empty line followed by a separator line
407
+ if id is not None:
408
+ id = str(id) # Convert ID to string
409
+ print(f"Node-specific Last Helds (node_id:{int(id)})")
410
+ else:
411
+ print(f"Global Last Helds:")
412
+ for key in ["results", "latent", "images", "vae_decode"]:
413
+ entries_with_id = last_helds[key] if id is None else [entry for entry in last_helds[key] if id == entry[-1]]
414
+ if not entries_with_id: # If no entries with the chosen ID, print None and skip this key
415
+ continue
416
+ print(f"{key.capitalize()}:")
417
+ for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1
418
+ if isinstance(entry[0], bool): # Special handling for boolean types
419
+ output = entry[0]
420
+ else:
421
+ output = len(entry[0])
422
+ if id is None:
423
+ print(f" [{i}] Output: {output} (id: {entry[-1]})")
424
+ else:
425
+ print(f" [{i}] Output: {output}")
426
+ print("-" * 40) # Print a separator line
427
+ print("\n") # Print an empty line
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workflows/SimpleEval_Node_Examples.txt ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ The Evaluate Integers, Floats, and Strings nodes
2
+ now employ the SimpleEval library, enabling secure
3
+ creation and execution of custom Python expressions.
4
+
5
+ (https://github.com/danthedeckie/simpleeval)
6
+
7
+ Below is a short list of what is possible.
8
+ ______________________________________________
9
+
10
+ "EVALUATE INTEGERS/FLOATS" NODE EXPRESSION EXAMPLES:
11
+
12
+ Addition: a + b + c
13
+ Subtraction: a - b - c
14
+ Multiplication: a * b * c
15
+ Division: a / b / c
16
+ Modulo: a % b % c
17
+ Exponentiation: a ** b ** c
18
+ Floor Division: a // b // c
19
+ Absolute Value: abs(a) + abs(b) + abs(c)
20
+ Maximum: max(a, b, c)
21
+ Minimum: min(a, b, c)
22
+ Sum of Squares: a**2 + b**2 + c**2
23
+ Bitwise And: a & b & c
24
+ Bitwise Or: a | b | c
25
+ Bitwise Xor: a ^ b ^ c
26
+ Left Shift: a << 1 + b << 1 + c << 1
27
+ Right Shift: a >> 1 + b >> 1 + c >> 1
28
+ Greater Than Comparison: a > b > c
29
+ Less Than Comparison: a < b < c
30
+ Equal To Comparison: a == b == c
31
+ Not Equal To Comparison: a != b != c
32
+ ______________________________________________
33
+
34
+ "EVALUATE STRINGS" NODE EXPRESSION EXAMPLES:
35
+
36
+ Concatenate: a + b + c
37
+ Format: f'{a} {b} {c}'
38
+ Length: len(a) + len(b) + len(c)
39
+ Uppercase: a.upper() + b.upper() + c.upper()
40
+ Lowercase: a.lower() + b.lower() + c.lower()
41
+ Capitalize: a.capitalize() + b.capitalize() + c.capitalize()
42
+ Title Case: a.title() + b.title() + c.title()
43
+ Strip: a.strip() + b.strip() + c.strip()
44
+ Find Substring: a.find('sub') + b.find('sub') + c.find('sub')
45
+ Replace Substring: a.replace('old', 'new') + b.replace('old', 'new') + c.replace('old', 'new')
46
+ Count Substring: a.count('sub') + b.count('sub') + c.count('sub')
47
+ Check Numeric: a.isnumeric() + b.isnumeric() + c.isnumeric()
48
+ Check Alphabetic: a.isalpha() + b.isalpha() + c.isalpha()
49
+ Check Alphanumeric: a.isalnum() + b.isalnum() + c.isalnum()
50
+ Check Start: a.startswith('prefix') + b.startswith('prefix') + c.startswith('prefix')
51
+ Check End: a.endswith('suffix') + b.endswith('suffix') + c.endswith('suffix')
52
+ Split: a.split(' ') + b.split(' ') + c.split(' ')
53
+ Zero Fill: a.zfill(5) + b.zfill(5) + c.zfill(5)
54
+ Slice: a[:5] + b[:5] + c[:5]
55
+ Reverse: a[::-1] + b[::-1] + c[::-1]
56
+ ______________________________________________
workflows/XYplot/Manual Entry Notes.txt ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ EFFICIENCY NODES - XY Plot Manual Entry Syntax Notes
2
+ ______________________________________________
3
+ Type: Sampler
4
+
5
+ if not defined by X/Y_value syntax, the default_scheduler = Ksampler (Efficient)'s Scheduler
6
+
7
+ 1. sampler_1;sampler_2 = sampler_1(default_scheduler), sampler_2(default_scheduler)
8
+ 2. sampler_1,scheduler_1;sampler_2; = sampler_1(scheduler_1), sampler_2(default_scheduler)
9
+ 3. sampler_1;sampler_2;,default_scheduler = sampler_1(default_scheduler), sampler_2(default_scheduler)
10
+ 4. sampler_1;sampler_2,scheduler_2;,default_scheduler = sampler_1(default_scheduler), sampler_2(scheduler_2)
11
+
12
+ ______________________________________________
13
+ Type: Checkpoint
14
+
15
+ if not defined by X/Y_value syntax, the default_clip_skip = Efficient Loader's Clip Skip
16
+
17
+ 1. ckpt_1;ckpt_2 = ckpt_1(default_clip_skip), ckpt_2(default_clip_skip)
18
+ 2. ckpt_1_1,clip_skip_1;ckpt_2; = ckpt_1(clip_skip_1), ckpt_2(default_clip_skip)
19
+ 3. ckpt_1;ckpt_2;,default_clip_skip = ckpt_1(default_clip_skip), ckpt_2(default_clip_skip)
20
+ 4. ckpt_1;ckpt_2,clip_skip_2;,default_clip_skip = ckpt_1(default_clip_skip), ckpt_2(clip_skip_2)
21
+
22
+ ______________________________________________
23
+
24
+ Type: LoRA
25
+
26
+ if not defined by X/Y_value syntax, the default_lora_model_str = default_lora_clip_str = 1
27
+
28
+ def_mod_str = default_lora_model_str
29
+ def_clip_str = default_lora_clip_str
30
+
31
+ 1. lora_1;lora_2 = lora_1(default_clip_skip), lora_2(default_clip_skip)
32
+ 2. lora_1,mod_str_1;lora_2; = lora_1(mod_str_1,1), lora_2(1,1)
33
+ 3. lora_1;lora_2;,def_mod_str = lora_1(def_mod_str,1), lora_2(def_mod_str,1)
34
+ 4. lora_1;lora_2,mod_str_2;,def_mod_str,def_clip_str = lora_1(def_mod_str,def_clip_str), lora_2(mod_str_2,def_clip_str)
35
+
36
+ ______________________________________________
37
+
38
+ Notes:
39
+ - Trailing semicolons, new entry lines, and spaces are removed when parsing input strings.
40
+ - Any parameter not defined by the XY Plot's inputs are defined by the Efficient Loader and Ksampler (Efficient).
41
+ For example, if the scheduler isn't specified when plotting Samplers, the script will use
42
+ the scheduler set by the Ksampler (Efficient).
43
+ - LoRA stacking through the XY Plot Manual Entry node is currently not supported.
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