WebUI 1.6.0 fix with my own guide and recommendations.

docs/15b-AstolfoMix-08b09b.recipe.txt

@@ -0,0 +1,63 @@
+modelA=08b-AstolfoMix-_13a_14a.safetensors
+modelB=09b-AstolfoMix-_15a_16a.safetensors
+modelO=15b-AstolfoMix-08b09b
+position_id_fix=Keep Original
+output_mode=Max Precision
+0.4,0.9,0.5,0.6,0.5,0.0,0.9,0.9,1.0,0.4,0.3,0.8,0.3,0.9,0.6,0.8,0.9,0.7,0.6,1.0,0.9,0.6,0.7,0.3,0.6,1.0,0.0
+
+[0] hyper_score (Bayesian Optimization):  35%| | 95/270 [48:30:32<89:21:31, 1838.24s/it, best_iter=0, best
+
+
+Results: 'hyper_score'  
+   Best score: 0.6132534303822829  
+   Best parameter set:
+      '0'  : 0.4  
+      '1'  : 0.9  
+      '2'  : 0.5  
+      '3'  : 0.6  
+      '4'  : 0.5  
+      '5'  : 0.0  
+      '6'  : 0.9  
+      '7'  : 0.9  
+      '8'  : 1.0  
+      '9'  : 0.4  
+      '10' : 0.3  
+      '11' : 0.8  
+      '12' : 0.3  
+      '13' : 1.0  
+      '14' : 0.9  
+      '15' : 0.6  
+      '16' : 0.8  
+      '17' : 0.9  
+      '18' : 0.7  
+      '19' : 0.6  
+      '20' : 1.0  
+      '21' : 0.9  
+      '22' : 0.6  
+      '23' : 0.7  
+      '24' : 0.3  
+      '25' : 0.6  
+      '26' : 0.0
+   Best iteration: 0
+
+   Random seed: 367813992
+
+   Evaluation time   : 174590.83615255356 sec    [99.98 %]
+   Optimization time : 34.05722665786743 sec    [0.02 %]
+   Iteration time    : 174624.89337921143 sec    [646.76 sec/iter]
+
+2023-11-21 00:16:36,848 - AutoMBWrt - INFO - Writing merge history to E:\NOVELAI\stable-diffusion-webui\stable-diffusion-webui\extensions\auto-MBW-rt\csv\history.tsv
+2023-11-21 00:16:36,873 - AutoMBWrt - INFO - Saving checkpoint to 15b-AstolfoMix-08b09b
+2023-11-21 00:16:36,873 - MBWrt - DEBUG - Gathering MBW info
+2023-11-21 00:16:36,873 - MBWrt - DEBUG - Reading model A
+2023-11-21 00:16:40,076 - MBWrt - DEBUG - Saving recipe file as E:\NOVELAI\stable-diffusion-webui\stable-diffusion-webui\models\Stable-diffusion\15b-AstolfoMix-08b09b.recipe.txt
+2023-11-21 00:16:42,189 - MBWrt - INFO - Checkpoint saved to E:\NOVELAI\stable-diffusion-webui\stable-diffusion-webui\models\Stable-diffusion\15b-AstolfoMix-08b09b.safetensors.
+2023-11-21 00:16:42,321 - AutoMBWrt - INFO - merge completed.
+2023-11-21 00:16:42,321 - AutoMBWrt - INFO - Disable injection.
+
+[0] hyper_score (Bayesian Optimization):   0%|                                    | 0/270 [00:00<?, ?it/s]2023-11-21 01:27:27,668 - AutoMBWrt - INFO - testweights: 0.4,0.9,0.5,0.6,0.5,0.0,0.9,0.9,1.0,0.4,0.3,0.8,0.3,1.0,0.9,0.6,0.8,0.9,0.7,0.6,1.0,0.9,0.6,0.7,0.3,0.6,0.0
+2023-11-21 02:02:24,217 - AutoMBWrt - DEBUG - <Response [200]>████████████| 96/96 [02:39<00:00,  3.00s/it] 
+load checkpoint from C:\Users\User/.cache/ImageReward\ImageReward.pt
+checkpoint loaded
+2023-11-21 02:02:40,953 - AutoMBWrt - INFO - test score: 0.6132534303822829
+[0] hyper_score (Bayesian Optimization):   0%| | 1/270 [35:13<157:54:39, 2113.31s/it, best_iter=0, best_po]

docs/recover_from_log.py

@@ -0,0 +1,95 @@
+import numpy as np
+
+raw_log = """
+[0] hyper_score (Bayesian Optimization):  12%| | 31/250 [7:10:18<50:39:56, 832.86s/it, best_iter=0, bes
+
+
+Results: 'hyper_score'  
+   Best score: 0.44875530845332806  
+   Best parameter set:
+      '0'  : 0.35  
+      '1'  : 0.05  
+      '2'  : 1.0  
+      '3'  : 0.7  
+      '4'  : 0.35  
+      '5'  : 0.75  
+      '6'  : 0.7
+      '7'  : 1.0
+      '8'  : 0.95
+      '9'  : 0.65
+      '10' : 0.9
+      '11' : 0.05
+      '12' : 0.45
+      '13' : 1.0
+      '14' : 0.4
+      '15' : 0.4
+      '16' : 0.3
+      '17' : 1.0
+      '18' : 0.5
+      '19' : 0.7
+      '20' : 0.8
+      '21' : 0.6
+      '22' : 0.95
+      '23' : 0.55
+      '24' : 0.45
+      '25' : 0.2
+      '26' : 0.2
+   Best iteration: 0
+
+   Random seed: 60156508
+
+   Evaluation time   : 25650.57901096344 sec    [99.74 %]
+   Optimization time : 66.75476264953613 sec    [0.26 %]
+   Iteration time    : 25717.333773612976 sec    [102.87 sec/iter]
+
+2023-11-05 15:40:31,671 - AutoMBWrt - INFO - merge completed.
+2023-11-05 15:40:32,474 - AutoMBWrt - INFO - injection disabled (hopefully).
+"""
+
+print("Recover Weights from console log: ")
+print(raw_log)
+print("log = weights = slider")
+
+# sliders = sliders_in + [sl_M_00] + [sl_OUT] + sliders_out + [sl_TIME_EMBED] = sl_ALL_nat
+
+# sl_ALL_nat = [*sl_INPUT, *sl_MID, sl_OUT, *sl_OUTPUT, sl_TIME_EMBED]
+# sl_ALL = [*sl_INPUT, *sl_MID, *sl_OUTPUT, sl_TIME_EMBED, sl_OUT]
+
+print("Repo1: https://github.com/Xerxemi/auto-MBW-rt")
+print("Repo2: https://github.com/Xynonners/sd-webui-runtime-block-merge/blob/master/scripts/runtime_block_merge.py")
+
+def on_save_checkpoint(*weights, ):
+    current_weights_nat = weights[:27]
+    weights_output_recipe = weights[27:]
+    print("current_weights_nat:")
+    print(current_weights_nat)
+    print("weights_output_recipe:")
+    print(f"{','.join([str(w) for w in weights_output_recipe])}\n")
+    print("Which is [*sl_INPUT, *sl_MID, *sl_OUTPUT, sl_OUT, sl_TIME_EMBED]")
+    print("However in code sl_ALL (Repo2): ")
+    print("sl_ALL = [*sl_INPUT, *sl_MID, *sl_OUTPUT, sl_TIME_EMBED, sl_OUT]")
+
+def main(weights):
+    weights_s = weights.copy()
+    out = weights_s.pop(13)
+    time_embed = weights_s.pop(-1)
+    weights_s.append(out)
+    weights_s.append(time_embed)
+    print("weights:")
+    print(weights)
+    print("weights_s:")
+    print(weights_s) # sliders_in + [sl_M_00] + sliders_out + [sl_OUT] + [sl_TIME_EMBED] = !sl_ALL
+    on_save_checkpoint(*weights,*weights_s) # Merge is fine, recipe is bad.
+
+sliders = list(range(0,27))
+
+print("(Repo1): sliders = sliders_in + [sl_M_00] + [sl_OUT] + sliders_out + [sl_TIME_EMBED] = sl_ALL_nat")
+print(sliders)
+
+main(sliders)
+
+print("Due to the recipe bug, you should swap OUT and TIME_EMBED on UI if you are reading the recipe.")
+
+actual_recipe = "0.35,0.05,1.0,0.7,0.35,0.75,0.7,1.0,0.95,0.65,0.9,0.05,0.45,0.4,0.4,0.3,1.0,0.5,0.7,0.8,0.6,0.95,0.55,0.45,0.2,1.0,0.2"
+print("Verify with Actual recipe: ")
+print(actual_recipe)

install.py

@@ -52,9 +52,9 @@
     if "SettingsInjector" in open(script_path, "r").read():
         pass
     else:
-        raise Exception("autoMBW [error]: runtime-block-merge extension missing injector. MERGING WILL NOT WORK - https://github.com/Xynonners/sd-webui-runtime-block-merge")
+        raise Exception("autoMBW [error]: runtime-block-merge extension missing injector. MERGING WILL NOT WORK - https://github.com/6DammK9/sd-webui-runtime-block-merge")
 else:
-    raise Exception("autoMBW [error]: runtime-block-merge extension not found. MERGING WILL NOT WORK - https://github.com/Xynonners/sd-webui-runtime-block-merge")
+    raise Exception("autoMBW [error]: runtime-block-merge extension not found. MERGING WILL NOT WORK - https://github.com/6DammK9/sd-webui-runtime-block-merge")
 
 if not os.path.isdir(os.path.join(__extensions__, msgspec.toml.decode(open(config_path, "rb").read())["extension_counterpart_lora"])):
     raise Exception("autoMBW [error]: lora-block-weight extension not found. LoRA merging will not work - https://github.com/hako-mikan/sd-webui-lora-block-weight")

metadata.ini

@@ -0,0 +1,3 @@
+[Extension]
+Name=auto-MBW-rt
+Requires=sd-webui-runtime-block-merge

scripts/classifiers/THUDM/score_image_reward.py

@@ -3,15 +3,23 @@
 import math
 import ImageReward as reward
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 def sigmoid(x):
     return 1 / (1 + math.exp(-x))
 
 model = None
+
 def score(image, prompt="", reverse=False):
+    #I think it is the culprit or "locked score".
     global model
     if model == None:
+        logger.info("Loading ImageReward...")
         model = reward.load("ImageReward-v1.0")
     score_origin = model.score(prompt, image)
+    #Why it can be identical?
+    logger.debug(f"Image address: {image}")
+    logger.debug(f"Raw model score: {score_origin}")
     if reverse:
         score_origin = score_origin*-1
     score = sigmoid(score_origin)

scripts/classifiers/aesthetic/score_aes_B32_v0.py

@@ -34,6 +34,8 @@ def forward(self, x):
 import safetensors
 from transformers import CLIPModel, CLIPProcessor
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 dirname = os.path.dirname(__file__)
 aesthetic_path = os.path.join(dirname, "aes-B32-v0.safetensors")
 clip_name = 'openai/clip-vit-base-patch32'
@@ -46,6 +48,6 @@ def score(image, prompt="", reverse=False):
     image_embeds = image_embeddings_direct(image, clipmodel, clipprocessor)
     prediction = aes_model(torch.from_numpy(image_embeds).float().to('cuda'))
     if reverse:
-        print("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
+        logger.warning("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
     return prediction.item()
 

scripts/classifiers/cafe_aesthetic/aesthetic.py

@@ -8,7 +8,7 @@
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
-from webui import wrap_gradio_gpu_call
+from modules.call_queue import wrap_gradio_gpu_call
 from modules import shared, scripts, script_callbacks, ui
 from modules import generation_parameters_copypaste as parameters_copypaste
 import launch

scripts/classifiers/cafe_aesthetic/score_cafe_aesthetic.py

@@ -4,9 +4,11 @@
 from transformers import CLIPModel, CLIPProcessor
 from scripts.classifiers.cafe_aesthetic.aesthetic import judge
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 def score(image, prompt="", reverse=False):
     aesthetic, _, _ = judge(image)
     if reverse:
-        print("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
+        logger.warning("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
     return aesthetic["aesthetic"]
 

scripts/classifiers/cafe_aesthetic/score_cafe_waifu.py

@@ -4,9 +4,11 @@
 from transformers import CLIPModel, CLIPProcessor
 from scripts.classifiers.cafe_aesthetic.aesthetic import judge
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 def score(image, prompt="", reverse=False):
     _, _, waifu = judge(image)
     if reverse:
-        print("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
+        logger.warning("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
     return waifu["waifu"]
 

scripts/classifiers/hybridIQA/models.py

@@ -347,7 +347,7 @@ def resnet152_backbone(lda_out_channels, in_chn, pretrained=False, **kwargs):
 
 def weights_init_xavier(m):
     classname = m.__class__.__name__
-    # print(classname)
+    # logger.debug(classname)
     # if isinstance(m, nn.Conv2d):
     if classname.find('Conv') != -1:
         init.kaiming_normal_(m.weight.data)

scripts/classifiers/hybridIQA/score_hybridIQA_V1.py

@@ -4,6 +4,8 @@
 import scripts.classifiers.hybridIQA.models as models
 import numpy as np
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 # state_name = "hyperIQA_hybrid_latest.pth"
 # dirname = os.path.dirname(__file__)
 # pth_path = os.path.join(dirname, state_name)
@@ -27,7 +29,7 @@
 # random crop 25 patches and calculate mean quality score
 def score(image, prompt="", reverse=False):
     if reverse:
-        print("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
+        logger.warning("Reverse scoring not supported with this classifier. Are you sure you want to continue?")
     image = image.convert("RGB")
     pred_scores = []
     for i in range(25):

scripts/classifiers/laion_v1/score_laion_avs1.py

@@ -6,6 +6,8 @@
 import clip
 import os
 
+from scripts.util.auto_mbw_rt_logger import logger_autombwrt as logger
+
 state_name = "sa_0_4_vit_l_14_linear.pth"
 dirname = os.path.dirname(__file__)
 aesthetic_path = os.path.join(dirname, state_name)
@@ -44,6 +46,6 @@ def score(image, prompt="", reverse=False):
     image_features = get_image_features(image)
     score = predictor(torch.from_numpy(image_features).to(device).float())
     if reverse:
-        print("Reverse scoring currently not supported with this classifier. Are you sure you want to continue?")
+        logger.warning("Reverse scoring currently not supported with this classifier. Are you sure you want to continue?")
     return score.item()