improve: LBW - efficient patch

fix: LBW - preset patch

__init__.py

@@ -7,7 +7,7 @@
 
 import importlib
 
-version_code = [0, 82, 5]
+version_code = [0, 85, 1]
 version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 print(f"### Loading: ComfyUI-Inspire-Pack ({version_str})")
 

inspire/list_nodes.py

@@ -18,9 +18,14 @@ class FloatRange:
     CATEGORY = "InspirePack/Util"
 
     def doit(self, start, stop, step, limit, ensure_end):
-        if start >= stop or step == 0:
+        if start == stop or step == 0:
             return ([start], )
 
+        reverse = False
+        if start > stop:
+            reverse = True
+            start, stop = stop, start
+
         res = []
         x = start
         last = x
@@ -36,6 +41,9 @@ class FloatRange:
 
             res.append(stop)
 
+        if reverse:
+            res.reverse()
+
         return (res, )
 
 

inspire/lora_block_weight.py

@@ -165,8 +165,15 @@ class LoraLoaderBlockWeight:
         input_blocks = []
         middle_blocks = []
         output_blocks = []
+        double_blocks = []
+        single_blocks = []
         others = []
-        for k, v in loaded.items():
+        for key, v in loaded.items():
+            if isinstance(key, tuple):
+                k = key[0]
+            else:
+                k = key
+
             k_unet = k[len("diffusion_model."):]
 
             if k_unet.startswith("input_blocks."):
@@ -178,18 +185,26 @@ class LoraLoaderBlockWeight:
             elif k_unet.startswith("output_blocks."):
                 k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2]
                 output_blocks.append((k, v, parse_unet_num(k_unet_num), k_unet))
+            elif k_unet.startswith("double_blocks."):
+                k_unet_num = k_unet[len("double_blocks."):len("double_blocks.")+2]
+                double_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet))
+            elif k_unet.startswith("single_blocks."):
+                k_unet_num = k_unet[len("single_blocks."):len("single_blocks.")+2]
+                single_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet))
             else:
                 others.append((k, v, k_unet))
 
         input_blocks = sorted(input_blocks, key=lambda x: x[2])
         middle_blocks = sorted(middle_blocks, key=lambda x: x[2])
         output_blocks = sorted(output_blocks, key=lambda x: x[2])
+        double_blocks = sorted(double_blocks, key=lambda x: x[2])
+        single_blocks = sorted(single_blocks, key=lambda x: x[2])
 
         # prepare patch
         np.random.seed(seed % (2**31))
         populated_vector_list = []
         ratios = []
-        for k, v, k_unet_num, k_unet in (input_blocks + middle_blocks + output_blocks):
+        for k, v, k_unet_num, k_unet in (input_blocks + middle_blocks + output_blocks + double_blocks + single_blocks):
             if last_k_unet_num != k_unet_num and len(vector) > vector_i:
                 ratios = LoraLoaderBlockWeight.convert_vector_value(A, B, vector[vector_i].strip())
                 ratio = ratios.pop(0)
@@ -213,7 +228,9 @@ class LoraLoaderBlockWeight:
 
             last_k_unet_num = k_unet_num
 
-            new_modelpatcher.add_patches({k: v}, strength_model * populated_ratio)
+            if populated_ratio > 0:
+                new_modelpatcher.add_patches({k: v}, strength_model * populated_ratio)
+
             # if inverse:
             #     print(f"\t{k_unet} -> inv({ratio}) ")
             # else:
@@ -485,7 +502,7 @@ class XYInput_LoraBlockWeight:
                         XY_Capsule_LoraBlockWeight(0, 2, '', 'diff', storage, common_params),
                         XY_Capsule_LoraBlockWeight(0, 3, '', 'heatmap', storage, common_params)]
 
-        return ((xy_type, x_values), (xy_type, y_values), )
+        return (xy_type, x_values), (xy_type, y_values),
 
 
 class LoraBlockInfo:
@@ -535,8 +552,21 @@ class LoraBlockInfo:
         text_blocks = []
         text_blocks_map = {}
 
+        double_block_count = set()
+        double_blocks = []
+        double_blocks_map = {}
+
+        single_block_count = set()
+        single_blocks = []
+        single_blocks_map = {}
+
         others = []
-        for k, v in loaded.items():
+        for key, v in loaded.items():
+            if isinstance(key, tuple):
+                k = key[0]
+            else:
+                k = key
+
             k_unet = k[len("diffusion_model."):]
 
             if k_unet.startswith("input_blocks."):
@@ -572,6 +602,28 @@ class LoraBlockInfo:
                 else:
                     output_blocks_map[k_unet_int] = [k_unet]
 
+            elif k_unet.startswith("double_blocks."):
+                k_unet_num = k_unet[len("double_blocks."):len("double_blocks.") + 2]
+                k_unet_int = parse_unet_num(k_unet_num)
+
+                double_block_count.add(k_unet_int)
+                double_blocks.append(k_unet)
+                if k_unet_int in double_blocks_map:
+                    double_blocks_map[k_unet_int].append(k_unet)
+                else:
+                    double_blocks_map[k_unet_int] = [k_unet]
+
+            elif k_unet.startswith("single_blocks."):
+                k_unet_num = k_unet[len("single_blocks."):len("single_blocks.") + 2]
+                k_unet_int = parse_unet_num(k_unet_num)
+
+                single_block_count.add(k_unet_int)
+                single_blocks.append(k_unet)
+                if k_unet_int in single_blocks_map:
+                    single_blocks_map[k_unet_int].append(k_unet)
+                else:
+                    single_blocks_map[k_unet_int] = [k_unet]
+
             elif k_unet.startswith("er.text_model.encoder.layers."):
                 k_unet_num = k_unet[len("er.text_model.encoder.layers."):len("er.text_model.encoder.layers.")+2]
                 k_unet_int = parse_unet_num(k_unet_num)
@@ -591,22 +643,39 @@ class LoraBlockInfo:
         input_blocks = sorted(input_blocks)
         middle_blocks = sorted(middle_blocks)
         output_blocks = sorted(output_blocks)
+        double_blocks = sorted(double_blocks)
+        single_blocks = sorted(single_blocks)
         others = sorted(others)
 
-        text += f"\n-------[Input blocks] ({len(input_block_count)}, Subs={len(input_blocks)})-------\n"
-        input_keys = sorted(input_blocks_map.keys())
-        for x in input_keys:
-            text += f" IN{x}: {len(input_blocks_map[x])}\n"
+        if len(input_block_count) > 0:
+            text += f"\n-------[Input blocks] ({len(input_block_count)}, Subs={len(input_blocks)})-------\n"
+            input_keys = sorted(input_blocks_map.keys())
+            for x in input_keys:
+                text += f" IN{x}: {len(input_blocks_map[x])}\n"
 
-        text += f"\n-------[Middle blocks] ({len(middle_block_count)}, Subs={len(middle_blocks)})-------\n"
-        middle_keys = sorted(middle_blocks_map.keys())
-        for x in middle_keys:
-            text += f" MID{x}: {len(middle_blocks_map[x])}\n"
+        if len(middle_block_count) > 0:
+            text += f"\n-------[Middle blocks] ({len(middle_block_count)}, Subs={len(middle_blocks)})-------\n"
+            middle_keys = sorted(middle_blocks_map.keys())
+            for x in middle_keys:
+                text += f" MID{x}: {len(middle_blocks_map[x])}\n"
 
-        text += f"\n-------[Output blocks] ({len(output_block_count)}, Subs={len(output_blocks)})-------\n"
-        output_keys = sorted(output_blocks_map.keys())
-        for x in output_keys:
-            text += f" OUT{x}: {len(output_blocks_map[x])}\n"
+        if len(output_block_count) > 0:
+            text += f"\n-------[Output blocks] ({len(output_block_count)}, Subs={len(output_blocks)})-------\n"
+            output_keys = sorted(output_blocks_map.keys())
+            for x in output_keys:
+                text += f" OUT{x}: {len(output_blocks_map[x])}\n"
+
+        if len(double_block_count) > 0:
+            text += f"\n-------[Double blocks(MMDiT)] ({len(double_block_count)}, Subs={len(double_blocks)})-------\n"
+            double_keys = sorted(double_blocks_map.keys())
+            for x in double_keys:
+                text += f" DOUBLE{x}: {len(double_blocks_map[x])}\n"
+
+        if len(single_block_count) > 0:
+            text += f"\n-------[Single blocks(DiT)] ({len(single_block_count)}, Subs={len(single_blocks)})-------\n"
+            single_keys = sorted(single_blocks_map.keys())
+            for x in single_keys:
+                text += f" SINGLE{x}: {len(single_blocks_map[x])}\n"
 
         text += f"\n-------[Base blocks] ({len(text_block_count) + len(others)}, Subs={len(text_blocks) + len(others)})-------\n"
         text_keys = sorted(text_blocks_map.keys())

inspire/model_nodes.py

@@ -63,6 +63,9 @@ class IPAdapterModelHelper:
                 "insightface_provider": (["CPU", "CUDA", "ROCM"], ),
                 "cache_mode": (["insightface only", "clip_vision only", "all", "none"], {"default": "insightface only"}),
             },
+            "optional": {
+                "insightface_model_name": (['buffalo_l', 'antelopev2'],),
+            },
             "hidden": {"unique_id": "UNIQUE_ID"}
         }
 
@@ -72,7 +75,7 @@ class IPAdapterModelHelper:
 
     CATEGORY = "InspirePack/models"
 
-    def doit(self, model, clip, preset, lora_strength_model, lora_strength_clip, insightface_provider, cache_mode="none", unique_id=None):
+    def doit(self, model, clip, preset, lora_strength_model, lora_strength_clip, insightface_provider, cache_mode="none", unique_id=None, insightface_model_name='buffalo_l'):
         if 'IPAdapter' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
                                           "To use 'IPAdapterModelHelper' node, 'ComfyUI IPAdapter Plus' extension is required.")
@@ -157,7 +160,7 @@ class IPAdapterModelHelper:
             if cache_mode in ["insightface only", "all"]:
                 icache_key = 'insightface-' + insightface_provider
                 if icache_key not in backend_support.cache:
-                    backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(insightface_provider)[0]))
+                    backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(provider=insightface_provider, model_name=insightface_model_name)[0]))
                 _, (_, insightface) = backend_support.cache[icache_key]
             else:
                 insightface = insight_face_loader(insightface_provider)[0]

inspire/prompt_support.py

@@ -121,7 +121,7 @@ class LoadPromptsFromFile:
 
         prompts = []
         try:
-            if text_data_opt is None:
+            if not text_data_opt:
                 with open(prompt_path, "r", encoding="utf-8") as file:
                     prompt_data = file.read()
             else:
@@ -182,7 +182,7 @@ class LoadSinglePromptFromFile:
 
         prompts = []
         try:
-            if text_data_opt is None:
+            if not text_data_opt:
                 with open(prompt_path, "r", encoding="utf-8") as file:
                     prompt_data = file.read()
             else:

inspire/sampler_nodes.py

@@ -24,6 +24,7 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
                     "noise_mode": (["GPU(=A1111)", "CPU"],),
                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
                     "omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
+                    "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
                     },
                 "optional": {
                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
@@ -36,7 +37,8 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
     RETURN_NAMES = ("latent", "progress_latent")
 
     @staticmethod
-    def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval, omit_start_latent, scheduler_func_opt=None):
+    def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
+             interval, omit_start_latent, omit_final_latent, scheduler_func_opt=None):
         adv_steps = int(steps / denoise)
 
         if omit_start_latent:
@@ -44,19 +46,20 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
         else:
             result = [latent_image['samples']]
 
-        result = []
-
         def progress_callback(step, x0, x, total_steps):
             if (total_steps-1) != step and step % interval != 0:
                 return
 
             x = model.model.process_latent_out(x)
-            x = x.to(model_management.intermediate_device())
+            x = x.cpu()
             result.append(x)
 
         latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, True, seed, adv_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, (adv_steps-steps),
                                                                            adv_steps, noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
 
+        if not omit_final_latent:
+            result.append(latent_image['samples'].cpu())
+
         if len(result) > 0:
             result = torch.cat(result)
             result = {'samples': result}
@@ -86,6 +89,7 @@ class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
                     "omit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+                    "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
                     },
                 "optional": {
                     "prev_progress_latent_opt": ("LATENT",),
@@ -100,26 +104,29 @@ class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
     RETURN_TYPES = ("LATENT", "LATENT")
     RETURN_NAMES = ("latent", "progress_latent")
 
-    def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step,
-             noise_mode, return_with_leftover_noise, interval, omit_start_latent, prev_progress_latent_opt=None, scheduler_func_opt=None):
+    def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+             start_at_step, end_at_step, noise_mode, return_with_leftover_noise, interval, omit_start_latent, omit_final_latent,
+             prev_progress_latent_opt=None, scheduler_func_opt=None):
+
         if omit_start_latent:
             result = []
         else:
             result = [latent_image['samples']]
 
-        result = []
-
         def progress_callback(step, x0, x, total_steps):
             if (total_steps-1) != step and step % interval != 0:
                 return
 
             x = model.model.process_latent_out(x)
-            x = x.to(model_management.intermediate_device())
+            x = x.cpu()
             result.append(x)
 
         latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step,
                                                                            noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
 
+        if not omit_final_latent:
+            result.append(latent_image['samples'].cpu())
+
         if len(result) > 0:
             result = torch.cat(result)
             result = {'samples': result}
@@ -165,6 +172,15 @@ def logarithmic_interpolation(from_cfg, to_cfg, i, steps):
     return from_cfg + (to_cfg - from_cfg) * t
 
 
+def cosine_interpolation(from_cfg, to_cfg, i, steps):
+    if (i == 0) or (i == steps-1):
+        return from_cfg
+
+    t = (1.0 + math.cos(math.pi*2*(i/steps))) / 2
+
+    return from_cfg + (to_cfg - from_cfg) * t
+
+
 class Guider_scheduled(CFGGuider):
     def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule):
         super().__init__(model_patcher)
@@ -194,6 +210,8 @@ class Guider_scheduled(CFGGuider):
                 self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             elif self.schedule == 'log':
                 self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
+            elif self.schedule == 'cos':
+                self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             else:
                 self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
 
@@ -245,6 +263,8 @@ class Guider_PerpNeg_scheduled(Guider_PerpNeg):
                 self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             elif self.schedule == 'log':
                 self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
+            elif self.schedule == 'cos':
+                self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             else:
                 self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
 
@@ -276,7 +296,7 @@ class ScheduledCFGGuider:
                     "sigmas": ("SIGMAS", ),
                     "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
                     "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                    "schedule": (["linear", "log", "exp"], {'default': 'log'})
+                    "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
                     }
                 }
 
@@ -303,7 +323,7 @@ class ScheduledPerpNegCFGGuider:
                     "sigmas": ("SIGMAS", ),
                     "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
                     "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                    "schedule": (["linear", "log", "exp"], {'default': 'log'})
+                    "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
                     }
                 }
 

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "comfyui-inspire-pack"
 description = "This extension provides various nodes to support Lora Block Weight and the Impact Pack. Provides many easily applicable regional features and applications for Variation Seed."
-version = "0.82.5"
-license = "LICENSE"
+version = "0.85.1"
+license = { file = "LICENSE" }
 dependencies = ["matplotlib", "cachetools"]
 
 [project.urls]

resources/lbw-preset.txt

@@ -29,6 +29,16 @@ SDXL-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 SDXL-LyC-INALL:1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
 SDXL-LyC-MIDALL:1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0
 SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
+FLUX-DBL-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+FLUX-DBL-FRONT7:1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-DBL-MID6:1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0
+FLUX-DBL-TAIL6:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1
+FLUX-SINGLE-ALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+FLUX-SINGLE-1to10:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-SINGLE-11to20:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-SINGLE-21to30:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0
+FLUX-SINGLE-31to37:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1
+FLUX-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 @SD-FULL-TEST:17
 @SD-BLOCK1-TEST:17,12,1
 @SD-BLOCK2-TEST:17,12,2
@@ -49,4 +59,64 @@ SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
 @SD-BLOCK17-TEST:17,12,17
 @SD-LyC-FULL-TEST:27
 @SDXL-FULL-TEST:12
-@SDXL-LyC-FULL-TEST:21
+@SDXL-LyC-FULL-TEST:21
+@FLUX-DBL-FULL:19
+@FLUX-DBL-SGL-FULL:58
+@FLUX-DBL0-TEST:19,14,2
+@FLUX-DBL1-TEST:19,14,3
+@FLUX-DBL2-TEST:19,14,4
+@FLUX-DBL3-TEST:19,14,5
+@FLUX-DBL4-TEST:19,14,6
+@FLUX-DBL5-TEST:19,14,7
+@FLUX-DBL6-TEST:19,14,8
+@FLUX-DBL7-TEST:19,14,9
+@FLUX-DBL8-TEST:19,14,10
+@FLUX-DBL9-TEST:19,14,11
+@FLUX-DBL10-TEST:19,14,12
+@FLUX-DBL11-TEST:19,14,13
+@FLUX-DBL12-TEST:19,14,14
+@FLUX-DBL13-TEST:19,14,15
+@FLUX-DBL14-TEST:19,14,16
+@FLUX-DBL15-TEST:19,14,17
+@FLUX-DBL16-TEST:19,14,18
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