improve: ForEachList - display progress of iteration

https://github.com/ltdrdata/ComfyUI-Inspire-Pack/issues/216

__init__.py

@@ -6,10 +6,11 @@
 """
 
 import importlib
+import logging
 
-version_code = [1, 12]
+version_code = [1, 15]
 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})")
+logging.info(f"### Loading: ComfyUI-Inspire-Pack ({version_str})")
 
 node_list = [
     "lora_block_weight",

inspire/a1111_compat.py

@@ -5,12 +5,20 @@ from einops import rearrange
 import random
 import math
 from .libs import common
+import logging
+
+
+supported_noise_modes = ["GPU(=A1111)", "CPU", "GPU+internal_seed", "CPU+internal_seed"]
 
 
 class Inspire_RandomNoise:
-    def __init__(self, seed, mode, incremental_seed_mode, variation_seed, variation_strength, variation_method="linear"):
+    def __init__(self, seed, mode, incremental_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None):
         device = comfy.model_management.get_torch_device()
-        self.seed = seed
+        # HOTFIX: https://github.com/comfyanonymous/ComfyUI/commit/916d1e14a93ef331adef7c0deff2fdcf443b05cf#commitcomment-151914788
+        # seed value should be different with generated noise
+
+        self.seed = internal_seed
+        self.noise_seed = seed
         self.noise_device = "cpu" if mode == "CPU" else device
         self.incremental_seed_mode = incremental_seed_mode
         self.variation_seed = variation_seed
@@ -20,7 +28,7 @@ class Inspire_RandomNoise:
     def generate_noise(self, input_latent):
         latent_image = input_latent["samples"]
         batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
-        noise = utils.prepare_noise(latent_image, self.seed, batch_inds, self.noise_device, self.incremental_seed_mode,
+        noise = utils.prepare_noise(latent_image, self.noise_seed, batch_inds, self.noise_device, self.incremental_seed_mode,
                                     variation_seed=self.variation_seed, variation_strength=self.variation_strength, variation_method=self.variation_method)
         return noise.cpu()
 
@@ -29,30 +37,36 @@ class RandomNoise:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
                     "noise_mode": (["GPU(=A1111)", "CPU"],),
                     "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                     "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     },
                 "optional":
-                    {"variation_method": (["linear", "slerp"],), }
+                    {
+                        "variation_method": (["linear", "slerp"],),
+                        "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
+                    }
                 }
 
     RETURN_TYPES = ("NOISE",)
     FUNCTION = "get_noise"
     CATEGORY = "InspirePack/a1111_compat"
 
-    def get_noise(self, noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method="linear"):
-        return (Inspire_RandomNoise(noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method=variation_method),)
+    def get_noise(self, noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method="linear", internal_seed=None):
+        if internal_seed is None:
+            internal_seed = noise_seed
+
+        return (Inspire_RandomNoise(noise_seed, noise_mode, batch_seed_mode, variation_seed, variation_strength, variation_method=variation_method, internal_seed=internal_seed),)
 
 
 def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
                      noise_mode="CPU", disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
                      incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, noise=None, callback=None, variation_method="linear",
-                     scheduler_func=None):
+                     scheduler_func=None, internal_seed=None):
     device = comfy.model_management.get_torch_device()
-    noise_device = "cpu" if noise_mode == "CPU" else device
+    noise_device = "cpu" if 'cpu' in noise_mode.lower() else device
     latent_image = latent["samples"]
     if hasattr(comfy.sample, 'fix_empty_latent_channels'):
         latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
@@ -60,7 +74,7 @@ def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     latent = latent.copy()
 
     if noise is not None and latent_image.shape[1] != noise.shape[1]:
-        print("[Inspire Pack] inspire_ksampler: The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.")
+        logging.info("[Inspire Pack] inspire_ksampler: The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.")
         raise Exception("The type of latent input for noise generation does not match the model's latent type. When using the SD3 model, you must use the SD3 Empty Latent.")
 
     if noise is None:
@@ -80,6 +94,12 @@ def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
             start_step = advanced_steps - steps
             steps = advanced_steps
 
+    if internal_seed is None:
+        internal_seed = seed
+
+    if 'internal_seed' in noise_mode:
+        seed = internal_seed
+
     try:
         samples = common.impact_sampling(
             model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
@@ -87,7 +107,7 @@ def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
             scheduler_func=scheduler_func)
     except Exception as e:
         if "unexpected keyword argument 'scheduler_func'" in str(e):
-            print(f"[Inspire Pack] Impact Pack is outdated. (Cannot use GITS scheduler.)")
+            logging.info("[Inspire Pack] Impact Pack is outdated. (Cannot use GITS scheduler.)")
 
             samples = common.impact_sampling(
                 model=model, add_noise=not disable_noise, seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
@@ -103,7 +123,7 @@ class KSampler_inspire:
     def INPUT_TYPES(s):
         return {"required":
                     {"model": ("MODEL",),
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
@@ -112,7 +132,7 @@ class KSampler_inspire:
                      "negative": ("CONDITIONING", ),
                      "latent_image": ("LATENT", ),
                      "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "noise_mode": (supported_noise_modes,),
                      "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                      "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                      "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
@@ -121,6 +141,7 @@ class KSampler_inspire:
                     {
                         "variation_method": (["linear", "slerp"],),
                         "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                        "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
                     }
                 }
 
@@ -131,10 +152,11 @@ class KSampler_inspire:
 
     @staticmethod
     def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
-             batch_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear", scheduler_func_opt=None):
+             batch_seed_mode="comfy", variation_seed=None, variation_strength=None, variation_method="linear", scheduler_func_opt=None,
+             internal_seed=None):
         return (inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
                                  incremental_seed_mode=batch_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method,
-                                 scheduler_func=scheduler_func_opt)[0], )
+                                 scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0], )
 
 
 class KSamplerAdvanced_inspire:
@@ -143,7 +165,7 @@ class KSamplerAdvanced_inspire:
         return {"required":
                     {"model": ("MODEL",),
                      "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
@@ -153,7 +175,7 @@ class KSamplerAdvanced_inspire:
                      "latent_image": ("LATENT", ),
                      "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                      "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "noise_mode": (supported_noise_modes,),
                      "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
                      "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                      "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@@ -164,6 +186,7 @@ class KSamplerAdvanced_inspire:
                         "variation_method": (["linear", "slerp"],),
                         "noise_opt": ("NOISE_IMAGE",),
                         "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                        "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
                     }
                 }
 
@@ -174,7 +197,7 @@ class KSamplerAdvanced_inspire:
 
     @staticmethod
     def sample(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,
-               denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, callback=None, variation_method="linear", scheduler_func_opt=None):
+               denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, callback=None, variation_method="linear", scheduler_func_opt=None, internal_seed=None):
         force_full_denoise = True
 
         if return_with_leftover_noise:
@@ -189,7 +212,7 @@ class KSamplerAdvanced_inspire:
                                 denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step,
                                 force_full_denoise=force_full_denoise, noise_mode=noise_mode, incremental_seed_mode=batch_seed_mode,
                                 variation_seed=variation_seed, variation_strength=variation_strength, noise=noise_opt, callback=callback, variation_method=variation_method,
-                                scheduler_func=scheduler_func_opt)
+                                scheduler_func=scheduler_func_opt, internal_seed=internal_seed)
 
     def doit(self, *args, **kwargs):
         return (self.sample(*args, **kwargs)[0],)
@@ -200,14 +223,14 @@ class KSampler_inspire_pipe:
     def INPUT_TYPES(s):
         return {"required":
                     {"basic_pipe": ("BASIC_PIPE",),
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                      "scheduler": (common.SCHEDULERS, ),
                      "latent_image": ("LATENT", ),
                      "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "noise_mode": (supported_noise_modes,),
                      "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                      "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                      "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
@@ -215,6 +238,7 @@ class KSampler_inspire_pipe:
                 "optional":
                     {
                         "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                        "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
                     }
                 }
 
@@ -224,10 +248,10 @@ class KSampler_inspire_pipe:
     CATEGORY = "InspirePack/a1111_compat"
 
     def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise, noise_mode, batch_seed_mode="comfy",
-               variation_seed=None, variation_strength=None, scheduler_func_opt=None):
+               variation_seed=None, variation_strength=None, scheduler_func_opt=None, internal_seed=None):
         model, clip, vae, positive, negative = basic_pipe
         latent = inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode,
-                                  variation_seed=variation_seed, variation_strength=variation_strength, scheduler_func=scheduler_func_opt)[0]
+                                  variation_seed=variation_seed, variation_strength=variation_strength, scheduler_func=scheduler_func_opt, internal_seed=internal_seed)[0]
         return latent, vae
 
 
@@ -237,7 +261,7 @@ class KSamplerAdvanced_inspire_pipe:
         return {"required":
                     {"basic_pipe": ("BASIC_PIPE",),
                      "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed for the initial noise applied to the latent."}),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
@@ -245,7 +269,7 @@ class KSamplerAdvanced_inspire_pipe:
                      "latent_image": ("LATENT", ),
                      "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                      "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "noise_mode": (["GPU(=A1111)", "CPU"],),
+                     "noise_mode": (supported_noise_modes,),
                      "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
                      "batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
                      "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@@ -255,6 +279,7 @@ class KSamplerAdvanced_inspire_pipe:
                     {
                         "noise_opt": ("NOISE_IMAGE",),
                         "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                        "internal_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "This is the seed used for generating noise in intermediate steps when using ancestral and SDE-based samplers.\nNOTE: If `noise_mode` is in GPU mode and `internal_seed` is the same as `seed`, the generated image may be distorted."}),
                     }
                 }
 
@@ -264,7 +289,7 @@ class KSamplerAdvanced_inspire_pipe:
     CATEGORY = "InspirePack/a1111_compat"
 
     def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise,
-               denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, scheduler_func_opt=None):
+               denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None, scheduler_func_opt=None, internal_seed=None):
         model, clip, vae, positive, negative = basic_pipe
         latent = KSamplerAdvanced_inspire().sample(model=model, add_noise=add_noise, noise_seed=noise_seed,
                                                    steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler,
@@ -272,7 +297,8 @@ class KSamplerAdvanced_inspire_pipe:
                                                    start_at_step=start_at_step, end_at_step=end_at_step,
                                                    noise_mode=noise_mode, return_with_leftover_noise=return_with_leftover_noise,
                                                    denoise=denoise, batch_seed_mode=batch_seed_mode, variation_seed=variation_seed,
-                                                   variation_strength=variation_strength, noise_opt=noise_opt, scheduler_func_opt=scheduler_func_opt)[0]
+                                                   variation_strength=variation_strength, noise_opt=noise_opt, scheduler_func_opt=scheduler_func_opt,
+                                                   internal_seed=internal_seed)[0]
         return latent, vae
 
 
@@ -387,7 +413,7 @@ class HyperTileInspire:
                 nh = random_divisor(h, latent_tile_size * factor, swap_size, rand_obj)
                 nw = random_divisor(w, latent_tile_size * factor, swap_size, rand_obj)
 
-                print(f"factor: {factor} <--- params.depth: {apply_to.index(model_chans)} / scale_depth: {scale_depth} / latent_tile_size={latent_tile_size}")
+                logging.debug(f"factor: {factor} <--- params.depth: {apply_to.index(model_chans)} / scale_depth: {scale_depth} / latent_tile_size={latent_tile_size}")
                 # print(f"h: {h}, w:{w} --> nh: {nh}, nw: {nw}")
 
                 if nh * nw > 1:
@@ -396,7 +422,7 @@ class HyperTileInspire:
                 # else:
                 #     temp = None
 
-                print(f"q={q} / k={k} / v={v}")
+                logging.debug(f"q={q} / k={k} / v={v}")
                 return q, k, v
 
             return q, k, v

inspire/backend_support.py

@@ -16,7 +16,7 @@ try:
     with open(settings_file) as f:
         cache_settings = json.load(f)
 except Exception as e:
-    print(e)
+    logging.error(e)
     cache_settings = {}
 cache = TaggedCache(cache_settings)
 cache_count = {}
@@ -60,11 +60,13 @@ class CacheBackendData:
 
     OUTPUT_NODE = True
 
-    def doit(self, key, tag, data):
+    @staticmethod
+    def doit(key, tag, data):
         global cache
 
         if key == '*':
-            print(f"[Inspire Pack] CacheBackendData: '*' is reserved key. Cannot use that key")
+            logging.warning("[Inspire Pack] CacheBackendData: '*' is reserved key. Cannot use that key")
+            return (None,)
 
         update_cache(key, tag, (False, data))
         return (data,)
@@ -90,7 +92,8 @@ class CacheBackendDataNumberKey:
 
     OUTPUT_NODE = True
 
-    def doit(self, key, tag, data):
+    @staticmethod
+    def doit(key, tag, data):
         global cache
 
         update_cache(key, tag, (False, data))
@@ -120,11 +123,13 @@ class CacheBackendDataList:
 
     OUTPUT_NODE = True
 
-    def doit(self, key, tag, data):
+    @staticmethod
+    def doit(key, tag, data):
         global cache
 
         if key == '*':
-            print(f"[Inspire Pack] CacheBackendDataList: '*' is reserved key. Cannot use that key")
+            logging.warning("[Inspire Pack] CacheBackendDataList: '*' is reserved key. Cannot use that key")
+            return (None,)
 
         update_cache(key[0], tag[0], (True, data))
         return (data,)
@@ -183,7 +188,7 @@ class RetrieveBackendData:
         v = cache.get(key)
 
         if v is None:
-            print(f"[RetrieveBackendData] '{key}' is unregistered key.")
+            logging.warning(f"[RetrieveBackendData] '{key}' is unregistered key.")
             return (None,)
 
         is_list, data = v[1]
@@ -238,7 +243,7 @@ class RemoveBackendData:
         elif key in cache:
             del cache[key]
         else:
-            print(f"[Inspire Pack] RemoveBackendData: invalid data key {key}")
+            logging.warning(f"[Inspire Pack] RemoveBackendData: invalid data key {key}")
 
         return (signal_opt,)
 
@@ -262,7 +267,7 @@ class RemoveBackendDataNumberKey(RemoveBackendData):
         if key in cache:
             del cache[key]
         else:
-            print(f"[Inspire Pack] RemoveBackendDataNumberKey: invalid data key {key}")
+            logging.warning(f"[Inspire Pack] RemoveBackendDataNumberKey: invalid data key {key}")
 
         return (signal_opt,)
 
@@ -322,7 +327,6 @@ class ShowCachedInfo:
             # tag settings is not changed
             return
 
-        # print(f'set to {new_tag_settings}')
         new_cache = TaggedCache(new_tag_settings)
         for k, v in cache.items():
             new_cache[k] = v
@@ -370,10 +374,10 @@ class CheckpointLoaderSimpleShared(nodes.CheckpointLoaderSimple):
             res = self.load_checkpoint(ckpt_name)
             update_cache(key, "ckpt", (False, res))
             cache_kind = 'ckpt'
-            print(f"[Inspire Pack] CheckpointLoaderSimpleShared: Ckpt '{ckpt_name}' is cached to '{key}'.")
+            logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Ckpt '{ckpt_name}' is cached to '{key}'.")
         else:
             cache_kind, (_, res) = cache[key]
-            print(f"[Inspire Pack] CheckpointLoaderSimpleShared: Cached ckpt '{key}' is loaded. (Loading skip)")
+            logging.info(f"[Inspire Pack] CheckpointLoaderSimpleShared: Cached ckpt '{key}' is loaded. (Loading skip)")
 
         if cache_kind == 'ckpt':
             model, clip, vae = res
@@ -432,10 +436,10 @@ class LoadDiffusionModelShared(nodes.UNETLoader):
         if key not in cache or mode == 'Override Cache':
             model = self.load_unet(model_name, weight_dtype)[0]
             update_cache(key, "diffusion", (False, model))
-            print(f"[Inspire Pack] LoadDiffusionModelShared: diffusion model '{model_name}' is cached to '{key}'.")
+            logging.info(f"[Inspire Pack] LoadDiffusionModelShared: diffusion model '{model_name}' is cached to '{key}'.")
         else:
             _, (_, model) = cache[key]
-            print(f"[Inspire Pack] LoadDiffusionModelShared: Cached diffusion model '{key}' is loaded. (Loading skip)")
+            logging.info(f"[Inspire Pack] LoadDiffusionModelShared: Cached diffusion model '{key}' is loaded. (Loading skip)")
 
         return model, key
 
@@ -540,10 +544,10 @@ class LoadTextEncoderShared:
                 res = nodes.NODE_CLASS_MAPPINGS["CLIPLoader"]().load_clip(model_name1, type=type, device=device)[0]
 
             update_cache(key, "diffusion", (False, res))
-            print(f"[Inspire Pack] LoadTextEncoderShared: text encoder model set is cached to '{key}'.")
+            logging.info(f"[Inspire Pack] LoadTextEncoderShared: text encoder model set is cached to '{key}'.")
         else:
             _, (_, res) = cache[key]
-            print(f"[Inspire Pack] LoadTextEncoderShared: Cached text encoder model set '{key}' is loaded. (Loading skip)")
+            logging.info(f"[Inspire Pack] LoadTextEncoderShared: Cached text encoder model set '{key}' is loaded. (Loading skip)")
 
         return res, key
 
@@ -626,10 +630,10 @@ class StableCascade_CheckpointLoader:
             if key_b not in cache:
                 res_b = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
                 update_cache(key_b, "ckpt", (False, res_b))
-                print(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_b}' is cached to '{key_b}'.")
+                logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_b}' is cached to '{key_b}'.")
             else:
                 _, (_, res_b) = cache[key_b]
-                print(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_b}' is loaded. (Loading skip)")
+                logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_b}' is loaded. (Loading skip)")
             b_model, clip, b_vae = res_b
         else:
             b_model, clip, b_vae = nodes.CheckpointLoaderSimple().load_checkpoint(ckpt_name=stage_b)
@@ -638,10 +642,10 @@ class StableCascade_CheckpointLoader:
             if key_c not in cache:
                 res_c = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)
                 update_cache(key_c, "unclip_ckpt", (False, res_c))
-                print(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_c}' is cached to '{key_c}'.")
+                logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Ckpt '{stage_c}' is cached to '{key_c}'.")
             else:
                 _, (_, res_c) = cache[key_c]
-                print(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_c}' is loaded. (Loading skip)")
+                logging.info(f"[Inspire Pack] StableCascade_CheckpointLoader: Cached ckpt '{key_c}' is loaded. (Loading skip)")
             c_model, _, c_vae, clip_vision = res_c
         else:
             c_model, _, c_vae, clip_vision = nodes.unCLIPCheckpointLoader().load_checkpoint(ckpt_name=stage_c)

inspire/conditioning_nodes.py

@@ -3,6 +3,7 @@ import nodes
 import inspect
 from .libs import utils
 from nodes import MAX_RESOLUTION
+import logging
 
 
 class ConcatConditioningsWithMultiplier:
@@ -51,7 +52,7 @@ class ConcatConditioningsWithMultiplier:
 
             out = []
             if len(conditioning_from) > 1:
-                print(f"Warning: ConcatConditioningsWithMultiplier {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
+                logging.warning(f"[Inspire Pack] ConcatConditioningsWithMultiplier {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
 
             mkey = 'multiplier' + k[12:]
             multiplier = float(kwargs[mkey])

inspire/image_util.py

@@ -2,11 +2,19 @@ import os
 
 import torch
 from PIL import ImageOps
+try:
+    import pillow_jxl      # noqa: F401
+    jxl = True
+except ImportError:
+    jxl = False
 import comfy
 import folder_paths
 import base64
 from io import BytesIO
-from .libs.utils import *
+from .libs.utils import ByPassTypeTuple, empty_pil_tensor, empty_latent
+from PIL import Image
+import numpy as np
+import logging
 
 
 class LoadImagesFromDirBatch:
@@ -18,7 +26,7 @@ class LoadImagesFromDirBatch:
             },
             "optional": {
                 "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
-                "start_index": ("INT", {"default": 0, "min": -1, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "step": 1}),
                 "load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
             }
         }
@@ -44,6 +52,8 @@ class LoadImagesFromDirBatch:
 
         # Filter files by extension
         valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
+        if jxl:
+            valid_extensions.extend('.jxl')
         dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
 
         dir_files = sorted(dir_files)
@@ -121,7 +131,7 @@ class LoadImagesFromDirList:
             },
             "optional": {
                 "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
-                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                 "load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
             }
         }
@@ -150,6 +160,8 @@ class LoadImagesFromDirList:
 
         # Filter files by extension
         valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
+        if jxl:
+            valid_extensions.extend('.jxl')
         dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
 
         dir_files = sorted(dir_files)
@@ -248,7 +260,7 @@ class ChangeImageBatchSize:
                 output_tensor = input_tensor[:batch_size, :, :, :]
             return output_tensor
         else:
-            print(f"[WARN] ChangeImage(Latent)BatchSize: Unknown mode `{mode}` - ignored")
+            logging.warning(f"[Inspire Pack] ChangeImage(Latent)BatchSize: Unknown mode `{mode}` - ignored")
             return input_tensor
 
     @staticmethod
@@ -393,7 +405,7 @@ class ColorMapToMasks:
 
     def doit(self, color_map, max_count, min_pixels):
         if len(color_map) > 0:
-            print(f"[Inspire Pack] WARN: ColorMapToMasks - Sure, here's the translation: `color_map` can only be a single image. Only the first image will be processed. If you want to utilize the remaining images, convert the Image Batch to an Image List.")
+            logging.warning("[Inspire Pack] ColorMapToMasks - Sure, here's the translation: `color_map` can only be a single image. Only the first image will be processed. If you want to utilize the remaining images, convert the Image Batch to an Image List.")
 
         top_colors = top_k_colors(color_map[0], max_count, min_pixels)
 

inspire/inspire_server.py

@@ -7,6 +7,7 @@ from . import prompt_support
 from aiohttp import web
 from . import backend_support
 from .libs import common
+import logging
 
 
 max_seed = 2**32 - 1
@@ -50,7 +51,7 @@ async def set_cache_settings(request):
     try:
         backend_support.ShowCachedInfo.set_cache_settings(data)
         return web.Response(text='OK', status=200)
-    except Exception as e: # pylint: disable=broad-except
+    except Exception as e:
         return web.Response(text=f"{e}", status=500)
 
 
@@ -263,9 +264,9 @@ def populate_wildcards(json_data):
 
     if 'ImpactWildcardProcessor' in nodes.NODE_CLASS_MAPPINGS:
         if not hasattr(nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'], 'process'):
-            print(f"[Inspire Pack] Your Impact Pack is outdated. Please update to the latest version.")
+            logging.warning("[Inspire Pack] Your Impact Pack is outdated. Please update to the latest version.")
             return
-        
+
         wildcard_process = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardProcessor'].process
         updated_widget_values = {}
         mbp_updated_widget_values = {}
@@ -295,7 +296,7 @@ def populate_wildcards(json_data):
                                 if not isinstance(input_seed, int):
                                     continue
                             else:
-                                print(f"[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
+                                logging.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
                                 continue
                         except:
                             continue
@@ -326,7 +327,7 @@ def populate_wildcards(json_data):
                                 if not isinstance(input_seed, int):
                                     continue
                             else:
-                                print(f"[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
+                                logging.warning("[Inspire Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
                                 continue
                         except:
                             continue

inspire/libs/common.py

@@ -1,6 +1,11 @@
+import traceback
+
 import comfy
 import nodes
 from . import utils
+import logging
+from server import PromptServer
+
 
 SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', "GITS[coeff=1.2]"]
 
@@ -9,7 +14,7 @@ def impact_sampling(*args, **kwargs):
     if 'RegionalSampler' not in nodes.NODE_CLASS_MAPPINGS:
         utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                       "'Impact Pack' extension is required.")
-        raise Exception(f"[ERROR] You need to install 'ComfyUI-Impact-Pack'")
+        raise Exception("[ERROR] You need to install 'ComfyUI-Impact-Pack'")
 
     return nodes.NODE_CLASS_MAPPINGS['RegionalSampler'].separated_sample(*args, **kwargs)
 
@@ -36,6 +41,45 @@ def is_changed(uid, value):
 
     changed_cache[uid] = value
 
-    print(f"keys: {changed_cache.keys()}")
+    logging.info(f"keys: {changed_cache.keys()}")
 
     return res
+
+
+def update_node_status(node, text, progress=None):
+    if PromptServer.instance.client_id is None:
+        return
+
+    PromptServer.instance.send_sync("inspire/update_status", {
+        "node": node,
+        "progress": progress,
+        "text": text
+    }, PromptServer.instance.client_id)
+
+
+class ListWrapper:
+    def __init__(self, data, aux=None):
+        if isinstance(data, ListWrapper):
+            self._data = data
+            if aux is None:
+                self.aux = data.aux
+            else:
+                self.aux = aux
+        else:
+            self._data = list(data)
+            self.aux = aux
+
+    def __getitem__(self, index):
+        if isinstance(index, slice):
+            return ListWrapper(self._data[index], self.aux)
+        else:
+            return self._data[index]
+
+    def __setitem__(self, index, value):
+        self._data[index] = value
+
+    def __len__(self):
+        return len(self._data)
+
+    def __repr__(self):
+        return f"ListWrapper({self._data}, aux={self.aux})"

inspire/libs/utils.py

@@ -6,6 +6,7 @@ from PIL import Image, ImageDraw
 import math
 import cv2
 import folder_paths
+import logging
 
 
 def apply_variation_noise(latent_image, noise_device, variation_seed, variation_strength, mask=None, variation_method='linear'):
@@ -198,9 +199,9 @@ def try_install_custom_node(custom_node_url, msg):
         import cm_global
         cm_global.try_call(api='cm.try-install-custom-node',
                            sender="Inspire Pack", custom_node_url=custom_node_url, msg=msg)
-    except Exception as e:
-        print(msg)
-        print(f"[Inspire Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.")
+    except Exception as e:  # noqa: F841
+        logging.error(msg)
+        logging.error("[Inspire Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.")
 
 
 def empty_latent():

inspire/list_nodes.py

@@ -1,5 +1,6 @@
 from comfy_execution.graph_utils import GraphBuilder, is_link
 from .libs.utils import any_typ
+from .libs.common import update_node_status, ListWrapper
 
 class FloatRange:
     @classmethod
@@ -105,11 +106,18 @@ class ForeachListBegin:
         if initial_input is None:
             initial_input = item_list[0]
             item_list = item_list[1:]
-            
-        if len(item_list) > 0:
-            return ("stub", item_list[1:], item_list[0], initial_input)
 
-        return ("stub", [], None, initial_input)
+        if len(item_list) > 0:
+            next_list = ListWrapper(item_list[1:])
+            next_item = item_list[0]
+        else:
+            next_list = ListWrapper([])
+            next_item = None
+
+        if next_list.aux is None:
+            next_list.aux = len(item_list), None
+
+        return "stub", next_list, next_item, initial_input
 
 
 class ForeachListEnd:
@@ -157,11 +165,15 @@ class ForeachListEnd:
                 self.collect_contained(child_id, upstream, contained)
 
     def doit(self, flow_control, remained_list, intermediate_output, dynprompt, unique_id):
+        if remained_list.aux[1] is None:
+            remained_list.aux = (remained_list.aux[0], unique_id)
+
+        update_node_status(remained_list.aux[1], f"{(remained_list.aux[0]-len(remained_list))}/{remained_list.aux[0]} steps", (remained_list.aux[0]-len(remained_list))/remained_list.aux[0])
+
         if len(remained_list) == 0:
             return (intermediate_output,)
 
         # We want to loop
-        this_node = dynprompt.get_node(unique_id)
         upstream = {}
 
         # Get the list of all nodes between the open and close nodes
@@ -192,6 +204,7 @@ class ForeachListEnd:
                     node.set_input(k, v)
 
         new_open = graph.lookup_node(open_node)
+
         new_open.set_input("item_list", remained_list)
         new_open.set_input("initial_input", intermediate_output)
 

inspire/lora_block_weight.py

@@ -10,7 +10,7 @@ import json
 from comfy.cli_args import args
 from safetensors.torch import safe_open
 import ast
-
+import logging
 
 from server import PromptServer
 from .libs import utils
@@ -51,9 +51,6 @@ def parse_unet_num(s):
 
 
 class MakeLBW:
-    def __init__(self):
-        self.loaded_lora = None
-
     @classmethod
     def INPUT_TYPES(s):
         preset = ["Preset"]  # 20
@@ -573,10 +570,10 @@ class XY_Capsule_LoraBlockWeight:
 
     def set_result(self, image, latent):
         if self.another_capsule is not None:
-            print(f"XY_Capsule_LoraBlockWeight: ({self.another_capsule.x, self.y}) is processed.")
+            logging.info(f"XY_Capsule_LoraBlockWeight: ({self.another_capsule.x, self.y}) is processed.")
             self.storage[(self.another_capsule.x, self.y)] = image
         else:
-            print(f"XY_Capsule_LoraBlockWeight: ({self.x, self.y}) is processed.")
+            logging.info(f"XY_Capsule_LoraBlockWeight: ({self.x, self.y}) is processed.")
 
     def patch_model(self, model, clip):
         lora_name, strength_model, strength_clip, inverse, block_vectors, seed, A, B, heatmap_palette, heatmap_alpha, heatmap_strength, xyplot_mode = self.params

inspire/model_nodes.py

@@ -5,6 +5,8 @@ import server
 from .libs import utils
 from . import backend_support
 from comfy import sdxl_clip
+import logging
+
 
 model_preset = {
     # base
@@ -48,7 +50,7 @@ def lookup_model(model_dir, name):
     if len(resolved_name) > 0:
         return resolved_name[0], "OK"
     else:
-        print(f"[ERROR] IPAdapterModelHelper: The `{name}` model file does not exist in `{model_dir}` model dir.")
+        logging.error(f"[Inspire Pack] IPAdapterModelHelper: The `{name}` model file does not exist in `{model_dir}` model dir.")
         return None, "FAIL"
 
 
@@ -81,7 +83,7 @@ class IPAdapterModelHelper:
         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.")
-            raise Exception(f"[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
+            raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
 
         is_sdxl_preset = 'SDXL' in preset
         if clip is not None:
@@ -95,7 +97,7 @@ class IPAdapterModelHelper:
             server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 3, "label": "INSIGHTFACE (fail)"})
             server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 4, "label": "MODEL (fail)"})
             server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 5, "label": "CLIP (fail)"})
-            print(f"[ERROR] IPAdapterModelHelper: You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.")
+            logging.error("[Inspire Pack] IPAdapterModelHelper: You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.")
             raise Exception("[ERROR] You cannot mix SDXL and SD1.5 in the checkpoint and IPAdapter.")
 
         ipadapter, clipvision, lora, is_insightface = model_preset[preset]
@@ -154,13 +156,13 @@ class IPAdapterModelHelper:
         if 'IPAdapterInsightFaceLoader' in nodes.NODE_CLASS_MAPPINGS:
             insight_face_loader = nodes.NODE_CLASS_MAPPINGS['IPAdapterInsightFaceLoader']().load_insightface
         else:
-            print("'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
+            logging.warning("'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
             insight_face_loader = None
 
         icache_key = ""
         if is_insightface:
             if insight_face_loader is None:
-                raise Exception(f"[ERROR] 'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
+                raise Exception("[ERROR] 'ComfyUI IPAdapter Plus' extension is either too outdated or not installed.")
 
             if cache_mode in ["insightface only", "all"]:
                 icache_key = 'insightface-' + insightface_provider

inspire/prompt_support.py

@@ -18,6 +18,7 @@ from server import PromptServer
 from .libs import utils, common
 from .backend_support import CheckpointLoaderSimpleShared
 
+import logging
 
 model_path = folder_paths.models_dir
 utils.add_folder_path_and_extensions("inspire_prompts", [os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "prompts"))], {'.txt'})
@@ -39,8 +40,8 @@ try:
 
     with open(pb_yaml_path, 'r', encoding="utf-8") as f:
         prompt_builder_preset = yaml.load(f, Loader=yaml.FullLoader)
-except Exception as e:
-    print(f"[Inspire Pack] Failed to load 'prompt-builder.yaml'\nNOTE: Only files with UTF-8 encoding are supported.")
+except Exception as e:  # noqa: F841
+    logging.error("[Inspire Pack] Failed to load 'prompt-builder.yaml'\nNOTE: Only files with UTF-8 encoding are supported.")
 
 
 class LoadPromptsFromDir:
@@ -61,7 +62,7 @@ class LoadPromptsFromDir:
                 "optional": {
                     "reload": ("BOOLEAN", { "default": False, "label_on": "if file changed", "label_off": "if value changed"}),
                     "load_cap": ("INT", {"default": 0, "min": 0, "step": 1, "advanced": True, "tooltip": "The amount of prompts to load at once:\n0: Load all\n1 or higher: Load a specified number"}),
-                    "start_index": ("INT", {"default": 0, "min": -1, "step": 1, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}),
+                    "start_index": ("INT", {"default": 0, "min": -1, "step": 1, "max": 0xffffffffffffffff, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}),
                     }
                 }
 
@@ -121,7 +122,7 @@ class LoadPromptsFromDir:
 
         prompts = []
         for file_name in prompt_files:
-            print(f"file_name: {file_name}")
+            logging.info(f"file_name: {file_name}")
             try:
                 with open(file_name, "r", encoding="utf-8") as file:
                     prompt_data = file.read()
@@ -138,9 +139,9 @@ class LoadPromptsFromDir:
                             result_tuple = (positive_text, negative_text, name_text)
                             prompts.append(result_tuple)
                         else:
-                            print(f"[WARN] LoadPromptsFromDir: invalid prompt format in '{file_name}'")
+                            logging.warning(f"[Inspire Pack] LoadPromptsFromDir: invalid prompt format in '{file_name}'")
             except Exception as e:
-                print(f"[ERROR] LoadPromptsFromDir: an error occurred while processing '{file_name}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
+                logging.error(f"[Inspire Pack] LoadPromptsFromDir: an error occurred while processing '{file_name}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         # slicing [start_index ~ start_index + load_cap]
         total_prompts = len(prompts)
@@ -176,7 +177,7 @@ class LoadPromptsFromFile:
                         "text_data_opt": ("STRING", {"defaultInput": True}),
                         "reload": ("BOOLEAN", {"default": False, "label_on": "if file changed", "label_off": "if value changed"}),
                         "load_cap": ("INT", {"default": 0, "min": 0, "step": 1, "advanced": True, "tooltip": "The amount of prompts to load at once:\n0: Load all\n1 or higher: Load a specified number"}),
-                        "start_index": ("INT", {"default": 0, "min": -1, "step": 1, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}),
+                        "start_index": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff, "step": 1, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}),
                         }
                 }
 
@@ -229,9 +230,9 @@ class LoadPromptsFromFile:
                 matched_path = None
 
         if matched_path:
-            print(f"[INFO] LoadPromptsFromFile: file found '{prompt_file}'")
+            logging.info(f"[Inspire Pack] LoadPromptsFromFile: file found '{prompt_file}'")
         else:
-            print(f"[WARN] LoadPromptsFromFile: file not found '{prompt_file}'")
+            logging.warning(f"[Inspire Pack] LoadPromptsFromFile: file not found '{prompt_file}'")
 
         prompts = []
         try:
@@ -255,9 +256,9 @@ class LoadPromptsFromFile:
                     result_tuple = (positive_text, negative_text, name_text)
                     prompts.append(result_tuple)
                 else:
-                    print(f"[WARN] LoadPromptsFromFile: invalid prompt format in '{prompt_file}'")
+                    logging.warning(f"[Inspire Pack] LoadPromptsFromFile: invalid prompt format in '{prompt_file}'")
         except Exception as e:
-            print(f"[ERROR] LoadPromptsFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
+            logging.error(f"[Inspire Pack] LoadPromptsFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         # slicing [start_index ~ start_index + load_cap]
         total_prompts = len(prompts)
@@ -312,9 +313,9 @@ class LoadSinglePromptFromFile:
                 prompt_path = None
 
         if prompt_path:
-            print(f"[INFO] LoadSinglePromptFromFile: file found '{prompt_file}'")
+            logging.info(f"[Inspire Pack] LoadSinglePromptFromFile: file found '{prompt_file}'")
         else:
-            print(f"[WARN] LoadSinglePromptFromFile: file not found '{prompt_file}'")
+            logging.warning(f"[Inspire Pack] LoadSinglePromptFromFile: file not found '{prompt_file}'")
 
         prompts = []
         try:
@@ -323,7 +324,7 @@ class LoadSinglePromptFromFile:
                     prompt_data = file.read()
             else:
                 prompt_data = text_data_opt
-                
+
             prompt_list = re.split(r'\n\s*-+\s*\n', prompt_data)
             try:
                 prompt = prompt_list[index]
@@ -340,9 +341,9 @@ class LoadSinglePromptFromFile:
                 result_tuple = (positive_text, negative_text, name_text)
                 prompts.append(result_tuple)
             else:
-                print(f"[WARN] LoadSinglePromptFromFile: invalid prompt format in '{prompt_file}'")
+                logging.warning(f"[Inspire Pack] LoadSinglePromptFromFile: invalid prompt format in '{prompt_file}'")
         except Exception as e:
-            print(f"[ERROR] LoadSinglePromptFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
+            logging.error(f"[Inspire Pack] LoadSinglePromptFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         return (prompts, )
 
@@ -563,7 +564,7 @@ class BNK_EncoderWrapper:
         if 'BNK_CLIPTextEncodeAdvanced' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb',
                                           "To use 'WildcardEncodeInspire' node, 'ComfyUI_ADV_CLIP_emb' extension is required.")
-            raise Exception(f"[ERROR] To use WildcardEncodeInspire, you need to install 'Advanced CLIP Text Encode'")
+            raise Exception("[ERROR] To use WildcardEncodeInspire, you need to install 'Advanced CLIP Text Encode'")
         return nodes.NODE_CLASS_MAPPINGS['BNK_CLIPTextEncodeAdvanced']().encode(clip, text, self.token_normalization, self.weight_interpretation)
 
 
@@ -604,7 +605,7 @@ class WildcardEncodeInspire:
         if 'ImpactWildcardEncode' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                           "To use 'Wildcard Encode (Inspire)' node, 'Impact Pack' extension is required.")
-            raise Exception(f"[ERROR] To use 'Wildcard Encode (Inspire)', you need to install 'Impact Pack'")
+            raise Exception("[ERROR] To use 'Wildcard Encode (Inspire)', you need to install 'Impact Pack'")
 
         processed = []
         model, clip, conditioning = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode'].process_with_loras(wildcard_opt=populated, model=kwargs['model'], clip=kwargs['clip'], seed=kwargs['seed'], clip_encoder=clip_encoder, processed=processed)
@@ -637,7 +638,6 @@ class MakeBasicPipe:
                         "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"], {'default': 'comfy++'}),
 
                         "stop_at_clip_layer": ("INT", {"default": -2, "min": -24, "max": -1, "step": 1}),
-            
                         "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     },
                 "optional": {
@@ -660,7 +660,7 @@ class MakeBasicPipe:
         if 'ImpactWildcardEncode' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                           "To use 'Make Basic Pipe (Inspire)' node, 'Impact Pack' extension is required.")
-            raise Exception(f"[ERROR] To use 'Make Basic Pipe (Inspire)', you need to install 'Impact Pack'")
+            raise Exception("[ERROR] To use 'Make Basic Pipe (Inspire)', you need to install 'Impact Pack'")
 
         model, clip, vae, key = CheckpointLoaderSimpleShared().doit(ckpt_name=kwargs['ckpt_name'], key_opt=kwargs['ckpt_key_opt'])
         clip = nodes.CLIPSetLastLayer().set_last_layer(clip, kwargs['stop_at_clip_layer'])[0]
@@ -738,7 +738,7 @@ class SeedExplorer:
 
                     noise = utils.apply_variation_noise(noise, noise_device, variation_seed, variation_strength, mask=mask, variation_method=variation_method)
                 except Exception:
-                    print(f"[ERROR] IGNORED: SeedExplorer failed to processing '{x}'")
+                    logging.error(f"[Inspire Pack] IGNORED: SeedExplorer failed to processing '{x}'")
                     traceback.print_exc()
         return noise
 
@@ -779,7 +779,7 @@ class SeedExplorer:
             return (noise,)
 
         except Exception:
-            print(f"[ERROR] IGNORED: SeedExplorer failed")
+            logging.error("[Inspire Pack] IGNORED: SeedExplorer failed")
             traceback.print_exc()
 
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,

inspire/regional_nodes.py

@@ -9,6 +9,7 @@ import webcolors
 from . import prompt_support
 from .libs import utils, common
 
+import logging
 
 class RegionalPromptSimple:
     @classmethod
@@ -43,7 +44,7 @@ class RegionalPromptSimple:
         if 'RegionalPrompt' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                           "To use 'RegionalPromptSimple' node, 'Impact Pack' extension is required.")
-            raise Exception(f"[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'")
+            raise Exception("[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'")
 
         model, clip, vae, positive, negative = basic_pipe
 
@@ -88,7 +89,7 @@ def color_to_mask(color_mask, mask_color):
         else:
             selected = int(mask_color, 10)
     except Exception:
-        raise Exception(f"[ERROR] Invalid mask_color value. mask_color should be a color value for RGB")
+        raise Exception("[ERROR] Invalid mask_color value. mask_color should be a color value for RGB")
 
     temp = (torch.clamp(color_mask, 0, 1.0) * 255.0).round().to(torch.int)
     temp = torch.bitwise_left_shift(temp[:, :, :, 0], 16) + torch.bitwise_left_shift(temp[:, :, :, 1], 8) + temp[:, :, :, 2]
@@ -260,7 +261,7 @@ class IPAdapterConditioning:
         if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
                                           "To use 'Regional IPAdapter' node, 'ComfyUI IPAdapter Plus' extension is required.")
-            raise Exception(f"[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
+            raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
 
         if self.embeds is None:
             obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']
@@ -287,7 +288,7 @@ def IPADAPTER_WEIGHT_TYPES():
         try:
             IPADAPTER_WEIGHT_TYPES_CACHE = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']().INPUT_TYPES()['required']['weight_type'][0]
         except Exception:
-            print(f"[Inspire Pack] IPAdapterPlus is not installed.")
+            logging.error("[Inspire Pack] IPAdapterPlus is not installed.")
             IPADAPTER_WEIGHT_TYPES_CACHE = ["IPAdapterPlus is not installed"]
 
     return IPADAPTER_WEIGHT_TYPES_CACHE
@@ -334,7 +335,6 @@ class RegionalIPAdapterColorMask:
             "required": {
                 "color_mask": ("IMAGE",),
                 "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
-                
                 "image": ("IMAGE",),
                 "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
                 "noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
@@ -501,7 +501,7 @@ class RegionalSeedExplorerMask:
 
             noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
         except Exception:
-            print(f"[ERROR] IGNORED: RegionalSeedExplorerColorMask is failed.")
+            logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
             traceback.print_exc()
 
         noise = noise.cpu()
@@ -559,7 +559,7 @@ class RegionalSeedExplorerColorMask:
 
             noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
         except Exception:
-            print(f"[ERROR] IGNORED: RegionalSeedExplorerColorMask is failed.")
+            logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
             traceback.print_exc()
 
         color_mask.cpu()

inspire/sampler_nodes.py

@@ -2,11 +2,11 @@ import torch
 from . import a1111_compat
 import comfy
 from .libs import common
-from comfy import model_management
 from comfy.samplers import CFGGuider
 from comfy_extras.nodes_perpneg import Guider_PerpNeg
 import math
 
+
 class KSampler_progress(a1111_compat.KSampler_inspire):
     @classmethod
     def INPUT_TYPES(s):
@@ -21,7 +21,7 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
                     "negative": ("CONDITIONING", ),
                     "latent_image": ("LATENT", ),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                    "noise_mode": (["GPU(=A1111)", "CPU"],),
+                    "noise_mode": (a1111_compat.supported_noise_modes,),
                     "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"}),
@@ -85,7 +85,7 @@ class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
                     "latent_image": ("LATENT", ),
                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                    "noise_mode": (["GPU(=A1111)", "CPU"],),
+                    "noise_mode": (a1111_compat.supported_noise_modes,),
                     "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"}),

inspire/segs_support.py

@@ -2,7 +2,7 @@ import nodes
 import numpy as np
 import torch
 from .libs import utils
-
+import logging
 
 def normalize_size_base_64(w, h):
     short_side = min(w, h)
@@ -28,12 +28,12 @@ class MediaPipeFaceMeshDetector:
         if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'MediaPipeFaceMeshDetector' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         if 'MediaPipeFaceMeshToSEGS' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                           "To use 'MediaPipeFaceMeshDetector' node, 'Impact Pack' extension is required.")
-            raise Exception(f"[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI-Impact-Pack'")
+            raise Exception("[ERROR] To use MediaPipeFaceMeshDetector, you need to install 'ComfyUI-Impact-Pack'")
 
         pre_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipe-FaceMeshPreprocessor']
         seg_obj = nodes.NODE_CLASS_MAPPINGS['MediaPipeFaceMeshToSEGS']
@@ -63,7 +63,7 @@ class MediaPipe_FaceMesh_Preprocessor_wrapper:
         if 'MediaPipe-FaceMeshPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         if self.upscale_factor != 1.0:
             image = nodes.ImageScaleBy().upscale(image, 'bilinear', self.upscale_factor)[0]
@@ -78,7 +78,7 @@ class AnimeLineArt_Preprocessor_wrapper:
         if 'AnimeLineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'AnimeLineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use AnimeLineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use AnimeLineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['AnimeLineArtPreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -90,7 +90,7 @@ class Manga2Anime_LineArt_Preprocessor_wrapper:
         if 'Manga2Anime_LineArt_Preprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'Manga2Anime_LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use Manga2Anime_LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use Manga2Anime_LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['Manga2Anime_LineArt_Preprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -102,7 +102,7 @@ class Color_Preprocessor_wrapper:
         if 'ColorPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'Color_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use Color_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use Color_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['ColorPreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -112,12 +112,12 @@ class Color_Preprocessor_wrapper:
 class InpaintPreprocessor_wrapper:
     def __init__(self, black_pixel_for_xinsir_cn):
         self.black_pixel_for_xinsir_cn = black_pixel_for_xinsir_cn
-    
+
     def apply(self, image, mask=None):
         if 'InpaintPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'InpaintPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use InpaintPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use InpaintPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['InpaintPreprocessor']()
         if mask is None:
@@ -130,7 +130,7 @@ class InpaintPreprocessor_wrapper:
                 raise e
             else:
                 res = obj.preprocess(image, mask)[0]
-                print(f"[Inspire Pack] Installed 'ComfyUI's ControlNet Auxiliary Preprocessors.' is outdated.")
+                logging.warning("[Inspire Pack] Installed 'ComfyUI's ControlNet Auxiliary Preprocessors.' is outdated.")
 
         return res
 
@@ -143,7 +143,7 @@ class TilePreprocessor_wrapper:
         if 'TilePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'TilePreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use TilePreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use TilePreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -155,7 +155,7 @@ class MeshGraphormerDepthMapPreprocessorProvider_wrapper:
         if 'MeshGraphormer-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'MeshGraphormerDepthMapPreprocessorProvider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use MeshGraphormerDepthMapPreprocessorProvider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use MeshGraphormerDepthMapPreprocessorProvider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['MeshGraphormer-DepthMapPreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -170,7 +170,7 @@ class LineArt_Preprocessor_wrapper:
         if 'LineArtPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'LineArt_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use LineArt_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         coarse = 'enable' if self.coarse else 'disable'
 
@@ -190,7 +190,7 @@ class OpenPose_Preprocessor_wrapper:
         if 'OpenposePreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'OpenPose_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use OpenPose_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use OpenPose_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         detect_hand = 'enable' if self.detect_hand else 'disable'
         detect_body = 'enable' if self.detect_body else 'disable'
@@ -217,7 +217,7 @@ class DWPreprocessor_wrapper:
         if 'DWPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'DWPreprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use DWPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use DWPreprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         detect_hand = 'enable' if self.detect_hand else 'disable'
         detect_body = 'enable' if self.detect_body else 'disable'
@@ -241,7 +241,7 @@ class LeReS_DepthMap_Preprocessor_wrapper:
         if 'LeReS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'LeReS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use LeReS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use LeReS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         boost = 'enable' if self.boost else 'disable'
 
@@ -259,7 +259,7 @@ class MiDaS_DepthMap_Preprocessor_wrapper:
         if 'MiDaS-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'MiDaS_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use MiDaS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use MiDaS_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['MiDaS-DepthMapPreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])
@@ -271,7 +271,7 @@ class Zoe_DepthMap_Preprocessor_wrapper:
         if 'Zoe-DepthMapPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
                                           "To use 'Zoe_DepthMap_Preprocessor_Provider' node, 'ComfyUI's ControlNet Auxiliary Preprocessors.' extension is required.")
-            raise Exception(f"[ERROR] To use Zoe_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
+            raise Exception("[ERROR] To use Zoe_DepthMap_Preprocessor_Provider, you need to install 'ComfyUI's ControlNet Auxiliary Preprocessors.'")
 
         obj = nodes.NODE_CLASS_MAPPINGS['Zoe-DepthMapPreprocessor']()
         resolution = normalize_size_base_64(image.shape[2], image.shape[1])

js/inspire-loop.js

@@ -0,0 +1,7 @@
+import { inspireProgressBadge } from "./progress-badge.js"
+
+export function register_loop_node(nodeType, nodeData, app) {
+	if(nodeData.name == 'ForeachListEnd //Inspire') {
+		inspireProgressBadge.addStatusHandler(nodeType);
+	}
+}

js/inspire-pack.js

@@ -1,11 +1,13 @@
 import { ComfyApp, app } from "../../scripts/app.js";
 import { register_concat_conditionings_with_multiplier_node, register_splitter } from "./inspire-flex.js";
 import { register_cache_info } from "./inspire-backend.js";
+import { register_loop_node } from "./inspire-loop.js";
 
 app.registerExtension({
 	name: "Comfy.Inspire",
 	async beforeRegisterNodeDef(nodeType, nodeData, app) {
 		await register_concat_conditionings_with_multiplier_node(nodeType, nodeData, app);
+		await register_loop_node(nodeType, nodeData, app);
 	},
 
 	nodeCreated(node, app) {

js/progress-badge.js

@@ -0,0 +1,76 @@
+import { api } from "../../scripts/api.js";
+
+// copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger
+class InspireProgressBadge {
+	constructor() {
+		if (!window.__progress_badge__) {
+			window.__progress_badge__ = Symbol("__inspire_progress_badge__");
+		}
+		this.symbol = window.__progress_badge__;
+	}
+
+	getState(node) {
+		return node[this.symbol] || {};
+	}
+
+	setState(node, state) {
+		node[this.symbol] = state;
+		app.canvas.setDirty(true);
+	}
+
+	addStatusHandler(nodeType) {
+		if (nodeType[this.symbol]?.statusTagHandler) {
+			return;
+		}
+		if (!nodeType[this.symbol]) {
+			nodeType[this.symbol] = {};
+		}
+		nodeType[this.symbol] = {
+			statusTagHandler: true,
+		};
+
+		api.addEventListener("inspire/update_status", ({ detail }) => {
+			let { node, progress, text } = detail;
+			const n = app.graph.getNodeById(+(node || app.runningNodeId));
+			if (!n) return;
+			const state = this.getState(n);
+			state.status = Object.assign(state.status || {}, { progress: text ? progress : null, text: text || null });
+			this.setState(n, state);
+		});
+
+		const self = this;
+		const onDrawForeground = nodeType.prototype.onDrawForeground;
+		nodeType.prototype.onDrawForeground = function (ctx) {
+			const r = onDrawForeground?.apply?.(this, arguments);
+			const state = self.getState(this);
+			if (!state?.status?.text) {
+				return r;
+			}
+
+			const { fgColor, bgColor, text, progress, progressColor } = { ...state.status };
+
+			ctx.save();
+			ctx.font = "12px sans-serif";
+			const sz = ctx.measureText(text);
+			ctx.fillStyle = bgColor || "dodgerblue";
+			ctx.beginPath();
+			ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5);
+			ctx.fill();
+
+			if (progress) {
+				ctx.fillStyle = progressColor || "green";
+				ctx.beginPath();
+				ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, (sz.width + 12) * progress, 20, 5);
+				ctx.fill();
+			}
+
+			ctx.fillStyle = fgColor || "#fff";
+			ctx.fillText(text, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6);
+			ctx.restore();
+			return r;
+		};
+	}
+}
+
+export const inspireProgressBadge = new InspireProgressBadge();
+

js/prompt.js

@@ -102,7 +102,7 @@ app.registerExtension({
 						else
 							node._mode_value = value; // combo value
 
-						populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
+						populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
@@ -193,8 +193,8 @@ app.registerExtension({
 						else
 							node._mode_value = value; // combo value
 
-						pos_populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
-						neg_populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
+						pos_populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
+						neg_populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-inspire-pack"
 description = "This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils, Noise(Seed) Utils, ... and the Impact Pack."
-version = "1.12"
+version = "1.15"
 license = { file = "LICENSE" }
 dependencies = ["matplotlib", "cachetools"]