feat: samplers - support internal_seed

- Avoiding the issue where the GPU seed causes problems in ancestral mode.

https://github.com/comfyanonymous/ComfyUI/commit/916d1e14a93ef331adef7c0deff2fdcf443b05cf#commitcomment-151914788

README.md

@@ -63,9 +63,15 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
     * Specify the directories located under `ComfyUI-Inspire-Pack/prompts/`
     * One prompts file can have multiple prompts separated by `---`. 
     * e.g. `prompts/example`
-  * `Load Prompts From File (Inspire)`: It sequentially reads prompts from the specified file. The output it returns is ZIPPED_PROMPT.
+    * **NOTE**: This node provides advanced option via `Show advanced`
+      * load_cap, start_index
+
+    * `Load Prompts From File (Inspire)`: It sequentially reads prompts from the specified file. The output it returns is ZIPPED_PROMPT.
     * Specify the file located under `ComfyUI-Inspire-Pack/prompts/`
     * e.g. `prompts/example/prompt2.txt`
+    * **NOTE**: This node provides advanced option via `Show advanced`
+      * load_cap, start_index
+
   * `Load Single Prompt From File (Inspire)`: Loads a single prompt from a file containing multiple prompts by using an index.
   * The prompts file directory can be specified as `inspire_prompts` in `extra_model_paths.yaml`
   * `Unzip Prompt (Inspire)`: Separate ZIPPED_PROMPT into `positive`, `negative`, and name components. 
@@ -147,6 +153,9 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
   * `Shared Checkpoint Loader (Inspire)`: When loading a checkpoint through this loader, it is automatically cached in the backend cache. Additionally, if it is already cached, it retrieves it from the cache instead of loading it anew.
     * When `key_opt` is empty, the `ckpt_name` is set as the cache key. The cache key output can be used for deletion purposes with Remove Back End.
     * This node resolves the issue of reloading checkpoints during workflow switching.
+  * `Shared Diffusion Model Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Diffusion models instead of Checkpoints.
+  * `Shared Text Encoder Loader (Inspire)`: Similar to the `Shared Checkpoint Loader (Inspire)` but used for loading Text Encoder models instead of Checkpoints.
+    * This node also functions as a unified node for `CLIPLoader`, `DualCLIPLoader`, and `TripleCLIPLoader`. 
   * `Stable Cascade Checkpoint Loader (Inspire)`: This node provides a feature that allows you to load the `stage_b` and `stage_c` checkpoints of Stable Cascade at once, and it also provides a backend caching feature, optionally.
   * `Is Cached (Inspire)`: Returns whether the cache exists.
 

__init__.py

@@ -7,7 +7,7 @@
 
 import importlib
 
-version_code = [1, 9, 1]
+version_code = [1, 13]
 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/a1111_compat.py

@@ -8,9 +8,13 @@ from .libs import common
 
 
 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 +24,7 @@ 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()
 
@@ -29,28 +33,34 @@ 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
     latent_image = latent["samples"]
@@ -80,9 +90,12 @@ 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
+
     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,
+            model=model, add_noise=not disable_noise, seed=internal_seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
             latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback,
             scheduler_func=scheduler_func)
     except Exception as e:
@@ -90,7 +103,7 @@ def inspire_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
             print(f"[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,
+                model=model, add_noise=not disable_noise, seed=internal_seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler, positive=positive, negative=negative,
                 latent_image=latent, start_at_step=start_step, end_at_step=last_step, return_with_leftover_noise=not force_full_denoise, noise=noise, callback=callback)
         else:
             raise e
@@ -103,7 +116,7 @@ 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, ),
@@ -121,6 +134,7 @@ 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 +145,11 @@ 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 +158,7 @@ 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, ),
@@ -164,6 +179,7 @@ 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 +190,7 @@ 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 +205,7 @@ 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],)
@@ -200,7 +216,7 @@ 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, ),
@@ -215,6 +231,7 @@ 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 +241,10 @@ 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 +254,7 @@ 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, ),
@@ -255,6 +272,7 @@ 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 +282,7 @@ 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 +290,8 @@ 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
 
 

inspire/backend_support.py

@@ -8,6 +8,8 @@ from server import PromptServer
 
 from .libs.utils import TaggedCache, any_typ
 
+import logging
+
 root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 settings_file = os.path.join(root_dir, 'cache_settings.json')
 try:
@@ -401,6 +403,174 @@ class CheckpointLoaderSimpleShared(nodes.CheckpointLoaderSimple):
         return (None, cache_weak_hash(key))
 
 
+class LoadDiffusionModelShared(nodes.UNETLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "Diffusion Model Name"}),
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],),
+                              "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}),
+                              "mode": (['Auto', 'Override Cache', 'Read Only'],),
+                              }
+                }
+    RETURN_TYPES = ("MODEL", "STRING")
+    RETURN_NAMES = ("model", "cache key")
+
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Backend"
+
+    def doit(self, model_name, weight_dtype, key_opt, mode='Auto'):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = f"{model_name}_{weight_dtype}"
+        else:
+            key = key_opt.strip()
+
+        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}'.")
+        else:
+            _, (_, model) = cache[key]
+            print(f"[Inspire Pack] LoadDiffusionModelShared: Cached diffusion model '{key}' is loaded. (Loading skip)")
+
+        return model, key
+
+    @staticmethod
+    def IS_CHANGED(model_name, weight_dtype, key_opt, mode='Auto'):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoadDiffusionModelShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = f"{model_name}_{weight_dtype}"
+        else:
+            key = key_opt.strip()
+
+        if mode == 'Read Only':
+            return None, cache_weak_hash(key)
+        elif mode == 'Override Cache':
+            return model_name, key
+
+        return None, cache_weak_hash(key)
+
+
+class LoadTextEncoderShared:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model_name1": (folder_paths.get_filename_list("text_encoders"), ),
+                              "model_name2": (["None"] + folder_paths.get_filename_list("text_encoders"), ),
+                              "model_name3": (["None"] + folder_paths.get_filename_list("text_encoders"), ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "sdxl", "flux", "hunyuan_video"], ),
+                              "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'model_name' as the key."}),
+                              "mode": (['Auto', 'Override Cache', 'Read Only'],),
+                              },
+                "optional": { "device": (["default", "cpu"], {"advanced": True}), }
+                }
+    RETURN_TYPES = ("CLIP", "STRING")
+    RETURN_NAMES = ("clip", "cache key")
+
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Backend"
+
+    DESCRIPTION = \
+        ("[Recipes single]\n"
+         "stable_diffusion: clip-l\n"
+         "stable_cascade: clip-g\n"
+         "sd3: t5 / clip-g / clip-l\n"
+         "stable_audio: t5\n"
+         "mochi: t5\n"
+         "cosmos: old t5 xxl\n\n"
+         "[Recipes dual]\n"
+         "sdxl: clip-l, clip-g\n"
+         "sd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\n"
+         "flux: clip-l, t5\n\n"
+         "[Recipes triple]\n"
+         "sd3: clip-l, clip-g, t5")
+
+    def doit(self, model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = model_name1
+            if model_name2 is not None:
+                key += f"_{model_name2}"
+            if model_name3 is not None:
+                key += f"_{model_name3}"
+            key += f"_{type}_{device}"
+        else:
+            key = key_opt.strip()
+
+        if key not in cache or mode == 'Override Cache':
+            if model_name2 != "None" and model_name3 != "None": # triple text encoder
+                if len({model_name1, model_name2, model_name3}) < 3:
+                    logging.error("[LoadTextEncoderShared] The same model has been selected multiple times.")
+                    raise ValueError("The same model has been selected multiple times.")
+
+                if type not in ["sd3"]:
+                    logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sd3`.")
+                    raise ValueError("Currently, the triple text encoder is only supported in `sd3`.")
+
+                res = nodes.NODE_CLASS_MAPPINGS["TripleCLIPLoader"]().load_clip(model_name1, model_name2, model_name3)[0]
+
+            elif model_name2 != "None" or model_name3 != "None": # dual text encoder
+                second_model = model_name2 if model_name2 != "None" else model_name3
+
+                if model_name1 == second_model:
+                    logging.error("[LoadTextEncoderShared] You have selected the same model for both.")
+                    raise ValueError("[LoadTextEncoderShared] You have selected the same model for both.")
+
+                if type not in ["sdxl", "sd3", "flux", "hunyuan_video"]:
+                    logging.error("[LoadTextEncoderShared] Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.")
+                    raise ValueError("Currently, the triple text encoder is only supported in `sdxl, sd3, flux, hunyuan_video`.")
+
+                res = nodes.NODE_CLASS_MAPPINGS["DualCLIPLoader"]().load_clip(model_name1, second_model, type=type, device=device)[0]
+
+            else: # single text encoder
+                if type not in ["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos"]:
+                    logging.error("[LoadTextEncoderShared] Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.")
+                    raise ValueError("Currently, the single text encoder is only supported in `stable_diffusion, stable_cascade, sd3, stable_audio, mochi, ltxv, pixart, cosmos`.")
+
+                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}'.")
+        else:
+            _, (_, res) = cache[key]
+            print(f"[Inspire Pack] LoadTextEncoderShared: Cached text encoder model set '{key}' is loaded. (Loading skip)")
+
+        return res, key
+
+    @staticmethod
+    def IS_CHANGED(model_name1, model_name2, model_name3, type, key_opt, mode='Auto', device="default"):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoadTextEncoderShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = model_name1
+            if model_name2 is not None:
+                key += f"_{model_name2}"
+            if model_name3 is not None:
+                key += f"_{model_name3}"
+            key += f"_{type}_{device}"
+        else:
+            key = key_opt.strip()
+
+        if mode == 'Read Only':
+            return None, cache_weak_hash(key)
+        elif mode == 'Override Cache':
+            return f"{model_name1}_{model_name2}_{model_name3}_{type}_{device}", key
+
+        return None, cache_weak_hash(key)
+
+
 class StableCascade_CheckpointLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -558,6 +728,8 @@ NODE_CLASS_MAPPINGS = {
     "RemoveBackendDataNumberKey //Inspire": RemoveBackendDataNumberKey,
     "ShowCachedInfo //Inspire": ShowCachedInfo,
     "CheckpointLoaderSimpleShared //Inspire": CheckpointLoaderSimpleShared,
+    "LoadDiffusionModelShared //Inspire": LoadDiffusionModelShared,
+    "LoadTextEncoderShared //Inspire": LoadTextEncoderShared,
     "StableCascade_CheckpointLoader //Inspire": StableCascade_CheckpointLoader,
     "IsCached //Inspire": IsCached,
     # "CacheBridge //Inspire": CacheBridge,
@@ -574,6 +746,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "RemoveBackendDataNumberKey //Inspire": "Remove Backend Data [NumberKey] (Inspire)",
     "ShowCachedInfo //Inspire": "Show Cached Info (Inspire)",
     "CheckpointLoaderSimpleShared //Inspire": "Shared Checkpoint Loader (Inspire)",
+    "LoadDiffusionModelShared //Inspire": "Shared Diffusion Model Loader (Inspire)",
+    "LoadTextEncoderShared //Inspire": "Shared Text Encoder Loader (Inspire)",
     "StableCascade_CheckpointLoader //Inspire": "Stable Cascade Checkpoint Loader (Inspire)",
     "IsCached //Inspire": "Is Cached (Inspire)",
     # "CacheBridge //Inspire": "Cache Bridge (Inspire)"

inspire/image_util.py

@@ -18,7 +18,7 @@ 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"}),
             }
         }
@@ -121,7 +121,7 @@ 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"}),
             }
         }

inspire/inspire_server.py

@@ -272,7 +272,17 @@ def populate_wildcards(json_data):
         for k, v in prompt.items():
             if 'class_type' in v and v['class_type'] == 'WildcardEncode //Inspire':
                 inputs = v['inputs']
-                if inputs['mode'] and isinstance(inputs['populated_text'], str):
+
+                # legacy adapter
+                if isinstance(inputs['mode'], bool):
+                    if inputs['mode']:
+                        new_mode = 'populate'
+                    else:
+                        new_mode = 'fixed'
+
+                    inputs['mode'] = new_mode
+
+                if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
                     if isinstance(inputs['seed'], list):
                         try:
                             input_node = prompt[inputs['seed'][0]]
@@ -293,14 +303,17 @@ def populate_wildcards(json_data):
                         input_seed = int(inputs['seed'])
 
                     inputs['populated_text'] = wildcard_process(text=inputs['wildcard_text'], seed=input_seed)
-                    inputs['mode'] = False
+                    inputs['mode'] = 'reproduce'
 
                     server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "text", "data": inputs['populated_text']})
                     updated_widget_values[k] = inputs['populated_text']
 
+                if inputs['mode'] == 'reproduce':
+                    server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "mode", "type": "text", "value": 'populate'})
+
             elif 'class_type' in v and v['class_type'] == 'MakeBasicPipe //Inspire':
                 inputs = v['inputs']
-                if inputs['wildcard_mode'] and (isinstance(inputs['positive_populated_text'], str) or isinstance(inputs['negative_populated_text'], str)):
+                if inputs['wildcard_mode'] == 'populate' and (isinstance(inputs['positive_populated_text'], str) or isinstance(inputs['negative_populated_text'], str)):
                     if isinstance(inputs['seed'], list):
                         try:
                             input_node = prompt[inputs['seed'][0]]
@@ -328,9 +341,12 @@ def populate_wildcards(json_data):
                         inputs['negative_populated_text'] = wildcard_process(text=inputs['negative_wildcard_text'], seed=input_seed)
                         server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "negative_populated_text", "type": "text", "data": inputs['negative_populated_text']})
 
-                    inputs['wildcard_mode'] = False
+                    inputs['wildcard_mode'] = 'reproduce'
                     mbp_updated_widget_values[k] = inputs['positive_populated_text'], inputs['negative_populated_text']
 
+                if inputs['wildcard_mode'] == 'reproduce':
+                    server.PromptServer.instance.send_sync("inspire-node-feedback", {"node_id": k, "widget_name": "wildcard_mode", "type": "text", "value": 'populate'})
+
         if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
             extra_pnginfo = json_data['extra_data']['extra_pnginfo']
             if 'workflow' in extra_pnginfo and extra_pnginfo['workflow'] is not None and 'nodes' in extra_pnginfo['workflow']:
@@ -338,11 +354,11 @@ def populate_wildcards(json_data):
                     key = str(node['id'])
                     if key in updated_widget_values:
                         node['widgets_values'][3] = updated_widget_values[key]
-                        node['widgets_values'][4] = False
+                        node['widgets_values'][4] = 'reproduce'
                     if key in mbp_updated_widget_values:
                         node['widgets_values'][7] = mbp_updated_widget_values[key][0]
                         node['widgets_values'][8] = mbp_updated_widget_values[key][1]
-                        node['widgets_values'][5] = False
+                        node['widgets_values'][5] = 'reproduce'
 
 
 def force_reset_useless_params(json_data):

inspire/prompt_support.py

@@ -60,21 +60,23 @@ 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, "max": 0xffffffffffffffff, "advanced": True, "tooltip": "Starting index for loading prompts:\n-1: The last prompt\n0 or higher: Load from the specified index"}),
                     }
                 }
 
-    RETURN_TYPES = ("ZIPPED_PROMPT", "INT")
-    RETURN_NAMES = ("zipped_prompt", "count")
-    OUTPUT_IS_LIST = (True,)
+    RETURN_TYPES = ("ZIPPED_PROMPT", "INT", "INT")
+    RETURN_NAMES = ("zipped_prompt", "count", "remaining_count")
+    OUTPUT_IS_LIST = (True, False, False)
 
     FUNCTION = "doit"
 
     CATEGORY = "InspirePack/Prompt"
 
     @staticmethod
-    def IS_CHANGED(prompt_dir, reload=False):
+    def IS_CHANGED(prompt_dir, reload=False, load_cap=0, start_index=-1):
         if not reload:
-            return prompt_dir
+            return prompt_dir, load_cap, start_index
         else:
             candidates = []
             for d in folder_paths.get_folder_paths('inspire_prompts'):
@@ -100,10 +102,10 @@ class LoadPromptsFromDir:
                             break
                         md5.update(chunk)
 
-            return md5.hexdigest()
+            return md5.hexdigest(), load_cap, start_index
 
     @staticmethod
-    def doit(prompt_dir, reload=False):
+    def doit(prompt_dir, reload=False, load_cap=0, start_index=-1):
         candidates = []
         for d in folder_paths.get_folder_paths('inspire_prompts'):
             candidates.append(os.path.join(d, prompt_dir))
@@ -140,7 +142,15 @@ class LoadPromptsFromDir:
             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.")
 
-        return (prompts, len(prompts),)
+        # slicing [start_index ~ start_index + load_cap]
+        total_prompts = len(prompts)
+        prompts = prompts[start_index:]
+        remaining_count = False
+        if load_cap > 0:
+            remaining_count = max(0, len(prompts) - load_cap)
+            prompts = prompts[:load_cap]
+
+        return prompts, total_prompts, remaining_count
 
 
 class LoadPromptsFromFile:
@@ -165,26 +175,28 @@ class LoadPromptsFromFile:
                 "optional": {
                         "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, "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"}),
                         }
                 }
 
-    RETURN_TYPES = ("ZIPPED_PROMPT", "INT")
-    RETURN_NAMES = ("zipped_prompt", "count")
-    OUTPUT_IS_LIST = (True,)
+    RETURN_TYPES = ("ZIPPED_PROMPT", "INT", "INT")
+    RETURN_NAMES = ("zipped_prompt", "count", "remaining_count")
+    OUTPUT_IS_LIST = (True, False, False)
 
     FUNCTION = "doit"
 
     CATEGORY = "InspirePack/Prompt"
 
     @staticmethod
-    def IS_CHANGED(prompt_file, text_data_opt=None, reload=False):
+    def IS_CHANGED(prompt_file, text_data_opt=None, reload=False, load_cap=0, start_index=-1):
         md5 = hashlib.md5()
 
         if text_data_opt is not None:
             md5.update(text_data_opt)
-            return md5.hexdigest()
+            return md5.hexdigest(), load_cap, start_index
         elif not reload:
-            return prompt_file
+            return prompt_file, load_cap, start_index
         else:
             matched_path = None
             for x in folder_paths.get_folder_paths('inspire_prompts'):
@@ -204,10 +216,10 @@ class LoadPromptsFromFile:
                         break
                     md5.update(chunk)
 
-            return md5.hexdigest()
+            return md5.hexdigest(), load_cap, start_index
 
     @staticmethod
-    def doit(prompt_file, text_data_opt=None, reload=False):
+    def doit(prompt_file, text_data_opt=None, reload=False, load_cap=0, start_index=-1):
         matched_path = None
         for d in folder_paths.get_folder_paths('inspire_prompts'):
             matched_path = os.path.join(d, prompt_file)
@@ -247,7 +259,15 @@ class LoadPromptsFromFile:
         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.")
 
-        return (prompts, len(prompts),)
+        # slicing [start_index ~ start_index + load_cap]
+        total_prompts = len(prompts)
+        prompts = prompts[start_index:]
+        remaining_count = 0
+        if load_cap > 0:
+            remaining_count = max(0, len(prompts) - load_cap)
+            prompts = prompts[:load_cap]
+
+        return prompts, total_prompts, remaining_count
 
 
 class LoadSinglePromptFromFile:
@@ -557,7 +577,13 @@ class WildcardEncodeInspire:
                         "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"], {'default': 'comfy++'}),
                         "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Wildcard Prompt (User Input)'}),
                         "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Prompt (Will be generated automatically)'}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
+
+                        "mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip":
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n"
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."
+                                                                               }),
+
                         "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ),
                         "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                         "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@@ -598,7 +624,11 @@ class MakeBasicPipe:
                         "Add selection to": ("BOOLEAN", {"default": True, "label_on": "Positive", "label_off": "Negative"}),
                         "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
                         "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
-                        "wildcard_mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
+                        "wildcard_mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip":
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n"
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."
+                                                                               }),
 
                         "positive_populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Positive Prompt (Will be generated automatically)'}),
                         "negative_populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, 'placeholder': 'Populated Negative Prompt (Will be generated automatically)'}),

js/prompt.js

@@ -95,14 +95,20 @@ app.registerExtension({
 			// mode combo
 			Object.defineProperty(mode_widget, "value", {
 				set: (value) => {
-						node._mode_value = value == true || value == "Populate";
-						populated_text_widget.inputEl.disabled = value == true || value == "Populate";
+						if(value == true)
+							node._mode_value = "populate";
+						else if(value == false)
+							node._mode_value = "fixed";
+						else
+							node._mode_value = value; // combo value
+
+						populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
 							return node._mode_value;
 						else
-							return true;
+							return 'populate';
 					 }
 			});
 		}
@@ -180,15 +186,21 @@ app.registerExtension({
 			// mode combo
 			Object.defineProperty(mode_widget, "value", {
 				set: (value) => {
-						pos_populated_text_widget.inputEl.disabled = node._mode_value;
-						neg_populated_text_widget.inputEl.disabled = node._mode_value;
-						node._mode_value = value;
+						if(value == true)
+							node._mode_value = "populate";
+						else if(value == false)
+							node._mode_value = "fixed";
+						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';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
 							return node._mode_value;
 						else
-							return true;
+							return 'populate';
 					 }
 			});
 		}

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 = "1.9.1"
+version = "1.13"
 license = { file = "LICENSE" }
 dependencies = ["matplotlib", "cachetools"]