Merge 3b87c2c397 into 439bd807f8

comfy/sd.py

@@ -468,6 +468,9 @@ class CLIP:
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
 
+    def is_dynamic(self):
+        return self.patcher.is_dynamic()
+
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
@@ -1251,6 +1254,8 @@ class VAE:
         except:
             return None
 
+    def is_dynamic(self):
+        return self.patcher.is_dynamic()
 
 class StyleModel:
     def __init__(self, model, device="cpu"):

comfy_execution/caching.py

@@ -503,6 +503,21 @@ RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
+
+def all_outputs_dynamic(outputs):
+    if outputs is None:
+        return False
+
+    for output in outputs:
+        if isinstance(output, (list, tuple)):
+            if not all_outputs_dynamic(output):
+                return False
+        elif not hasattr(output, "is_dynamic") or not output.is_dynamic():
+            return False
+
+    return True
+
+
 class RAMPressureCache(LRUCache):
 
     def __init__(self, key_class, enable_providers=False):
@@ -533,7 +548,11 @@ class RAMPressureCache(LRUCache):
         for key, cache_entry in self.cache.items():
             if not free_active and self.used_generation[key] == self.generation:
                 continue
-            oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
+
+            if all_outputs_dynamic(cache_entry.outputs) and self.used_generation[key] == self.generation:
+                continue
+
+            oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
             def scan_list_for_ram_usage(outputs):

comfy_extras/nodes_ideogram4.py

@@ -1,6 +1,7 @@
 """Ideogram 4 sampling helper
 """
 
+import enum
 import math
 
 import torch
@@ -10,6 +11,45 @@ from comfy_api.latest import ComfyExtension, io
 _LOGSNR_MIN = -15.0
 _LOGSNR_MAX = 18.0
 
+class Ideogram4Enum(enum.Enum):
+    QUALITY = "Quality"
+    HIGH = "High"
+    DEFAULT = "Default"
+    FAST = "Fast"
+    TURBO = "Turbo"
+
+IDEOGRAM4_PRESET_CONFIGS = {
+  Ideogram4Enum.QUALITY.value: {
+    "num_steps": 48,
+    "mu": 0.0,
+    "std": 1.5,
+    "preset_id": "V4_QUALITY_48"
+  },
+  Ideogram4Enum.HIGH.value: {
+    "num_steps": 34,
+    "mu": 0.0,
+    "std": 1.6875,
+    "preset_id": "V4_HIGH_34"
+  },
+  Ideogram4Enum.DEFAULT.value: {
+    "num_steps": 20,
+    "mu": 0.0,
+    "std": 1.75,
+    "preset_id": "V4_DEFAULT_20"
+  },
+  Ideogram4Enum.FAST.value: {
+    "num_steps": 16,
+    "mu": 0.25,
+    "std": 1.8375,
+    "preset_id": "V4_FAST_16"
+  },
+  Ideogram4Enum.TURBO.value: {
+    "num_steps": 12,
+    "mu": 0.5,
+    "std": 1.75,
+    "preset_id": "V4_TURBO_12"
+  }
+}
 
 def _logit_normal_schedule(u, mean, std):
     # Reference time (0=noise..1=clean) via the probit/ndtri quantile.
@@ -54,10 +94,41 @@ class Ideogram4Scheduler(io.ComfyNode):
         return io.NodeOutput(ideogram4_sigmas(steps, width, height, mu, std))
 
 
+class Ideogram4SchedulerPreset(Ideogram4Scheduler):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="Ideogram4SchedulerPreset",
+            display_name="Ideogram 4 Scheduler (Presets)",
+            category="sampling/custom_sampling/schedulers",
+            description="Schedule Presets for Ideogram 4. They are as follows: Quality=48, High=34, Default=20, Fast=16, Turbo=12",
+            inputs=[
+                io.Combo.Input("preset", options=[e.value for e in Ideogram4Enum], default=Ideogram4Enum.DEFAULT.value),
+                io.Int.Input("width", default=1024, min=256, max=8192, step=16),
+                io.Int.Input("height", default=1024, min=256, max=8192, step=16),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
+
+    @classmethod
+    def execute(cls, preset, width, height) -> io.NodeOutput:
+        config = IDEOGRAM4_PRESET_CONFIGS.get(preset)
+        if not config:
+            raise ValueError(f"Invalid preset: {preset}")
+
+        return super().execute(
+            steps=config["num_steps"],
+            width=width,
+            height=height,
+            mu=config["mu"],
+            std=config["std"]
+        )
+
+
 class Ideogram4Extension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [Ideogram4Scheduler]
+        return [Ideogram4Scheduler, Ideogram4SchedulerPreset]
 
 
 async def comfy_entrypoint() -> Ideogram4Extension: