Merge branch 'master' into fix/color-curves-shader-nested-sampler

comfy/cli_args.py

@@ -110,11 +110,13 @@ parser.add_argument("--preview-method", type=LatentPreviewMethod, default=Latent
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
 
+CACHE_RAM_AUTO_GB = -1.0
+
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=4.0, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB")
+cache_group.add_argument("--cache-ram", nargs='?', const=CACHE_RAM_AUTO_GB, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threshold the cache removes large items to free RAM. Default (when no value is provided): 25%% of system RAM (min 4GB, max 32GB).")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")

comfy/memory_management.py

@@ -141,3 +141,17 @@ def interpret_gathered_like(tensors, gathered):
     return dest_views
 
 aimdo_enabled = False
+
+extra_ram_release_callback = None
+RAM_CACHE_HEADROOM = 0
+
+def set_ram_cache_release_state(callback, headroom):
+    global extra_ram_release_callback
+    global RAM_CACHE_HEADROOM
+    extra_ram_release_callback = callback
+    RAM_CACHE_HEADROOM = max(0, int(headroom))
+
+def extra_ram_release(target):
+    if extra_ram_release_callback is None:
+        return 0
+    return extra_ram_release_callback(target)

comfy/model_management.py

@@ -669,7 +669,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
-        if shift_model.device == device:
+        if device is None or shift_model.device == device:
             if shift_model not in keep_loaded and not shift_model.is_dead():
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
@@ -679,8 +679,8 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
         i = x[-1]
         memory_to_free = 1e32
         pins_to_free = 1e32
-        if not DISABLE_SMART_MEMORY:
-            memory_to_free = memory_required - get_free_memory(device)
+        if not DISABLE_SMART_MEMORY or device is None:
+            memory_to_free = 0 if device is None else memory_required - get_free_memory(device)
             pins_to_free = pins_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
@@ -708,7 +708,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
-    else:
+    elif device is not None:
         if vram_state != VRAMState.HIGH_VRAM:
             mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
             if mem_free_torch > mem_free_total * 0.25:

comfy/model_patcher.py

@@ -300,9 +300,6 @@ class ModelPatcher:
     def model_mmap_residency(self, free=False):
         return comfy.model_management.module_mmap_residency(self.model, free=free)
 
-    def get_ram_usage(self):
-        return self.model_size()
-
     def loaded_size(self):
         return self.model.model_loaded_weight_memory
 

comfy/pinned_memory.py

@@ -2,6 +2,7 @@ import comfy.model_management
 import comfy.memory_management
 import comfy_aimdo.host_buffer
 import comfy_aimdo.torch
+import psutil
 
 from comfy.cli_args import args
 
@@ -12,6 +13,11 @@ def pin_memory(module):
     if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None:
         return
     #FIXME: This is a RAM cache trigger event
+    ram_headroom = comfy.memory_management.RAM_CACHE_HEADROOM
+    #we split the difference and assume half the RAM cache headroom is for us
+    if ram_headroom > 0 and psutil.virtual_memory().available < (ram_headroom * 0.5):
+        comfy.memory_management.extra_ram_release(ram_headroom)
+
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
 
     if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:

comfy/sd.py

@@ -280,9 +280,6 @@ class CLIP:
         n.apply_hooks_to_conds = self.apply_hooks_to_conds
         return n
 
-    def get_ram_usage(self):
-        return self.patcher.get_ram_usage()
-
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         return self.patcher.add_patches(patches, strength_patch, strength_model)
 
@@ -840,9 +837,6 @@ class VAE:
         self.size = comfy.model_management.module_size(self.first_stage_model)
         return self.size
 
-    def get_ram_usage(self):
-        return self.model_size()
-
     def throw_exception_if_invalid(self):
         if self.first_stage_model is None:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")

comfy_execution/caching.py

@@ -1,6 +1,5 @@
 import asyncio
 import bisect
-import gc
 import itertools
 import psutil
 import time
@@ -475,6 +474,10 @@ class LRUCache(BasicCache):
         self._mark_used(node_id)
         return await self._set_immediate(node_id, value)
 
+    def set_local(self, node_id, value):
+        self._mark_used(node_id)
+        BasicCache.set_local(self, node_id, value)
+
     async def ensure_subcache_for(self, node_id, children_ids):
         # Just uses subcaches for tracking 'live' nodes
         await super()._ensure_subcache(node_id, children_ids)
@@ -489,15 +492,10 @@ class LRUCache(BasicCache):
         return self
 
 
-#Iterating the cache for usage analysis might be expensive, so if we trigger make sure
-#to take a chunk out to give breathing space on high-node / low-ram-per-node flows.
+#Small baseline weight used when a cache entry has no measurable CPU tensors.
+#Keeps unknown-sized entries in eviction scoring without dominating tensor-backed entries.
 
-RAM_CACHE_HYSTERESIS = 1.1
-
-#This is kinda in GB but not really. It needs to be non-zero for the below heuristic
-#and as long as Multi GB models dwarf this it will approximate OOM scoring OK
-
-RAM_CACHE_DEFAULT_RAM_USAGE = 0.1
+RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 #Exponential bias towards evicting older workflows so garbage will be taken out
 #in constantly changing setups.
@@ -521,19 +519,17 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         return await super().get(node_id)
 
-    def poll(self, ram_headroom):
-        def _ram_gb():
-            return psutil.virtual_memory().available / (1024**3)
+    def set_local(self, node_id, value):
+        self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
+        super().set_local(node_id, value)
 
-        if _ram_gb() > ram_headroom:
-            return
-        gc.collect()
-        if _ram_gb() > ram_headroom:
+    def ram_release(self, target):
+        if psutil.virtual_memory().available >= target:
             return
 
         clean_list = []
 
-        for key, (outputs, _), in self.cache.items():
+        for key, cache_entry in self.cache.items():
             oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
@@ -542,22 +538,20 @@ class RAMPressureCache(LRUCache):
                 if outputs is None:
                     return
                 for output in outputs:
-                    if isinstance(output, list):
+                    if isinstance(output, (list, tuple)):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
-                        #score Tensors at a 50% discount for RAM usage as they are likely to
-                        #be high value intermediates
-                        ram_usage += (output.numel() * output.element_size()) * 0.5
-                    elif hasattr(output, "get_ram_usage"):
-                        ram_usage += output.get_ram_usage()
-            scan_list_for_ram_usage(outputs)
+                        ram_usage += output.numel() * output.element_size()
+            scan_list_for_ram_usage(cache_entry.outputs)
 
             oom_score *= ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
             bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
 
-        while _ram_gb() < ram_headroom * RAM_CACHE_HYSTERESIS and clean_list:
+        while psutil.virtual_memory().available < target and clean_list:
             _, _, key = clean_list.pop()
             del self.cache[key]
-            gc.collect()
+            self.used_generation.pop(key, None)
+            self.timestamps.pop(key, None)
+            self.children.pop(key, None)

execution.py

@@ -724,6 +724,9 @@ class PromptExecutor:
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
         self._notify_prompt_lifecycle("start", prompt_id)
+        ram_headroom = int(self.cache_args["ram"] * (1024 ** 3))
+        ram_release_callback = self.caches.outputs.ram_release if self.cache_type == CacheType.RAM_PRESSURE else None
+        comfy.memory_management.set_ram_cache_release_state(ram_release_callback, ram_headroom)
 
         try:
             with torch.inference_mode():
@@ -773,7 +776,10 @@ class PromptExecutor:
                         execution_list.unstage_node_execution()
                     else: # result == ExecutionResult.SUCCESS:
                         execution_list.complete_node_execution()
-                    self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
+
+                    if self.cache_type == CacheType.RAM_PRESSURE:
+                        comfy.model_management.free_memory(0, None, pins_required=ram_headroom, ram_required=ram_headroom)
+                        comfy.memory_management.extra_ram_release(ram_headroom)
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed
@@ -801,6 +807,7 @@ class PromptExecutor:
                 if comfy.model_management.DISABLE_SMART_MEMORY:
                     comfy.model_management.unload_all_models()
         finally:
+            comfy.memory_management.set_ram_cache_release_state(None, 0)
             self._notify_prompt_lifecycle("end", prompt_id)
 
 

main.py

@@ -275,15 +275,19 @@ def _collect_output_absolute_paths(history_result: dict) -> list[str]:
 
 def prompt_worker(q, server_instance):
     current_time: float = 0.0
+    cache_ram = args.cache_ram
+    if cache_ram < 0:
+        cache_ram = min(32.0, max(4.0, comfy.model_management.total_ram * 0.25 / 1024.0))
+
     cache_type = execution.CacheType.CLASSIC
     if args.cache_lru > 0:
         cache_type = execution.CacheType.LRU
-    elif args.cache_ram > 0:
+    elif cache_ram > 0:
         cache_type = execution.CacheType.RAM_PRESSURE
     elif args.cache_none:
         cache_type = execution.CacheType.NONE
 
-    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : args.cache_ram } )
+    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : cache_ram } )
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0