models

.github/workflows/test-launch.yml

@@ -13,7 +13,7 @@ jobs:
     - name: Checkout ComfyUI
       uses: actions/checkout@v4
       with:
-        repository: "Comfy-Org/ComfyUI"
+        repository: "comfyanonymous/ComfyUI"
         path: "ComfyUI"
     - uses: actions/setup-python@v4
       with:

README.md

@@ -183,7 +183,7 @@ Simply download, extract with [7-Zip](https://7-zip.org) or with the windows exp
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
-The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
+Update your Nvidia drivers if it doesn't start.
 
 #### Alternative Downloads:
 
@@ -212,7 +212,7 @@ Python 3.14 works but you may encounter issues with the torch compile node. The
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
-torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
+torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch unless it is less than 2 weeks old.
 
 ### Instructions:
 

app/assets/database/bulk_ops.py

@@ -92,23 +92,14 @@ def seed_from_paths_batch(
         session.execute(ins_asset, chunk)
 
     # try to claim AssetCacheState (file_path)
-    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
+    winners_by_path: set[str] = set()
     ins_state = (
         sqlite.insert(AssetCacheState)
         .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
+        .returning(AssetCacheState.file_path)
     )
     for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
-        session.execute(ins_state, chunk)
-
-    # Query to find which of our paths won (were actually inserted)
-    winners_by_path: set[str] = set()
-    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetCacheState.file_path)
-            .where(AssetCacheState.file_path.in_(chunk))
-            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
-        )
-        winners_by_path.update(result.scalars().all())
+        winners_by_path.update((session.execute(ins_state, chunk)).scalars().all())
 
     all_paths_set = set(path_list)
     losers_by_path = all_paths_set - winners_by_path
@@ -121,23 +112,16 @@ def seed_from_paths_batch(
         return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
 
     # insert AssetInfo only for winners
-    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
     winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
     ins_info = (
         sqlite.insert(AssetInfo)
         .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
+        .returning(AssetInfo.id)
     )
-    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
-        session.execute(ins_info, chunk)
 
-    # Query to find which info rows were actually inserted (by matching our generated IDs)
-    all_info_ids = [row["id"] for row in winner_info_rows]
     inserted_info_ids: set[str] = set()
-    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
-        result = session.execute(
-            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
-        )
-        inserted_info_ids.update(result.scalars().all())
+    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
+        inserted_info_ids.update((session.execute(ins_info, chunk)).scalars().all())
 
     # build and insert tag + meta rows for the AssetInfo
     tag_rows: list[dict] = []

app/subgraph_manager.py

@@ -10,7 +10,6 @@
 
 class Source:
     custom_node = "custom_node"
-    templates = "templates"
 
 class SubgraphEntry(TypedDict):
     source: str
@@ -39,18 +38,6 @@ class CustomNodeSubgraphEntryInfo(TypedDict):
 class SubgraphManager:
     def __init__(self):
         self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
-        self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None
-
-    def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:
-        """Create a subgraph entry from a file path. Expects normalized path (forward slashes)."""
-        entry_id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
-        entry: SubgraphEntry = {
-            "source": source,
-            "name": os.path.splitext(os.path.basename(file))[0],
-            "path": file,
-            "info": {"node_pack": node_pack},
-        }
-        return entry_id, entry
 
     async def load_entry_data(self, entry: SubgraphEntry):
         with open(entry['path'], 'r') as f:
@@ -73,60 +60,53 @@ async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=
         return entries
 
     async def get_custom_node_subgraphs(self, loadedModules, force_reload=False):
-        """Load subgraphs from custom nodes."""
+        # if not forced to reload and cached, return cache
         if not force_reload and self.cached_custom_node_subgraphs is not None:
             return self.cached_custom_node_subgraphs
-
+        # Load subgraphs from custom nodes
+        subfolder = "subgraphs"
         subgraphs_dict: dict[SubgraphEntry] = {}
+
         for folder in folder_paths.get_folder_paths("custom_nodes"):
-            pattern = os.path.join(folder, "*/subgraphs/*.json")
-            for file in glob.glob(pattern):
+            pattern = os.path.join(folder, f"*/{subfolder}/*.json")
+            matched_files = glob.glob(pattern)
+            for file in matched_files:
+                # replace backslashes with forward slashes
                 file = file.replace('\\', '/')
-                node_pack = "custom_nodes." + file.split('/')[-3]
-                entry_id, entry = self._create_entry(file, Source.custom_node, node_pack)
-                subgraphs_dict[entry_id] = entry
-
+                info: CustomNodeSubgraphEntryInfo = {
+                    "node_pack": "custom_nodes." + file.split('/')[-3]
+                }
+                source = Source.custom_node
+                # hash source + path to make sure id will be as unique as possible, but
+                # reproducible across backend reloads
+                id = hashlib.sha256(f"{source}{file}".encode()).hexdigest()
+                entry: SubgraphEntry = {
+                    "source": Source.custom_node,
+                    "name": os.path.splitext(os.path.basename(file))[0],
+                    "path": file,
+                    "info": info,
+                }
+                subgraphs_dict[id] = entry
         self.cached_custom_node_subgraphs = subgraphs_dict
         return subgraphs_dict
 
-    async def get_blueprint_subgraphs(self, force_reload=False):
-        """Load subgraphs from the blueprints directory."""
-        if not force_reload and self.cached_blueprint_subgraphs is not None:
-            return self.cached_blueprint_subgraphs
-
-        subgraphs_dict: dict[SubgraphEntry] = {}
-        blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')
-
-        if os.path.exists(blueprints_dir):
-            for file in glob.glob(os.path.join(blueprints_dir, "*.json")):
-                file = file.replace('\\', '/')
-                entry_id, entry = self._create_entry(file, Source.templates, "comfyui")
-                subgraphs_dict[entry_id] = entry
-
-        self.cached_blueprint_subgraphs = subgraphs_dict
-        return subgraphs_dict
-
-    async def get_all_subgraphs(self, loadedModules, force_reload=False):
-        """Get all subgraphs from all sources (custom nodes and blueprints)."""
-        custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload)
-        blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload)
-        return {**custom_node_subgraphs, **blueprint_subgraphs}
-
-    async def get_subgraph(self, id: str, loadedModules):
-        """Get a specific subgraph by ID from any source."""
-        entry = (await self.get_all_subgraphs(loadedModules)).get(id)
-        if entry is not None and entry.get('data') is None:
+    async def get_custom_node_subgraph(self, id: str, loadedModules):
+        subgraphs = await self.get_custom_node_subgraphs(loadedModules)
+        entry: SubgraphEntry = subgraphs.get(id, None)
+        if entry is not None and entry.get('data', None) is None:
             await self.load_entry_data(entry)
         return entry
 
     def add_routes(self, routes, loadedModules):
         @routes.get("/global_subgraphs")
         async def get_global_subgraphs(request):
-            subgraphs_dict = await self.get_all_subgraphs(loadedModules)
+            subgraphs_dict = await self.get_custom_node_subgraphs(loadedModules)
+            # NOTE: we may want to include other sources of global subgraphs such as templates in the future;
+            # that's the reasoning for the current implementation
             return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True))
 
         @routes.get("/global_subgraphs/{id}")
         async def get_global_subgraph(request):
             id = request.match_info.get("id", None)
-            subgraph = await self.get_subgraph(id, loadedModules)
+            subgraph = await self.get_custom_node_subgraph(id, loadedModules)
             return web.json_response(await self.sanitize_entry(subgraph))

comfy/clip_model.py

@@ -1,7 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
-import math
 
 def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
     image = image[:, :, :, :3] if image.shape[3] > 3 else image
@@ -22,39 +21,6 @@ def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], s
     image = torch.clip((255. * image), 0, 255).round() / 255.0
     return (image - mean.view([3,1,1])) / std.view([3,1,1])
 
-def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, eps=1e-5):
-    def scale_dim(size, scale):
-        scaled = math.ceil(size * scale / patch_size) * patch_size
-        return max(patch_size, int(scaled))
-
-    # Binary search for optimal scale
-    lo, hi = eps / 10, 100.0
-    while hi - lo >= eps:
-        mid = (lo + hi) / 2
-        h, w = scale_dim(oh, mid), scale_dim(ow, mid)
-        if (h // patch_size) * (w // patch_size) <= max_num_patches:
-            lo = mid
-        else:
-            hi = mid
-
-    return scale_dim(oh, lo), scale_dim(ow, lo)
-
-def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True):
-    if size > 0:
-        return clip_preprocess(image, size=size, mean=mean, std=std, crop=crop)
-
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-
-    b, c, h, w = image.shape
-    h, w = siglip2_flex_calc_resolution(h, w, patch_size, num_patches)
-
-    image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear", antialias=True)
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3, 1, 1])) / std.view([3, 1, 1])
-
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
         super().__init__()
@@ -209,27 +175,6 @@ def forward(self, *args, **kwargs):
         out = self.text_projection(x[2])
         return (x[0], x[1], out, x[2])
 
-def siglip2_pos_embed(embed_weight, embeds, orig_shape):
-    embed_weight_len = round(embed_weight.shape[0] ** 0.5)
-    embed_weight = comfy.ops.cast_to_input(embed_weight, embeds).movedim(1, 0).reshape(1, -1, embed_weight_len, embed_weight_len)
-    embed_weight = torch.nn.functional.interpolate(embed_weight, size=orig_shape, mode="bilinear", align_corners=False, antialias=True)
-    embed_weight = embed_weight.reshape(-1, embed_weight.shape[-2] * embed_weight.shape[-1]).movedim(0, 1)
-    return embeds + embed_weight
-
-class Siglip2Embeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", num_patches=None, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.patch_embedding = operations.Linear(num_channels * patch_size * patch_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
-        self.patch_size = patch_size
-
-    def forward(self, pixel_values):
-        b, c, h, w = pixel_values.shape
-        img = pixel_values.movedim(1, -1).reshape(b, h // self.patch_size, self.patch_size, w // self.patch_size, self.patch_size, c)
-        img = img.permute(0, 1, 3, 2, 4, 5)
-        img = img.reshape(b, img.shape[1] * img.shape[2], -1)
-        img = self.patch_embedding(img)
-        return siglip2_pos_embed(self.position_embedding.weight, img, (h // self.patch_size, w // self.patch_size))
 
 class CLIPVisionEmbeddings(torch.nn.Module):
     def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
@@ -273,11 +218,8 @@ def __init__(self, config_dict, dtype, device, operations):
         intermediate_activation = config_dict["hidden_act"]
         model_type = config_dict["model_type"]
 
-        if model_type in ["siglip2_vision_model"]:
-            self.embeddings = Siglip2Embeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, num_patches=config_dict.get("num_patches", None), dtype=dtype, device=device, operations=operations)
-        else:
-            self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
-        if model_type in ["siglip_vision_model", "siglip2_vision_model"]:
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
+        if model_type == "siglip_vision_model":
             self.pre_layrnorm = lambda a: a
             self.output_layernorm = True
         else:

comfy/clip_vision.py

@@ -21,7 +21,6 @@ def __setitem__(self, key, item):
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
-    "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "dinov2": comfy.image_encoders.dino2.Dinov2Model,
 }
 
@@ -33,10 +32,9 @@ def __init__(self, json_config):
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
-        self.model_type = config.get("model_type", "clip_vision_model")
-        self.config = config.copy()
-        model_class = IMAGE_ENCODERS.get(self.model_type)
-        if self.model_type == "siglip_vision_model":
+        model_type = config.get("model_type", "clip_vision_model")
+        model_class = IMAGE_ENCODERS.get(model_type)
+        if model_type == "siglip_vision_model":
             self.return_all_hidden_states = True
         else:
             self.return_all_hidden_states = False
@@ -57,10 +55,7 @@ def get_sd(self):
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        if self.model_type == "siglip2_vision_model":
-            pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()
-        else:
-            pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()
@@ -112,14 +107,10 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
         embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0]
         if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
-            patch_embedding_shape = sd["vision_model.embeddings.patch_embedding.weight"].shape
-            if len(patch_embedding_shape) == 2:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip2_base_naflex.json")
-            else:
-                if embed_shape == 729:
-                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
-                elif embed_shape == 1024:
-                    json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
+            if embed_shape == 729:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+            elif embed_shape == 1024:
+                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
         elif embed_shape == 577:
             if "multi_modal_projector.linear_1.bias" in sd:
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json")