Merge branch 'master' into assets-redo-part2

* Not sure about this one, but try removing assets from old sessions.

* Simplify _prune_orphaned_assets: merge functions, use list comprehensions

Amp-Thread-ID: https://ampcode.com/threads/T-019c0917-0dc3-75ab-870d-a32b3fdc1927
Co-authored-by: Amp <amp@ampcode.com>

* Refactor _prune_orphaned_assets for readability

Amp-Thread-ID: https://ampcode.com/threads/T-019c0917-0dc3-75ab-870d-a32b3fdc1927
Co-authored-by: Amp <amp@ampcode.com>

* Add unit tests for pruning

* Add unit tests for _prune_orphaned_assets

Tests cover:

- Orphaned seed assets pruned when file removed

- Seed assets with valid files survive

- Hashed assets not pruned even without file

- Multi-root pruning

- SQL LIKE escape handling for %, _, spaces

Amp-Thread-ID: https://ampcode.com/threads/T-019c0c7a-5c8a-7548-b6c3-823e9829ce74
Co-authored-by: Amp <amp@ampcode.com>

* Ruff fix

---------

Co-authored-by: Amp <amp@ampcode.com>

comfy/text_encoders/llama.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from dataclasses import dataclass
-from typing import Optional, Any, Tuple
+from typing import Optional, Any
 import math
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
@@ -32,7 +32,6 @@ class Llama2Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Mistral3Small24BConfig:
@@ -55,7 +54,6 @@ class Mistral3Small24BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_3BConfig:
@@ -78,7 +76,6 @@ class Qwen25_3BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_06BConfig:
@@ -101,7 +98,6 @@ class Qwen3_06BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_4BConfig:
@@ -124,7 +120,6 @@ class Qwen3_4BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_8BConfig:
@@ -147,7 +142,6 @@ class Qwen3_8BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Ovis25_2BConfig:
@@ -170,7 +164,6 @@ class Ovis25_2BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -193,7 +186,6 @@ class Qwen25_7BVLI_Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma2_2B_Config:
@@ -217,7 +209,6 @@ class Gemma2_2B_Config:
     sliding_attention = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_4B_Config:
@@ -241,7 +232,6 @@ class Gemma3_4B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_12B_Config:
@@ -265,7 +255,6 @@ class Gemma3_12B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
     vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
     mm_tokens_per_image = 256
 
@@ -367,7 +356,6 @@ class Attention(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
         xq = self.q_proj(hidden_states)
@@ -385,30 +373,11 @@ class Attention(nn.Module):
 
         xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
 
-        present_key_value = None
-        if past_key_value is not None:
-            index = 0
-            num_tokens = xk.shape[2]
-            if len(past_key_value) > 0:
-                past_key, past_value, index = past_key_value
-                if past_key.shape[2] >= (index + num_tokens):
-                    past_key[:, :, index:index + xk.shape[2]] = xk
-                    past_value[:, :, index:index + xv.shape[2]] = xv
-                    xk = past_key[:, :, :index + xk.shape[2]]
-                    xv = past_value[:, :, :index + xv.shape[2]]
-                    present_key_value = (past_key, past_value, index + num_tokens)
-                else:
-                    xk = torch.cat((past_key[:, :, :index], xk), dim=2)
-                    xv = torch.cat((past_value[:, :, :index], xv), dim=2)
-                    present_key_value = (xk, xv, index + num_tokens)
-            else:
-                present_key_value = (xk, xv, index + num_tokens)
-
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
         xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
 
         output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
-        return self.o_proj(output), present_key_value
+        return self.o_proj(output)
 
 class MLP(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -439,17 +408,15 @@ class TransformerBlock(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
         x = residual + x
 
@@ -459,7 +426,7 @@ class TransformerBlock(nn.Module):
         x = self.mlp(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class TransformerBlockGemma2(nn.Module):
     def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
@@ -484,7 +451,6 @@ class TransformerBlockGemma2(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
@@ -502,12 +468,11 @@ class TransformerBlockGemma2(nn.Module):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
 
         x = self.post_attention_layernorm(x)
@@ -520,7 +485,7 @@ class TransformerBlockGemma2(nn.Module):
         x = self.post_feedforward_layernorm(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
@@ -551,10 +516,9 @@ class Llama2_(nn.Module):
         else:
             self.norm = None
 
-        if config.lm_head:
-            self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+        # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
-    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None):
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]):
         if embeds is not None:
             x = embeds
         else:
@@ -563,13 +527,8 @@ class Llama2_(nn.Module):
         if self.normalize_in:
             x *= self.config.hidden_size ** 0.5
 
-        seq_len = x.shape[1]
-        past_len = 0
-        if past_key_values is not None and len(past_key_values) > 0:
-            past_len = past_key_values[0][2]
-
         if position_ids is None:
-            position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)
+            position_ids = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0)
 
         freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          position_ids,
@@ -580,16 +539,14 @@ class Llama2_(nn.Module):
 
         mask = None
         if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
             mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
-        if seq_len > 1:
-            causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
-            if mask is not None:
-                mask += causal_mask
-            else:
-                mask = causal_mask
-
+        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+        if mask is not None:
+            mask += causal_mask
+        else:
+            mask = causal_mask
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
         intermediate = None
@@ -605,27 +562,16 @@ class Llama2_(nn.Module):
             elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
-        next_key_values = []
         for i, layer in enumerate(self.layers):
             if all_intermediate is not None:
                 if only_layers is None or (i in only_layers):
                     all_intermediate.append(x.unsqueeze(1).clone())
-
-            past_kv = None
-            if past_key_values is not None:
-                past_kv = past_key_values[i] if len(past_key_values) > 0 else []
-
-            x, current_kv = layer(
+            x = layer(
                 x=x,
                 attention_mask=mask,
                 freqs_cis=freqs_cis,
                 optimized_attention=optimized_attention,
-                past_key_value=past_kv,
             )
-
-            if current_kv is not None:
-                next_key_values.append(current_kv)
-
             if i == intermediate_output:
                 intermediate = x.clone()
 
@@ -642,10 +588,7 @@ class Llama2_(nn.Module):
         if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
             intermediate = self.norm(intermediate)
 
-        if len(next_key_values) > 0:
-            return x, intermediate, next_key_values
-        else:
-            return x, intermediate
+        return x, intermediate
 
 
 class Gemma3MultiModalProjector(torch.nn.Module):

comfy_api/latest/_io.py

@@ -1248,7 +1248,6 @@ class Hidden(str, Enum):
 class NodeInfoV1:
     input: dict=None
     input_order: dict[str, list[str]]=None
-    is_input_list: bool=None
     output: list[str]=None
     output_is_list: list[bool]=None
     output_name: list[str]=None
@@ -1475,7 +1474,6 @@ class Schema:
         info = NodeInfoV1(
             input=input,
             input_order={key: list(value.keys()) for (key, value) in input.items()},
-            is_input_list=self.is_input_list,
             output=output,
             output_is_list=output_is_list,
             output_name=output_name,

comfy_extras/nodes_painter.py

@@ -1,132 +0,0 @@
-from __future__ import annotations
-
-import hashlib
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-
-import folder_paths
-import node_helpers
-from comfy_api.latest import ComfyExtension, io
-from typing_extensions import override
-
-
-def hex_to_rgb(hex_color: str) -> tuple[float, float, float]:
-    hex_color = hex_color.lstrip("#")
-    if len(hex_color) != 6:
-        return (0.0, 0.0, 0.0)
-    r = int(hex_color[0:2], 16) / 255.0
-    g = int(hex_color[2:4], 16) / 255.0
-    b = int(hex_color[4:6], 16) / 255.0
-    return (r, g, b)
-
-
-class PainterNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="Painter",
-            display_name="Painter",
-            category="image",
-            inputs=[
-                io.Image.Input(
-                    "image",
-                    optional=True,
-                    tooltip="Optional base image to paint over",
-                ),
-                io.String.Input(
-                    "mask",
-                    default="",
-                    socketless=True,
-                    extra_dict={"widgetType": "PAINTER", "image_upload": True},
-                ),
-                io.Int.Input(
-                    "width",
-                    default=512,
-                    min=64,
-                    max=4096,
-                    step=64,
-                    socketless=True,
-                    extra_dict={"hidden": True},
-                ),
-                io.Int.Input(
-                    "height",
-                    default=512,
-                    min=64,
-                    max=4096,
-                    step=64,
-                    socketless=True,
-                    extra_dict={"hidden": True},
-                ),
-                io.String.Input(
-                    "bg_color",
-                    default="#000000",
-                    socketless=True,
-                    extra_dict={"hidden": True, "widgetType": "COLOR"},
-                ),
-            ],
-            outputs=[
-                io.Image.Output("IMAGE"),
-                io.Mask.Output("MASK"),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, mask, width, height, bg_color="#000000", image=None) -> io.NodeOutput:
-        if image is not None:
-            h, w = image.shape[1], image.shape[2]
-            base_image = image
-        else:
-            h, w = height, width
-            r, g, b = hex_to_rgb(bg_color)
-            base_image = torch.zeros((1, h, w, 3), dtype=torch.float32)
-            base_image[0, :, :, 0] = r
-            base_image[0, :, :, 1] = g
-            base_image[0, :, :, 2] = b
-
-        if mask and mask.strip():
-            mask_path = folder_paths.get_annotated_filepath(mask)
-            painter_img = node_helpers.pillow(Image.open, mask_path)
-            painter_img = painter_img.convert("RGBA")
-
-            if painter_img.size != (w, h):
-                painter_img = painter_img.resize((w, h), Image.LANCZOS)
-
-            painter_np = np.array(painter_img).astype(np.float32) / 255.0
-            painter_rgb = painter_np[:, :, :3]
-            painter_alpha = painter_np[:, :, 3:4]
-
-            mask_tensor = torch.from_numpy(painter_np[:, :, 3]).unsqueeze(0)
-
-            base_np = base_image[0].cpu().numpy()
-            composited = painter_rgb * painter_alpha + base_np * (1.0 - painter_alpha)
-            out_image = torch.from_numpy(composited).unsqueeze(0)
-        else:
-            mask_tensor = torch.zeros((1, h, w), dtype=torch.float32)
-            out_image = base_image
-
-        return io.NodeOutput(out_image, mask_tensor)
-
-    @classmethod
-    def fingerprint_inputs(cls, mask, width, height, bg_color="#000000", image=None):
-        if mask and mask.strip():
-            mask_path = folder_paths.get_annotated_filepath(mask)
-            if os.path.exists(mask_path):
-                m = hashlib.sha256()
-                with open(mask_path, "rb") as f:
-                    m.update(f.read())
-                return m.digest().hex()
-        return ""
-
-
-
-class PainterExtension(ComfyExtension):
-    @override
-    async def get_node_list(self):
-        return [PainterNode]
-
-
-async def comfy_entrypoint():
-    return PainterExtension()

nodes.py

@@ -2433,8 +2433,7 @@ async def init_builtin_extra_nodes():
         "nodes_image_compare.py",
         "nodes_zimage.py",
         "nodes_lora_debug.py",
-        "nodes_color.py",
-        "nodes_painter.py"
+        "nodes_color.py"
     ]
 
     import_failed = []

server.py

@@ -656,7 +656,6 @@ class PromptServer():
             info = {}
             info['input'] = obj_class.INPUT_TYPES()
             info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
-            info['is_input_list'] = getattr(obj_class, "INPUT_IS_LIST", False)
             info['output'] = obj_class.RETURN_TYPES
             info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
             info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']