Track custom node startup errors and expose via API endpoint

Store import and prestartup errors in NODE_STARTUP_ERRORS dict (nodes.py,
main.py) and add GET /custom_node_startup_errors endpoint (server.py) so
the frontend/Manager can distinguish failed imports from missing nodes.

Ref: ComfyUI-Launcher#303
Amp-Thread-ID: https://ampcode.com/threads/T-019d2346-6e6f-75e0-a97f-cdb6e26859f7
Co-authored-by: Amp <amp@ampcode.com>

README.md

@@ -232,7 +232,7 @@ Put your VAE in: models/vae
 
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
 
 This is the command to install the nightly with ROCm 7.2 which might have some performance improvements:
 

app/assets/services/path_utils.py

@@ -93,13 +93,12 @@ def compute_relative_filename(file_path: str) -> str | None:
 
 def get_asset_category_and_relative_path(
     file_path: str,
-) -> tuple[Literal["input", "output", "temp", "models"], str]:
+) -> tuple[Literal["input", "output", "models"], str]:
     """Determine which root category a file path belongs to.
 
     Categories:
       - 'input': under folder_paths.get_input_directory()
       - 'output': under folder_paths.get_output_directory()
-      - 'temp': under folder_paths.get_temp_directory()
       - 'models': under any base path from get_comfy_models_folders()
 
     Returns:
@@ -130,12 +129,7 @@ def get_asset_category_and_relative_path(
     if _check_is_within(fp_abs, output_base):
         return "output", _compute_relative(fp_abs, output_base)
 
-    # 3) temp
-    temp_base = os.path.abspath(folder_paths.get_temp_directory())
-    if _check_is_within(fp_abs, temp_base):
-        return "temp", _compute_relative(fp_abs, temp_base)
-
-    # 4) models (check deepest matching base to avoid ambiguity)
+    # 3) models (check deepest matching base to avoid ambiguity)
     best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
     for bucket, bases in get_comfy_models_folders():
         for b in bases:
@@ -152,7 +146,7 @@ def get_asset_category_and_relative_path(
         return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
 
     raise ValueError(
-        f"Path is not within input, output, temp, or configured model bases: {file_path}"
+        f"Path is not within input, output, or configured model bases: {file_path}"
     )
 
 

blueprints/.glsl/Color_Balance_15.frag

@@ -1,90 +0,0 @@
-#version 300 es
-precision highp float;
-
-uniform sampler2D u_image0;
-uniform float u_float0;
-uniform float u_float1;
-uniform float u_float2;
-uniform float u_float3;
-uniform float u_float4;
-uniform float u_float5;
-uniform float u_float6;
-uniform float u_float7;
-uniform float u_float8;
-uniform bool u_bool0;
-
-in vec2 v_texCoord;
-out vec4 fragColor;
-
-vec3 rgb2hsl(vec3 c) {
-    float maxC = max(c.r, max(c.g, c.b));
-    float minC = min(c.r, min(c.g, c.b));
-    float l = (maxC + minC) * 0.5;
-    if (maxC == minC) return vec3(0.0, 0.0, l);
-    float d = maxC - minC;
-    float s = l > 0.5 ? d / (2.0 - maxC - minC) : d / (maxC + minC);
-    float h;
-    if (maxC == c.r) {
-        h = (c.g - c.b) / d + (c.g < c.b ? 6.0 : 0.0);
-    } else if (maxC == c.g) {
-        h = (c.b - c.r) / d + 2.0;
-    } else {
-        h = (c.r - c.g) / d + 4.0;
-    }
-    h /= 6.0;
-    return vec3(h, s, l);
-}
-
-float hue2rgb(float p, float q, float t) {
-    if (t < 0.0) t += 1.0;
-    if (t > 1.0) t -= 1.0;
-    if (t < 1.0 / 6.0) return p + (q - p) * 6.0 * t;
-    if (t < 1.0 / 2.0) return q;
-    if (t < 2.0 / 3.0) return p + (q - p) * (2.0 / 3.0 - t) * 6.0;
-    return p;
-}
-
-vec3 hsl2rgb(vec3 hsl) {
-    float h = hsl.x, s = hsl.y, l = hsl.z;
-    if (s == 0.0) return vec3(l);
-    float q = l < 0.5 ? l * (1.0 + s) : l + s - l * s;
-    float p = 2.0 * l - q;
-    return vec3(
-        hue2rgb(p, q, h + 1.0 / 3.0),
-        hue2rgb(p, q, h),
-        hue2rgb(p, q, h - 1.0 / 3.0)
-    );
-}
-
-void main() {
-    vec4 tex = texture(u_image0, v_texCoord);
-    vec3 color = tex.rgb;
-
-    vec3 shadows = vec3(u_float0, u_float1, u_float2) * 0.01;
-    vec3 midtones = vec3(u_float3, u_float4, u_float5) * 0.01;
-    vec3 highlights = vec3(u_float6, u_float7, u_float8) * 0.01;
-
-    float maxC = max(color.r, max(color.g, color.b));
-    float minC = min(color.r, min(color.g, color.b));
-    float lightness = (maxC + minC) * 0.5;
-
-    // GIMP weight curves: linear ramps with constants a=0.25, b=0.333, scale=0.7
-    const float a = 0.25;
-    const float b = 0.333;
-    const float scale = 0.7;
-
-    float sw = clamp((lightness - b) / -a + 0.5, 0.0, 1.0) * scale;
-    float mw = clamp((lightness - b) / a + 0.5, 0.0, 1.0) *
-               clamp((lightness + b - 1.0) / -a + 0.5, 0.0, 1.0) * scale;
-    float hw = clamp((lightness + b - 1.0) / a + 0.5, 0.0, 1.0) * scale;
-
-    color += sw * shadows + mw * midtones + hw * highlights;
-
-    if (u_bool0) {
-        vec3 hsl = rgb2hsl(clamp(color, 0.0, 1.0));
-        hsl.z = lightness;
-        color = hsl2rgb(hsl);
-    }
-
-    fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);
-}

blueprints/.glsl/Color_Curves_8.frag

@@ -1,49 +0,0 @@
-#version 300 es
-precision highp float;
-
-uniform sampler2D u_image0;
-uniform sampler2D u_curve0;  // RGB master curve (256x1 LUT)
-uniform sampler2D u_curve1;  // Red channel curve
-uniform sampler2D u_curve2;  // Green channel curve
-uniform sampler2D u_curve3;  // Blue channel curve
-
-in vec2 v_texCoord;
-layout(location = 0) out vec4 fragColor0;
-
-// GIMP-compatible curve lookup with manual linear interpolation.
-// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:
-//   index = value * (n_samples - 1)
-//   f = fract(index)
-//   result = (1-f) * samples[floor] + f * samples[ceil]
-//
-// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues
-// that occur with texture() + GL_LINEAR on small 256x1 LUTs.
-float applyCurve(sampler2D curve, float value) {
-    value = clamp(value, 0.0, 1.0);
-
-    float pos = value * 255.0;
-    int lo = int(floor(pos));
-    int hi = min(lo + 1, 255);
-    float f = pos - float(lo);
-
-    float a = texelFetch(curve, ivec2(lo, 0), 0).r;
-    float b = texelFetch(curve, ivec2(hi, 0), 0).r;
-
-    return a + f * (b - a);
-}
-
-void main() {
-    vec4 color = texture(u_image0, v_texCoord);
-
-    // GIMP order: per-channel curves first, then RGB master curve.
-    // See gimp_curve_map_pixels() default case in gimpcurve-map.c:
-    //   dest = colors_curve( channel_curve( src ) )
-    float tmp_r = applyCurve(u_curve1, color.r);
-    float tmp_g = applyCurve(u_curve2, color.g);
-    float tmp_b = applyCurve(u_curve3, color.b);
-    color.r = applyCurve(u_curve0, tmp_r);
-    color.g = applyCurve(u_curve0, tmp_g);
-    color.b = applyCurve(u_curve0, tmp_b);
-
-    fragColor0 = vec4(color.rgb, color.a);
-}

comfy/cli_args.py

@@ -110,13 +110,11 @@ 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=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).")
+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")
 
 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/ldm/rt_detr/rtdetr_v4.py

@@ -1,725 +0,0 @@
-from collections import OrderedDict
-from typing import List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-import comfy.model_management
-from comfy.ldm.modules.attention import optimized_attention_for_device
-
-COCO_CLASSES = [
-    'person','bicycle','car','motorcycle','airplane','bus','train','truck','boat',
-    'traffic light','fire hydrant','stop sign','parking meter','bench','bird','cat',
-    'dog','horse','sheep','cow','elephant','bear','zebra','giraffe','backpack',
-    'umbrella','handbag','tie','suitcase','frisbee','skis','snowboard','sports ball',
-    'kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket',
-    'bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple',
-    'sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair',
-    'couch','potted plant','bed','dining table','toilet','tv','laptop','mouse',
-    'remote','keyboard','cell phone','microwave','oven','toaster','sink',
-    'refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush',
-]
-
-# ---------------------------------------------------------------------------
-# HGNetv2 backbone
-# ---------------------------------------------------------------------------
-
-class ConvBNAct(nn.Module):
-    """Conv→BN→ReLU.  padding='same' adds asymmetric zero-pad (stem)."""
-    def __init__(self, ic, oc, k=3, s=1, groups=1, use_act=True, device=None, dtype=None, operations=None):
-        super().__init__()
-
-        self.conv = operations.Conv2d(ic, oc, k, s, (k - 1) // 2, groups=groups, bias=False, device=device, dtype=dtype)
-        self.bn   = nn.BatchNorm2d(oc, device=device, dtype=dtype)
-        self.act  = nn.ReLU() if use_act else nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-class LightConvBNAct(nn.Module):
-    def __init__(self, ic, oc, k, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.conv1 = ConvBNAct(ic, oc, 1, use_act=False, device=device, dtype=dtype, operations=operations)
-        self.conv2 = ConvBNAct(oc, oc, k, groups=oc, use_act=True, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, x):
-        return self.conv2(self.conv1(x))
-
-class _StemBlock(nn.Module):
-    def __init__(self, ic, mc, oc, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.stem1  = ConvBNAct(ic,    mc,    3, 2, device=device, dtype=dtype, operations=operations)
-        # stem2a/stem2b use kernel=2, stride=1, no internal padding;
-        # padding is applied manually in forward (matching PaddlePaddle original)
-        self.stem2a = ConvBNAct(mc,    mc//2, 2, 1, device=device, dtype=dtype, operations=operations)
-        self.stem2b = ConvBNAct(mc//2, mc,    2, 1, device=device, dtype=dtype, operations=operations)
-        self.stem3  = ConvBNAct(mc*2,  mc,    3, 2, device=device, dtype=dtype, operations=operations)
-        self.stem4  = ConvBNAct(mc,    oc,    1, device=device, dtype=dtype, operations=operations)
-        self.pool   = nn.MaxPool2d(2, 1, ceil_mode=True)
-
-    def forward(self, x):
-        x  = self.stem1(x)
-        x  = F.pad(x, (0, 1, 0, 1))   # pad before pool and stem2a
-        x2 = self.stem2a(x)
-        x2 = F.pad(x2, (0, 1, 0, 1))  # pad before stem2b
-        x2 = self.stem2b(x2)
-        x1 = self.pool(x)
-        return self.stem4(self.stem3(torch.cat([x1, x2], 1)))
-
-
-class _HG_Block(nn.Module):
-    def __init__(self, ic, mc, oc, layer_num, k=3, residual=False, light=False, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.residual = residual
-        if light:
-            self.layers = nn.ModuleList(
-                [LightConvBNAct(ic if i == 0 else mc, mc, k, device=device, dtype=dtype, operations=operations) for i in range(layer_num)])
-        else:
-            self.layers = nn.ModuleList(
-                [ConvBNAct(ic if i == 0 else mc, mc, k, device=device, dtype=dtype, operations=operations) for i in range(layer_num)])
-        total = ic + layer_num * mc
-
-        self.aggregation = nn.Sequential(
-            ConvBNAct(total,   oc // 2, 1, device=device, dtype=dtype, operations=operations),
-            ConvBNAct(oc // 2, oc,      1, device=device, dtype=dtype, operations=operations))
-
-    def forward(self, x):
-        identity = x
-        outs = [x]
-        for layer in self.layers:
-            x = layer(x)
-            outs.append(x)
-        x = self.aggregation(torch.cat(outs, 1))
-        return x + identity if self.residual else x
-
-
-class _HG_Stage(nn.Module):
-    # config order: ic, mc, oc, num_blocks, downsample, light, k, layer_num
-    def __init__(self, ic, mc, oc, num_blocks, downsample=True, light=False, k=3, layer_num=6, device=None, dtype=None, operations=None):
-        super().__init__()
-        if downsample:
-            self.downsample = ConvBNAct(ic, ic, 3, 2, groups=ic, use_act=False, device=device, dtype=dtype, operations=operations)
-        else:
-            self.downsample = nn.Identity()
-        self.blocks = nn.Sequential(*[
-            _HG_Block(ic if i == 0 else oc, mc, oc, layer_num,
-                      k=k, residual=(i != 0), light=light, device=device, dtype=dtype, operations=operations)
-            for i in range(num_blocks)
-        ])
-
-    def forward(self, x):
-        return self.blocks(self.downsample(x))
-
-
-class HGNetv2(nn.Module):
-    # B5 config: stem=[3,32,64], stages=[ic, mc, oc, blocks, down, light, k, layers]
-    _STAGE_CFGS = [[64,  64,  128,  1, False, False, 3, 6],
-                   [128, 128, 512,  2, True,  False, 3, 6],
-                   [512, 256, 1024, 5, True,  True,  5, 6],
-                   [1024,512, 2048, 2, True,  True,  5, 6]]
-
-    def __init__(self, return_idx=(1, 2, 3), device=None, dtype=None, operations=None):
-        super().__init__()
-        self.stem   = _StemBlock(3, 32, 64, device=device, dtype=dtype, operations=operations)
-        self.stages = nn.ModuleList([_HG_Stage(*cfg, device=device, dtype=dtype, operations=operations) for cfg in self._STAGE_CFGS])
-        self.return_idx  = list(return_idx)
-        self.out_channels = [self._STAGE_CFGS[i][2] for i in return_idx]
-
-    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
-        x = self.stem(x)
-        outs = []
-        for i, stage in enumerate(self.stages):
-            x = stage(x)
-            if i in self.return_idx:
-                outs.append(x)
-        return outs
-
-
-# ---------------------------------------------------------------------------
-# Encoder — HybridEncoder  (dfine version: RepNCSPELAN4 + SCDown PAN)
-# ---------------------------------------------------------------------------
-
-class ConvNormLayer(nn.Module):
-    """Conv→act (expects pre-fused BN weights)."""
-    def __init__(self, ic, oc, k, s, g=1, padding=None, act=None, device=None, dtype=None, operations=None):
-        super().__init__()
-        p = (k - 1) // 2 if padding is None else padding
-        self.conv = operations.Conv2d(ic, oc, k, s, p, groups=g, bias=True, device=device, dtype=dtype)
-        self.act  = nn.SiLU() if act == 'silu' else nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.conv(x))
-
-
-class VGGBlock(nn.Module):
-    """Rep-VGG block (expects pre-fused weights)."""
-    def __init__(self, ic, oc, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv2d(ic, oc, 3, 1, padding=1, bias=True, device=device, dtype=dtype)
-        self.act  = nn.SiLU()
-
-    def forward(self, x):
-        return self.act(self.conv(x))
-
-
-class CSPLayer(nn.Module):
-    def __init__(self, ic, oc, num_blocks=3, expansion=1.0, act='silu', device=None, dtype=None, operations=None):
-        super().__init__()
-        h = int(oc * expansion)
-        self.conv1 = ConvNormLayer(ic, h, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
-        self.conv2 = ConvNormLayer(ic, h, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
-        self.bottlenecks = nn.Sequential(*[VGGBlock(h, h, device=device, dtype=dtype, operations=operations) for _ in range(num_blocks)])
-        self.conv3 = ConvNormLayer(h, oc, 1, 1, act=act, device=device, dtype=dtype, operations=operations) if h != oc else nn.Identity()
-
-    def forward(self, x):
-        return self.conv3(self.bottlenecks(self.conv1(x)) + self.conv2(x))
-
-
-class RepNCSPELAN4(nn.Module):
-    """CSP-ELAN block — the FPN/PAN block in RTv4's HybridEncoder."""
-    def __init__(self, c1, c2, c3, c4, n=3, act='silu', device=None, dtype=None, operations=None):
-        super().__init__()
-        self.c = c3 // 2
-        self.cv1 = ConvNormLayer(c1, c3, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
-        self.cv2 = nn.Sequential(CSPLayer(c3 // 2, c4, n, 1.0, act=act, device=device, dtype=dtype, operations=operations), ConvNormLayer(c4, c4, 3, 1, act=act, device=device, dtype=dtype, operations=operations))
-        self.cv3 = nn.Sequential(CSPLayer(c4, c4, n, 1.0, act=act, device=device, dtype=dtype, operations=operations), ConvNormLayer(c4, c4, 3, 1, act=act, device=device, dtype=dtype, operations=operations))
-        self.cv4 = ConvNormLayer(c3 + 2 * c4, c2, 1, 1, act=act, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, x):
-        y = list(self.cv1(x).split((self.c, self.c), 1))
-        y.extend(m(y[-1]) for m in [self.cv2, self.cv3])
-        return self.cv4(torch.cat(y, 1))
-
-
-class SCDown(nn.Module):
-    """Separable conv downsampling used in HybridEncoder PAN bottom-up path."""
-    def __init__(self, ic, oc, k, s, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.cv1 = ConvNormLayer(ic, oc, 1, 1, device=device, dtype=dtype, operations=operations)
-        self.cv2 = ConvNormLayer(oc, oc, k, s, g=oc, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, x):
-        return self.cv2(self.cv1(x))
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, embed_dim, num_heads, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim  = embed_dim // num_heads
-        self.q_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
-        self.k_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
-        self.v_proj   = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
-
-    def forward(self, query, key, value, attn_mask=None):
-        optimized_attention = optimized_attention_for_device(query.device, False, small_input=True)
-        q, k, v = self.q_proj(query), self.k_proj(key), self.v_proj(value)
-        out = optimized_attention(q, k, v, heads=self.num_heads, mask=attn_mask)
-        return self.out_proj(out)
-
-
-class _TransformerEncoderLayer(nn.Module):
-    """Single AIFI encoder layer (pre- or post-norm, GELU by default)."""
-    def __init__(self, d_model, nhead, dim_feedforward, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn  = SelfAttention(d_model, nhead, device=device, dtype=dtype, operations=operations)
-        self.linear1    = operations.Linear(d_model, dim_feedforward, device=device, dtype=dtype)
-        self.linear2    = operations.Linear(dim_feedforward, d_model, device=device, dtype=dtype)
-        self.norm1      = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.norm2      = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.activation = nn.GELU()
-
-    def forward(self, src, src_mask=None, pos_embed=None):
-        q = k = src if pos_embed is None else src + pos_embed
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)
-        src = self.norm1(src + src2)
-        src2 = self.linear2(self.activation(self.linear1(src)))
-        return self.norm2(src + src2)
-
-
-class _TransformerEncoder(nn.Module):
-    """Thin wrapper so state-dict keys are  encoder.0.layers.N.*"""
-    def __init__(self, num_layers, d_model, nhead, dim_feedforward, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.layers = nn.ModuleList([
-            _TransformerEncoderLayer(d_model, nhead, dim_feedforward, device=device, dtype=dtype, operations=operations)
-            for _ in range(num_layers)
-        ])
-
-    def forward(self, src, src_mask=None, pos_embed=None):
-        for layer in self.layers:
-            src = layer(src, src_mask=src_mask, pos_embed=pos_embed)
-        return src
-
-
-class HybridEncoder(nn.Module):
-    def __init__(self, in_channels=(512, 1024, 2048), feat_strides=(8, 16, 32), hidden_dim=256, nhead=8, dim_feedforward=2048, use_encoder_idx=(2,), num_encoder_layers=1,
-                 pe_temperature=10000, expansion=1.0, depth_mult=1.0, act='silu', eval_spatial_size=(640, 640), device=None, dtype=None, operations=None):
-        super().__init__()
-        self.in_channels       = list(in_channels)
-        self.feat_strides      = list(feat_strides)
-        self.hidden_dim        = hidden_dim
-        self.use_encoder_idx   = list(use_encoder_idx)
-        self.pe_temperature    = pe_temperature
-        self.eval_spatial_size = eval_spatial_size
-        self.out_channels      = [hidden_dim] * len(in_channels)
-        self.out_strides       = list(feat_strides)
-
-        # channel projection (expects pre-fused weights)
-        self.input_proj = nn.ModuleList([
-            nn.Sequential(OrderedDict([('conv', operations.Conv2d(ch, hidden_dim, 1, bias=True, device=device, dtype=dtype))]))
-            for ch in in_channels
-        ])
-
-        # AIFI transformer — use _TransformerEncoder so keys are  encoder.0.layers.N.*
-        self.encoder = nn.ModuleList([
-            _TransformerEncoder(num_encoder_layers, hidden_dim, nhead, dim_feedforward, device=device, dtype=dtype, operations=operations)
-            for _ in range(len(use_encoder_idx))
-        ])
-
-        nb  = round(3 * depth_mult)
-        exp = expansion
-
-        # top-down FPN  (dfine: lateral conv has no act)
-        self.lateral_convs = nn.ModuleList(
-            [ConvNormLayer(hidden_dim, hidden_dim, 1, 1, device=device, dtype=dtype, operations=operations)
-             for _ in range(len(in_channels) - 1)])
-        self.fpn_blocks = nn.ModuleList(
-            [RepNCSPELAN4(hidden_dim * 2, hidden_dim, hidden_dim * 2, round(exp * hidden_dim // 2), nb, act=act, device=device, dtype=dtype, operations=operations)
-             for _ in range(len(in_channels) - 1)])
-
-        # bottom-up PAN  (dfine: nn.Sequential(SCDown) — keeps checkpoint key  .0.cv1/.0.cv2)
-        self.downsample_convs = nn.ModuleList(
-            [nn.Sequential(SCDown(hidden_dim, hidden_dim, 3, 2, device=device, dtype=dtype, operations=operations))
-             for _ in range(len(in_channels) - 1)])
-        self.pan_blocks = nn.ModuleList(
-            [RepNCSPELAN4(hidden_dim * 2, hidden_dim, hidden_dim * 2, round(exp * hidden_dim // 2), nb, act=act, device=device, dtype=dtype, operations=operations)
-             for _ in range(len(in_channels) - 1)])
-
-        # cache positional embeddings for fixed spatial size
-        if eval_spatial_size:
-            for idx in self.use_encoder_idx:
-                stride = self.feat_strides[idx]
-                pe = self._build_pe(eval_spatial_size[1] // stride,
-                                    eval_spatial_size[0] // stride,
-                                    hidden_dim, pe_temperature)
-                setattr(self, f'pos_embed{idx}', pe)
-
-    @staticmethod
-    def _build_pe(w, h, dim=256, temp=10000.):
-        assert dim % 4 == 0
-        gw = torch.arange(w, dtype=torch.float32)
-        gh = torch.arange(h, dtype=torch.float32)
-        gw, gh = torch.meshgrid(gw, gh, indexing='ij')
-        pdim  = dim // 4
-        omega = 1. / (temp ** (torch.arange(pdim, dtype=torch.float32) / pdim))
-        ow = gw.flatten()[:, None] @ omega[None]
-        oh = gh.flatten()[:, None] @ omega[None]
-        return torch.cat([ow.sin(), ow.cos(), oh.sin(), oh.cos()], 1)[None]
-
-    def forward(self, feats: List[torch.Tensor]) -> List[torch.Tensor]:
-        proj = [self.input_proj[i](f) for i, f in enumerate(feats)]
-
-        for i, enc_idx in enumerate(self.use_encoder_idx):
-            h, w = proj[enc_idx].shape[2:]
-            src  = proj[enc_idx].flatten(2).permute(0, 2, 1)
-            pe = getattr(self, f'pos_embed{enc_idx}').to(device=src.device, dtype=src.dtype)
-            for layer in self.encoder[i].layers:
-                src = layer(src, pos_embed=pe)
-            proj[enc_idx] = src.permute(0, 2, 1).reshape(-1, self.hidden_dim, h, w).contiguous()
-
-        n = len(self.in_channels)
-        inner = [proj[-1]]
-        for k in range(n - 1, 0, -1):
-            j = n - 1 - k
-            top = self.lateral_convs[j](inner[0])
-            inner[0] = top
-            up = F.interpolate(top, scale_factor=2., mode='nearest')
-            inner.insert(0, self.fpn_blocks[j](torch.cat([up, proj[k - 1]], 1)))
-
-        outs = [inner[0]]
-        for k in range(n - 1):
-            outs.append(self.pan_blocks[k](
-                torch.cat([self.downsample_convs[k](outs[-1]), inner[k + 1]], 1)))
-        return outs
-
-
-# ---------------------------------------------------------------------------
-# Decoder — DFINETransformer
-# ---------------------------------------------------------------------------
-
-def _deformable_attn_v2(value: list, spatial_shapes, sampling_locations: torch.Tensor, attention_weights: torch.Tensor, num_points_list: List[int]) -> torch.Tensor:
-    """
-    value            : list of per-level tensors  [bs*n_head, c, h_l, w_l]
-    sampling_locations: [bs, Lq, n_head, sum(pts), 2]  in [0,1]
-    attention_weights : [bs, Lq, n_head, sum(pts)]
-    """
-    _, c = value[0].shape[:2]      # bs*n_head, c
-    _, Lq, n_head, _, _ = sampling_locations.shape
-    bs = sampling_locations.shape[0]
-    n_h = n_head
-
-    grids = (2 * sampling_locations - 1)          # [bs, Lq, n_head, sum_pts, 2]
-    grids = grids.permute(0, 2, 1, 3, 4).flatten(0, 1)  # [bs*n_head, Lq, sum_pts, 2]
-    grids_per_lvl = grids.split(num_points_list, dim=2)  # list of [bs*n_head, Lq, pts_l, 2]
-
-    sampled = []
-    for lvl, (h, w) in enumerate(spatial_shapes):
-        val_l = value[lvl].reshape(bs * n_h, c, h, w)
-        sv = F.grid_sample(val_l, grids_per_lvl[lvl], mode='bilinear', padding_mode='zeros', align_corners=False)
-        sampled.append(sv) # sv: [bs*n_head, c, Lq, pts_l]
-
-    attn = attention_weights.permute(0, 2, 1, 3)  # [bs, n_head, Lq, sum_pts]
-    attn = attn.flatten(0, 1).unsqueeze(1)         # [bs*n_head, 1, Lq, sum_pts]
-    out  = (torch.cat(sampled, -1) * attn).sum(-1) # [bs*n_head, c, Lq]
-    out  = out.reshape(bs, n_h * c, Lq)
-    return out.permute(0, 2, 1)                    # [bs, Lq, hidden]
-
-
-class MSDeformableAttention(nn.Module):
-    def __init__(self, embed_dim=256, num_heads=8, num_levels=3, num_points=4, offset_scale=0.5, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.embed_dim, self.num_heads = embed_dim, num_heads
-        self.head_dim  = embed_dim // num_heads
-        pts = num_points if isinstance(num_points, list) else [num_points] * num_levels
-        self.num_points_list = pts
-        self.offset_scale    = offset_scale
-        total = num_heads * sum(pts)
-        self.register_buffer('num_points_scale', torch.tensor([1. / n for n in pts for _ in range(n)], dtype=torch.float32))
-        self.sampling_offsets  = operations.Linear(embed_dim, total * 2, device=device, dtype=dtype)
-        self.attention_weights = operations.Linear(embed_dim, total, device=device, dtype=dtype)
-
-    def forward(self, query, ref_pts, value, spatial_shapes):
-        bs, Lq = query.shape[:2]
-        offsets = self.sampling_offsets(query).reshape(
-            bs, Lq, self.num_heads, sum(self.num_points_list), 2)
-        attn_w  = F.softmax(
-            self.attention_weights(query).reshape(
-                bs, Lq, self.num_heads, sum(self.num_points_list)), -1)
-        scale   = self.num_points_scale.to(query).unsqueeze(-1)
-        offset  = offsets * scale * ref_pts[:, :, None, :, 2:] * self.offset_scale
-        locs    = ref_pts[:, :, None, :, :2] + offset  # [bs, Lq, n_head, sum_pts, 2]
-        return _deformable_attn_v2(value, spatial_shapes, locs, attn_w, self.num_points_list)
-
-
-class Gate(nn.Module):
-    def __init__(self, d_model, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.gate = operations.Linear(2 * d_model, 2 * d_model, device=device, dtype=dtype)
-        self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    def forward(self, x1, x2):
-        g1, g2 = torch.sigmoid(self.gate(torch.cat([x1, x2], -1))).chunk(2, -1)
-        return self.norm(g1 * x1 + g2 * x2)
-
-
-class MLP(nn.Module):
-    def __init__(self, in_dim, hidden_dim, out_dim, num_layers, device=None, dtype=None, operations=None):
-        super().__init__()
-        dims = [in_dim] + [hidden_dim] * (num_layers - 1) + [out_dim]
-        self.layers = nn.ModuleList(operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) for i in range(num_layers))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = nn.SiLU()(layer(x)) if i < len(self.layers) - 1 else layer(x)
-        return x
-
-
-class TransformerDecoderLayer(nn.Module):
-    def __init__(self, d_model=256, nhead=8, dim_feedforward=1024, num_levels=3, num_points=4, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.self_attn  = SelfAttention(d_model, nhead, device=device, dtype=dtype, operations=operations)
-        self.norm1      = operations.LayerNorm(d_model, device=device, dtype=dtype)
-        self.cross_attn = MSDeformableAttention(d_model, nhead, num_levels, num_points, device=device, dtype=dtype, operations=operations)
-        self.gateway    = Gate(d_model, device=device, dtype=dtype, operations=operations)
-        self.linear1    = operations.Linear(d_model, dim_feedforward, device=device, dtype=dtype)
-        self.activation = nn.ReLU()
-        self.linear2    = operations.Linear(dim_feedforward, d_model, device=device, dtype=dtype)
-        self.norm3      = operations.LayerNorm(d_model, device=device, dtype=dtype)
-
-    def forward(self, target, ref_pts, value, spatial_shapes, attn_mask=None, query_pos=None):
-        q = k = target if query_pos is None else target + query_pos
-        t2 = self.self_attn(q, k, value=target, attn_mask=attn_mask)
-        target = self.norm1(target + t2)
-        t2 = self.cross_attn(
-            target if query_pos is None else target + query_pos,
-            ref_pts, value, spatial_shapes)
-        target = self.gateway(target, t2)
-        t2 = self.linear2(self.activation(self.linear1(target)))
-        target = self.norm3((target + t2).clamp(-65504, 65504))
-        return target
-
-
-# ---------------------------------------------------------------------------
-# FDR utilities
-# ---------------------------------------------------------------------------
-
-def weighting_function(reg_max, up, reg_scale):
-    """Non-uniform weighting function W(n) for FDR box regression."""
-    ub1 = (abs(up[0]) * abs(reg_scale)).item()
-    ub2 = ub1 * 2
-    step = (ub1 + 1) ** (2 / (reg_max - 2))
-    left  = [-(step ** i) + 1 for i in range(reg_max // 2 - 1, 0, -1)]
-    right = [ (step ** i) - 1 for i in range(1, reg_max // 2)]
-    vals  = [-ub2] + left + [0] + right + [ub2]
-    return torch.tensor(vals, dtype=up.dtype, device=up.device)
-
-
-def distance2bbox(points, distance, reg_scale):
-    """Decode edge-distances → cxcywh boxes."""
-    rs = abs(reg_scale).to(dtype=points.dtype)
-    x1 = points[..., 0] - (0.5 * rs + distance[..., 0]) * (points[..., 2] / rs)
-    y1 = points[..., 1] - (0.5 * rs + distance[..., 1]) * (points[..., 3] / rs)
-    x2 = points[..., 0] + (0.5 * rs + distance[..., 2]) * (points[..., 2] / rs)
-    y2 = points[..., 1] + (0.5 * rs + distance[..., 3]) * (points[..., 3] / rs)
-    x0, y0, x1_, y1_ = (x1 + x2) / 2, (y1 + y2) / 2, x2 - x1, y2 - y1
-    return torch.stack([x0, y0, x1_, y1_], -1)
-
-
-class Integral(nn.Module):
-    """Sum Pr(n)·W(n) over the distribution bins."""
-    def __init__(self, reg_max=32):
-        super().__init__()
-        self.reg_max = reg_max
-
-    def forward(self, x, project):
-        shape = x.shape
-        x = F.softmax(x.reshape(-1, self.reg_max + 1), 1)
-        x = F.linear(x, project.to(device=x.device, dtype=x.dtype)).reshape(-1, 4)
-        return x.reshape(list(shape[:-1]) + [-1])
-
-
-class LQE(nn.Module):
-    """Location Quality Estimator — refines class scores using corner distribution."""
-    def __init__(self, k=4, hidden_dim=64, num_layers=2, reg_max=32, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.k, self.reg_max = k, reg_max
-        self.reg_conf = MLP(4 * (k + 1), hidden_dim, 1, num_layers, device=device, dtype=dtype, operations=operations)
-
-    def forward(self, scores, pred_corners):
-        B, L, _ = pred_corners.shape
-        prob     = F.softmax(pred_corners.reshape(B, L, 4, self.reg_max + 1), -1)
-        topk, _  = prob.topk(self.k, -1)
-        stat     = torch.cat([topk, topk.mean(-1, keepdim=True)], -1)
-        return scores + self.reg_conf(stat.reshape(B, L, -1))
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, hidden_dim, nhead, dim_feedforward, num_levels, num_points, num_layers, reg_max, reg_scale, up, eval_idx=-1, device=None, dtype=None, operations=None):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.num_layers = num_layers
-        self.nhead      = nhead
-        self.eval_idx   = eval_idx if eval_idx >= 0 else num_layers + eval_idx
-        self.up, self.reg_scale, self.reg_max = up, reg_scale, reg_max
-        self.layers = nn.ModuleList([
-            TransformerDecoderLayer(hidden_dim, nhead, dim_feedforward, num_levels, num_points, device=device, dtype=dtype, operations=operations)
-            for _ in range(self.eval_idx + 1)
-        ])
-        self.lqe_layers = nn.ModuleList([LQE(4, 64, 2, reg_max, device=device, dtype=dtype, operations=operations) for _ in range(self.eval_idx + 1)])
-        self.register_buffer('project', weighting_function(reg_max, up, reg_scale))
-
-    def _value_op(self, memory, spatial_shapes):
-        """Reshape memory to per-level value tensors for deformable attention."""
-        c = self.hidden_dim // self.nhead
-        split = [h * w for h, w in spatial_shapes]
-        val = memory.reshape(memory.shape[0], memory.shape[1], self.nhead, c) # memory: [bs, sum(h*w), hidden_dim]
-        # → [bs, n_head, c, sum_hw]
-        val = val.permute(0, 2, 3, 1).flatten(0, 1)  # [bs*n_head, c, sum_hw]
-        return val.split(split, dim=-1)  # list of [bs*n_head, c, h_l*w_l]
-
-    def forward(self, target, ref_pts_unact, memory, spatial_shapes, bbox_head, score_head, query_pos_head, pre_bbox_head, integral):
-        val_split_flat = self._value_op(memory, spatial_shapes) # pre-split value for deformable attention
-
-        # reshape to [bs*n_head, c, h_l, w_l]
-        value = []
-        for lvl, (h, w) in enumerate(spatial_shapes):
-            v = val_split_flat[lvl]   # [bs*n_head, c, h*w]
-            value.append(v.reshape(v.shape[0], v.shape[1], h, w))
-
-        ref_pts  = F.sigmoid(ref_pts_unact)
-        output   = target
-        output_detach = pred_corners_undetach = 0
-
-        dec_bboxes, dec_logits = [], []
-
-        for i, layer in enumerate(self.layers):
-            ref_input    = ref_pts.unsqueeze(2)           # [bs, Lq, 1, 4]
-            query_pos    = query_pos_head(ref_pts).clamp(-10, 10)
-            output       = layer(output, ref_input, value, spatial_shapes, query_pos=query_pos)
-
-            if i == 0:
-                ref_unact = ref_pts.clamp(1e-5, 1 - 1e-5)
-                ref_unact = torch.log(ref_unact / (1 - ref_unact))
-                pre_bboxes = F.sigmoid(pre_bbox_head(output) + ref_unact)
-                ref_pts_initial = pre_bboxes.detach()
-
-            pred_corners = bbox_head[i](output + output_detach) + pred_corners_undetach
-            inter_ref_bbox = distance2bbox(ref_pts_initial, integral(pred_corners, self.project), self.reg_scale)
-
-            if i == self.eval_idx:
-                scores = score_head[i](output)
-                scores = self.lqe_layers[i](scores, pred_corners)
-                dec_bboxes.append(inter_ref_bbox)
-                dec_logits.append(scores)
-                break
-
-            pred_corners_undetach = pred_corners
-            ref_pts        = inter_ref_bbox.detach()
-            output_detach  = output.detach()
-
-        return torch.stack(dec_bboxes), torch.stack(dec_logits)
-
-
-class DFINETransformer(nn.Module):
-    def __init__(self, num_classes=80, hidden_dim=256, num_queries=300, feat_channels=[256, 256, 256], feat_strides=[8, 16, 32],
-                 num_levels=3, num_points=[3, 6, 3], nhead=8, num_layers=6, dim_feedforward=1024, eval_idx=-1, eps=1e-2, reg_max=32,
-                 reg_scale=8.0, eval_spatial_size=(640, 640), device=None, dtype=None, operations=None):
-        super().__init__()
-        assert len(feat_strides) == len(feat_channels)
-        self.hidden_dim  = hidden_dim
-        self.num_queries = num_queries
-        self.num_levels  = num_levels
-        self.eps         = eps
-        self.eval_spatial_size = eval_spatial_size
-
-        self.feat_strides = list(feat_strides)
-        for i in range(num_levels - len(feat_strides)):
-            self.feat_strides.append(feat_strides[-1] * 2 ** (i + 1))
-
-        # input projection (expects pre-fused weights)
-        self.input_proj = nn.ModuleList()
-        for ch in feat_channels:
-            if ch == hidden_dim:
-                self.input_proj.append(nn.Identity())
-            else:
-                self.input_proj.append(nn.Sequential(OrderedDict([
-                    ('conv', operations.Conv2d(ch, hidden_dim, 1, bias=True, device=device, dtype=dtype))])))
-        in_ch = feat_channels[-1]
-        for i in range(num_levels - len(feat_channels)):
-            self.input_proj.append(nn.Sequential(OrderedDict([
-                ('conv', operations.Conv2d(in_ch if i == 0 else hidden_dim,
-                                           hidden_dim, 3, 2, 1, bias=True, device=device, dtype=dtype))])))
-            in_ch = hidden_dim
-
-        # FDR parameters (non-trainable placeholders, set from config)
-        self.up        = nn.Parameter(torch.tensor([0.5]),      requires_grad=False)
-        self.reg_scale = nn.Parameter(torch.tensor([reg_scale]), requires_grad=False)
-
-        pts = num_points if isinstance(num_points, (list, tuple)) else [num_points] * num_levels
-        self.decoder = TransformerDecoder(hidden_dim, nhead, dim_feedforward, num_levels, pts,
-                                          num_layers, reg_max, self.reg_scale, self.up, eval_idx, device=device, dtype=dtype, operations=operations)
-
-        self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, 2, device=device, dtype=dtype, operations=operations)
-        self.enc_output     = nn.Sequential(OrderedDict([
-            ('proj', operations.Linear(hidden_dim, hidden_dim, device=device, dtype=dtype)),
-            ('norm', operations.LayerNorm(hidden_dim, device=device, dtype=dtype))]))
-        self.enc_score_head = operations.Linear(hidden_dim, num_classes, device=device, dtype=dtype)
-        self.enc_bbox_head  = MLP(hidden_dim, hidden_dim, 4, 3, device=device, dtype=dtype, operations=operations)
-
-        self.eval_idx_ = eval_idx if eval_idx >= 0 else num_layers + eval_idx
-        self.dec_score_head = nn.ModuleList(
-            [operations.Linear(hidden_dim, num_classes, device=device, dtype=dtype) for _ in range(self.eval_idx_ + 1)])
-        self.pre_bbox_head  = MLP(hidden_dim, hidden_dim, 4, 3, device=device, dtype=dtype, operations=operations)
-        self.dec_bbox_head  = nn.ModuleList(
-            [MLP(hidden_dim, hidden_dim, 4 * (reg_max + 1), 3, device=device, dtype=dtype, operations=operations)
-             for _ in range(self.eval_idx_ + 1)])
-        self.integral = Integral(reg_max)
-
-        if eval_spatial_size:
-            # Register as buffers so checkpoint values override the freshly-computed defaults
-            anchors, valid_mask = self._gen_anchors()
-            self.register_buffer('anchors', anchors)
-            self.register_buffer('valid_mask', valid_mask)
-
-    def _gen_anchors(self, spatial_shapes=None, grid_size=0.05, dtype=torch.float32, device='cpu'):
-        if spatial_shapes is None:
-            h0, w0 = self.eval_spatial_size
-            spatial_shapes = [[int(h0 / s), int(w0 / s)] for s in self.feat_strides]
-        anchors = []
-        for lvl, (h, w) in enumerate(spatial_shapes):
-            gy, gx = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
-            gxy = (torch.stack([gx, gy], -1).float() + 0.5) / torch.tensor([w, h], dtype=dtype)
-            wh  = torch.ones_like(gxy) * grid_size * (2. ** lvl)
-            anchors.append(torch.cat([gxy, wh], -1).reshape(-1, h * w, 4))
-        anchors    = torch.cat(anchors, 1).to(device)
-        valid_mask = ((anchors > self.eps) & (anchors < 1 - self.eps)).all(-1, keepdim=True)
-        anchors    = torch.log(anchors / (1 - anchors))
-        anchors    = torch.where(valid_mask, anchors, torch.full_like(anchors, float('inf')))
-        return anchors, valid_mask
-
-    def _encoder_input(self, feats: List[torch.Tensor]):
-        proj = [self.input_proj[i](f) for i, f in enumerate(feats)]
-        for i in range(len(feats), self.num_levels):
-            proj.append(self.input_proj[i](feats[-1] if i == len(feats) else proj[-1]))
-        flat, shapes = [], []
-        for f in proj:
-            _, _, h, w = f.shape
-            flat.append(f.flatten(2).permute(0, 2, 1))
-            shapes.append([h, w])
-        return torch.cat(flat, 1), shapes
-
-    def _decoder_input(self, memory: torch.Tensor):
-        anchors, valid_mask = self.anchors.to(memory), self.valid_mask
-        if memory.shape[0] > 1:
-            anchors = anchors.repeat(memory.shape[0], 1, 1)
-
-        mem      = valid_mask.to(memory) * memory
-        out_mem  = self.enc_output(mem)
-        logits   = self.enc_score_head(out_mem)
-        _, idx   = torch.topk(logits.max(-1).values, self.num_queries, dim=-1)
-        idx_e    = idx.unsqueeze(-1)
-        topk_mem = out_mem.gather(1, idx_e.expand(-1, -1, out_mem.shape[-1]))
-        topk_anc = anchors.gather(1, idx_e.expand(-1, -1, anchors.shape[-1]))
-        topk_ref = self.enc_bbox_head(topk_mem) + topk_anc
-        return topk_mem.detach(), topk_ref.detach()
-
-    def forward(self, feats: List[torch.Tensor]):
-        memory, shapes = self._encoder_input(feats)
-        content, ref   = self._decoder_input(memory)
-        out_bboxes, out_logits = self.decoder(
-            content, ref, memory, shapes,
-            self.dec_bbox_head, self.dec_score_head,
-            self.query_pos_head, self.pre_bbox_head, self.integral)
-        return {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1]}
-
-
-# ---------------------------------------------------------------------------
-# Main model
-# ---------------------------------------------------------------------------
-
-class RTv4(nn.Module):
-    def __init__(self, num_classes=80, num_queries=300, enc_h=256, dec_h=256, enc_ff=2048, dec_ff=1024, feat_strides=[8, 16, 32], device=None, dtype=None, operations=None, **kwargs):
-        super().__init__()
-        self.device = device
-        self.dtype = dtype
-        self.operations = operations
-
-        self.backbone = HGNetv2(device=device, dtype=dtype, operations=operations)
-        self.encoder  = HybridEncoder(hidden_dim=enc_h, dim_feedforward=enc_ff, device=device, dtype=dtype, operations=operations)
-        self.decoder  = DFINETransformer(num_classes=num_classes, hidden_dim=dec_h, num_queries=num_queries,
-            feat_channels=[enc_h] * len(feat_strides), feat_strides=feat_strides, dim_feedforward=dec_ff, device=device, dtype=dtype, operations=operations)
-
-        self.num_classes = num_classes
-        self.num_queries = num_queries
-        self.load_device = comfy.model_management.get_torch_device()
-
-    def _forward(self, x: torch.Tensor):
-        return self.decoder(self.encoder(self.backbone(x)))
-
-    def postprocess(self, outputs, orig_size: tuple = (640, 640)) -> List[dict]:
-        logits = outputs['pred_logits']
-        boxes  = torchvision.ops.box_convert(outputs['pred_boxes'], 'cxcywh', 'xyxy')
-        boxes  = boxes * torch.tensor(orig_size, device=boxes.device, dtype=boxes.dtype).repeat(1, 2).unsqueeze(1)
-        scores = F.sigmoid(logits)
-        scores, idx = torch.topk(scores.flatten(1), self.num_queries, dim=-1)
-        labels = idx % self.num_classes
-        boxes  = boxes.gather(1, (idx // self.num_classes).unsqueeze(-1).expand(-1, -1, 4))
-        return [{'labels': lbl, 'boxes': b, 'scores': s} for lbl, b, s in zip(labels, boxes, scores)]
-
-    def forward(self, x: torch.Tensor, orig_size: tuple = (640, 640), **kwargs):
-        outputs = self._forward(x.to(device=self.load_device, dtype=self.dtype))
-        return self.postprocess(outputs, orig_size)

comfy/memory_management.py

@@ -141,17 +141,3 @@ 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_base.py

@@ -52,7 +52,6 @@ import comfy.ldm.qwen_image.model
 import comfy.ldm.kandinsky5.model
 import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
-import comfy.ldm.rt_detr.rtdetr_v4
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -891,7 +890,7 @@ class Flux(BaseModel):
         return torch.cat((image, mask), dim=1)
 
     def encode_adm(self, **kwargs):
-        return kwargs.get("pooled_output", None)
+        return kwargs["pooled_output"]
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -1958,7 +1957,3 @@ class Kandinsky5Image(Kandinsky5):
 
     def concat_cond(self, **kwargs):
         return None
-
-class RT_DETR_v4(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4)

comfy/model_detection.py

@@ -698,12 +698,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["audio_model"] = "ace1.5"
         return dit_config
 
-    if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
-        dit_config = {}
-        dit_config["image_model"] = "RT_DETR_v4"
-        dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
-        return dit_config
-
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 

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 device is None or shift_model.device == device:
+        if 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 or device is None:
-            memory_to_free = 0 if device is None else memory_required - get_free_memory(device)
+        if not DISABLE_SMART_MEMORY:
+            memory_to_free = 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()
-    elif device is not None:
+    else:
         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:
@@ -1326,9 +1326,9 @@ MAX_PINNED_MEMORY = -1
 if not args.disable_pinned_memory:
     if is_nvidia() or is_amd():
         if WINDOWS:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.40  # Windows limit is apparently 50%
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
         else:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.90
+            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
         logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
 
 PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"])
@@ -1403,6 +1403,8 @@ def unpin_memory(tensor):
 
     if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
         TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
+        if len(PINNED_MEMORY) == 0:
+            TOTAL_PINNED_MEMORY = 0
         return True
     else:
         logging.warning("Unpin error.")

comfy/model_patcher.py

@@ -300,6 +300,9 @@ 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/ops.py

@@ -928,7 +928,6 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 weight = state_dict.pop(weight_key, None)
                 if weight is None:
                     logging.warning(f"Missing weight for layer {layer_name}")
-                    self.weight = None
                     return
 
                 manually_loaded_keys = [weight_key]
@@ -1035,9 +1034,6 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 if self.bias is not None:
                     sd["{}bias".format(prefix)] = self.bias
 
-                if self.weight is None:
-                    return sd
-
                 if isinstance(self.weight, QuantizedTensor):
                     sd_out = self.weight.state_dict("{}weight".format(prefix))
                     for k in sd_out:

comfy/pinned_memory.py

@@ -2,7 +2,6 @@ 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
 
@@ -13,11 +12,6 @@ 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

@@ -61,7 +61,6 @@ import comfy.text_encoders.newbie
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
-import comfy.text_encoders.qwen35
 
 import comfy.model_patcher
 import comfy.lora
@@ -280,6 +279,9 @@ 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)
 
@@ -423,13 +425,13 @@ class CLIP:
     def get_key_patches(self):
         return self.patcher.get_key_patches()
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
         self.cond_stage_model.reset_clip_options()
 
         self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
-        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)
+        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
 
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
@@ -837,6 +839,9 @@ 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.")
@@ -1223,11 +1228,6 @@ class TEModel(Enum):
     QWEN3_8B = 20
     QWEN3_06B = 21
     GEMMA_3_4B_VISION = 22
-    QWEN35_08B = 23
-    QWEN35_2B = 24
-    QWEN35_4B = 25
-    QWEN35_9B = 26
-    QWEN35_27B = 27
 
 
 def detect_te_model(sd):
@@ -1267,17 +1267,6 @@ def detect_te_model(sd):
             return TEModel.QWEN25_3B
         if weight.shape[0] == 512:
             return TEModel.QWEN25_7B
-    if "model.language_model.layers.0.linear_attn.A_log" in sd and "model.language_model.layers.0.input_layernorm.weight" in sd:
-        weight = sd['model.language_model.layers.0.input_layernorm.weight']
-        if weight.shape[0] == 1024:
-            return TEModel.QWEN35_08B
-        if weight.shape[0] == 2560:
-            return TEModel.QWEN35_4B
-        if weight.shape[0] == 4096:
-            return TEModel.QWEN35_9B
-        if weight.shape[0] == 5120:
-            return TEModel.QWEN35_27B
-        return TEModel.QWEN35_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         weight = sd['model.layers.0.post_attention_layernorm.weight']
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1310,12 +1299,11 @@ def t5xxl_detect(clip_data):
     return {}
 
 def llama_detect(clip_data):
-    weight_names = ["model.layers.0.self_attn.k_proj.weight", "model.layers.0.linear_attn.in_proj_a.weight"]
+    weight_name = "model.layers.0.self_attn.k_proj.weight"
 
     for sd in clip_data:
-        for weight_name in weight_names:
-            if weight_name in sd:
-                return comfy.text_encoders.hunyuan_video.llama_detect(sd)
+        if weight_name in sd:
+            return comfy.text_encoders.hunyuan_video.llama_detect(sd)
 
     return {}
 
@@ -1443,11 +1431,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.JINA_CLIP_2:
             clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
             clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
-        elif te_model in (TEModel.QWEN35_08B, TEModel.QWEN35_2B, TEModel.QWEN35_4B, TEModel.QWEN35_9B, TEModel.QWEN35_27B):
-            clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
-            qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
-            clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
-            clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
         elif te_model == TEModel.QWEN3_06B:
             clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
@@ -1736,16 +1719,15 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
     """
     dtype = model_options.get("dtype", None)
 
-    custom_operations = model_options.get("custom_operations", None)
-    if custom_operations is None:
-        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
-
     #Allow loading unets from checkpoint files
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
     if len(temp_sd) > 0:
         sd = temp_sd
 
+    custom_operations = model_options.get("custom_operations", None)
+    if custom_operations is None:
+        sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
     parameters = comfy.utils.calculate_parameters(sd)
     weight_dtype = comfy.utils.weight_dtype(sd)
 

comfy/sd1_clip.py

@@ -308,14 +308,14 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
         if isinstance(tokens, dict):
             tokens_only = next(iter(tokens.values())) # todo: get this better?
         else:
             tokens_only = tokens
         tokens_only = [[t[0] for t in b] for b in tokens_only]
         embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=presence_penalty)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
 
 def parse_parentheses(string):
     result = []
@@ -740,5 +740,5 @@ class SD1ClipModel(torch.nn.Module):
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
-        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)

comfy/supported_models.py

@@ -1734,21 +1734,6 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
 
-
-class RT_DETR_v4(supported_models_base.BASE):
-    unet_config = {
-        "image_model": "RT_DETR_v4",
-    }
-
-    supported_inference_dtypes = [torch.float16, torch.float32]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.RT_DETR_v4(self, device=device)
-        return out
-
-    def clip_target(self, state_dict={}):
-        return None
-
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]

comfy/text_encoders/llama.py

@@ -224,7 +224,7 @@ class Qwen3_8BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = True
+    lm_head: bool = False
     stop_tokens = [151643, 151645]
 
 @dataclass
@@ -655,17 +655,6 @@ class Llama2_(nn.Module):
         if config.lm_head:
             self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
-    def get_past_len(self, past_key_values):
-        return past_key_values[0][2]
-
-    def compute_freqs_cis(self, position_ids, device):
-        return precompute_freqs_cis(self.config.head_dim,
-                                    position_ids,
-                                    self.config.rope_theta,
-                                    self.config.rope_scale,
-                                    self.config.rope_dims,
-                                    device=device)
-
     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):
         if embeds is not None:
             x = embeds
@@ -678,12 +667,17 @@ class Llama2_(nn.Module):
         seq_len = x.shape[1]
         past_len = 0
         if past_key_values is not None and len(past_key_values) > 0:
-            past_len = self.get_past_len(past_key_values)
+            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)
 
-        freqs_cis = self.compute_freqs_cis(position_ids, x.device)
+        freqs_cis = precompute_freqs_cis(self.config.head_dim,
+                                         position_ids,
+                                         self.config.rope_theta,
+                                         self.config.rope_scale,
+                                         self.config.rope_dims,
+                                         device=x.device)
 
         mask = None
         if attention_mask is not None:
@@ -818,16 +812,9 @@ class BaseGenerate:
         comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
         return x
 
-    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
-        model_config = self.model.config
-        past_key_values = []
-        for x in range(model_config.num_hidden_layers):
-            past_key_values.append((torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
-                                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
-        return past_key_values
-
-    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0):
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):
         device = embeds.device
+        model_config = self.model.config
 
         if stop_tokens is None:
             stop_tokens = self.model.config.stop_tokens
@@ -842,8 +829,11 @@ class BaseGenerate:
         if embeds.ndim == 2:
             embeds = embeds.unsqueeze(0)
 
+        past_key_values = [] #kv_cache init
         max_cache_len = embeds.shape[1] + max_length
-        past_key_values = self.init_kv_cache(embeds.shape[0], max_cache_len, device, execution_dtype)
+        for x in range(model_config.num_hidden_layers):
+            past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                                    torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
 
         generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None
 
@@ -854,7 +844,7 @@ class BaseGenerate:
         for step in tqdm(range(max_length), desc="Generating tokens"):
             x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)
             logits = self.logits(x)[:, -1]
-            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty)
+            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)
             token_id = next_token[0].item()
             generated_token_ids.append(token_id)
 
@@ -866,7 +856,7 @@ class BaseGenerate:
 
         return generated_token_ids
 
-    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True, presence_penalty=0.0):
+    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):
 
         if not do_sample or temperature == 0.0:
             return torch.argmax(logits, dim=-1, keepdim=True)
@@ -877,11 +867,6 @@ class BaseGenerate:
                 for token_id in set(token_history):
                     logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
 
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
-
         if temperature != 1.0:
             logits = logits / temperature
 
@@ -912,9 +897,6 @@ class BaseGenerate:
 class BaseQwen3:
     def logits(self, x):
         input = x[:, -1:]
-        if self.model.config.lm_head:
-            return self.model.lm_head(input)
-
         module = self.model.embed_tokens
 
         offload_stream = None

comfy/text_encoders/lt.py

@@ -91,11 +91,11 @@ class Gemma3_12BModel(sd1_clip.SDClipModel):
         self.dtypes.add(dtype)
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
         tokens_only = [[t[0] for t in b] for b in tokens]
         embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
         comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106], presence_penalty=presence_penalty)  # 106 is <end_of_turn>
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
 
 class DualLinearProjection(torch.nn.Module):
     def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):
@@ -189,8 +189,8 @@ class LTXAVTEModel(torch.nn.Module):
 
         return out.to(device=out_device, dtype=torch.float), pooled, extra
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty):
-        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty)
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+        return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
 
     def load_sd(self, sd):
         if "model.layers.47.self_attn.q_norm.weight" in sd:

comfy/text_encoders/qwen35.py

@@ -1,833 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from dataclasses import dataclass, field
-import os
-import math
-
-import comfy.model_management
-from comfy.ldm.modules.attention import optimized_attention_for_device
-from comfy import sd1_clip
-import comfy.text_encoders.qwen_vl
-
-from .llama import BaseLlama, BaseGenerate, Llama2_, MLP, RMSNorm, apply_rope
-
-
-def _qwen35_layer_types(n):
-    return [("full_attention" if (i + 1) % 4 == 0 else "linear_attention") for i in range(n)]
-
-@dataclass
-class Qwen35Config:
-    vocab_size: int = 248320
-    hidden_size: int = 2048
-    intermediate_size: int = 6144
-    num_hidden_layers: int = 24
-    # Full attention params
-    num_attention_heads: int = 8
-    num_key_value_heads: int = 2
-    head_dim: int = 256
-    partial_rotary_factor: float = 0.25
-    # Linear attention (DeltaNet) params
-    linear_num_key_heads: int = 16
-    linear_num_value_heads: int = 16
-    linear_key_head_dim: int = 128
-    linear_value_head_dim: int = 128
-    conv_kernel_size: int = 4
-    # Shared params
-    max_position_embeddings: int = 32768
-    rms_norm_eps: float = 1e-6
-    rope_theta: float = 10000000.0
-    mrope_section: list = field(default_factory=lambda: [11, 11, 10])
-    layer_types: list = field(default_factory=lambda: _qwen35_layer_types(24))
-    rms_norm_add: bool = True
-    mlp_activation: str = "silu"
-    qkv_bias: bool = False
-    final_norm: bool = True
-    lm_head: bool = False
-    stop_tokens: list = field(default_factory=lambda: [248044, 248046])
-    # These are needed for BaseLlama/BaseGenerate compatibility but unused directly
-    transformer_type: str = "qwen35_2b"
-    rope_dims: list = None
-    rope_scale: float = None
-
-QWEN35_VISION_DEFAULTS = dict(hidden_size=1024, num_heads=16, intermediate_size=4096, depth=24, patch_size=16, temporal_patch_size=2, in_channels=3, spatial_merge_size=2, num_position_embeddings=2304)
-
-QWEN35_MODELS = {
-    "qwen35_08b": dict(hidden_size=1024, intermediate_size=3584, vision=dict(hidden_size=768, num_heads=12, intermediate_size=3072, depth=12)),
-    "qwen35_2b": dict(hidden_size=2048, intermediate_size=6144, num_hidden_layers=24, num_attention_heads=8, num_key_value_heads=2, linear_num_value_heads=16),
-    "qwen35_4b": dict(hidden_size=2560, intermediate_size=9216, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32),
-    "qwen35_9b": dict(hidden_size=4096, intermediate_size=12288, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
-    "qwen35_27b": dict(hidden_size=5120, intermediate_size=17408, num_hidden_layers=64, num_attention_heads=24, num_key_value_heads=4, linear_num_value_heads=48, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
-}
-
-
-def _make_config(model_type, config_dict={}):
-    overrides = QWEN35_MODELS.get(model_type, {}).copy()
-    overrides.pop("vision", None)
-    if "num_hidden_layers" in overrides:
-        overrides["layer_types"] = _qwen35_layer_types(overrides["num_hidden_layers"])
-    overrides.update(config_dict)
-    return Qwen35Config(**overrides)
-
-
-class RMSNormGated(RMSNorm):
-    def forward(self, x, gate):
-        return super().forward(x) * F.silu(gate.to(x.dtype))
-
-def torch_chunk_gated_delta_rule(query, key, value, g, beta, chunk_size=64, initial_state=None, output_final_state=False):
-    initial_dtype = query.dtype
-    query = F.normalize(query, dim=-1)
-    key = F.normalize(key, dim=-1)
-    query, key, value, beta, g = [x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)]
-
-    batch_size, num_heads, sequence_length, k_head_dim = key.shape
-    v_head_dim = value.shape[-1]
-    pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size
-    query = F.pad(query, (0, 0, 0, pad_size))
-    key = F.pad(key, (0, 0, 0, pad_size))
-    value = F.pad(value, (0, 0, 0, pad_size))
-    beta = F.pad(beta, (0, pad_size))
-    g = F.pad(g, (0, pad_size))
-    total_sequence_length = sequence_length + pad_size
-    scale = 1 / (query.shape[-1] ** 0.5)
-    query = query * scale
-
-    v_beta = value * beta.unsqueeze(-1)
-    k_beta = key * beta.unsqueeze(-1)
-    query, key, value, k_beta, v_beta = [x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta)]
-    g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
-    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
-
-    g = g.cumsum(dim=-1)
-    decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
-    attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
-    for i in range(1, chunk_size):
-        row = attn[..., i, :i].clone()
-        sub = attn[..., :i, :i].clone()
-        attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
-    attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
-    value = attn @ v_beta
-    k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
-    last_recurrent_state = (
-        torch.zeros(batch_size, num_heads, k_head_dim, v_head_dim).to(value)
-        if initial_state is None
-        else initial_state.to(value)
-    )
-    core_attn_out = torch.zeros_like(value)
-    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
-
-    for i in range(0, total_sequence_length // chunk_size):
-        q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
-        attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-        v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-        v_new = v_i - v_prime
-        attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-        core_attn_out[:, :, i] = attn_inter + attn @ v_new
-        last_recurrent_state = (
-            last_recurrent_state * g[:, :, i, -1, None, None].exp()
-            + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
-        )
-
-    if not output_final_state:
-        last_recurrent_state = None
-    core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
-    core_attn_out = core_attn_out[:, :, :sequence_length]
-    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
-    return core_attn_out, last_recurrent_state
-
-
-def torch_causal_conv1d_update(x, conv_state, weight, bias=None):
-    # conv_state: [B, channels, kernel_size-1], x: [B, channels, 1]
-    # weight: [channels, kernel_size]
-    state_len = conv_state.shape[-1]
-    combined = torch.cat([conv_state, x], dim=-1).to(weight.dtype)  # [B, channels, kernel_size]
-    conv_state.copy_(combined[:, :, -state_len:])
-    out = (combined * weight).sum(dim=-1, keepdim=True)  # [B, channels, 1]
-    if bias is not None:
-        out = out + bias.unsqueeze(0).unsqueeze(-1)
-    return F.silu(out).to(x.dtype)
-
-
-# GatedDeltaNet - Linear Attention Layer
-
-class GatedDeltaNet(nn.Module):
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        hidden = config.hidden_size
-        self.num_key_heads = config.linear_num_key_heads
-        self.num_value_heads = config.linear_num_value_heads
-        self.key_head_dim = config.linear_key_head_dim
-        self.value_head_dim = config.linear_value_head_dim
-        self.conv_kernel_size = config.conv_kernel_size
-
-        key_dim = self.num_key_heads * self.key_head_dim
-        value_dim = self.num_value_heads * self.value_head_dim
-        self.key_dim = key_dim
-        self.value_dim = value_dim
-        conv_dim = key_dim * 2 + value_dim
-
-        self.in_proj_qkv = ops.Linear(hidden, conv_dim, bias=False, device=device, dtype=dtype)
-        self.in_proj_z = ops.Linear(hidden, value_dim, bias=False, device=device, dtype=dtype)
-        self.in_proj_b = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
-        self.in_proj_a = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
-        self.out_proj = ops.Linear(value_dim, hidden, bias=False, device=device, dtype=dtype)
-
-        self.dt_bias = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
-        self.A_log = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
-
-        self.conv1d = ops.Conv1d(in_channels=conv_dim, out_channels=conv_dim, bias=False, kernel_size=self.conv_kernel_size,
-            groups=conv_dim, padding=self.conv_kernel_size - 1, device=device, dtype=dtype)
-
-        self.norm = RMSNormGated(self.value_head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype)
-
-    def forward(self, x, past_key_value=None, **kwargs):
-        batch_size, seq_len, _ = x.shape
-
-        use_recurrent = (
-            past_key_value is not None
-            and past_key_value[2] > 0
-            and seq_len == 1
-        )
-
-        # Projections (shared)
-        mixed_qkv = self.in_proj_qkv(x).transpose(1, 2)  # [B, conv_dim, seq_len]
-        z = self.in_proj_z(x)
-        b = self.in_proj_b(x)
-        a = self.in_proj_a(x)
-
-        # Conv1d
-        if use_recurrent:
-            recurrent_state, conv_state, step_index = past_key_value
-            conv_weight = comfy.model_management.cast_to_device(self.conv1d.weight, mixed_qkv.device, mixed_qkv.dtype).squeeze(1)
-            conv_bias = comfy.model_management.cast_to_device(self.conv1d.bias, mixed_qkv.device, mixed_qkv.dtype) if self.conv1d.bias is not None else None
-            mixed_qkv = torch_causal_conv1d_update(mixed_qkv, conv_state, conv_weight, conv_bias)
-        else:
-            if past_key_value is not None:
-                recurrent_state, conv_state, step_index = past_key_value
-                conv_state_init = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                conv_state.copy_(conv_state_init[:, :, -conv_state.shape[-1]:])
-            mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
-
-        # Split QKV and compute beta/g
-        mixed_qkv = mixed_qkv.transpose(1, 2)  # [B, seq_len, conv_dim]
-        query, key, value = mixed_qkv.split([self.key_dim, self.key_dim, self.value_dim], dim=-1)
-        beta = b.sigmoid()
-        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias.float())
-
-        # Delta rule
-        if use_recurrent:
-            # single-token path: work in [B, heads, dim] without seq dim
-            query = query.reshape(batch_size, self.num_key_heads, self.key_head_dim)
-            key = key.reshape(batch_size, self.num_key_heads, self.key_head_dim)
-            value = value.reshape(batch_size, self.num_value_heads, self.value_head_dim)
-
-            if self.num_value_heads != self.num_key_heads:
-                rep = self.num_value_heads // self.num_key_heads
-                query = query.repeat_interleave(rep, dim=1)
-                key = key.repeat_interleave(rep, dim=1)
-
-            scale = self.key_head_dim ** -0.5
-            q = F.normalize(query.float(), dim=-1) * scale
-            k = F.normalize(key.float(), dim=-1)
-            v = value.float()
-            beta_t = beta.reshape(batch_size, -1)
-            g_t = g.reshape(batch_size, -1).exp()
-
-            # In-place state update: [B, heads, k_dim, v_dim]
-            recurrent_state.mul_(g_t[:, :, None, None])
-            kv_mem = torch.einsum('bhk,bhkv->bhv', k, recurrent_state)
-            delta = (v - kv_mem) * beta_t[:, :, None]
-            recurrent_state.add_(k.unsqueeze(-1) * delta.unsqueeze(-2))
-            core_attn_out = torch.einsum('bhk,bhkv->bhv', q, recurrent_state)
-
-            core_attn_out = core_attn_out.to(x.dtype).unsqueeze(1)
-            present_key_value = (recurrent_state, conv_state, step_index + 1)
-        else:
-            query = query.reshape(batch_size, seq_len, -1, self.key_head_dim)
-            key = key.reshape(batch_size, seq_len, -1, self.key_head_dim)
-            value = value.reshape(batch_size, seq_len, -1, self.value_head_dim)
-
-            if self.num_value_heads != self.num_key_heads:
-                rep = self.num_value_heads // self.num_key_heads
-                query = query.repeat_interleave(rep, dim=2)
-                key = key.repeat_interleave(rep, dim=2)
-
-            core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule(
-                query, key, value, g=g, beta=beta,
-                initial_state=None,
-                output_final_state=past_key_value is not None,
-            )
-
-            present_key_value = None
-            if past_key_value is not None:
-                if last_recurrent_state is not None:
-                    recurrent_state.copy_(last_recurrent_state.to(recurrent_state.dtype))
-                present_key_value = (recurrent_state, conv_state, step_index + seq_len)
-
-        # Gated norm + output projection (shared)
-        core_attn_out = self.norm(core_attn_out.reshape(-1, self.value_head_dim), z.reshape(-1, self.value_head_dim))
-        output = self.out_proj(core_attn_out.reshape(batch_size, seq_len, -1))
-        return output, present_key_value
-
-
-# GatedAttention - Full Attention with output gating
-def precompute_partial_rope(head_dim, rotary_dim, position_ids, theta, device=None, mrope_section=None):
-    """Compute RoPE frequencies for partial rotary embeddings."""
-    theta_numerator = torch.arange(0, rotary_dim, 2, device=device).float()
-    inv_freq = 1.0 / (theta ** (theta_numerator / rotary_dim))
-
-    inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-    position_ids_expanded = position_ids[:, None, :].float()
-    freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-    emb = torch.cat((freqs, freqs), dim=-1)
-    cos = emb.cos()
-    sin = emb.sin()
-
-    if mrope_section is not None and position_ids.shape[0] == 3:
-        mrope_section_2 = [s * 2 for s in mrope_section]
-        cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
-        sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
-
-    cos = cos.unsqueeze(1)
-    sin = sin.unsqueeze(1)
-    sin_split = sin.shape[-1] // 2
-    return (cos, sin[..., :sin_split], -sin[..., sin_split:])
-
-
-def apply_partial_rope(xq, xk, freqs_cis, rotary_dim):
-    """Apply RoPE to only the first rotary_dim dimensions."""
-    xq_rot = xq[..., :rotary_dim]
-    xq_pass = xq[..., rotary_dim:]
-    xk_rot = xk[..., :rotary_dim]
-    xk_pass = xk[..., rotary_dim:]
-
-    xq_rot, xk_rot = apply_rope(xq_rot, xk_rot, freqs_cis)
-
-    xq = torch.cat([xq_rot, xq_pass], dim=-1)
-    xk = torch.cat([xk_rot, xk_pass], dim=-1)
-    return xq, xk
-
-
-class GatedAttention(nn.Module):
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        self.num_heads = config.num_attention_heads
-        self.num_kv_heads = config.num_key_value_heads
-        self.head_dim = config.head_dim
-        self.hidden_size = config.hidden_size
-        self.inner_size = self.num_heads * self.head_dim
-        self.rotary_dim = int(self.head_dim * config.partial_rotary_factor)
-
-        # q_proj outputs 2x: query + gate
-        self.q_proj = ops.Linear(config.hidden_size, self.inner_size * 2, bias=config.qkv_bias, device=device, dtype=dtype)
-        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
-        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
-        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
-
-        # QK norms with (1+weight) scaling
-        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-
-    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
-        batch_size, seq_length, _ = x.shape
-
-        # Project Q (with gate), K, V
-        qg = self.q_proj(x)
-        # Split into query and gate: each is [B, seq, inner_size]
-        qg = qg.view(batch_size, seq_length, self.num_heads, self.head_dim * 2)
-        xq, gate = qg[..., :self.head_dim], qg[..., self.head_dim:]
-        gate = gate.reshape(batch_size, seq_length, -1)  # [B, seq, inner_size]
-
-        xk = self.k_proj(x)
-        xv = self.v_proj(x)
-
-        xq = self.q_norm(xq).transpose(1, 2)  # [B, heads, seq, head_dim]
-        xk = self.k_norm(xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim)).transpose(1, 2)
-        xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
-
-        # Apply partial RoPE
-        xq, xk = apply_partial_rope(xq, xk, freqs_cis, self.rotary_dim)
-
-        # KV cache
-        present_key_value = None
-        if past_key_value is not None:
-            past_key, past_value, index = past_key_value
-            num_tokens = xk.shape[2]
-            if past_key.shape[2] >= (index + num_tokens):
-                past_key[:, :, index:index + num_tokens] = xk
-                past_value[:, :, index:index + num_tokens] = xv
-                xk = past_key[:, :, :index + num_tokens]
-                xv = past_value[:, :, :index + num_tokens]
-                present_key_value = (past_key, past_value, index + num_tokens)
-            else:
-                if index > 0:
-                    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)
-
-        # Expand KV heads for GQA
-        if self.num_heads != self.num_kv_heads:
-            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)
-        output = output * gate.sigmoid()
-
-        return self.o_proj(output), present_key_value
-
-
-# Hybrid Transformer Block
-class Qwen35TransformerBlock(nn.Module):
-    def __init__(self, config, index, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.layer_type = config.layer_types[index]
-        if self.layer_type == "linear_attention":
-            self.linear_attn = GatedDeltaNet(config, device=device, dtype=dtype, ops=ops)
-        else:
-            self.self_attn = GatedAttention(config, device=device, dtype=dtype, ops=ops)
-        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
-        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-
-    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
-        if self.layer_type == "linear_attention":
-            h, present_key_value = self.linear_attn(self.input_layernorm(x), attention_mask=attention_mask, past_key_value=past_key_value)
-        else:
-            h, present_key_value = self.self_attn(self.input_layernorm(x), attention_mask=attention_mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_key_value)
-
-        x = x + h
-        x = x + self.mlp(self.post_attention_layernorm(x))
-        return x, present_key_value
-
-
-# Qwen35 Transformer Backbone
-class Qwen35Transformer(Llama2_):
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        nn.Module.__init__(self)
-        self.config = config
-        self.vocab_size = config.vocab_size
-        self.normalize_in = False
-
-        self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype)
-        self.layers = nn.ModuleList([
-            Qwen35TransformerBlock(config, index=i, device=device, dtype=dtype, ops=ops)
-            for i in range(config.num_hidden_layers)
-        ])
-
-        if config.final_norm:
-            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        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)
-
-    def get_past_len(self, past_key_values):
-        for i, layer in enumerate(self.layers):
-            if layer.layer_type == "full_attention":
-                if len(past_key_values) > i:
-                    return past_key_values[i][2]
-                break
-        return 0
-
-    def compute_freqs_cis(self, position_ids, device):
-        rotary_dim = int(self.config.head_dim * self.config.partial_rotary_factor)
-        return precompute_partial_rope(
-            self.config.head_dim, rotary_dim, position_ids,
-            self.config.rope_theta, device=device,
-            mrope_section=self.config.mrope_section,
-        )
-
-
-# Vision Encoder
-class Qwen35VisionPatchEmbed(nn.Module):
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.patch_size = config["patch_size"]
-        self.temporal_patch_size = config["temporal_patch_size"]
-        self.in_channels = config["in_channels"]
-        self.embed_dim = config["hidden_size"]
-        kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
-        self.proj = ops.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        target_dtype = self.proj.weight.dtype
-        x = x.view(-1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size)
-        return self.proj(x.to(target_dtype)).view(-1, self.embed_dim)
-
-
-class Qwen35VisionMLP(nn.Module):
-    def __init__(self, hidden_size, intermediate_size, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        self.linear_fc1 = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype)
-        self.linear_fc2 = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype)
-
-    def forward(self, hidden_state):
-        return self.linear_fc2(F.gelu(self.linear_fc1(hidden_state), approximate="tanh"))
-
-
-class Qwen35VisionRotaryEmbedding(nn.Module):
-    def __init__(self, dim, theta=10000.0):
-        super().__init__()
-        self.dim = dim
-        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-    def forward(self, seqlen):
-        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
-        freqs = torch.outer(seq, self.inv_freq)
-        return freqs
-
-
-class Qwen35VisionAttention(nn.Module):
-    def __init__(self, hidden_size, num_heads, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        self.dim = hidden_size
-        self.num_heads = num_heads
-        self.head_dim = self.dim // self.num_heads
-        self.qkv = ops.Linear(self.dim, self.dim * 3, bias=True, device=device, dtype=dtype)
-        self.proj = ops.Linear(self.dim, self.dim, device=device, dtype=dtype)
-
-    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
-        seq_length = x.shape[0]
-        query_states, key_states, value_states = (
-            self.qkv(x).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
-        )
-        query_states, key_states = apply_rope(query_states, key_states, position_embeddings)
-
-        # Process per-sequence attention
-        lengths = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
-        q_splits = torch.split(query_states, lengths, dim=0)
-        k_splits = torch.split(key_states, lengths, dim=0)
-        v_splits = torch.split(value_states, lengths, dim=0)
-
-        attn_outputs = []
-        for q, k, v in zip(q_splits, k_splits, v_splits):
-            q = q.transpose(0, 1).unsqueeze(0)
-            k = k.transpose(0, 1).unsqueeze(0)
-            v = v.transpose(0, 1).unsqueeze(0)
-            attn_outputs.append(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
-
-        attn_output = torch.cat(attn_outputs, dim=1)
-        attn_output = attn_output.reshape(seq_length, -1)
-        return self.proj(attn_output)
-
-
-class Qwen35VisionBlock(nn.Module):
-    def __init__(self, hidden_size, num_heads, intermediate_size, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        self.norm1 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
-        self.norm2 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
-        self.attn = Qwen35VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops)
-        self.mlp = Qwen35VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops)
-
-    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
-        x = x + self.attn(self.norm1(x), cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
-        return x + self.mlp(self.norm2(x))
-
-
-class Qwen35VisionPatchMerger(nn.Module):
-    def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
-        super().__init__()
-
-        merge_dim = hidden_size * (spatial_merge_size ** 2)
-        self.norm = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
-        self.linear_fc1 = ops.Linear(merge_dim, merge_dim, device=device, dtype=dtype)
-        self.linear_fc2 = ops.Linear(merge_dim, out_hidden_size, device=device, dtype=dtype)
-        self.merge_dim = merge_dim
-
-    def forward(self, x):
-        x = self.norm(x).view(-1, self.merge_dim)
-        return self.linear_fc2(F.gelu(self.linear_fc1(x)))
-
-
-class Qwen35VisionModel(nn.Module):
-    def __init__(self, config, device=None, dtype=None, ops=None):
-        super().__init__()
-        self.spatial_merge_size = config["spatial_merge_size"]
-        self.patch_size = config["patch_size"]
-        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
-
-        self.hidden_size = config["hidden_size"]
-        self.num_heads = config["num_heads"]
-        self.num_position_embeddings = config["num_position_embeddings"]
-
-        self.patch_embed = Qwen35VisionPatchEmbed(config, device=device, dtype=dtype, ops=ops)
-        self.pos_embed = ops.Embedding(self.num_position_embeddings, self.hidden_size, device=device, dtype=dtype)
-        self.num_grid_per_side = int(self.num_position_embeddings ** 0.5)
-        self.rotary_pos_emb = Qwen35VisionRotaryEmbedding(self.hidden_size // self.num_heads // 2)
-        self.blocks = nn.ModuleList([
-            Qwen35VisionBlock(self.hidden_size, self.num_heads, config["intermediate_size"], device=device, dtype=dtype, ops=ops)
-            for _ in range(config["depth"])
-        ])
-        self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
-
-    def rot_pos_emb(self, grid_thw):
-        merge_size = self.spatial_merge_size
-        grid_thw_list = grid_thw.tolist()
-        max_hw = max(max(h, w) for _, h, w in grid_thw_list)
-        freq_table = self.rotary_pos_emb(max_hw)
-        device = freq_table.device
-        total_tokens = sum(int(t * h * w) for t, h, w in grid_thw_list)
-        pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
-        offset = 0
-        for num_frames, height, width in grid_thw_list:
-            num_frames, height, width = int(num_frames), int(height), int(width)
-            merged_h, merged_w = height // merge_size, width // merge_size
-            block_rows = torch.arange(merged_h, device=device)
-            block_cols = torch.arange(merged_w, device=device)
-            intra_row = torch.arange(merge_size, device=device)
-            intra_col = torch.arange(merge_size, device=device)
-            row_idx = block_rows[:, None, None, None] * merge_size + intra_row[None, None, :, None]
-            col_idx = block_cols[None, :, None, None] * merge_size + intra_col[None, None, None, :]
-            row_idx = row_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
-            col_idx = col_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
-            coords = torch.stack((row_idx, col_idx), dim=-1)
-            if num_frames > 1:
-                coords = coords.repeat(num_frames, 1)
-            num_tokens = coords.shape[0]
-            pos_ids[offset:offset + num_tokens] = coords
-            offset += num_tokens
-        embeddings = freq_table[pos_ids]
-        embeddings = embeddings.flatten(1)
-        return embeddings
-
-    def fast_pos_embed_interpolate(self, grid_thw):
-        grid_thw_list = grid_thw.tolist()
-        grid_ts = [int(row[0]) for row in grid_thw_list]
-        grid_hs = [int(row[1]) for row in grid_thw_list]
-        grid_ws = [int(row[2]) for row in grid_thw_list]
-        device = self.pos_embed.weight.device
-        idx_list = [[] for _ in range(4)]
-        weight_list = [[] for _ in range(4)]
-        for t, h, w in grid_thw_list:
-            h, w = int(h), int(w)
-            h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h, device=device)
-            w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w, device=device)
-            h_idxs_floor = h_idxs.int()
-            w_idxs_floor = w_idxs.int()
-            h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
-            w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
-            dh = h_idxs - h_idxs_floor
-            dw = w_idxs - w_idxs_floor
-            base_h = h_idxs_floor * self.num_grid_per_side
-            base_h_ceil = h_idxs_ceil * self.num_grid_per_side
-            indices = [
-                (base_h[None].T + w_idxs_floor[None]).flatten(),
-                (base_h[None].T + w_idxs_ceil[None]).flatten(),
-                (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
-                (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
-            ]
-            weights = [
-                ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
-                ((1 - dh)[None].T * dw[None]).flatten(),
-                (dh[None].T * (1 - dw)[None]).flatten(),
-                (dh[None].T * dw[None]).flatten(),
-            ]
-            for j in range(4):
-                idx_list[j].extend(indices[j].tolist())
-                weight_list[j].extend(weights[j].tolist())
-        idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=device)
-        weight_tensor = torch.tensor(weight_list, dtype=self.pos_embed.weight.dtype, device=device)
-        pos_embeds = self.pos_embed(idx_tensor).to(device) * weight_tensor[:, :, None]
-        patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
-        patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
-        patch_pos_embeds_permute = []
-        merge_size = self.spatial_merge_size
-        for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
-            pos_embed = pos_embed.repeat(t, 1)
-            pos_embed = (
-                pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
-                .permute(0, 1, 3, 2, 4, 5)
-                .flatten(0, 4)
-            )
-            patch_pos_embeds_permute.append(pos_embed)
-        return torch.cat(patch_pos_embeds_permute)
-
-    def forward(self, x, grid_thw):
-        x = self.patch_embed(x)
-        pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device)
-        x = x + pos_embeds
-        rotary_pos_emb = self.rot_pos_emb(grid_thw)
-        seq_len = x.shape[0]
-        x = x.reshape(seq_len, -1)
-        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
-        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
-        cos = emb.cos().unsqueeze(-2)
-        sin = emb.sin().unsqueeze(-2)
-        sin_half = sin.shape[-1] // 2
-        position_embeddings = (cos, sin[..., :sin_half], -sin[..., sin_half:])
-        cu_seqlens = torch.repeat_interleave(
-            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
-        ).cumsum(dim=0, dtype=torch.int32)
-        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
-        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
-        for blk in self.blocks:
-            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
-        merged = self.merger(x)
-        return merged
-
-# Model Wrapper
-class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
-    model_type = "qwen35_2b"
-
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = _make_config(self.model_type, config_dict)
-        self.num_layers = config.num_hidden_layers
-        self.model = Qwen35Transformer(config, device=device, dtype=dtype, ops=operations)
-        vision_overrides = QWEN35_MODELS.get(self.model_type, {}).get("vision", {})
-        vision_config = {**QWEN35_VISION_DEFAULTS, **vision_overrides, "out_hidden_size": config.hidden_size}
-        self.visual = Qwen35VisionModel(vision_config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
-
-    def preprocess_embed(self, embed, device):
-        if embed["type"] == "image":
-            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16)
-            return self.visual(image.to(device, dtype=torch.float32), grid), grid
-        return None, None
-
-    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
-        grid = None
-        position_ids = None
-        offset = 0
-        for e in embeds_info:
-            if e.get("type") == "image":
-                grid = e.get("extra", None)
-                start = e.get("index")
-                if position_ids is None:
-                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
-                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
-                end = e.get("size") + start
-                len_max = int(grid.max()) // 2
-                start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
-                position_ids[0, start:end] = start + offset
-                max_d = int(grid[0][1]) // 2
-                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
-                max_d = int(grid[0][2]) // 2
-                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
-                offset += len_max - (end - start)
-
-        if grid is None:
-            position_ids = None
-
-        return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values)
-
-    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
-        model_config = self.model.config
-        past_key_values = []
-        for i in range(model_config.num_hidden_layers):
-            if model_config.layer_types[i] == "linear_attention":
-                recurrent_state = torch.zeros(
-                    [batch, model_config.linear_num_value_heads, model_config.linear_key_head_dim, model_config.linear_value_head_dim],
-                    device=device, dtype=torch.float32
-                )
-                conv_dim = model_config.linear_num_key_heads * model_config.linear_key_head_dim * 2 + model_config.linear_num_value_heads * model_config.linear_value_head_dim
-                conv_state = torch.zeros(
-                    [batch, conv_dim, model_config.conv_kernel_size - 1],
-                    device=device, dtype=execution_dtype
-                )
-                past_key_values.append((recurrent_state, conv_state, 0))
-            else:
-                past_key_values.append((
-                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
-                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
-                    0
-                ))
-        return past_key_values
-
-# Tokenizer and Text Encoder Wrappers
-
-class Qwen35Tokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=2048, embedding_key="qwen35_2b"):
-        from transformers import Qwen2Tokenizer
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen35_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer,
-            has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=248044, tokenizer_data=tokenizer_data)
-
-
-class Qwen35ImageTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen35_2b"):
-        embedding_size = QWEN35_MODELS.get(model_type, {}).get("hidden_size", 2048)
-        tokenizer = lambda *a, **kw: Qwen35Tokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
-        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
-        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
-        self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
-
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
-        image = kwargs.get("image", None)
-        if image is not None and len(images) == 0:
-            images = [image]
-
-        skip_template = False
-        if text.startswith('<|im_start|>'):
-            skip_template = True
-        if prevent_empty_text and text == '':
-            text = ' '
-
-        if skip_template:
-            llama_text = text
-        else:
-            if llama_template is None:
-                if len(images) > 0:
-                    llama_text = self.llama_template_images.format(text)
-                else:
-                    llama_text = self.llama_template.format(text)
-            else:
-                llama_text = llama_template.format(text)
-            if not thinking:
-                llama_text += "<think>\n</think>\n"
-
-        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
-        key_name = next(iter(tokens))
-        embed_count = 0
-        qwen_tokens = tokens[key_name]
-        for r in qwen_tokens:
-            for i in range(len(r)):
-                if r[i][0] == 248056:  # <|image_pad|>
-                    if len(images) > embed_count:
-                        r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
-                        embed_count += 1
-        return tokens
-
-
-class Qwen35ClipModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}, model_type="qwen35_2b"):
-        class Qwen35_(Qwen35):
-            pass
-        Qwen35_.model_type = model_type
-
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
-            dtype=dtype, special_tokens={"pad": 248044}, layer_norm_hidden_state=False,
-            model_class=Qwen35_, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
-
-
-class Qwen35TEModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen35_2b"):
-        clip_model = lambda **kw: Qwen35ClipModel(**kw, model_type=model_type)
-        super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
-
-
-def tokenizer(model_type="qwen35_2b"):
-    class Qwen35ImageTokenizer_(Qwen35ImageTokenizer):
-        def __init__(self, embedding_directory=None, tokenizer_data={}):
-            super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
-    return Qwen35ImageTokenizer_
-
-
-def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen35_2b"):
-    class Qwen35TEModel_(Qwen35TEModel):
-        def __init__(self, device="cpu", dtype=None, model_options={}):
-            if dtype_llama is not None:
-                dtype = dtype_llama
-            if llama_quantization_metadata is not None:
-                model_options = model_options.copy()
-                model_options["quantization_metadata"] = llama_quantization_metadata
-            super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
-    return Qwen35TEModel_

comfy_api/latest/_io.py

@@ -43,55 +43,9 @@ class UploadType(str, Enum):
     model = "file_upload"
 
 
-class RemoteItemSchema:
-    """Describes how to map API response objects to rich dropdown items.
-
-    All *_field parameters use dot-path notation (e.g. ``"labels.gender"``).
-    ``label_field`` additionally supports template strings with ``{field}``
-    placeholders (e.g. ``"{name} ({labels.accent})"``).
-    """
-    def __init__(
-        self,
-        value_field: str,
-        label_field: str,
-        preview_url_field: str | None = None,
-        preview_type: Literal["image", "video", "audio"] = "image",
-        description_field: str | None = None,
-        search_fields: list[str] | None = None,
-        filter_field: str | None = None,
-    ):
-        self.value_field = value_field
-        """Dot-path to the unique identifier within each item. This value is stored in the widget and passed to execute()."""
-        self.label_field = label_field
-        """Dot-path to the display name, or a template string with {field} placeholders."""
-        self.preview_url_field = preview_url_field
-        """Dot-path to a preview media URL. If None, no preview is shown."""
-        self.preview_type = preview_type
-        """How to render the preview: "image", "video", or "audio"."""
-        self.description_field = description_field
-        """Optional dot-path or template for a subtitle line shown below the label."""
-        self.search_fields = search_fields
-        """Dot-paths to fields included in the search index. Defaults to [label_field]."""
-        self.filter_field = filter_field
-        """Optional dot-path to a categorical field for filter tabs."""
-
-    def as_dict(self):
-        return prune_dict({
-            "value_field": self.value_field,
-            "label_field": self.label_field,
-            "preview_url_field": self.preview_url_field,
-            "preview_type": self.preview_type,
-            "description_field": self.description_field,
-            "search_fields": self.search_fields,
-            "filter_field": self.filter_field,
-        })
-
-
 class RemoteOptions:
     def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first",
-                 timeout: int=None, max_retries: int=None, refresh: int=None,
-                 response_key: str=None, query_params: dict[str, str]=None,
-                 item_schema: RemoteItemSchema=None):
+                 timeout: int=None, max_retries: int=None, refresh: int=None):
         self.route = route
         """The route to the remote source."""
         self.refresh_button = refresh_button
@@ -104,12 +58,6 @@ class RemoteOptions:
         """The maximum number of retries before aborting the request."""
         self.refresh = refresh
         """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
-        self.response_key = response_key
-        """Dot-path to the items array in the response. If None, the entire response is used."""
-        self.query_params = query_params
-        """Static query parameters appended to the request URL."""
-        self.item_schema = item_schema
-        """When present, the frontend renders a rich dropdown with previews instead of a plain combo widget."""
 
     def as_dict(self):
         return prune_dict({
@@ -119,9 +67,6 @@ class RemoteOptions:
             "timeout": self.timeout,
             "max_retries": self.max_retries,
             "refresh": self.refresh,
-            "response_key": self.response_key,
-            "query_params": self.query_params,
-            "item_schema": self.item_schema.as_dict() if self.item_schema else None,
         })
 
 
@@ -1428,7 +1373,6 @@ class NodeInfoV1:
     price_badge: dict | None = None
     search_aliases: list[str]=None
     essentials_category: str=None
-    has_intermediate_output: bool=None
 
 
 @dataclass
@@ -1552,16 +1496,6 @@ class Schema:
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
     essentials_category: str | None = None
     """Optional category for the Essentials tab. Path-based like category field (e.g., 'Basic', 'Image Tools/Editing')."""
-    has_intermediate_output: bool=False
-    """Flags this node as having intermediate output that should persist across page refreshes.
-
-    Nodes with this flag behave like output nodes (their UI results are cached and resent
-    to the frontend) but do NOT automatically get added to the execution list. This means
-    they will only execute if they are on the dependency path of a real output node.
-
-    Use this for nodes with interactive/operable UI regions that produce intermediate outputs
-    (e.g., Image Crop, Painter) rather than final outputs (e.g., Save Image).
-    """
 
     def validate(self):
         '''Validate the schema:
@@ -1661,7 +1595,6 @@ class Schema:
             category=self.category,
             description=self.description,
             output_node=self.is_output_node,
-            has_intermediate_output=self.has_intermediate_output,
             deprecated=self.is_deprecated,
             experimental=self.is_experimental,
             dev_only=self.is_dev_only,
@@ -1953,14 +1886,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._OUTPUT_NODE
 
-    _HAS_INTERMEDIATE_OUTPUT = None
-    @final
-    @classproperty
-    def HAS_INTERMEDIATE_OUTPUT(cls):  # noqa
-        if cls._HAS_INTERMEDIATE_OUTPUT is None:
-            cls.GET_SCHEMA()
-        return cls._HAS_INTERMEDIATE_OUTPUT
-
     _INPUT_IS_LIST = None
     @final
     @classproperty
@@ -2053,8 +1978,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls._API_NODE = schema.is_api_node
         if cls._OUTPUT_NODE is None:
             cls._OUTPUT_NODE = schema.is_output_node
-        if cls._HAS_INTERMEDIATE_OUTPUT is None:
-            cls._HAS_INTERMEDIATE_OUTPUT = schema.has_intermediate_output
         if cls._INPUT_IS_LIST is None:
             cls._INPUT_IS_LIST = schema.is_input_list
         if cls._NOT_IDEMPOTENT is None:
@@ -2239,7 +2162,6 @@ class NodeReplace:
 __all__ = [
     "FolderType",
     "UploadType",
-    "RemoteItemSchema",
     "RemoteOptions",
     "NumberDisplay",
     "ControlAfterGenerate",

comfy_api_nodes/apis/phota_labs.py

@@ -1,49 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class PhotaGenerateRequest(BaseModel):
-    prompt: str = Field(...)
-    num_output_images: int = Field(1)
-    aspect_ratio: str = Field(...)
-    resolution: str = Field(...)
-    profile_ids: list[str] | None = Field(None)
-
-
-class PhotaEditRequest(BaseModel):
-    prompt: str = Field(...)
-    images: list[str] = Field(...)
-    num_output_images: int = Field(1)
-    aspect_ratio: str = Field(...)
-    resolution: str = Field(...)
-    profile_ids: list[str] | None = Field(None)
-
-
-class PhotaEnhanceRequest(BaseModel):
-    image: str = Field(...)
-    num_output_images: int = Field(1)
-
-
-class PhotaKnownGeneratedSubjectCounts(BaseModel):
-    counts: dict[str, int] = Field(default_factory=dict)
-
-
-class PhotoStudioResponse(BaseModel):
-    images: list[str] = Field(..., description="Base64-encoded PNG output images.")
-    known_subjects: PhotaKnownGeneratedSubjectCounts = Field(default_factory=PhotaKnownGeneratedSubjectCounts)
-
-
-class PhotaAddProfileRequest(BaseModel):
-    image_urls: list[str] = Field(...)
-
-
-class PhotaAddProfileResponse(BaseModel):
-    profile_id: str = Field(...)
-
-
-class PhotaProfileStatusResponse(BaseModel):
-    profile_id: str = Field(...)
-    status: str = Field(
-        ...,
-        description="Current profile status: VALIDATING, QUEUING, IN_PROGRESS, READY, ERROR, or INACTIVE.",
-    )
-    message: str | None = Field(default=None, description="Optional error or status message.")

comfy_api_nodes/nodes_elevenlabs.py

@@ -233,45 +233,6 @@ class ElevenLabsVoiceSelector(IO.ComfyNode):
         return IO.NodeOutput(voice_id)
 
 
-class ElevenLabsRichVoiceSelector(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="ElevenLabsRichVoiceSelector",
-            display_name="ElevenLabs Voice Selector (Rich)",
-            category="api node/audio/ElevenLabs",
-            description="Select an ElevenLabs voice with audio preview and rich metadata.",
-            inputs=[
-                IO.Combo.Input(
-                    "voice",
-                    options=ELEVENLABS_VOICE_OPTIONS,
-                    remote=IO.RemoteOptions(
-                        route="http://localhost:9000/elevenlabs/voices",
-                        refresh_button=True,
-                        item_schema=IO.RemoteItemSchema(
-                            value_field="voice_id",
-                            label_field="name",
-                            preview_url_field="preview_url",
-                            preview_type="audio",
-                            search_fields=["name", "labels.gender", "labels.accent"],
-                            filter_field="labels.use_case",
-                        ),
-                    ),
-                    tooltip="Choose a voice with audio preview.",
-                ),
-            ],
-            outputs=[
-                IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
-            ],
-            is_api_node=False,
-        )
-
-    @classmethod
-    def execute(cls, voice: str) -> IO.NodeOutput:
-        # voice is already the voice_id from item_schema.value_field
-        return IO.NodeOutput(voice)
-
-
 class ElevenLabsTextToSpeech(IO.ComfyNode):
     @classmethod
     def define_schema(cls) -> IO.Schema:
@@ -950,7 +911,6 @@ class ElevenLabsExtension(ComfyExtension):
         return [
             ElevenLabsSpeechToText,
             ElevenLabsVoiceSelector,
-            ElevenLabsRichVoiceSelector,
             ElevenLabsTextToSpeech,
             ElevenLabsAudioIsolation,
             ElevenLabsTextToSoundEffects,

comfy_api_nodes/nodes_gemini.py

@@ -201,16 +201,6 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
             returned_image = await download_url_to_image_tensor(part.fileData.fileUri)
         image_tensors.append(returned_image)
     if len(image_tensors) == 0:
-        if not thought:
-            # No images generated --> extract text response for a meaningful error
-            model_message = get_text_from_response(response).strip()
-            if model_message:
-                raise ValueError(f"Gemini did not generate an image. Model response: {model_message}")
-            raise ValueError(
-                "Gemini did not generate an image. "
-                "Try rephrasing your prompt or changing the response modality to 'IMAGE+TEXT' "
-                "to see the model's reasoning."
-            )
         return torch.zeros((1, 1024, 1024, 4))
     return torch.cat(image_tensors, dim=0)
 

comfy_api_nodes/nodes_hunyuan3d.py

@@ -132,7 +132,7 @@ class TencentTextToModelNode(IO.ComfyNode):
                     tooltip="The LowPoly option is unavailable for the `3.1` model.",
                 ),
                 IO.String.Input("prompt", multiline=True, default="", tooltip="Supports up to 1024 characters."),
-                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
                 IO.DynamicCombo.Input(
                     "generate_type",
                     options=[
@@ -251,7 +251,7 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.Image.Input("image_left", optional=True),
                 IO.Image.Input("image_right", optional=True),
                 IO.Image.Input("image_back", optional=True),
-                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.Int.Input("face_count", default=500000, min=40000, max=1500000),
                 IO.DynamicCombo.Input(
                     "generate_type",
                     options=[
@@ -422,7 +422,6 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             outputs=[
                 IO.File3DOBJ.Output(display_name="OBJ"),
                 IO.File3DFBX.Output(display_name="FBX"),
-                IO.Image.Output(display_name="uv_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -469,16 +468,9 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        uv_image_file = get_file_from_response(result.ResultFile3Ds, "uv_image", raise_if_not_found=False)
-        uv_image = (
-            await download_url_to_image_tensor(uv_image_file.Url)
-            if uv_image_file is not None
-            else torch.zeros(1, 1, 1, 3)
-        )
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
-            uv_image,
         )
 
 

comfy_api_nodes/nodes_phota_labs.py

@@ -1,350 +0,0 @@
-import base64
-from io import BytesIO
-
-from typing_extensions import override
-
-from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.phota_labs import (
-    PhotaAddProfileRequest,
-    PhotaAddProfileResponse,
-    PhotaEditRequest,
-    PhotaEnhanceRequest,
-    PhotaGenerateRequest,
-    PhotaProfileStatusResponse,
-    PhotoStudioResponse,
-)
-from comfy_api_nodes.util import (
-    ApiEndpoint,
-    bytesio_to_image_tensor,
-    poll_op,
-    sync_op,
-    upload_images_to_comfyapi,
-    upload_image_to_comfyapi,
-    validate_string,
-)
-
-# Direct API endpoint (comment out this class to use proxy)
-class ApiEndpoint(ApiEndpoint):
-    """Temporary override to use direct API instead of proxy."""
-
-    def __init__(
-        self,
-        path: str,
-        method: str = "GET",
-        *,
-        query_params: dict | None = None,
-        headers: dict | None = None,
-    ):
-        self.path = path.replace("/proxy/phota/", "https://api.photalabs.com/")
-        self.method = method
-        self.query_params = query_params or {}
-        self.headers = headers or {}
-        if "api.photalabs.com" in self.path:
-            self.headers["X-API-Key"] = "YOUR_PHOTA_API_KEY"
-
-
-PHOTA_LABS_PROFILE_ID = "PHOTA_LABS_PROFILE_ID"
-
-
-class PhotaLabsGenerate(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="PhotaLabsGenerate",
-            display_name="Phota Labs Generate",
-            category="api node/image/Phota Labs",
-            description="Generate images from a text prompt using Phota Labs.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text prompt describing the desired image.",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=["auto", "1:1", "3:4", "4:3", "9:16", "16:9"],
-                ),
-                IO.Combo.Input(
-                    "resolution",
-                    options=["1K", "4K"],
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        aspect_ratio: str,
-        resolution: str,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False, min_length=1)
-        pid_list = None  # list(profile_ids.values()) if profile_ids else None
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/phota/v1/phota/generate", method="POST"),
-            response_model=PhotoStudioResponse,
-            data=PhotaGenerateRequest(
-                prompt=prompt,
-                aspect_ratio=aspect_ratio,
-                resolution=resolution,
-                profile_ids=pid_list or None,
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
-
-
-class PhotaLabsEdit(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="PhotaLabsEdit",
-            display_name="Phota Labs Edit",
-            category="api node/image/Phota Labs",
-            description="Edit images based on a text prompt using Phota Labs. "
-            "Provide input images and a prompt describing the desired edit.",
-            inputs=[
-                IO.Autogrow.Input(
-                    "images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="image",
-                        min=1,
-                        max=10,
-                    ),
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=["auto", "1:1", "3:4", "4:3", "9:16", "16:9"],
-                ),
-                IO.Combo.Input(
-                    "resolution",
-                    options=["1K", "4K"],
-                ),
-                IO.Autogrow.Input(
-                    "profile_ids",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Custom(PHOTA_LABS_PROFILE_ID).Input("profile_id"),
-                        prefix="profile_id",
-                        min=0,
-                        max=5,
-                    ),
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        images: IO.Autogrow.Type,
-        prompt: str,
-        aspect_ratio: str,
-        resolution: str,
-        profile_ids: IO.Autogrow.Type = None,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False, min_length=1)
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/phota/v1/phota/edit", method="POST"),
-            response_model=PhotoStudioResponse,
-            data=PhotaEditRequest(
-                prompt=prompt,
-                images=await upload_images_to_comfyapi(cls, list(images.values()), max_images=10),
-                aspect_ratio=aspect_ratio,
-                resolution=resolution,
-                profile_ids=list(profile_ids.values()) if profile_ids else None,
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
-
-
-class PhotaLabsEnhance(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="PhotaLabsEnhance",
-            display_name="Phota Labs Enhance",
-            category="api node/image/Phota Labs",
-            description="Automatically enhance a photo using Phota Labs. "
-            "No text prompt is required — enhancement parameters are inferred automatically.",
-            inputs=[
-                IO.Image.Input(
-                    "image",
-                    tooltip="Input image to enhance.",
-                ),
-                IO.Autogrow.Input(
-                    "profile_ids",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Custom(PHOTA_LABS_PROFILE_ID).Input("profile_id"),
-                        prefix="profile_id",
-                        min=0,
-                        max=5,
-                    ),
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: Input.Image,
-            profile_ids: IO.Autogrow.Type = None,
-    ) -> IO.NodeOutput:
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/phota/v1/phota/enhance", method="POST"),
-            response_model=PhotoStudioResponse,
-            data=PhotaEnhanceRequest(
-                image=await upload_image_to_comfyapi(cls, image),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
-
-
-class PhotaLabsSelectProfile(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="PhotaLabsSelectProfile",
-            display_name="Phota Labs Select Profile",
-            category="api node/image/Phota Labs",
-            description="Select a trained Phota Labs profile for use in generation.",
-            inputs=[
-                IO.Combo.Input(
-                    "profile_id",
-                    options=[],
-                    remote=IO.RemoteOptions(
-                        route="http://localhost:9000/phota/profiles",
-                        refresh_button=True,
-                        item_schema=IO.RemoteItemSchema(
-                            value_field="profile_id",
-                            label_field="profile_id",
-                            preview_url_field="preview_url",
-                            preview_type="image",
-                        ),
-                    ),
-                ),
-            ],
-            outputs=[IO.Custom(PHOTA_LABS_PROFILE_ID).Output(display_name="profile_id")],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(cls, profile_id: str) -> IO.NodeOutput:
-        return IO.NodeOutput(profile_id)
-
-
-class PhotaLabsAddProfile(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="PhotaLabsAddProfile",
-            display_name="Phota Labs Add Profile",
-            category="api node/image/Phota Labs",
-            description="Create a training profile from 30-50 reference images using Phota Labs. "
-            "Uploads images and starts asynchronous training, returning the profile ID once training is queued.",
-            inputs=[
-                IO.Autogrow.Input(
-                    "images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="image",
-                        min=30,
-                        max=50,
-                    ),
-                ),
-            ],
-            outputs=[
-                IO.Custom(PHOTA_LABS_PROFILE_ID).Output(display_name="profile_id"),
-                IO.String.Output(display_name="status"),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        images: IO.Autogrow.Type,
-    ) -> IO.NodeOutput:
-        image_urls = await upload_images_to_comfyapi(
-            cls,
-            list(images.values()),
-            max_images=50,
-            wait_label="Uploading training images",
-        )
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/phota/v1/phota/profiles/add", method="POST"),
-            response_model=PhotaAddProfileResponse,
-            data=PhotaAddProfileRequest(image_urls=image_urls),
-        )
-        # Poll until validation passes and training is queued/in-progress/ready
-        status_response = await poll_op(
-            cls,
-            ApiEndpoint(
-                path=f"/proxy/phota/v1/phota/profiles/{response.profile_id}/status"
-            ),
-            response_model=PhotaProfileStatusResponse,
-            status_extractor=lambda r: r.status,
-            completed_statuses=["QUEUING", "IN_PROGRESS", "READY"],
-            failed_statuses=["ERROR", "INACTIVE"],
-        )
-        return IO.NodeOutput(response.profile_id, status_response.status)
-
-
-class PhotaLabsExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            PhotaLabsGenerate,
-            PhotaLabsEdit,
-            PhotaLabsEnhance,
-            PhotaLabsSelectProfile,
-            PhotaLabsAddProfile,
-        ]
-
-
-async def comfy_entrypoint() -> PhotaLabsExtension:
-    return PhotaLabsExtension()

comfy_api_nodes/nodes_reve.py

@@ -145,20 +145,7 @@ class ReveImageCreateNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["upscale", "upscale.upscale_factor"],
-                ),
-                expr="""
-                (
-                    $factor := $lookup(widgets, "upscale.upscale_factor");
-                    $fmt := {"approximate": true, "note": "(base)"};
-                    widgets.upscale = "enabled" ? (
-                        $factor = 4 ? {"type": "usd", "usd": 0.0762, "format": $fmt}
-                        : $factor = 3 ? {"type": "usd", "usd": 0.0591, "format": $fmt}
-                        : {"type": "usd", "usd": 0.0457, "format": $fmt}
-                    ) : {"type": "usd", "usd": 0.03432, "format": $fmt}
-                )
-                """,
+                expr="""{"type":"usd","usd":0.03432,"format":{"approximate":true,"note":"(base)"}}""",
             ),
         )
 
@@ -238,21 +225,13 @@ class ReveImageEditNode(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
-                    widgets=["model", "upscale", "upscale.upscale_factor"],
+                    widgets=["model"],
                 ),
                 expr="""
                 (
-                    $fmt := {"approximate": true, "note": "(base)"};
                     $isFast := $contains(widgets.model, "fast");
-                    $enabled := widgets.upscale = "enabled";
-                    $factor := $lookup(widgets, "upscale.upscale_factor");
-                    $isFast
-                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
-                        : $enabled ? (
-                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
-                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
-                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
-                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
+                    $base := $isFast ? 0.01001 : 0.0572;
+                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
                 )
                 """,
             ),
@@ -348,21 +327,13 @@ class ReveImageRemixNode(IO.ComfyNode):
             is_api_node=True,
             price_badge=IO.PriceBadge(
                 depends_on=IO.PriceBadgeDepends(
-                    widgets=["model", "upscale", "upscale.upscale_factor"],
+                    widgets=["model"],
                 ),
                 expr="""
                 (
-                    $fmt := {"approximate": true, "note": "(base)"};
                     $isFast := $contains(widgets.model, "fast");
-                    $enabled := widgets.upscale = "enabled";
-                    $factor := $lookup(widgets, "upscale.upscale_factor");
-                    $isFast
-                        ? {"type": "usd", "usd": 0.01001, "format": $fmt}
-                        : $enabled ? (
-                            $factor = 4 ? {"type": "usd", "usd": 0.0991, "format": $fmt}
-                            : $factor = 3 ? {"type": "usd", "usd": 0.0819, "format": $fmt}
-                            : {"type": "usd", "usd": 0.0686, "format": $fmt}
-                        ) : {"type": "usd", "usd": 0.0572, "format": $fmt}
+                    $base := $isFast ? 0.01001 : 0.0572;
+                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
                 )
                 """,
             ),

comfy_api_nodes/nodes_topaz.py

@@ -38,7 +38,6 @@ from comfy_api_nodes.util import (
 UPSCALER_MODELS_MAP = {
     "Starlight (Astra) Fast": "slf-1",
     "Starlight (Astra) Creative": "slc-1",
-    "Starlight Precise 2.5": "slp-2.5",
 }
 
 

comfy_execution/caching.py

@@ -1,5 +1,6 @@
 import asyncio
 import bisect
+import gc
 import itertools
 import psutil
 import time
@@ -474,10 +475,6 @@ 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)
@@ -492,10 +489,15 @@ class LRUCache(BasicCache):
         return self
 
 
-#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.
+#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.
 
-RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
+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
 
 #Exponential bias towards evicting older workflows so garbage will be taken out
 #in constantly changing setups.
@@ -519,17 +521,19 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         return await super().get(node_id)
 
-    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)
+    def poll(self, ram_headroom):
+        def _ram_gb():
+            return psutil.virtual_memory().available / (1024**3)
 
-    def ram_release(self, target):
-        if psutil.virtual_memory().available >= target:
+        if _ram_gb() > ram_headroom:
+            return
+        gc.collect()
+        if _ram_gb() > ram_headroom:
             return
 
         clean_list = []
 
-        for key, cache_entry in self.cache.items():
+        for key, (outputs, _), 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
@@ -538,20 +542,22 @@ class RAMPressureCache(LRUCache):
                 if outputs is None:
                     return
                 for output in outputs:
-                    if isinstance(output, (list, tuple)):
+                    if isinstance(output, list):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
-                        ram_usage += output.numel() * output.element_size()
-            scan_list_for_ram_usage(cache_entry.outputs)
+                        #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)
 
             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 psutil.virtual_memory().available < target and clean_list:
+        while _ram_gb() < ram_headroom * RAM_CACHE_HYSTERESIS and clean_list:
             _, _, key = clean_list.pop()
             del self.cache[key]
-            self.used_generation.pop(key, None)
-            self.timestamps.pop(key, None)
-            self.children.pop(key, None)
+            gc.collect()

comfy_extras/nodes_glsl.py

@@ -87,9 +87,7 @@ class SizeModeInput(TypedDict):
 
 
 MAX_IMAGES = 5      # u_image0-4
-MAX_UNIFORMS = 20   # u_float0-19, u_int0-19
-MAX_BOOLS = 10      # u_bool0-9
-MAX_CURVES = 4      # u_curve0-3 (1D LUT textures)
+MAX_UNIFORMS = 5    # u_float0-4, u_int0-4
 MAX_OUTPUTS = 4     # fragColor0-3 (MRT)
 
 # Vertex shader using gl_VertexID trick - no VBO needed.
@@ -499,8 +497,6 @@ def _render_shader_batch(
     image_batches: list[list[np.ndarray]],
     floats: list[float],
     ints: list[int],
-    bools: list[bool] | None = None,
-    curves: list[np.ndarray] | None = None,
 ) -> list[list[np.ndarray]]:
     """
     Render a fragment shader for multiple batches efficiently.
@@ -515,8 +511,6 @@ def _render_shader_batch(
         image_batches: List of batches, each batch is a list of input images (H, W, C) float32 [0,1]
         floats: List of float uniforms
         ints: List of int uniforms
-        bools: List of bool uniforms (passed as int 0/1 to GLSL bool uniforms)
-        curves: List of 1D LUT arrays (float32) of arbitrary size for u_curve0-N
 
     Returns:
         List of batch outputs, each is a list of output images (H, W, 4) float32 [0,1]
@@ -539,17 +533,11 @@ def _render_shader_batch(
     # Detect multi-pass rendering
     num_passes = _detect_pass_count(fragment_code)
 
-    if bools is None:
-        bools = []
-    if curves is None:
-        curves = []
-
     # Track resources for cleanup
     program = None
     fbo = None
     output_textures = []
     input_textures = []
-    curve_textures = []
     ping_pong_textures = []
     ping_pong_fbos = []
 
@@ -636,28 +624,6 @@ def _render_shader_batch(
             if loc >= 0:
                 gl.glUniform1i(loc, v)
 
-        for i, v in enumerate(bools):
-            loc = gl.glGetUniformLocation(program, f"u_bool{i}")
-            if loc >= 0:
-                gl.glUniform1i(loc, 1 if v else 0)
-
-        # Create 1D LUT textures for curves (bound after image texture units)
-        for i, lut in enumerate(curves):
-            tex = gl.glGenTextures(1)
-            curve_textures.append(tex)
-            unit = MAX_IMAGES + i
-            gl.glActiveTexture(gl.GL_TEXTURE0 + unit)
-            gl.glBindTexture(gl.GL_TEXTURE_2D, tex)
-            gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_R32F, len(lut), 1, 0, gl.GL_RED, gl.GL_FLOAT, lut)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE)
-            gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE)
-
-            loc = gl.glGetUniformLocation(program, f"u_curve{i}")
-            if loc >= 0:
-                gl.glUniform1i(loc, unit)
-
         # Get u_pass uniform location for multi-pass
         pass_loc = gl.glGetUniformLocation(program, "u_pass")
 
@@ -752,8 +718,6 @@ def _render_shader_batch(
 
         for tex in input_textures:
             gl.glDeleteTextures(int(tex))
-        for tex in curve_textures:
-            gl.glDeleteTextures(int(tex))
         for tex in output_textures:
             gl.glDeleteTextures(int(tex))
         for tex in ping_pong_textures:
@@ -790,20 +754,6 @@ class GLSLShader(io.ComfyNode):
             max=MAX_UNIFORMS,
         )
 
-        bool_template = io.Autogrow.TemplatePrefix(
-            io.Boolean.Input("bool", default=False),
-            prefix="u_bool",
-            min=0,
-            max=MAX_BOOLS,
-        )
-
-        curve_template = io.Autogrow.TemplatePrefix(
-            io.Curve.Input("curve"),
-            prefix="u_curve",
-            min=0,
-            max=MAX_CURVES,
-        )
-
         return io.Schema(
             node_id="GLSLShader",
             display_name="GLSL Shader",
@@ -812,8 +762,6 @@ class GLSLShader(io.ComfyNode):
                 "Apply GLSL ES fragment shaders to images. "
                 "u_resolution (vec2) is always available."
             ),
-            is_experimental=True,
-            has_intermediate_output=True,
             inputs=[
                 io.String.Input(
                     "fragment_shader",
@@ -848,8 +796,6 @@ class GLSLShader(io.ComfyNode):
                 io.Autogrow.Input("images", template=image_template, tooltip=f"Images are available as u_image0-{MAX_IMAGES-1} (sampler2D) in the shader code"),
                 io.Autogrow.Input("floats", template=float_template, tooltip=f"Floats are available as u_float0-{MAX_UNIFORMS-1} in the shader code"),
                 io.Autogrow.Input("ints", template=int_template, tooltip=f"Ints are available as u_int0-{MAX_UNIFORMS-1} in the shader code"),
-                io.Autogrow.Input("bools", template=bool_template, tooltip=f"Booleans are available as u_bool0-{MAX_BOOLS-1} (bool) in the shader code"),
-                io.Autogrow.Input("curves", template=curve_template, tooltip=f"Curves are available as u_curve0-{MAX_CURVES-1} (sampler2D, 1D LUT) in the shader code. Sample with texture(u_curve0, vec2(x, 0.5)).r"),
             ],
             outputs=[
                 io.Image.Output(display_name="IMAGE0", tooltip="Available via layout(location = 0) out vec4 fragColor0 in the shader code"),
@@ -867,19 +813,13 @@ class GLSLShader(io.ComfyNode):
         images: io.Autogrow.Type,
         floats: io.Autogrow.Type = None,
         ints: io.Autogrow.Type = None,
-        bools: io.Autogrow.Type = None,
-        curves: io.Autogrow.Type = None,
         **kwargs,
     ) -> io.NodeOutput:
-
         image_list = [v for v in images.values() if v is not None]
         float_list = (
             [v if v is not None else 0.0 for v in floats.values()] if floats else []
         )
         int_list = [v if v is not None else 0 for v in ints.values()] if ints else []
-        bool_list = [v if v is not None else False for v in bools.values()] if bools else []
-
-        curve_luts = [v.to_lut().astype(np.float32) for v in curves.values() if v is not None] if curves else []
 
         if not image_list:
             raise ValueError("At least one input image is required")
@@ -906,8 +846,6 @@ class GLSLShader(io.ComfyNode):
             image_batches,
             float_list,
             int_list,
-            bool_list,
-            curve_luts,
         )
 
         # Collect outputs into tensors

comfy_extras/nodes_images.py

@@ -59,7 +59,6 @@ class ImageCropV2(IO.ComfyNode):
             display_name="Image Crop",
             category="image/transform",
             essentials_category="Image Tools",
-            has_intermediate_output=True,
             inputs=[
                 IO.Image.Input("image"),
                 IO.BoundingBox.Input("crop_region", component="ImageCrop"),

comfy_extras/nodes_number_convert.py

@@ -44,13 +44,8 @@ class NumberConvertNode(io.ComfyNode):
     def execute(cls, value) -> io.NodeOutput:
         if isinstance(value, bool):
             float_val = 1.0 if value else 0.0
-            int_val = 1 if value else 0
-        elif isinstance(value, int):
+        elif isinstance(value, (int, float)):
             float_val = float(value)
-            int_val = value
-        elif isinstance(value, float):
-            float_val = value
-            int_val = int(value)
         elif isinstance(value, str):
             text = value.strip()
             if not text:
@@ -61,14 +56,6 @@ class NumberConvertNode(io.ComfyNode):
                 raise ValueError(
                     f"Cannot convert string to number: {value!r}"
                 ) from None
-            if not math.isfinite(float_val):
-                raise ValueError(
-                    f"Cannot convert non-finite value to number: {float_val}"
-                )
-            try:
-                int_val = int(text)
-            except ValueError:
-                int_val = int(float_val)
         else:
             raise TypeError(
                 f"Unsupported input type: {type(value).__name__}"
@@ -79,7 +66,7 @@ class NumberConvertNode(io.ComfyNode):
                 f"Cannot convert non-finite value to number: {float_val}"
             )
 
-        return io.NodeOutput(float_val, int_val)
+        return io.NodeOutput(float_val, int(float_val))
 
 
 class NumberConvertExtension(ComfyExtension):

comfy_extras/nodes_painter.py

@@ -30,7 +30,6 @@ class PainterNode(io.ComfyNode):
             node_id="Painter",
             display_name="Painter",
             category="image",
-            has_intermediate_output=True,
             inputs=[
                 io.Image.Input(
                     "image",

comfy_extras/nodes_post_processing.py

@@ -67,11 +67,11 @@ class Blend(io.ComfyNode):
     def g(cls, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
-def gaussian_kernel(kernel_size: int, sigma: float, device=None, dtype=torch.float32):
+def gaussian_kernel(kernel_size: int, sigma: float, device=None):
     x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
     d = torch.sqrt(x * x + y * y)
     g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
-    return (g / g.sum()).to(dtype)
+    return g / g.sum()
 
 class Blur(io.ComfyNode):
     @classmethod
@@ -99,7 +99,7 @@ class Blur(io.ComfyNode):
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype).repeat(channels, 1, 1).unsqueeze(1)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1)
 
         image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
         padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
@@ -200,7 +200,7 @@ class Sharpen(io.ComfyNode):
         image = image.to(comfy.model_management.get_torch_device())
 
         kernel_size = sharpen_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype) * -(alpha*10)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
         kernel = kernel.to(dtype=image.dtype)
         center = kernel_size // 2
         kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0

comfy_extras/nodes_rtdetr.py

@@ -1,154 +0,0 @@
-from typing_extensions import override
-
-import torch
-from comfy.ldm.rt_detr.rtdetr_v4 import COCO_CLASSES
-import comfy.model_management
-import comfy.utils
-from comfy_api.latest import ComfyExtension, io
-from torchvision.transforms import ToPILImage, ToTensor
-from PIL import ImageDraw, ImageFont
-
-
-class RTDETR_detect(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="RTDETR_detect",
-            display_name="RT-DETR Detect",
-            category="detection/",
-            search_aliases=["bbox", "bounding box", "object detection", "coco"],
-            inputs=[
-                io.Model.Input("model", display_name="model"),
-                io.Image.Input("image", display_name="image"),
-                io.Float.Input("threshold", display_name="threshold", default=0.5),
-                io.Combo.Input("class_name", options=["all"] + COCO_CLASSES, default="all", tooltip="Filter detections by class. Set to 'all' to disable filtering."),
-                io.Int.Input("max_detections", display_name="max_detections", default=100, tooltip="Maximum number of detections to return per image. In order of descending confidence score."),
-            ],
-            outputs=[
-                io.BoundingBox.Output("bboxes")],
-        )
-
-    @classmethod
-    def execute(cls, model, image, threshold, class_name, max_detections) -> io.NodeOutput:
-        B, H, W, C = image.shape
-
-        image_in = comfy.utils.common_upscale(image.movedim(-1, 1), 640, 640, "bilinear", crop="disabled")
-
-        comfy.model_management.load_model_gpu(model)
-        results = model.model.diffusion_model(image_in, (W, H))  # list of B dicts
-
-        all_bbox_dicts = []
-
-        for det in results:
-            keep   = det['scores'] > threshold
-            boxes  = det['boxes'][keep].cpu()
-            labels = det['labels'][keep].cpu()
-            scores = det['scores'][keep].cpu()
-
-            bbox_dicts = [
-                {
-                    "x": float(box[0]),
-                    "y": float(box[1]),
-                    "width": float(box[2] - box[0]),
-                    "height": float(box[3] - box[1]),
-                    "label": COCO_CLASSES[int(label)],
-                    "score": float(score)
-                }
-                for box, label, score in zip(boxes, labels, scores)
-                if class_name == "all" or COCO_CLASSES[int(label)] == class_name
-            ]
-            bbox_dicts.sort(key=lambda d: d["score"], reverse=True)
-            all_bbox_dicts.append(bbox_dicts[:max_detections])
-
-        return io.NodeOutput(all_bbox_dicts)
-
-
-class DrawBBoxes(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="DrawBBoxes",
-            display_name="Draw BBoxes",
-            category="detection/",
-            search_aliases=["bbox", "bounding box", "object detection", "rt_detr", "visualize detections", "coco"],
-            inputs=[
-                io.Image.Input("image", optional=True),
-                io.BoundingBox.Input("bboxes", force_input=True),
-            ],
-            outputs=[
-                io.Image.Output("out_image"),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, bboxes, image=None) -> io.NodeOutput:
-        # Normalise to list[list[dict]], then fit to batch size B.
-        B = image.shape[0] if image is not None else 1
-        if isinstance(bboxes, dict):
-            bboxes = [[bboxes]]
-        elif not isinstance(bboxes, list) or not bboxes:
-            bboxes = [[]]
-        elif isinstance(bboxes[0], dict):
-            bboxes = [bboxes]  # flat list → same detections for every image
-
-        if len(bboxes) == 1:
-            bboxes = bboxes * B
-        bboxes = (bboxes + [[]] * B)[:B]
-
-        if image is None:
-            B = len(bboxes)
-            max_w = max((int(d["x"] + d["width"])  for frame in bboxes for d in frame), default=640)
-            max_h = max((int(d["y"] + d["height"]) for frame in bboxes for d in frame), default=640)
-            image = torch.zeros((B, max_h, max_w, 3), dtype=torch.float32)
-
-        all_out_images = []
-        for i in range(B):
-            detections = bboxes[i]
-            if detections:
-                boxes  = torch.tensor([[d["x"], d["y"], d["x"] + d["width"], d["y"] + d["height"]] for d in detections])
-                labels = [d.get("label") if d.get("label") in COCO_CLASSES else None for d in detections]
-                scores = torch.tensor([d.get("score", 1.0) for d in detections])
-            else:
-                boxes  = torch.zeros((0, 4))
-                labels = []
-                scores = torch.zeros((0,))
-
-            pil_image = image[i].movedim(-1, 0)
-            img = ToPILImage()(pil_image)
-            if detections:
-                img = cls.draw_detections(img, boxes, labels, scores)
-            all_out_images.append(ToTensor()(img).unsqueeze(0).movedim(1, -1))
-
-        out_images = torch.cat(all_out_images, dim=0).to(comfy.model_management.intermediate_device())
-        return io.NodeOutput(out_images)
-
-    @classmethod
-    def draw_detections(cls, img, boxes, labels, scores):
-        draw = ImageDraw.Draw(img)
-        try:
-            font = ImageFont.truetype('arial.ttf', 16)
-        except Exception:
-            font = ImageFont.load_default()
-        colors = [(255,0,0),(0,200,0),(0,0,255),(255,165,0),(128,0,128),
-                (0,255,255),(255,20,147),(100,149,237)]
-        for box, label, score in sorted(zip(boxes, labels, scores), key=lambda x: x[2].item()):
-            x1, y1, x2, y2 = box.tolist()
-            color_idx = COCO_CLASSES.index(label) if label is not None else 0
-            c = colors[color_idx % len(colors)]
-            draw.rectangle([x1, y1, x2, y2], outline=c, width=3)
-            if label is not None:
-                draw.text((x1 + 2, y1 + 2), f'{label} {score:.2f}', fill=c, font=font)
-        return img
-
-
-class RTDETRExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
-            RTDETR_detect,
-            DrawBBoxes,
-        ]
-
-
-async def comfy_entrypoint() -> RTDETRExtension:
-    return RTDETRExtension()

comfy_extras/nodes_sdpose.py

@@ -661,7 +661,6 @@ class CropByBBoxes(io.ComfyNode):
                 io.Int.Input("output_width",  default=512, min=64, max=4096, step=8, tooltip="Width each crop is resized to."),
                 io.Int.Input("output_height", default=512, min=64, max=4096, step=8, tooltip="Height each crop is resized to."),
                 io.Int.Input("padding", default=0, min=0, max=1024, step=1, tooltip="Extra padding in pixels added on each side of the bbox before cropping."),
-                io.Combo.Input("keep_aspect", options=["stretch", "pad"], default="stretch", tooltip="Whether to stretch the crop to fit the output size, or pad with black pixels to preserve aspect ratio."),
             ],
             outputs=[
                 io.Image.Output(tooltip="All crops stacked into a single image batch."),
@@ -669,7 +668,7 @@ class CropByBBoxes(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, image, bboxes, output_width, output_height, padding, keep_aspect="stretch") -> io.NodeOutput:
+    def execute(cls, image, bboxes, output_width, output_height, padding) -> io.NodeOutput:
         total_frames = image.shape[0]
         img_h = image.shape[1]
         img_w = image.shape[2]
@@ -717,19 +716,7 @@ class CropByBBoxes(io.ComfyNode):
                 x1, y1, x2, y2 = fb_x1, fb_y1, fb_x2, fb_y2
 
             crop_chw = frame_chw[:, :, y1:y2, x1:x2]  # (1, C, crop_h, crop_w)
-
-            if keep_aspect == "pad":
-                crop_h, crop_w = y2 - y1, x2 - x1
-                scale = min(output_width / crop_w, output_height / crop_h)
-                scaled_w = int(round(crop_w * scale))
-                scaled_h = int(round(crop_h * scale))
-                scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
-                pad_left = (output_width  - scaled_w) // 2
-                pad_top  = (output_height - scaled_h) // 2
-                resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device)
-                resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
-            else:  # "stretch"
-                resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
+            resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
             crops.append(resized)
 
         if not crops:

comfy_extras/nodes_string.py

@@ -9,9 +9,9 @@ class StringConcatenate(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringConcatenate",
-            display_name="Text Concatenate",
+            display_name="Concatenate",
             category="utils/string",
-            search_aliases=["Concatenate", "text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
+            search_aliases=["text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
             inputs=[
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),
@@ -32,8 +32,8 @@ class StringSubstring(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringSubstring",
-            search_aliases=["Substring", "extract text", "text portion"],
-            display_name="Text Substring",
+            search_aliases=["extract text", "text portion"],
+            display_name="Substring",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -55,8 +55,8 @@ class StringLength(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringLength",
-            search_aliases=["character count", "text size", "string length"],
-            display_name="Text Length",
+            search_aliases=["character count", "text size"],
+            display_name="Length",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -76,8 +76,8 @@ class CaseConverter(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="CaseConverter",
-            search_aliases=["Case Converter", "text case", "uppercase", "lowercase", "capitalize"],
-            display_name="Text Case Converter",
+            search_aliases=["text case", "uppercase", "lowercase", "capitalize"],
+            display_name="Case Converter",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -109,8 +109,8 @@ class StringTrim(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringTrim",
-            search_aliases=["Trim", "clean whitespace", "remove whitespace", "strip"],
-            display_name="Text Trim",
+            search_aliases=["clean whitespace", "remove whitespace"],
+            display_name="Trim",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -140,8 +140,8 @@ class StringReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringReplace",
-            search_aliases=["Replace", "find and replace", "substitute", "swap text"],
-            display_name="Text Replace",
+            search_aliases=["find and replace", "substitute", "swap text"],
+            display_name="Replace",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -163,8 +163,8 @@ class StringContains(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringContains",
-            search_aliases=["Contains", "text includes", "string includes"],
-            display_name="Text Contains",
+            search_aliases=["text includes", "string includes"],
+            display_name="Contains",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -191,8 +191,8 @@ class StringCompare(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StringCompare",
-            search_aliases=["Compare", "text match", "string equals", "starts with", "ends with"],
-            display_name="Text Compare",
+            search_aliases=["text match", "string equals", "starts with", "ends with"],
+            display_name="Compare",
             category="utils/string",
             inputs=[
                 io.String.Input("string_a", multiline=True),
@@ -227,8 +227,8 @@ class RegexMatch(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexMatch",
-            search_aliases=["Regex Match", "regex", "pattern match", "text contains", "string match"],
-            display_name="Text Match",
+            search_aliases=["pattern match", "text contains", "string match"],
+            display_name="Regex Match",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -268,8 +268,8 @@ class RegexExtract(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexExtract",
-            search_aliases=["Regex Extract", "regex", "pattern extract", "text parser", "parse text"],
-            display_name="Text Extract Substring",
+            search_aliases=["pattern extract", "text parser", "parse text"],
+            display_name="Regex Extract",
             category="utils/string",
             inputs=[
                 io.String.Input("string", multiline=True),
@@ -343,8 +343,8 @@ class RegexReplace(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="RegexReplace",
-            search_aliases=["Regex Replace", "regex", "pattern replace", "regex replace", "substitution"],
-            display_name="Text Replace (Regex)",
+            search_aliases=["pattern replace", "find and replace", "substitution"],
+            display_name="Regex Replace",
             category="utils/string",
             description="Find and replace text using regex patterns.",
             inputs=[

comfy_extras/nodes_textgen.py

@@ -15,7 +15,6 @@ class TextGenerate(io.ComfyNode):
                     io.Float.Input("min_p", default=0.05, min=0.0, max=1.0, step=0.01),
                     io.Float.Input("repetition_penalty", default=1.05, min=0.0, max=5.0, step=0.01),
                     io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
-                    io.Float.Input("presence_penalty", optional=True, default=0.0, min=0.0, max=5.0, step=0.01),
                 ]
             ),
             io.DynamicCombo.Option(
@@ -26,7 +25,7 @@ class TextGenerate(io.ComfyNode):
 
         return io.Schema(
             node_id="TextGenerate",
-            category="textgen",
+            category="textgen/",
             search_aliases=["LLM", "gemma"],
             inputs=[
                 io.Clip.Input("clip"),
@@ -34,7 +33,6 @@ class TextGenerate(io.ComfyNode):
                 io.Image.Input("image", optional=True),
                 io.Int.Input("max_length", default=256, min=1, max=2048),
                 io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
-                io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."),
             ],
             outputs=[
                 io.String.Output(display_name="generated_text"),
@@ -42,9 +40,9 @@ class TextGenerate(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput:
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
 
-        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1, thinking=thinking)
+        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1)
 
         # Get sampling parameters from dynamic combo
         do_sample = sampling_mode.get("sampling_mode") == "on"
@@ -54,7 +52,6 @@ class TextGenerate(io.ComfyNode):
         min_p = sampling_mode.get("min_p", 0.0)
         seed = sampling_mode.get("seed", None)
         repetition_penalty = sampling_mode.get("repetition_penalty", 1.0)
-        presence_penalty = sampling_mode.get("presence_penalty", 0.0)
 
         generated_ids = clip.generate(
             tokens,
@@ -65,7 +62,6 @@ class TextGenerate(io.ComfyNode):
             top_p=top_p,
             min_p=min_p,
             repetition_penalty=repetition_penalty,
-            presence_penalty=presence_penalty,
             seed=seed
         )
 
@@ -160,12 +156,12 @@ class TextGenerateLTX2Prompt(TextGenerate):
         )
 
     @classmethod
-    def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput:
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
         if image is None:
             formatted_prompt = f"<start_of_turn>system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
         else:
             formatted_prompt = f"<start_of_turn>system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>\n\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
-        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image, thinking)
+        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image)
 
 
 class TextgenExtension(ComfyExtension):

execution.py

@@ -411,19 +411,6 @@ def format_value(x):
     else:
         return str(x)
 
-def _is_intermediate_output(dynprompt, node_id):
-    class_type = dynprompt.get_node(node_id)["class_type"]
-    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    return getattr(class_def, 'HAS_INTERMEDIATE_OUTPUT', False)
-
-def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs):
-    if server.client_id is None:
-        return
-    cached_ui = cached.ui or {}
-    server.send_sync("executed", { "node": node_id, "display_node": display_node_id, "output": cached_ui.get("output", None), "prompt_id": prompt_id }, server.client_id)
-    if cached.ui is not None:
-        ui_outputs[node_id] = cached.ui
-
 async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs):
     unique_id = current_item
     real_node_id = dynprompt.get_real_node_id(unique_id)
@@ -434,7 +421,11 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
     cached = await caches.outputs.get(unique_id)
     if cached is not None:
-        _send_cached_ui(server, unique_id, display_node_id, cached, prompt_id, ui_outputs)
+        if server.client_id is not None:
+            cached_ui = cached.ui or {}
+            server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_ui.get("output",None), "prompt_id": prompt_id }, server.client_id)
+            if cached.ui is not None:
+                ui_outputs[unique_id] = cached.ui
         get_progress_state().finish_progress(unique_id)
         execution_list.cache_update(unique_id, cached)
         return (ExecutionResult.SUCCESS, None, None)
@@ -724,9 +715,6 @@ 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():
@@ -776,22 +764,9 @@ class PromptExecutor:
                         execution_list.unstage_node_execution()
                     else: # result == ExecutionResult.SUCCESS:
                         execution_list.complete_node_execution()
-
-                    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)
+                    self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
                 else:
                     # Only execute when the while-loop ends without break
-                    # Send cached UI for intermediate output nodes that weren't executed
-                    for node_id in dynamic_prompt.all_node_ids():
-                        if node_id in executed:
-                            continue
-                        if not _is_intermediate_output(dynamic_prompt, node_id):
-                            continue
-                        cached = await self.caches.outputs.get(node_id)
-                        if cached is not None:
-                            display_node_id = dynamic_prompt.get_display_node_id(node_id)
-                            _send_cached_ui(self.server, node_id, display_node_id, cached, prompt_id, ui_node_outputs)
                     self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
                 ui_outputs = {}
@@ -807,7 +782,6 @@ 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)
 
 
@@ -991,10 +965,6 @@ async def validate_inputs(prompt_id, prompt, item, validated):
 
                 if isinstance(input_type, list) or input_type == io.Combo.io_type:
                     if input_type == io.Combo.io_type:
-                        # Skip validation for combos with remote options — options
-                        # are fetched client-side and not available on the server.
-                        if extra_info.get("remote"):
-                            continue
                         combo_options = extra_info.get("options", [])
                     else:
                         combo_options = input_type

main.py

@@ -139,7 +139,16 @@ def execute_prestartup_script():
             spec.loader.exec_module(module)
             return True
         except Exception as e:
+            import traceback
             logging.error(f"Failed to execute startup-script: {script_path} / {e}")
+            from nodes import NODE_STARTUP_ERRORS, get_module_name
+            node_module_name = get_module_name(os.path.dirname(script_path))
+            NODE_STARTUP_ERRORS[node_module_name] = {
+                "module_path": os.path.dirname(script_path),
+                "error": str(e),
+                "traceback": traceback.format_exc(),
+                "phase": "prestartup",
+            }
         return False
 
     node_paths = folder_paths.get_folder_paths("custom_nodes")
@@ -275,19 +284,15 @@ 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 cache_ram > 0:
+    elif args.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" : cache_ram } )
+    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : args.cache_ram } )
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1
+comfyui_manager==4.1b8

nodes.py

@@ -2181,6 +2181,9 @@ EXTENSION_WEB_DIRS = {}
 # Dictionary of successfully loaded module names and associated directories.
 LOADED_MODULE_DIRS = {}
 
+# Dictionary of custom node startup errors, keyed by module name.
+NODE_STARTUP_ERRORS: dict[str, dict] = {}
+
 
 def get_module_name(module_path: str) -> str:
     """
@@ -2298,6 +2301,13 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
     except Exception as e:
         logging.warning(traceback.format_exc())
         logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
+        module_name = get_module_name(module_path)
+        NODE_STARTUP_ERRORS[module_name] = {
+            "module_path": module_path,
+            "error": str(e),
+            "traceback": traceback.format_exc(),
+            "phase": "import",
+        }
         return False
 
 async def init_external_custom_nodes():
@@ -2457,7 +2467,6 @@ async def init_builtin_extra_nodes():
         "nodes_number_convert.py",
         "nodes_painter.py",
         "nodes_curve.py",
-        "nodes_rtdetr.py"
     ]
 
     import_failed = []

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.42.8
-comfyui-workflow-templates==0.9.39
+comfyui-workflow-templates==0.9.36
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

server.py

@@ -709,11 +709,6 @@ class PromptServer():
             else:
                 info['output_node'] = False
 
-            if hasattr(obj_class, 'HAS_INTERMEDIATE_OUTPUT') and obj_class.HAS_INTERMEDIATE_OUTPUT == True:
-                info['has_intermediate_output'] = True
-            else:
-                info['has_intermediate_output'] = False
-
             if hasattr(obj_class, 'CATEGORY'):
                 info['category'] = obj_class.CATEGORY
 
@@ -758,6 +753,10 @@ class PromptServer():
                 out[node_class] = node_info(node_class)
             return web.json_response(out)
 
+        @routes.get("/custom_node_startup_errors")
+        async def get_custom_node_startup_errors(request):
+            return web.json_response(nodes.NODE_STARTUP_ERRORS)
+
         @routes.get("/api/jobs")
         async def get_jobs(request):
             """List all jobs with filtering, sorting, and pagination.

tests-unit/assets_test/services/test_path_utils.py

@@ -1,81 +0,0 @@
-"""Tests for path_utils – asset category resolution."""
-import os
-import tempfile
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-
-from app.assets.services.path_utils import get_asset_category_and_relative_path
-
-
-@pytest.fixture
-def fake_dirs():
-    """Create temporary input, output, and temp directories."""
-    with tempfile.TemporaryDirectory() as root:
-        root_path = Path(root)
-        input_dir = root_path / "input"
-        output_dir = root_path / "output"
-        temp_dir = root_path / "temp"
-        models_dir = root_path / "models" / "checkpoints"
-        for d in (input_dir, output_dir, temp_dir, models_dir):
-            d.mkdir(parents=True)
-
-        with patch("app.assets.services.path_utils.folder_paths") as mock_fp:
-            mock_fp.get_input_directory.return_value = str(input_dir)
-            mock_fp.get_output_directory.return_value = str(output_dir)
-            mock_fp.get_temp_directory.return_value = str(temp_dir)
-
-            with patch(
-                "app.assets.services.path_utils.get_comfy_models_folders",
-                return_value=[("checkpoints", [str(models_dir)])],
-            ):
-                yield {
-                    "input": input_dir,
-                    "output": output_dir,
-                    "temp": temp_dir,
-                    "models": models_dir,
-                }
-
-
-class TestGetAssetCategoryAndRelativePath:
-    def test_input_file(self, fake_dirs):
-        f = fake_dirs["input"] / "photo.png"
-        f.touch()
-        cat, rel = get_asset_category_and_relative_path(str(f))
-        assert cat == "input"
-        assert rel == "photo.png"
-
-    def test_output_file(self, fake_dirs):
-        f = fake_dirs["output"] / "result.png"
-        f.touch()
-        cat, rel = get_asset_category_and_relative_path(str(f))
-        assert cat == "output"
-        assert rel == "result.png"
-
-    def test_temp_file(self, fake_dirs):
-        """Regression: temp files must be categorised, not raise ValueError."""
-        f = fake_dirs["temp"] / "GLSLShader_output_00004_.png"
-        f.touch()
-        cat, rel = get_asset_category_and_relative_path(str(f))
-        assert cat == "temp"
-        assert rel == "GLSLShader_output_00004_.png"
-
-    def test_temp_file_in_subfolder(self, fake_dirs):
-        sub = fake_dirs["temp"] / "sub"
-        sub.mkdir()
-        f = sub / "ComfyUI_temp_tczip_00004_.png"
-        f.touch()
-        cat, rel = get_asset_category_and_relative_path(str(f))
-        assert cat == "temp"
-        assert os.path.normpath(rel) == os.path.normpath("sub/ComfyUI_temp_tczip_00004_.png")
-
-    def test_model_file(self, fake_dirs):
-        f = fake_dirs["models"] / "model.safetensors"
-        f.touch()
-        cat, rel = get_asset_category_and_relative_path(str(f))
-        assert cat == "models"
-
-    def test_unknown_path_raises(self, fake_dirs):
-        with pytest.raises(ValueError, match="not within"):
-            get_asset_category_and_relative_path("/some/random/path.png")

tests-unit/comfy_extras_test/nodes_number_convert_test.py

@@ -90,63 +90,6 @@ class TestNumberConvertExecute:
         assert result[0] == 1000.0
         assert result[1] == 1000
 
-    # --- Large number precision (string input) ---
-
-    def test_string_large_int_above_2_53(self):
-        """Text-to-int must not lose precision for integers beyond 2^53."""
-        big = 2**53 + 1  # 9007199254740993
-        result = self._exec(str(big))
-        assert result[1] == big
-
-    def test_string_large_negative_int_above_2_53(self):
-        big = -(2**53 + 1)
-        result = self._exec(str(big))
-        assert result[1] == big
-
-    def test_string_very_large_int(self):
-        big = 2**63 + 42
-        result = self._exec(str(big))
-        assert result[1] == big
-
-    def test_string_large_int_float_output_is_float(self):
-        """FLOAT output is still a float (may lose precision, but must be float type)."""
-        result = self._exec(str(2**53 + 1))
-        assert isinstance(result[0], float)
-
-    # --- Large number precision (int input) ---
-
-    def test_int_large_above_2_53(self):
-        """Native int input must preserve its value in the INT output."""
-        big = 2**53 + 1
-        result = self._exec(big)
-        assert result[1] == big
-
-    def test_int_large_negative_above_2_53(self):
-        big = -(2**53 + 1)
-        result = self._exec(big)
-        assert result[1] == big
-
-    def test_int_very_large(self):
-        big = 2**100
-        result = self._exec(big)
-        assert result[1] == big
-
-    # --- String decimal / scientific notation fallback ---
-
-    def test_string_decimal_still_truncates(self):
-        """Strings with decimal points fall back to int(float(...)) truncation."""
-        result = self._exec("3.7")
-        assert result[1] == 3
-
-    def test_string_negative_decimal_truncates(self):
-        result = self._exec("-2.9")
-        assert result[1] == -2
-
-    def test_string_scientific_large(self):
-        result = self._exec("1e18")
-        assert result[0] == 1e18
-        assert result[1] == 10**18
-
     # --- STRING error paths ---
 
     def test_empty_string_raises(self):

utils/mime_types.py

@@ -24,7 +24,6 @@ def init_mime_types():
     # Web types (used by server.py for static file serving)
     mimetypes.add_type('application/javascript; charset=utf-8', '.js')
     mimetypes.add_type('image/webp', '.webp')
-    mimetypes.add_type('image/svg+xml', '.svg')
 
     # Model and data file types (used by asset scanning / metadata extraction)
     mimetypes.add_type("application/safetensors", ".safetensors")