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>

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

@@ -1,46 +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 ) )
-    color.r = applyCurve(u_curve0, applyCurve(u_curve1, color.r));
-    color.g = applyCurve(u_curve0, applyCurve(u_curve2, color.g));
-    color.b = applyCurve(u_curve0, applyCurve(u_curve3, color.b));
-
-    fragColor0 = vec4(color.rgb, color.a);
-}

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/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
@@ -426,13 +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)
@@ -1229,11 +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):
@@ -1273,17 +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:
@@ -1316,12 +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 {}
 
@@ -1449,11 +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

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/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_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_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",
@@ -812,7 +762,6 @@ class GLSLShader(io.ComfyNode):
                 "Apply GLSL ES fragment shaders to images. "
                 "u_resolution (vec2) is always available."
             ),
-            is_experimental=True,
             inputs=[
                 io.String.Input(
                     "fragment_shader",
@@ -847,8 +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"),
@@ -866,19 +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")
@@ -905,8 +846,6 @@ class GLSLShader(io.ComfyNode):
             image_batches,
             float_list,
             int_list,
-            bool_list,
-            curve_luts,
         )
 
         # Collect outputs into tensors

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_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_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):

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")

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():

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.42.8
-comfyui-workflow-templates==0.9.38
+comfyui-workflow-templates==0.9.36
 comfyui-embedded-docs==0.4.3
 torch
 torchsde

server.py

@@ -753,6 +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")