Merge branch 'master' into feat/core/expected_outputs

comfy/ldm/ace/ace_step15.py

@@ -7,6 +7,67 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 from comfy.ldm.flux.layers import timestep_embedding
 
+def get_silence_latent(length, device):
+    head = torch.tensor([[[ 0.5707,  0.0982,  0.6909, -0.5658,  0.6266,  0.6996, -0.1365, -0.1291,
+                        -0.0776, -0.1171, -0.2743, -0.8422, -0.1168,  1.5539, -4.6936,  0.7436,
+                        -1.1846, -0.2637,  0.6933, -6.7266,  0.0966, -0.1187, -0.3501, -1.1736,
+                        0.0587, -2.0517, -1.3651,  0.7508, -0.2490, -1.3548, -0.1290, -0.7261,
+                        1.1132, -0.3249,  0.2337,  0.3004,  0.6605, -0.0298, -0.1989, -0.4041,
+                        0.2843, -1.0963, -0.5519,  0.2639, -1.0436, -0.1183,  0.0640,  0.4460,
+                        -1.1001, -0.6172, -1.3241,  1.1379,  0.5623, -0.1507, -0.1963, -0.4742,
+                        -2.4697,  0.5302,  0.5381,  0.4636, -0.1782, -0.0687,  1.0333,  0.4202],
+                        [ 0.3040, -0.1367,  0.6200,  0.0665, -0.0642,  0.4655, -0.1187, -0.0440,
+                        0.2941, -0.2753,  0.0173, -0.2421, -0.0147,  1.5603, -2.7025,  0.7907,
+                        -0.9736, -0.0682,  0.1294, -5.0707, -0.2167,  0.3302, -0.1513, -0.8100,
+                        -0.3894, -0.2884, -0.3149,  0.8660, -0.3817, -1.7061,  0.5824, -0.4840,
+                        0.6938,  0.1859,  0.1753,  0.3081,  0.0195,  0.1403, -0.0754, -0.2091,
+                        0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320,  0.1284,  0.4974,
+                        -1.1889, -0.0344, -0.8313,  0.2953,  0.5445, -0.6249, -0.1595, -0.0682,
+                        -3.1412,  0.0484,  0.4153,  0.8260, -0.1526, -0.0625,  0.5366,  0.8473],
+                        [ 5.3524e-02, -1.7534e-01,  5.4443e-01, -4.3501e-01, -2.1317e-03,
+                        3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01,
+                        -7.7107e-02, -2.0593e-01, -3.2780e-01,  1.5142e+00, -2.6101e+00,
+                        5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00,
+                        1.1601e-01,  4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01,
+                        -6.3474e-01, -1.5893e-01,  8.2745e-01, -2.2992e-01, -1.6816e+00,
+                        5.4440e-01, -4.9579e-01,  5.5128e-01,  3.0477e-01,  8.3052e-02,
+                        -6.1782e-02,  5.9036e-03,  2.9553e-01, -8.0645e-02, -1.0060e-01,
+                        1.9144e-01, -3.8124e-01, -7.2949e-01,  2.4520e-02, -5.0814e-01,
+                        2.3977e-01,  9.2943e-02,  3.9256e-01, -1.1993e+00, -3.2752e-01,
+                        -7.2707e-01,  2.9476e-01,  4.3542e-01, -8.8597e-01, -4.1686e-01,
+                        -8.5390e-02, -2.9018e+00,  6.4988e-02,  5.3945e-01,  9.1988e-01,
+                        5.8762e-02, -7.0098e-02,  6.4772e-01,  8.9118e-01],
+                        [-3.2225e-02, -1.3195e-01,  5.6411e-01, -5.4766e-01, -5.2170e-03,
+                        3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01,
+                        -9.0984e-02, -1.9540e-01, -2.5590e-01,  1.5440e+00, -2.6349e+00,
+                        6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00,
+                        1.8818e-01,  5.0109e-01,  7.3546e-03, -6.8771e-01, -3.0676e-01,
+                        -7.3257e-01, -1.6687e-01,  9.2232e-01, -1.8987e-01, -1.7267e+00,
+                        5.3355e-01, -5.3179e-01,  4.4953e-01,  2.8820e-01,  1.3012e-01,
+                        -2.0943e-01, -1.1348e-01,  3.3929e-01, -1.5069e-01, -1.2919e-01,
+                        1.8929e-01, -3.6166e-01, -8.0756e-01,  6.6387e-02, -5.8867e-01,
+                        1.6978e-01,  1.0134e-01,  3.3877e-01, -1.2133e+00, -3.2492e-01,
+                        -8.1237e-01,  3.8101e-01,  4.3765e-01, -8.0596e-01, -4.4531e-01,
+                        -4.7513e-02, -2.9266e+00,  1.1741e-03,  4.5123e-01,  9.3075e-01,
+                        5.3688e-02, -1.9621e-01,  6.4530e-01,  9.3870e-01]]], device=device).movedim(-1, 1)
+
+    silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
+                                2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
+                                -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
+                                7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
+                                2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
+                                -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
+                                5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
+                                -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
+                                2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
+                                1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
+                                -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
+                                -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
+                                7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length)
+    silence_latent[:, :, :head.shape[-1]] = head
+    return silence_latent
+
+
 def get_layer_class(operations, layer_name):
     if operations is not None and hasattr(operations, layer_name):
         return getattr(operations, layer_name)
@@ -677,7 +738,7 @@ class AttentionPooler(nn.Module):
     def forward(self, x):
         B, T, P, D = x.shape
         x = self.embed_tokens(x)
-        special = self.special_token.expand(B, T, 1, -1)
+        special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)
         x = torch.cat([special, x], dim=2)
         x = x.view(B * T, P + 1, D)
 
@@ -728,7 +789,7 @@ class FSQ(nn.Module):
         self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)
 
     def bound(self, z):
-        levels_minus_1 = (self._levels - 1).to(z.dtype)
+        levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)
         scale = 2. / levels_minus_1
         bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5
 
@@ -743,8 +804,8 @@ class FSQ(nn.Module):
         return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1.
 
     def codes_to_indices(self, zhat):
-        zhat_normalized = (zhat + 1.) / (2. / (self._levels.to(zhat.dtype) - 1))
-        return (zhat_normalized * self._basis.to(zhat.dtype)).sum(dim=-1).round().to(torch.int32)
+        zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1))
+        return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32)
 
     def forward(self, z):
         orig_dtype = z.dtype
@@ -826,7 +887,7 @@ class ResidualFSQ(nn.Module):
         x = self.project_in(x)
 
         if hasattr(self, 'soft_clamp_input_value'):
-            sc_val = self.soft_clamp_input_value.to(x.dtype)
+            sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)
             x = (x / sc_val).tanh() * sc_val
 
         quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)
@@ -834,7 +895,7 @@ class ResidualFSQ(nn.Module):
         all_indices = []
 
         for layer, scale in zip(self.layers, self.scales):
-            scale = scale.to(residual.dtype)
+            scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)
 
             quantized, indices = layer(residual / scale)
             quantized = quantized * scale
@@ -1040,22 +1101,21 @@ class AceStepConditionGenerationModel(nn.Module):
             lm_hints = self.detokenizer(lm_hints_5Hz)
 
         lm_hints = lm_hints[:, :src_latents.shape[1], :]
-        if is_covers is None:
+        if is_covers is None or is_covers is True:
             src_latents = lm_hints
-        else:
-            src_latents = torch.where(is_covers.unsqueeze(-1).unsqueeze(-1) > 0, lm_hints, src_latents)
+        elif is_covers is False:
+            src_latents = refer_audio_acoustic_hidden_states_packed
 
         context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1)
 
         return encoder_hidden, encoder_mask, context_latents
 
-    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):
         text_attention_mask = None
         lyric_attention_mask = None
         refer_audio_order_mask = None
         attention_mask = None
         chunk_masks = None
-        is_covers = None
         src_latents = None
         precomputed_lm_hints_25Hz = None
         lyric_hidden_states = lyric_embed
@@ -1067,7 +1127,7 @@ class AceStepConditionGenerationModel(nn.Module):
         if refer_audio_order_mask is None:
             refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long)
 
-        if src_latents is None and is_covers is None:
+        if src_latents is None:
             src_latents = x
 
         if chunk_masks is None:
@@ -1080,6 +1140,9 @@ class AceStepConditionGenerationModel(nn.Module):
             src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes
         )
 
+        if replace_with_null_embeds:
+            enc_hidden[:] = self.null_condition_emb.to(enc_hidden)
+
         out = self.decoder(hidden_states=x,
                            timestep=timestep,
                            timestep_r=timestep,

comfy/ldm/cosmos/predict2.py

@@ -335,7 +335,7 @@ class FinalLayer(nn.Module):
         device=None, dtype=None, operations=None
     ):
         super().__init__()
-        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.linear = operations.Linear(
             hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
         )
@@ -463,6 +463,8 @@ class Block(nn.Module):
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
         transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
+        residual_dtype = x_B_T_H_W_D.dtype
+        compute_dtype = emb_B_T_D.dtype
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
 
@@ -512,7 +514,7 @@ class Block(nn.Module):
         result_B_T_H_W_D = rearrange(
             self.self_attn(
                 # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
                 transformer_options=transformer_options,
@@ -522,7 +524,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -536,7 +538,7 @@ class Block(nn.Module):
             )
             _result_B_T_H_W_D = rearrange(
                 self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
                     transformer_options=transformer_options,
@@ -555,7 +557,7 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
+        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -563,8 +565,8 @@ class Block(nn.Module):
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
         )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
         return x_B_T_H_W_D
 
 
@@ -876,6 +878,14 @@ class MiniTrainDIT(nn.Module):
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
             "transformer_options": kwargs.get("transformer_options", {}),
         }
+
+        # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
+        # in fp32, but run attention and MLP modules in fp16.
+        # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable
+        # quality degradation and visual artifacts.
+        if x_B_T_H_W_D.dtype == torch.float16:
+            x_B_T_H_W_D = x_B_T_H_W_D.float()
+
         for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
@@ -884,6 +894,6 @@ class MiniTrainDIT(nn.Module):
                 **block_kwargs,
             )
 
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
         x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
         return x_B_C_Tt_Hp_Wp

comfy/model_base.py

@@ -147,11 +147,11 @@ class BaseModel(torch.nn.Module):
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
             logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
+            comfy.model_management.archive_model_dtypes(self.diffusion_model)
+
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
-        comfy.model_management.archive_model_dtypes(self.diffusion_model)
-
         self.adm_channels = unet_config.get("adm_in_channels", None)
         if self.adm_channels is None:
             self.adm_channels = 0
@@ -1552,6 +1552,8 @@ class ACEStep15(BaseModel):
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
+            if torch.count_nonzero(cross_attn) == 0:
+                out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
@@ -1560,22 +1562,11 @@ class ACEStep15(BaseModel):
 
         refer_audio = kwargs.get("reference_audio_timbre_latents", None)
         if refer_audio is None or len(refer_audio) == 0:
-            refer_audio = torch.tensor([[[-1.3672e-01, -1.5820e-01,  5.8594e-01, -5.7422e-01,  3.0273e-02,
-                                        2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,
-                                        -2.7710e-02, -1.8066e-01, -2.9688e-01,  1.6016e+00, -2.6719e+00,
-                                        7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,
-                                        2.4316e-01,  4.7266e-01,  4.6387e-02, -6.6406e-01, -2.1973e-01,
-                                        -6.7578e-01, -1.5723e-01,  9.5312e-01, -2.0020e-01, -1.7109e+00,
-                                        5.8984e-01, -5.7422e-01,  5.1562e-01,  2.8320e-01,  1.4551e-01,
-                                        -1.8750e-01, -5.9814e-02,  3.6719e-01, -1.0059e-01, -1.5723e-01,
-                                        2.0605e-01, -4.3359e-01, -8.2812e-01,  4.5654e-02, -6.6016e-01,
-                                        1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
-                                        -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
-                                        -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
-                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
+            refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
             pass_audio_codes = True
         else:
             refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
+            out['is_covers'] = comfy.conds.CONDConstant(True)
             pass_audio_codes = False
 
         if pass_audio_codes:
@@ -1583,6 +1574,12 @@ class ACEStep15(BaseModel):
             if audio_codes is not None:
                 out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
                 refer_audio = refer_audio[:, :, :750]
+            else:
+                out['is_covers'] = comfy.conds.CONDConstant(False)
+
+        if refer_audio.shape[2] < noise.shape[2]:
+            pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
+            refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)
 
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out

comfy/supported_models.py

@@ -993,7 +993,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
@@ -1023,11 +1023,7 @@ class Anima(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float32]
-
-    def __init__(self, unet_config):
-        super().__init__(unet_config)
-        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Anima(self, device=device)
@@ -1038,6 +1034,12 @@ class Anima(supported_models_base.BASE):
         detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))
 
+    def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):
+        self.memory_usage_factor = (self.unet_config.get("model_channels", 2048) / 2048) * 0.95
+        if dtype is torch.float16:
+            self.memory_usage_factor *= 1.4
+        return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs)
+
 class CosmosI2VPredict2(CosmosT2IPredict2):
     unet_config = {
         "image_model": "cosmos_predict2",

comfy/text_encoders/anima.py

@@ -23,7 +23,7 @@ class AnimaTokenizer:
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["qwen3_06b"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        out["qwen3_06b"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
         out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 

comfy/text_encoders/lt.py

@@ -25,7 +25,7 @@ def ltxv_te(*args, **kwargs):
 class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}

comfy_api/latest/_io.py

@@ -1430,6 +1430,11 @@ class Schema:
     """Flags a node as expandable, allowing NodeOutput to include 'expand' property."""
     accept_all_inputs: bool=False
     """When True, all inputs from the prompt will be passed to the node as kwargs, even if not defined in the schema."""
+    lazy_outputs: bool=False
+    """When True, cache will invalidate when output connections change, and expected_outputs will be available.
+
+    Use this for nodes that can skip computing outputs that aren't connected downstream.
+    Access via `get_executing_context().expected_outputs` - outputs NOT in the set are definitely unused."""
 
     def validate(self):
         '''Validate the schema:
@@ -1875,6 +1880,14 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls.GET_SCHEMA()
         return cls._ACCEPT_ALL_INPUTS
 
+    _LAZY_OUTPUTS = None
+    @final
+    @classproperty
+    def LAZY_OUTPUTS(cls):  # noqa
+        if cls._LAZY_OUTPUTS is None:
+            cls.GET_SCHEMA()
+        return cls._LAZY_OUTPUTS
+
     @final
     @classmethod
     def INPUT_TYPES(cls) -> dict[str, dict]:
@@ -1917,6 +1930,8 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
             cls._NOT_IDEMPOTENT = schema.not_idempotent
         if cls._ACCEPT_ALL_INPUTS is None:
             cls._ACCEPT_ALL_INPUTS = schema.accept_all_inputs
+        if cls._LAZY_OUTPUTS is None:
+            cls._LAZY_OUTPUTS = schema.lazy_outputs
 
         if cls._RETURN_TYPES is None:
             output = []

comfy_execution/caching.py

@@ -5,7 +5,7 @@ import psutil
 import time
 import torch
 from typing import Sequence, Mapping, Dict
-from comfy_execution.graph import DynamicPrompt
+from comfy_execution.graph import DynamicPrompt, get_expected_outputs_for_node
 from abc import ABC, abstractmethod
 
 import nodes
@@ -115,6 +115,10 @@ class CacheKeySetInputSignature(CacheKeySet):
         signature = [class_type, await self.is_changed_cache.get(node_id)]
         if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
+        # Include expected_outputs in cache key for nodes that opt in via LAZY_OUTPUTS
+        if hasattr(class_def, 'LAZY_OUTPUTS') and class_def.LAZY_OUTPUTS:
+            expected = get_expected_outputs_for_node(dynprompt, node_id)
+            signature.append(("expected_outputs", tuple(sorted(expected))))
         inputs = node["inputs"]
         for key in sorted(inputs.keys()):
             if is_link(inputs[key]):

comfy_execution/graph.py

@@ -19,6 +19,15 @@ class NodeInputError(Exception):
 class NodeNotFoundError(Exception):
     pass
 
+
+def get_expected_outputs_for_node(dynprompt, node_id: str) -> frozenset:
+    """Get the set of output indices that are connected downstream.
+    Returns outputs that MIGHT be used.
+    Outputs NOT in this set are DEFINITELY not used and safe to skip.
+    """
+    return dynprompt.get_expected_outputs_map().get(node_id, frozenset())
+
+
 class DynamicPrompt:
     def __init__(self, original_prompt):
         # The original prompt provided by the user
@@ -27,6 +36,7 @@ class DynamicPrompt:
         self.ephemeral_prompt = {}
         self.ephemeral_parents = {}
         self.ephemeral_display = {}
+        self._expected_outputs_map = None
 
     def get_node(self, node_id):
         if node_id in self.ephemeral_prompt:
@@ -42,6 +52,7 @@ class DynamicPrompt:
         self.ephemeral_prompt[node_id] = node_info
         self.ephemeral_parents[node_id] = parent_id
         self.ephemeral_display[node_id] = display_id
+        self._expected_outputs_map = None
 
     def get_real_node_id(self, node_id):
         while node_id in self.ephemeral_parents:
@@ -59,6 +70,26 @@ class DynamicPrompt:
     def all_node_ids(self):
         return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
 
+    def _build_expected_outputs_map(self):
+        result = {}
+        for node_id in self.all_node_ids():
+            try:
+                node_data = self.get_node(node_id)
+            except NodeNotFoundError:
+                continue
+            for value in node_data.get("inputs", {}).values():
+                if is_link(value):
+                    from_node_id, from_socket = value
+                    if from_node_id not in result:
+                        result[from_node_id] = set()
+                    result[from_node_id].add(from_socket)
+        self._expected_outputs_map = {k: frozenset(v) for k, v in result.items()}
+
+    def get_expected_outputs_map(self):
+        if self._expected_outputs_map is None:
+            self._build_expected_outputs_map()
+        return self._expected_outputs_map
+
     def get_original_prompt(self):
         return self.original_prompt
 

comfy_execution/utils.py

@@ -1,23 +1,41 @@
 import contextvars
-from typing import Optional, NamedTuple
+from typing import NamedTuple, FrozenSet
 
 class ExecutionContext(NamedTuple):
     """
     Context information about the currently executing node.
 
     Attributes:
+        prompt_id: The ID of the current prompt execution
         node_id: The ID of the currently executing node
         list_index: The index in a list being processed (for operations on batches/lists)
+        expected_outputs: Set of output indices that might be used downstream.
+                         Outputs NOT in this set are definitely unused (safe to skip).
+                         None means the information is not available.
     """
     prompt_id: str
     node_id: str
-    list_index: Optional[int]
+    list_index: int | None
+    expected_outputs: FrozenSet[int] | None = None
 
-current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
+current_executing_context: contextvars.ContextVar[ExecutionContext | None] = contextvars.ContextVar("current_executing_context", default=None)
 
-def get_executing_context() -> Optional[ExecutionContext]:
+def get_executing_context() -> ExecutionContext | None:
     return current_executing_context.get(None)
 
+
+def is_output_needed(output_index: int) -> bool:
+    """Check if an output at the given index is connected downstream.
+
+    Returns True if the output might be used (should be computed).
+    Returns False if the output is definitely not connected (safe to skip).
+    """
+    ctx = get_executing_context()
+    if ctx is None or ctx.expected_outputs is None:
+        return True
+    return output_index in ctx.expected_outputs
+
+
 class CurrentNodeContext:
     """
     Context manager for setting the current executing node context.
@@ -25,15 +43,22 @@ class CurrentNodeContext:
     Sets the current_executing_context on enter and resets it on exit.
 
     Example:
-        with CurrentNodeContext(node_id="123", list_index=0):
+        with CurrentNodeContext(prompt_id="abc", node_id="123", list_index=0):
             # Code that should run with the current node context set
             process_image()
     """
-    def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
+    def __init__(
+        self,
+        prompt_id: str,
+        node_id: str,
+        list_index: int | None = None,
+        expected_outputs: FrozenSet[int] | None = None,
+    ):
         self.context = ExecutionContext(
-            prompt_id= prompt_id,
-            node_id= node_id,
-            list_index= list_index
+            prompt_id=prompt_id,
+            node_id=node_id,
+            list_index=list_index,
+            expected_outputs=expected_outputs,
         )
         self.token = None
 

comfy_extras/nodes_custom_sampler.py

@@ -622,6 +622,7 @@ class SamplerSASolver(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSASolver",
+            search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
@@ -666,6 +667,7 @@ class SamplerSEEDS2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSEEDS2",
+            search_aliases=["sde", "exp heun"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),

comfy_extras/nodes_easycache.py

@@ -9,6 +9,14 @@ if TYPE_CHECKING:
     from uuid import UUID
 
 
+def _extract_tensor(data, output_channels):
+    """Extract tensor from data, handling both single tensors and lists."""
+    if isinstance(data, list):
+        # LTX2 AV tensors: [video, audio]
+        return data[0][:, :output_channels], data[1][:, :output_channels]
+    return data[:, :output_channels], None
+
+
 def easycache_forward_wrapper(executor, *args, **kwargs):
     # get values from args
     transformer_options: dict[str] = args[-1]
@@ -17,7 +25,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if not transformer_options:
             transformer_options = args[-2]
     easycache: EasyCacheHolder = transformer_options["easycache"]
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    x, ax = _extract_tensor(args[0], easycache.output_channels)
     sigmas = transformer_options["sigmas"]
     uuids = transformer_options["uuids"]
     if sigmas is not None and easycache.is_past_end_timestep(sigmas):
@@ -35,7 +43,11 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
         if easycache.skip_current_step and can_apply_cache_diff:
             if easycache.verbose:
                 logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
-            return easycache.apply_cache_diff(x, uuids)
+            result = easycache.apply_cache_diff(x, uuids)
+            if ax is not None:
+                result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
+                return [result, result_audio]
+            return result
         if easycache.initial_step:
             easycache.first_cond_uuid = uuids[0]
             has_first_cond_uuid = easycache.has_first_cond_uuid(uuids)
@@ -51,13 +63,18 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
                         logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     # other conds should also skip this step, and instead use their cached values
                     easycache.skip_current_step = True
-                    return easycache.apply_cache_diff(x, uuids)
+                    result = easycache.apply_cache_diff(x, uuids)
+                    if ax is not None:
+                        result_audio = easycache.apply_cache_diff(ax, uuids, is_audio=True)
+                        return [result, result_audio]
+                    return result
                 else:
                     if easycache.verbose:
                         logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     easycache.cumulative_change_rate = 0.0
 
-    output: torch.Tensor = executor(*args, **kwargs)
+    full_output: torch.Tensor = executor(*args, **kwargs)
+    output, audio_output = _extract_tensor(full_output, easycache.output_channels)
     if has_first_cond_uuid and easycache.has_output_prev_norm():
         output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
         if easycache.verbose:
@@ -74,13 +91,15 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
             logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
     # TODO: allow cache_diff to be offloaded
     easycache.update_cache_diff(output, next_x_prev, uuids)
+    if audio_output is not None:
+        easycache.update_cache_diff(audio_output, ax, uuids, is_audio=True)
     if has_first_cond_uuid:
         easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids)
         easycache.output_prev_subsampled = easycache.subsample(output, uuids)
         easycache.output_prev_norm = output.flatten().abs().mean()
         if easycache.verbose:
             logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
-    return output
+    return full_output
 
 def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     # get values from args
@@ -89,8 +108,8 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
-    # prepare next x_prev
     x: torch.Tensor = args[0][:, :easycache.output_channels]
+    # prepare next x_prev
     next_x_prev = x
     input_change = None
     do_easycache = easycache.should_do_easycache(timestep)
@@ -197,6 +216,7 @@ class EasyCacheHolder:
         self.output_prev_subsampled: torch.Tensor = None
         self.output_prev_norm: torch.Tensor = None
         self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {}
+        self.uuid_cache_diffs_audio: dict[UUID, torch.Tensor] = {}
         self.output_change_rates = []
         self.approx_output_change_rates = []
         self.total_steps_skipped = 0
@@ -245,20 +265,21 @@ class EasyCacheHolder:
     def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
         return all(uuid in self.uuid_cache_diffs for uuid in uuids)
 
-    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]):
-        if self.first_cond_uuid in uuids:
+    def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
+        if self.first_cond_uuid in uuids and not is_audio:
             self.total_steps_skipped += 1
+        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
         batch_offset = x.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
             # slice out only what is relevant to this cond
             batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
             # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
-            if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]:
+            if x.shape[1:] != cache_diffs[uuid].shape[1:]:
                 if not self.allow_mismatch:
                     raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good")
                 slicing = []
                 skip_this_dim = True
-                for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape):
+                for dim_u, dim_x in zip(cache_diffs[uuid].shape, x.shape):
                     if skip_this_dim:
                         skip_this_dim = False
                         continue
@@ -270,10 +291,11 @@ class EasyCacheHolder:
                     else:
                         slicing.append(slice(None))
                 batch_slice = batch_slice + slicing
-            x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
+            x[tuple(batch_slice)] += cache_diffs[uuid].to(x.device)
         return x
 
-    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
+    def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID], is_audio: bool = False):
+        cache_diffs = self.uuid_cache_diffs_audio if is_audio else self.uuid_cache_diffs
         # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
         if output.shape[1:] != x.shape[1:]:
             if not self.allow_mismatch:
@@ -293,7 +315,7 @@ class EasyCacheHolder:
         diff = output - x
         batch_offset = diff.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
-            self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
+            cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...]
 
     def has_first_cond_uuid(self, uuids: list[UUID]) -> bool:
         return self.first_cond_uuid in uuids
@@ -324,6 +346,8 @@ class EasyCacheHolder:
         self.output_prev_norm = None
         del self.uuid_cache_diffs
         self.uuid_cache_diffs = {}
+        del self.uuid_cache_diffs_audio
+        self.uuid_cache_diffs_audio = {}
         self.total_steps_skipped = 0
         self.state_metadata = None
         return self

comfy_extras/nodes_latent.py

@@ -391,8 +391,9 @@ class LatentOperationTonemapReinhard(io.ComfyNode):
             latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
             normalized_latent = latent / latent_vector_magnitude
 
-            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            dims = list(range(1, latent_vector_magnitude.ndim))
+            mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
 
             top = (std * 5 + mean) * multiplier
 

comfy_extras/nodes_toolkit.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class CreateList(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template_matchtype = io.MatchType.Template("type")
+        template_autogrow = io.Autogrow.TemplatePrefix(
+            input=io.MatchType.Input("input", template=template_matchtype),
+            prefix="input",
+        )
+        return io.Schema(
+            node_id="CreateList",
+            display_name="Create List",
+            category="logic",
+            is_input_list=True,
+            search_aliases=["Image Iterator", "Text Iterator", "Iterator"],
+            inputs=[io.Autogrow.Input("inputs", template=template_autogrow)],
+            outputs=[
+                io.MatchType.Output(
+                    template=template_matchtype,
+                    is_output_list=True,
+                    display_name="list",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, inputs: io.Autogrow.Type) -> io.NodeOutput:
+        output_list = []
+        for input in inputs.values():
+            output_list += input
+        return io.NodeOutput(output_list)
+
+
+class ToolkitExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CreateList,
+        ]
+
+
+async def comfy_entrypoint() -> ToolkitExtension:
+    return ToolkitExtension()

execution.py

@@ -31,6 +31,7 @@ from comfy_execution.graph import (
     ExecutionBlocker,
     ExecutionList,
     get_input_info,
+    get_expected_outputs_for_node,
 )
 from comfy_execution.graph_utils import GraphBuilder, is_link
 from comfy_execution.validation import validate_node_input
@@ -227,7 +228,18 @@ async def resolve_map_node_over_list_results(results):
                 raise exc
         return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
 
-async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
+async def _async_map_node_over_list(
+    prompt_id,
+    unique_id,
+    obj,
+    input_data_all,
+    func,
+    allow_interrupt=False,
+    execution_block_cb=None,
+    pre_execute_cb=None,
+    v3_data=None,
+    expected_outputs=None,
+):
     # check if node wants the lists
     input_is_list = getattr(obj, "INPUT_IS_LIST", False)
 
@@ -277,10 +289,12 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
             else:
                 f = getattr(obj, func)
             if inspect.iscoroutinefunction(f):
-                async def async_wrapper(f, prompt_id, unique_id, list_index, args):
-                    with CurrentNodeContext(prompt_id, unique_id, list_index):
+                async def async_wrapper(f, prompt_id, unique_id, list_index, args, expected_outputs):
+                    with CurrentNodeContext(prompt_id, unique_id, list_index, expected_outputs):
                         return await f(**args)
-                task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs))
+                task = asyncio.create_task(
+                    async_wrapper(f, prompt_id, unique_id, index, args=inputs, expected_outputs=expected_outputs)
+                )
                 # Give the task a chance to execute without yielding
                 await asyncio.sleep(0)
                 if task.done():
@@ -289,7 +303,7 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                 else:
                     results.append(task)
             else:
-                with CurrentNodeContext(prompt_id, unique_id, index):
+                with CurrentNodeContext(prompt_id, unique_id, index, expected_outputs):
                     result = f(**inputs)
                 results.append(result)
         else:
@@ -327,8 +341,17 @@ def merge_result_data(results, obj):
             output.append([o[i] for o in results])
     return output
 
-async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
-    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
+async def get_output_data(
+    prompt_id,
+    unique_id,
+    obj,
+    input_data_all,
+    execution_block_cb=None,
+    pre_execute_cb=None,
+    v3_data=None,
+    expected_outputs=None,
+):
+    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data, expected_outputs=expected_outputs)
     has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values)
     if has_pending_task:
         return return_values, {}, False, has_pending_task
@@ -522,9 +545,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             #will cause all sorts of incompatible memory shapes to fragment the pytorch alloc
             #that we just want to cull out each model run.
             allocator = comfy.memory_management.aimdo_allocator
+            expected_outputs = get_expected_outputs_for_node(dynprompt, unique_id)
             with nullcontext() if allocator is None else torch.cuda.use_mem_pool(torch.cuda.MemPool(allocator.allocator())):
                 try:
-                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
+                    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data, expected_outputs=expected_outputs)
                 finally:
                     if allocator is not None:
                         comfy.model_management.reset_cast_buffers()

nodes.py

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

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.37.11
+comfyui-frontend-package==1.38.13
 comfyui-workflow-templates==0.8.31
-comfyui-embedded-docs==0.4.0
+comfyui-embedded-docs==0.4.1
 torch
 torchsde
 torchvision

tests-unit/execution_test/expected_outputs_test.py

@@ -0,0 +1,322 @@
+"""Unit tests for the expected_outputs feature.
+
+This feature allows nodes to know at runtime which outputs are connected downstream,
+enabling them to skip computing outputs that aren't needed.
+"""
+
+from comfy_api.latest import IO
+from comfy_execution.graph import DynamicPrompt, get_expected_outputs_for_node
+from comfy_execution.utils import (
+    CurrentNodeContext,
+    ExecutionContext,
+    get_executing_context,
+    is_output_needed,
+)
+
+
+class TestGetExpectedOutputsForNode:
+    """Tests for get_expected_outputs_for_node() function."""
+
+    def test_single_output_connected(self):
+        """Test node with single output connected to one downstream node."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {"class_type": "ConsumerNode", "inputs": {"image": ["1", 0]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0})
+
+    def test_multiple_outputs_partial_connected(self):
+        """Test node with multiple outputs, only some connected."""
+        prompt = {
+            "1": {"class_type": "MultiOutputNode", "inputs": {}},
+            "2": {"class_type": "ConsumerA", "inputs": {"input": ["1", 0]}},
+            # Output 1 is not connected
+            "3": {"class_type": "ConsumerC", "inputs": {"input": ["1", 2]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0, 2})
+        assert 1 not in expected  # Output 1 is definitely unused
+
+    def test_no_outputs_connected(self):
+        """Test node with no outputs connected."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {"class_type": "OtherNode", "inputs": {}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset()
+
+    def test_same_output_connected_multiple_times(self):
+        """Test same output connected to multiple downstream nodes."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {"class_type": "ConsumerA", "inputs": {"input": ["1", 0]}},
+            "3": {"class_type": "ConsumerB", "inputs": {"input": ["1", 0]}},
+            "4": {"class_type": "ConsumerC", "inputs": {"input": ["1", 0]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0})
+
+    def test_node_not_in_prompt(self):
+        """Test getting expected outputs for a node not in the prompt."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "999")
+        assert expected == frozenset()
+
+    def test_chained_nodes(self):
+        """Test expected outputs in a chain of nodes."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {"class_type": "MiddleNode", "inputs": {"input": ["1", 0]}},
+            "3": {"class_type": "EndNode", "inputs": {"input": ["2", 0]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+
+        # Node 1's output 0 is connected to node 2
+        expected_1 = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected_1 == frozenset({0})
+
+        # Node 2's output 0 is connected to node 3
+        expected_2 = get_expected_outputs_for_node(dynprompt, "2")
+        assert expected_2 == frozenset({0})
+
+        # Node 3 has no downstream connections
+        expected_3 = get_expected_outputs_for_node(dynprompt, "3")
+        assert expected_3 == frozenset()
+
+    def test_complex_graph(self):
+        """Test expected outputs in a complex graph with multiple connections."""
+        prompt = {
+            "1": {"class_type": "MultiOutputNode", "inputs": {}},
+            "2": {"class_type": "ProcessorA", "inputs": {"image": ["1", 0], "mask": ["1", 1]}},
+            "3": {"class_type": "ProcessorB", "inputs": {"data": ["1", 2]}},
+            "4": {"class_type": "Combiner", "inputs": {"a": ["2", 0], "b": ["3", 0]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+
+        # Node 1 has outputs 0, 1, 2 all connected
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0, 1, 2})
+
+    def test_constant_inputs_ignored(self):
+        """Test that constant (non-link) inputs don't affect expected outputs."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {
+                "class_type": "ConsumerNode",
+                "inputs": {
+                    "image": ["1", 0],
+                    "value": 42,
+                    "name": "test",
+                },
+            },
+        }
+        dynprompt = DynamicPrompt(prompt)
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0})
+
+    def test_ephemeral_node_invalidates_cache(self):
+        """Test that adding ephemeral nodes updates expected outputs."""
+        prompt = {
+            "1": {"class_type": "SourceNode", "inputs": {}},
+            "2": {"class_type": "ConsumerNode", "inputs": {"image": ["1", 0]}},
+        }
+        dynprompt = DynamicPrompt(prompt)
+
+        # Initially only output 0 is connected
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0})
+
+        # Add an ephemeral node that connects to output 1
+        dynprompt.add_ephemeral_node(
+            "eph_1",
+            {"class_type": "EphemeralNode", "inputs": {"data": ["1", 1]}},
+            parent_id="2",
+            display_id="2",
+        )
+
+        # Now both outputs 0 and 1 should be expected
+        expected = get_expected_outputs_for_node(dynprompt, "1")
+        assert expected == frozenset({0, 1})
+
+
+class TestExecutionContext:
+    """Tests for ExecutionContext with expected_outputs field."""
+
+    def test_context_with_expected_outputs(self):
+        """Test creating ExecutionContext with expected_outputs."""
+        ctx = ExecutionContext(
+            prompt_id="prompt-123", node_id="node-456", list_index=0, expected_outputs=frozenset({0, 2})
+        )
+        assert ctx.prompt_id == "prompt-123"
+        assert ctx.node_id == "node-456"
+        assert ctx.list_index == 0
+        assert ctx.expected_outputs == frozenset({0, 2})
+
+    def test_context_without_expected_outputs(self):
+        """Test ExecutionContext defaults to None for expected_outputs."""
+        ctx = ExecutionContext(prompt_id="prompt-123", node_id="node-456", list_index=0)
+        assert ctx.expected_outputs is None
+
+    def test_context_empty_expected_outputs(self):
+        """Test ExecutionContext with empty expected_outputs set."""
+        ctx = ExecutionContext(
+            prompt_id="prompt-123", node_id="node-456", list_index=None, expected_outputs=frozenset()
+        )
+        assert ctx.expected_outputs == frozenset()
+        assert len(ctx.expected_outputs) == 0
+
+
+class TestCurrentNodeContext:
+    """Tests for CurrentNodeContext context manager with expected_outputs."""
+
+    def test_context_manager_with_expected_outputs(self):
+        """Test CurrentNodeContext sets and resets context correctly."""
+        assert get_executing_context() is None
+
+        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 1})):
+            ctx = get_executing_context()
+            assert ctx is not None
+            assert ctx.prompt_id == "prompt-1"
+            assert ctx.node_id == "node-1"
+            assert ctx.list_index == 0
+            assert ctx.expected_outputs == frozenset({0, 1})
+
+        assert get_executing_context() is None
+
+    def test_context_manager_without_expected_outputs(self):
+        """Test CurrentNodeContext works without expected_outputs (backwards compatible)."""
+        with CurrentNodeContext("prompt-1", "node-1"):
+            ctx = get_executing_context()
+            assert ctx is not None
+            assert ctx.expected_outputs is None
+
+    def test_nested_context_managers(self):
+        """Test nested CurrentNodeContext managers."""
+        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0})):
+            ctx1 = get_executing_context()
+            assert ctx1.expected_outputs == frozenset({0})
+
+            with CurrentNodeContext("prompt-1", "node-2", 0, frozenset({1, 2})):
+                ctx2 = get_executing_context()
+                assert ctx2.expected_outputs == frozenset({1, 2})
+                assert ctx2.node_id == "node-2"
+
+            # After inner context exits, should be back to outer context
+            ctx1_again = get_executing_context()
+            assert ctx1_again.expected_outputs == frozenset({0})
+            assert ctx1_again.node_id == "node-1"
+
+    def test_output_check_pattern(self):
+        """Test the typical pattern nodes will use to check expected outputs."""
+        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
+            ctx = get_executing_context()
+
+            # Typical usage pattern
+            if ctx and ctx.expected_outputs is not None:
+                should_compute_0 = 0 in ctx.expected_outputs
+                should_compute_1 = 1 in ctx.expected_outputs
+                should_compute_2 = 2 in ctx.expected_outputs
+            else:
+                # Fallback when info not available
+                should_compute_0 = should_compute_1 = should_compute_2 = True
+
+            assert should_compute_0 is True
+            assert should_compute_1 is False  # Not in expected_outputs
+            assert should_compute_2 is True
+
+
+class TestSchemaLazyOutputs:
+    """Tests for lazy_outputs in V3 Schema."""
+
+    def test_schema_lazy_outputs_default(self):
+        """Test that lazy_outputs defaults to False."""
+        schema = IO.Schema(
+            node_id="TestNode",
+            inputs=[],
+            outputs=[IO.Float.Output()],
+        )
+        assert schema.lazy_outputs is False
+
+    def test_schema_lazy_outputs_true(self):
+        """Test setting lazy_outputs to True."""
+        schema = IO.Schema(
+            node_id="TestNode",
+            lazy_outputs=True,
+            inputs=[],
+            outputs=[IO.Float.Output()],
+        )
+        assert schema.lazy_outputs is True
+
+    def test_v3_node_lazy_outputs_property(self):
+        """Test that LAZY_OUTPUTS property works on V3 nodes."""
+
+        class TestNodeWithLazyOutputs(IO.ComfyNode):
+            @classmethod
+            def define_schema(cls):
+                return IO.Schema(
+                    node_id="TestNodeWithLazyOutputs",
+                    lazy_outputs=True,
+                    inputs=[],
+                    outputs=[IO.Float.Output()],
+                )
+
+            @classmethod
+            def execute(cls):
+                return IO.NodeOutput(1.0)
+
+        assert TestNodeWithLazyOutputs.LAZY_OUTPUTS is True
+
+    def test_v3_node_lazy_outputs_default(self):
+        """Test that LAZY_OUTPUTS defaults to False on V3 nodes."""
+
+        class TestNodeWithoutLazyOutputs(IO.ComfyNode):
+            @classmethod
+            def define_schema(cls):
+                return IO.Schema(
+                    node_id="TestNodeWithoutLazyOutputs",
+                    inputs=[],
+                    outputs=[IO.Float.Output()],
+                )
+
+            @classmethod
+            def execute(cls):
+                return IO.NodeOutput(1.0)
+
+        assert TestNodeWithoutLazyOutputs.LAZY_OUTPUTS is False
+
+
+class TestIsOutputNeeded:
+    """Tests for is_output_needed() helper function."""
+
+    def test_output_needed_when_in_expected(self):
+        """Test that output is needed when in expected_outputs."""
+        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
+            assert is_output_needed(0) is True
+            assert is_output_needed(2) is True
+
+    def test_output_not_needed_when_not_in_expected(self):
+        """Test that output is not needed when not in expected_outputs."""
+        with CurrentNodeContext("prompt-1", "node-1", 0, frozenset({0, 2})):
+            assert is_output_needed(1) is False
+            assert is_output_needed(3) is False
+
+    def test_output_needed_when_no_context(self):
+        """Test that output is needed when no context."""
+        assert get_executing_context() is None
+        assert is_output_needed(0) is True
+        assert is_output_needed(1) is True
+
+    def test_output_needed_when_expected_outputs_is_none(self):
+        """Test that output is needed when expected_outputs is None."""
+        with CurrentNodeContext("prompt-1", "node-1", 0, None):
+            assert is_output_needed(0) is True
+            assert is_output_needed(1) is True

tests/execution/test_execution.py

@@ -574,6 +574,104 @@ class TestExecution:
         else:
             assert result.did_run(test_node), "The execution should have been re-run"
 
+    def test_expected_outputs_all_connected(self, client: ComfyClient, builder: GraphBuilder):
+        """Test that expected_outputs contains all connected outputs."""
+        g = builder
+        # Create a node with 3 outputs, all connected
+        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
+
+        # Connect all 3 outputs to preview nodes
+        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
+        output1 = g.node("PreviewImage", images=expected_outputs_node.out(1))
+        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
+
+        result = client.run(g)
+
+        # All outputs should be white (255) since all are connected
+        images0 = result.get_images(output0)
+        images1 = result.get_images(output1)
+        images2 = result.get_images(output2)
+
+        assert len(images0) == 1, "Should have 1 image for output0"
+        assert len(images1) == 1, "Should have 1 image for output1"
+        assert len(images2) == 1, "Should have 1 image for output2"
+
+        # White pixels = 255, meaning output was in expected_outputs
+        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white (was expected)"
+        assert numpy.array(images1[0]).min() == 255, "Output 1 should be white (was expected)"
+        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white (was expected)"
+
+    def test_expected_outputs_partial_connected(self, client: ComfyClient, builder: GraphBuilder):
+        """Test that expected_outputs only contains connected outputs."""
+        g = builder
+        # Create a node with 3 outputs, only some connected
+        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
+
+        # Only connect outputs 0 and 2, leave output 1 disconnected
+        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
+        # output1 is intentionally not connected
+        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
+
+        result = client.run(g)
+
+        # Connected outputs should be white (255)
+        images0 = result.get_images(output0)
+        images2 = result.get_images(output2)
+
+        assert len(images0) == 1, "Should have 1 image for output0"
+        assert len(images2) == 1, "Should have 1 image for output2"
+
+        # White = expected, output 1 is not connected so we can't verify it directly but outputs 0 and 2 should be white
+        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white (was expected)"
+        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white (was expected)"
+
+    def test_expected_outputs_single_connected(self, client: ComfyClient, builder: GraphBuilder):
+        """Test that expected_outputs works with single connected output."""
+        g = builder
+        # Create a node with 3 outputs, only one connected
+        expected_outputs_node = g.node("TestExpectedOutputs", height=64, width=64)
+
+        # Only connect output 1
+        output1 = g.node("PreviewImage", images=expected_outputs_node.out(1))
+
+        result = client.run(g)
+
+        images1 = result.get_images(output1)
+        assert len(images1) == 1, "Should have 1 image for output1"
+
+        # Output 1 should be white (connected), others are not visible in this test
+        assert numpy.array(images1[0]).min() == 255, "Output 1 should be white (was expected)"
+
+    def test_expected_outputs_cache_invalidation(self, client: ComfyClient, builder: GraphBuilder, server):
+        """Test that cache invalidates when output connections change."""
+        g = builder
+        # Use unique dimensions to avoid cache collision with other expected_outputs tests
+        expected_outputs_node = g.node("TestExpectedOutputs", height=32, width=32)
+
+        # First run: only connect output 0
+        output0 = g.node("PreviewImage", images=expected_outputs_node.out(0))
+
+        result1 = client.run(g)
+        assert result1.did_run(expected_outputs_node), "First run should execute the node"
+
+        # Second run: same connections, should be cached
+        result2 = client.run(g)
+        if server["should_cache_results"]:
+            assert not result2.did_run(expected_outputs_node), "Second run should be cached"
+
+        # Third run: add connection to output 2
+        output2 = g.node("PreviewImage", images=expected_outputs_node.out(2))
+
+        result3 = client.run(g)
+        # Because LAZY_OUTPUTS=True, changing connections should invalidate cache
+        if server["should_cache_results"]:
+            assert result3.did_run(expected_outputs_node), "Adding output connection should invalidate cache"
+
+        # Verify both outputs are now white
+        images0 = result3.get_images(output0)
+        images2 = result3.get_images(output2)
+        assert numpy.array(images0[0]).min() == 255, "Output 0 should be white"
+        assert numpy.array(images2[0]).min() == 255, "Output 2 should be white"
 
     def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
         # Warmup execution to ensure server is fully initialized

tests/execution/testing_nodes/testing-pack/specific_tests.py

@@ -6,6 +6,7 @@ from .tools import VariantSupport
 from comfy_execution.graph_utils import GraphBuilder
 from comfy.comfy_types.node_typing import ComfyNodeABC
 from comfy.comfy_types import IO
+from comfy_execution.utils import get_executing_context
 
 class TestLazyMixImages:
     @classmethod
@@ -482,6 +483,57 @@ class TestOutputNodeWithSocketOutput:
         result = image * value
         return (result,)
 
+
+class TestExpectedOutputs:
+    """Test node for the expected_outputs feature.
+
+    This node has 3 IMAGE outputs that encode which outputs were expected:
+    - White image (255) if the output was in expected_outputs
+    - Black image (0) if the output was NOT in expected_outputs
+
+    This allows integration tests to verify which outputs were expected by checking pixel values.
+    """
+    LAZY_OUTPUTS = True  # Opt into cache invalidation on output connection changes
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "height": ("INT", {"default": 64, "min": 1, "max": 1024}),
+                "width": ("INT", {"default": 64, "min": 1, "max": 1024}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
+    RETURN_NAMES = ("output0", "output1", "output2")
+    FUNCTION = "execute"
+    CATEGORY = "_for_testing"
+
+    def execute(self, height, width):
+        ctx = get_executing_context()
+
+        # Default: assume all outputs are expected (backwards compatibility)
+        output0_expected = True
+        output1_expected = True
+        output2_expected = True
+
+        if ctx is not None and ctx.expected_outputs is not None:
+            output0_expected = 0 in ctx.expected_outputs
+            output1_expected = 1 in ctx.expected_outputs
+            output2_expected = 2 in ctx.expected_outputs
+
+        # Return white image if expected, black if not
+        # This allows tests to verify which outputs were expected via pixel values
+        white = torch.ones(1, height, width, 3)
+        black = torch.zeros(1, height, width, 3)
+
+        return (
+            white if output0_expected else black,
+            white if output1_expected else black,
+            white if output2_expected else black,
+        )
+
+
 TEST_NODE_CLASS_MAPPINGS = {
     "TestLazyMixImages": TestLazyMixImages,
     "TestVariadicAverage": TestVariadicAverage,
@@ -498,6 +550,7 @@ TEST_NODE_CLASS_MAPPINGS = {
     "TestSleep": TestSleep,
     "TestParallelSleep": TestParallelSleep,
     "TestOutputNodeWithSocketOutput": TestOutputNodeWithSocketOutput,
+    "TestExpectedOutputs": TestExpectedOutputs,
 }
 
 TEST_NODE_DISPLAY_NAME_MAPPINGS = {
@@ -516,4 +569,5 @@ TEST_NODE_DISPLAY_NAME_MAPPINGS = {
     "TestSleep": "Test Sleep",
     "TestParallelSleep": "Test Parallel Sleep",
     "TestOutputNodeWithSocketOutput": "Test Output Node With Socket Output",
+    "TestExpectedOutputs": "Test Expected Outputs",
 }