Merge branch 'master' into portable-manager-update

.ci/update_windows/update.py

@@ -53,6 +53,16 @@ try:
     repo.stash(ident)
 except KeyError:
     print("nothing to stash")  # noqa: T201
+except:
+    print("Could not stash, cleaning index and trying again.")  # noqa: T201
+    repo.state_cleanup()
+    repo.index.read_tree(repo.head.peel().tree)
+    repo.index.write()
+    try:
+        repo.stash(ident)
+    except KeyError:
+        print("nothing to stash.")  # noqa: T201
+
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
 print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:

api_server/routes/internal/internal_routes.py

@@ -58,8 +58,13 @@ class InternalRoutes:
                 return web.json_response({"error": "Invalid directory type"}, status=400)
 
             directory = get_directory_by_type(directory_type)
+
+            def is_visible_file(entry: os.DirEntry) -> bool:
+                """Filter out hidden files (e.g., .DS_Store on macOS)."""
+                return entry.is_file() and not entry.name.startswith('.')
+
             sorted_files = sorted(
-                (entry for entry in os.scandir(directory) if entry.is_file()),
+                (entry for entry in os.scandir(directory) if is_visible_file(entry)),
                 key=lambda entry: -entry.stat().st_mtime
             )
             return web.json_response([entry.name for entry in sorted_files], status=200)

comfy/cli_args.py

@@ -97,6 +97,13 @@ class LatentPreviewMethod(enum.Enum):
     Latent2RGB = "latent2rgb"
     TAESD = "taesd"
 
+    @classmethod
+    def from_string(cls, value: str):
+        for member in cls:
+            if member.value == value:
+                return member
+        return None
+
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")

comfy/context_windows.py

@@ -87,6 +87,7 @@ class IndexListCallbacks:
     COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results"
     EXECUTE_START = "execute_start"
     EXECUTE_CLEANUP = "execute_cleanup"
+    RESIZE_COND_ITEM = "resize_cond_item"
 
     def init_callbacks(self):
         return {}
@@ -166,6 +167,18 @@ class IndexListContextHandler(ContextHandlerABC):
                         new_cond_item = cond_item.copy()
                         # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
                         for cond_key, cond_value in new_cond_item.items():
+                            # Allow callbacks to handle custom conditioning items
+                            handled = False
+                            for callback in comfy.patcher_extension.get_all_callbacks(
+                                IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks
+                            ):
+                                result = callback(cond_key, cond_value, window, x_in, device, new_cond_item)
+                                if result is not None:
+                                    new_cond_item[cond_key] = result
+                                    handled = True
+                                    break
+                            if handled:
+                                continue
                             if isinstance(cond_value, torch.Tensor):
                                 if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
                                    (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):

comfy/k_diffusion/sampling.py

@@ -1557,10 +1557,13 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
 
 
 @torch.no_grad()
-def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
+def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type="phi_1"):
     """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
     arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
+    if solver_type not in {"phi_1", "phi_2"}:
+        raise ValueError("solver_type must be 'phi_1' or 'phi_2'")
+
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@@ -1600,8 +1603,14 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
         denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
         # Step 2
-        denoised_d = torch.lerp(denoised, denoised_2, fac)
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        if solver_type == "phi_1":
+            denoised_d = torch.lerp(denoised, denoised_2, fac)
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        elif solver_type == "phi_2":
+            b2 = ei_h_phi_2(-h_eta) / r
+            b1 = ei_h_phi_1(-h_eta) - b2
+            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)
+
         if inject_noise:
             segment_factor = (r - 1) * h * eta
             sde_noise = sde_noise * segment_factor.exp()

comfy/ldm/chroma_radiance/model.py

@@ -37,7 +37,7 @@ class ChromaRadianceParams(ChromaParams):
     nerf_final_head_type: str
     # None means use the same dtype as the model.
     nerf_embedder_dtype: Optional[torch.dtype]
-
+    use_x0: bool
 
 class ChromaRadiance(Chroma):
     """
@@ -159,6 +159,9 @@ class ChromaRadiance(Chroma):
         self.skip_dit = []
         self.lite = False
 
+        if params.use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
     @property
     def _nerf_final_layer(self) -> nn.Module:
         if self.params.nerf_final_head_type == "linear":
@@ -276,6 +279,12 @@ class ChromaRadiance(Chroma):
         params_dict |= overrides
         return params.__class__(**params_dict)
 
+    def _apply_x0_residual(self, predicted, noisy, timesteps):
+
+        # non zero during training to prevent 0 div
+        eps = 0.0
+        return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)
+
     def _forward(
         self,
         x: Tensor,
@@ -316,4 +325,11 @@ class ChromaRadiance(Chroma):
             transformer_options,
             attn_mask=kwargs.get("attention_mask", None),
         )
-        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]
+
+        # If x0 variant → v-pred, just return this instead
+        if hasattr(self, "__x0__"):
+            out = self._apply_x0_residual(out, img, timestep)
+        return out
+

comfy/ldm/hunyuan_video/model.py

@@ -43,6 +43,7 @@ class HunyuanVideoParams:
     meanflow: bool
     use_cond_type_embedding: bool
     vision_in_dim: int
+    meanflow_sum: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -317,7 +318,7 @@ class HunyuanVideo(nn.Module):
                 timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
                 timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
                 vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
-                vec = (vec + vec_r) / 2
+                vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2
 
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)

comfy/ldm/lumina/controlnet.py

@@ -41,6 +41,11 @@ class ZImage_Control(torch.nn.Module):
         ffn_dim_multiplier: float = (8.0 / 3.0),
         norm_eps: float = 1e-5,
         qk_norm: bool = True,
+        n_control_layers=6,
+        control_in_dim=16,
+        additional_in_dim=0,
+        broken=False,
+        refiner_control=False,
         dtype=None,
         device=None,
         operations=None,
@@ -49,10 +54,11 @@ class ZImage_Control(torch.nn.Module):
         super().__init__()
         operation_settings = {"operations": operations, "device": device, "dtype": dtype}
 
-        self.additional_in_dim = 0
-        self.control_in_dim = 16
+        self.broken = broken
+        self.additional_in_dim = additional_in_dim
+        self.control_in_dim = control_in_dim
         n_refiner_layers = 2
-        self.n_control_layers = 6
+        self.n_control_layers = n_control_layers
         self.control_layers = nn.ModuleList(
             [
                 ZImageControlTransformerBlock(
@@ -74,28 +80,49 @@ class ZImage_Control(torch.nn.Module):
         all_x_embedder = {}
         patch_size = 2
         f_patch_size = 1
-        x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * self.control_in_dim, dim, bias=True, device=device, dtype=dtype)
+        x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * (self.control_in_dim + self.additional_in_dim), dim, bias=True, device=device, dtype=dtype)
         all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
 
+        self.refiner_control = refiner_control
+
         self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
-        self.control_noise_refiner = nn.ModuleList(
-            [
-                JointTransformerBlock(
-                    layer_id,
-                    dim,
-                    n_heads,
-                    n_kv_heads,
-                    multiple_of,
-                    ffn_dim_multiplier,
-                    norm_eps,
-                    qk_norm,
-                    modulation=True,
-                    z_image_modulation=True,
-                    operation_settings=operation_settings,
-                )
-                for layer_id in range(n_refiner_layers)
-            ]
-        )
+        if self.refiner_control:
+            self.control_noise_refiner = nn.ModuleList(
+                [
+                    ZImageControlTransformerBlock(
+                        layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        multiple_of,
+                        ffn_dim_multiplier,
+                        norm_eps,
+                        qk_norm,
+                        block_id=layer_id,
+                        operation_settings=operation_settings,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+        else:
+            self.control_noise_refiner = nn.ModuleList(
+                [
+                    JointTransformerBlock(
+                        layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        multiple_of,
+                        ffn_dim_multiplier,
+                        norm_eps,
+                        qk_norm,
+                        modulation=True,
+                        z_image_modulation=True,
+                        operation_settings=operation_settings,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
 
     def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
         patch_size = 2
@@ -105,9 +132,29 @@ class ZImage_Control(torch.nn.Module):
         control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
 
         x_attn_mask = None
-        for layer in self.control_noise_refiner:
-            control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+        if not self.refiner_control:
+            for layer in self.control_noise_refiner:
+                control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+
         return control_context
 
+    def forward_noise_refiner_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+        if self.refiner_control:
+            if self.broken:
+                if layer_id == 0:
+                    return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+                if layer_id > 0:
+                    out = None
+                    for i in range(1, len(self.control_layers)):
+                        o, control_context = self.control_layers[i](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+                        if out is None:
+                            out = o
+
+                    return (out, control_context)
+            else:
+                return self.control_noise_refiner[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
+        else:
+            return (None, control_context)
+
     def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
         return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)

comfy/ldm/lumina/model.py

@@ -536,6 +536,7 @@ class NextDiT(nn.Module):
         bsz = len(x)
         pH = pW = self.patch_size
         device = x[0].device
+        orig_x = x
 
         if self.pad_tokens_multiple is not None:
             pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
@@ -572,13 +573,21 @@ class NextDiT(nn.Module):
 
         freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)
 
+        patches = transformer_options.get("patches", {})
+
         # refine context
         for layer in self.context_refiner:
             cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options)
 
         padded_img_mask = None
-        for layer in self.noise_refiner:
+        x_input = x
+        for i, layer in enumerate(self.noise_refiner):
             x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options)
+            if "noise_refiner" in patches:
+                for p in patches["noise_refiner"]:
+                    out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": freqs_cis[:, cap_pos_ids.shape[1]:], "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"})
+                    if "img" in out:
+                        x = out["img"]
 
         padded_full_embed = torch.cat((cap_feats, x), dim=1)
         mask = None
@@ -622,14 +631,15 @@ class NextDiT(nn.Module):
 
         patches = transformer_options.get("patches", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, transformer_options=transformer_options)
         freqs_cis = freqs_cis.to(img.device)
 
+        img_input = img
         for i, layer in enumerate(self.layers):
             img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
             if "double_block" in patches:
                 for p in patches["double_block"]:
-                    out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
                     if "img" in out:
                         img[:, cap_size[0]:] = out["img"]
                     if "txt" in out:

comfy/ldm/qwen_image/model.py

@@ -218,9 +218,24 @@ class QwenImageTransformerBlock(nn.Module):
             operations=operations,
         )
 
-    def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def _apply_gate(self, x, y, gate, timestep_zero_index=None):
+        if timestep_zero_index is not None:
+            return y + torch.cat((x[:, :timestep_zero_index] * gate[0], x[:, timestep_zero_index:] * gate[1]), dim=1)
+        else:
+            return torch.addcmul(y, gate, x)
+
+    def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor, timestep_zero_index=None) -> Tuple[torch.Tensor, torch.Tensor]:
         shift, scale, gate = torch.chunk(mod_params, 3, dim=-1)
-        return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)
+        if timestep_zero_index is not None:
+            actual_batch = shift.size(0) // 2
+            shift, shift_0 = shift[:actual_batch], shift[actual_batch:]
+            scale, scale_0 = scale[:actual_batch], scale[actual_batch:]
+            gate, gate_0 = gate[:actual_batch], gate[actual_batch:]
+            reg = torch.addcmul(shift.unsqueeze(1), x[:, :timestep_zero_index], 1 + scale.unsqueeze(1))
+            zero = torch.addcmul(shift_0.unsqueeze(1), x[:, timestep_zero_index:], 1 + scale_0.unsqueeze(1))
+            return torch.cat((reg, zero), dim=1), (gate.unsqueeze(1), gate_0.unsqueeze(1))
+        else:
+            return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)
 
     def forward(
         self,
@@ -229,14 +244,19 @@ class QwenImageTransformerBlock(nn.Module):
         encoder_hidden_states_mask: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        timestep_zero_index=None,
         transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         img_mod_params = self.img_mod(temb)
+
+        if timestep_zero_index is not None:
+            temb = temb.chunk(2, dim=0)[0]
+
         txt_mod_params = self.txt_mod(temb)
         img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)
         txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)
 
-        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1)
+        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1, timestep_zero_index)
         del img_mod1
         txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)
         del txt_mod1
@@ -251,15 +271,15 @@ class QwenImageTransformerBlock(nn.Module):
         del img_modulated
         del txt_modulated
 
-        hidden_states = hidden_states + img_gate1 * img_attn_output
+        hidden_states = self._apply_gate(img_attn_output, hidden_states, img_gate1, timestep_zero_index)
         encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
         del img_attn_output
         del txt_attn_output
         del img_gate1
         del txt_gate1
 
-        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2)
-        hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2))
+        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2, timestep_zero_index)
+        hidden_states = self._apply_gate(self.img_mlp(img_modulated2), hidden_states, img_gate2, timestep_zero_index)
 
         txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)
         encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))
@@ -391,11 +411,14 @@ class QwenImageTransformer2DModel(nn.Module):
         hidden_states, img_ids, orig_shape = self.process_img(x)
         num_embeds = hidden_states.shape[1]
 
+        timestep_zero_index = None
         if ref_latents is not None:
             h = 0
             w = 0
             index = 0
-            index_ref_method = kwargs.get("ref_latents_method", "index") == "index"
+            ref_method = kwargs.get("ref_latents_method", "index")
+            index_ref_method = (ref_method == "index") or (ref_method == "index_timestep_zero")
+            timestep_zero = ref_method == "index_timestep_zero"
             for ref in ref_latents:
                 if index_ref_method:
                     index += 1
@@ -415,6 +438,10 @@ class QwenImageTransformer2DModel(nn.Module):
                 kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 hidden_states = torch.cat([hidden_states, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+            if timestep_zero:
+                if index > 0:
+                    timestep = torch.cat([timestep, timestep * 0], dim=0)
+                    timestep_zero_index = num_embeds
 
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
@@ -446,7 +473,7 @@ class QwenImageTransformer2DModel(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
+                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], timestep_zero_index=timestep_zero_index, transformer_options=args["transformer_options"])
                     return out
                 out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                 hidden_states = out["img"]
@@ -458,6 +485,7 @@ class QwenImageTransformer2DModel(nn.Module):
                     encoder_hidden_states_mask=encoder_hidden_states_mask,
                     temb=temb,
                     image_rotary_emb=image_rotary_emb,
+                    timestep_zero_index=timestep_zero_index,
                     transformer_options=transformer_options,
                 )
 
@@ -474,6 +502,9 @@ class QwenImageTransformer2DModel(nn.Module):
                     if add is not None:
                         hidden_states[:, :add.shape[1]] += add
 
+        if timestep_zero_index is not None:
+            temb = temb.chunk(2, dim=0)[0]
+
         hidden_states = self.norm_out(hidden_states, temb)
         hidden_states = self.proj_out(hidden_states)
 

comfy/model_detection.py

@@ -180,8 +180,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["use_cond_type_embedding"] = False
         if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
             dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
+            dit_config["meanflow_sum"] = True
         else:
             dit_config["vision_in_dim"] = None
+            dit_config["meanflow_sum"] = False
         return dit_config
 
     if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
@@ -257,6 +259,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_tile_size"] = 512
                 dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
+                if "__x0__" in state_dict_keys: # x0 pred
+                    dit_config["use_x0"] = True
+                else:
+                    dit_config["use_x0"] = False
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
             dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys

comfy/model_patcher.py

@@ -454,6 +454,9 @@ class ModelPatcher:
     def set_model_post_input_patch(self, patch):
         self.set_model_patch(patch, "post_input")
 
+    def set_model_noise_refiner_patch(self, patch):
+        self.set_model_patch(patch, "noise_refiner")
+
     def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):
         rope_options = self.model_options["transformer_options"].get("rope_options", {})
         rope_options["scale_x"] = scale_x

comfy/ops.py

@@ -22,7 +22,6 @@ import comfy.model_management
 from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 import comfy.rmsnorm
-import contextlib
 import json
 
 def run_every_op():
@@ -94,13 +93,6 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     else:
         offload_stream = None
 
-    if offload_stream is not None:
-        wf_context = offload_stream
-        if hasattr(wf_context, "as_context"):
-            wf_context = wf_context.as_context(offload_stream)
-    else:
-        wf_context = contextlib.nullcontext()
-
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
     weight_has_function = len(s.weight_function) > 0
@@ -505,15 +497,14 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
             ) -> None:
                 super().__init__()
 
-                self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
-                # self.factory_kwargs = {"device": device, "dtype": dtype}
+                if dtype is None:
+                    dtype = MixedPrecisionOps._compute_dtype
+
+                self.factory_kwargs = {"device": device, "dtype": dtype}
 
                 self.in_features = in_features
                 self.out_features = out_features
-                if bias:
-                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
-                else:
-                    self.register_parameter("bias", None)
+                self._has_bias = bias
 
                 self.tensor_class = None
                 self._full_precision_mm = MixedPrecisionOps._full_precision_mm
@@ -538,7 +529,14 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     layer_conf = json.loads(layer_conf.numpy().tobytes())
 
                 if layer_conf is None:
-                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
+                    dtype = self.factory_kwargs["dtype"]
+                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=dtype), requires_grad=False)
+                    if dtype != MixedPrecisionOps._compute_dtype:
+                        self.comfy_cast_weights = True
+                    if self._has_bias:
+                        self.bias = torch.nn.Parameter(torch.empty(self.out_features, device=device, dtype=dtype))
+                    else:
+                        self.register_parameter("bias", None)
                 else:
                     self.quant_format = layer_conf.get("format", None)
                     if not self._full_precision_mm:
@@ -568,6 +566,11 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                         requires_grad=False
                     )
 
+                    if self._has_bias:
+                        self.bias = torch.nn.Parameter(torch.empty(self.out_features, device=device, dtype=MixedPrecisionOps._compute_dtype))
+                    else:
+                        self.register_parameter("bias", None)
+
                     for param_name in qconfig["parameters"]:
                         param_key = f"{prefix}{param_name}"
                         _v = state_dict.pop(param_key, None)
@@ -589,7 +592,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     quant_conf = {"format": self.quant_format}
                     if self._full_precision_mm:
                         quant_conf["full_precision_matrix_mult"] = True
-                    sd["{}comfy_quant".format(prefix)] = torch.frombuffer(json.dumps(quant_conf).encode('utf-8'), dtype=torch.uint8)
+                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
                 return sd
 
             def _forward(self, input, weight, bias):

comfy/quant_ops.py

@@ -399,7 +399,10 @@ class TensorCoreFP8Layout(QuantizedLayout):
         orig_dtype = tensor.dtype
 
         if isinstance(scale, str) and scale == "recalculate":
-            scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
+            scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(dtype).max
+            if tensor.dtype not in [torch.float32, torch.bfloat16]:  # Prevent scale from being too small
+                tensor_info = torch.finfo(tensor.dtype)
+                scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max))
 
         if scale is not None:
             if not isinstance(scale, torch.Tensor):

comfy/sd.py

@@ -549,8 +549,10 @@ class VAE:
                     ddconfig = {"dim": dim, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
                     self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
                     self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
-                    self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
-                    self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
+                    self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype)
+
+
             # Hunyuan 3d v2 2.0 & 2.1
             elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
 

comfy/supported_models.py

@@ -541,7 +541,7 @@ class SD3(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.SD3
 
-    memory_usage_factor = 1.2
+    memory_usage_factor = 1.6
 
     text_encoder_key_prefix = ["text_encoders."]
 
@@ -965,7 +965,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
-        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.CosmosPredict2(self, device=device)
@@ -1026,7 +1026,7 @@ class ZImage(Lumina2):
         "shift": 3.0,
     }
 
-    memory_usage_factor = 1.7
+    memory_usage_factor = 2.0
 
     supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
@@ -1289,7 +1289,7 @@ class ChromaRadiance(Chroma):
     latent_format = comfy.latent_formats.ChromaRadiance
 
     # Pixel-space model, no spatial compression for model input.
-    memory_usage_factor = 0.038
+    memory_usage_factor = 0.044
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.ChromaRadiance(self, device=device)
@@ -1332,7 +1332,7 @@ class Omnigen2(supported_models_base.BASE):
         "shift": 2.6,
     }
 
-    memory_usage_factor = 1.65 #TODO
+    memory_usage_factor = 1.95 #TODO
 
     unet_extra_config = {}
     latent_format = latent_formats.Flux
@@ -1397,7 +1397,7 @@ class HunyuanImage21(HunyuanVideo):
 
     latent_format = latent_formats.HunyuanImage21
 
-    memory_usage_factor = 7.7
+    memory_usage_factor = 8.7
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -1488,7 +1488,7 @@ class Kandinsky5(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.HunyuanVideo
 
-    memory_usage_factor = 1.1 #TODO
+    memory_usage_factor = 1.25 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -1517,7 +1517,7 @@ class Kandinsky5Image(Kandinsky5):
     }
 
     latent_format = latent_formats.Flux
-    memory_usage_factor = 1.1 #TODO
+    memory_usage_factor = 1.25 #TODO
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Kandinsky5Image(self, device=device)

comfy/utils.py

@@ -53,7 +53,7 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
     ALWAYS_SAFE_LOAD = True
     logging.info("Checkpoint files will always be loaded safely.")
 else:
-    logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
+    logging.warning("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended as older versions of pytorch are no longer supported.")
 
 def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     if device is None:
@@ -1262,6 +1262,6 @@ def convert_old_quants(state_dict, model_prefix="", metadata={}):
     if quant_metadata is not None:
         layers = quant_metadata["layers"]
         for k, v in layers.items():
-            state_dict["{}.comfy_quant".format(k)] = torch.frombuffer(json.dumps(v).encode('utf-8'), dtype=torch.uint8)
+            state_dict["{}.comfy_quant".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8)
 
     return state_dict, metadata

comfy_api/feature_flags.py

@@ -5,12 +5,12 @@ This module handles capability negotiation between frontend and backend,
 allowing graceful protocol evolution while maintaining backward compatibility.
 """
 
-from typing import Any, Dict
+from typing import Any
 
 from comfy.cli_args import args
 
 # Default server capabilities
-SERVER_FEATURE_FLAGS: Dict[str, Any] = {
+SERVER_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
@@ -18,7 +18,7 @@ SERVER_FEATURE_FLAGS: Dict[str, Any] = {
 
 
 def get_connection_feature(
-    sockets_metadata: Dict[str, Dict[str, Any]],
+    sockets_metadata: dict[str, dict[str, Any]],
     sid: str,
     feature_name: str,
     default: Any = False
@@ -42,7 +42,7 @@ def get_connection_feature(
 
 
 def supports_feature(
-    sockets_metadata: Dict[str, Dict[str, Any]],
+    sockets_metadata: dict[str, dict[str, Any]],
     sid: str,
     feature_name: str
 ) -> bool:
@@ -60,7 +60,7 @@ def supports_feature(
     return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
 
 
-def get_server_features() -> Dict[str, Any]:
+def get_server_features() -> dict[str, Any]:
     """
     Get the server's feature flags.
 

comfy_api/internal/api_registry.py

@@ -1,4 +1,4 @@
-from typing import Type, List, NamedTuple
+from typing import NamedTuple
 from comfy_api.internal.singleton import ProxiedSingleton
 from packaging import version as packaging_version
 
@@ -10,7 +10,7 @@ class ComfyAPIBase(ProxiedSingleton):
 
 class ComfyAPIWithVersion(NamedTuple):
     version: str
-    api_class: Type[ComfyAPIBase]
+    api_class: type[ComfyAPIBase]
 
 
 def parse_version(version_str: str) -> packaging_version.Version:
@@ -23,16 +23,16 @@ def parse_version(version_str: str) -> packaging_version.Version:
     return packaging_version.parse(version_str)
 
 
-registered_versions: List[ComfyAPIWithVersion] = []
+registered_versions: list[ComfyAPIWithVersion] = []
 
 
-def register_versions(versions: List[ComfyAPIWithVersion]):
+def register_versions(versions: list[ComfyAPIWithVersion]):
     versions.sort(key=lambda x: parse_version(x.version))
     global registered_versions
     registered_versions = versions
 
 
-def get_all_versions() -> List[ComfyAPIWithVersion]:
+def get_all_versions() -> list[ComfyAPIWithVersion]:
     """
     Returns a list of all registered ComfyAPI versions.
     """

comfy_api/internal/async_to_sync.py

@@ -8,7 +8,7 @@ import os
 import textwrap
 import threading
 from enum import Enum
-from typing import Optional, Type, get_origin, get_args, get_type_hints
+from typing import Optional, get_origin, get_args, get_type_hints
 
 
 class TypeTracker:
@@ -193,7 +193,7 @@ class AsyncToSyncConverter:
         return result_container["result"]
 
     @classmethod
-    def create_sync_class(cls, async_class: Type, thread_pool_size=10) -> Type:
+    def create_sync_class(cls, async_class: type, thread_pool_size=10) -> type:
         """
         Creates a new class with synchronous versions of all async methods.
 
@@ -563,7 +563,7 @@ class AsyncToSyncConverter:
 
     @classmethod
     def _generate_imports(
-        cls, async_class: Type, type_tracker: TypeTracker
+        cls, async_class: type, type_tracker: TypeTracker
     ) -> list[str]:
         """Generate import statements for the stub file."""
         imports = []
@@ -628,7 +628,7 @@ class AsyncToSyncConverter:
         return imports
 
     @classmethod
-    def _get_class_attributes(cls, async_class: Type) -> list[tuple[str, Type]]:
+    def _get_class_attributes(cls, async_class: type) -> list[tuple[str, type]]:
         """Extract class attributes that are classes themselves."""
         class_attributes = []
 
@@ -654,7 +654,7 @@ class AsyncToSyncConverter:
     def _generate_inner_class_stub(
         cls,
         name: str,
-        attr: Type,
+        attr: type,
         indent: str = "    ",
         type_tracker: Optional[TypeTracker] = None,
     ) -> list[str]:
@@ -782,7 +782,7 @@ class AsyncToSyncConverter:
         return processed
 
     @classmethod
-    def generate_stub_file(cls, async_class: Type, sync_class: Type) -> None:
+    def generate_stub_file(cls, async_class: type, sync_class: type) -> None:
         """
         Generate a .pyi stub file for the sync class to help IDEs with type checking.
         """
@@ -988,7 +988,7 @@ class AsyncToSyncConverter:
             logging.error(traceback.format_exc())
 
 
-def create_sync_class(async_class: Type, thread_pool_size=10) -> Type:
+def create_sync_class(async_class: type, thread_pool_size=10) -> type:
     """
     Creates a sync version of an async class
 

comfy_api/internal/singleton.py

@@ -1,4 +1,4 @@
-from typing import Type, TypeVar
+from typing import TypeVar
 
 class SingletonMetaclass(type):
     T = TypeVar("T", bound="SingletonMetaclass")
@@ -11,13 +11,13 @@ class SingletonMetaclass(type):
             )
         return cls._instances[cls]
 
-    def inject_instance(cls: Type[T], instance: T) -> None:
+    def inject_instance(cls: type[T], instance: T) -> None:
         assert cls not in SingletonMetaclass._instances, (
             "Cannot inject instance after first instantiation"
         )
         SingletonMetaclass._instances[cls] = instance
 
-    def get_instance(cls: Type[T], *args, **kwargs) -> T:
+    def get_instance(cls: type[T], *args, **kwargs) -> T:
         """
         Gets the singleton instance of the class, creating it if it doesn't exist.
         """

comfy_api/latest/__init__.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Type, TYPE_CHECKING
+from typing import TYPE_CHECKING
 from comfy_api.internal import ComfyAPIBase
 from comfy_api.internal.singleton import ProxiedSingleton
 from comfy_api.internal.async_to_sync import create_sync_class
@@ -113,7 +113,7 @@ ComfyAPI = ComfyAPI_latest
 if TYPE_CHECKING:
     import comfy_api.latest.generated.ComfyAPISyncStub  # type: ignore
 
-    ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
+    ComfyAPISync: type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
 ComfyAPISync = create_sync_class(ComfyAPI_latest)
 
 # create new aliases for io and ui

comfy_api/latest/_input/basic_types.py

@@ -1,5 +1,5 @@
 import torch
-from typing import TypedDict, List, Optional
+from typing import TypedDict, Optional
 
 ImageInput = torch.Tensor
 """
@@ -39,4 +39,4 @@ class LatentInput(TypedDict):
     Optional noise mask tensor in the same format as samples.
     """
 
-    batch_index: Optional[List[int]]
+    batch_index: Optional[list[int]]

comfy_api/latest/_io.py

@@ -774,6 +774,13 @@ class AudioEncoder(ComfyTypeIO):
 class AudioEncoderOutput(ComfyTypeIO):
     Type = Any
 
+@comfytype(io_type="TRACKS")
+class Tracks(ComfyTypeIO):
+    class TrackDict(TypedDict):
+        track_path: torch.Tensor
+        track_visibility: torch.Tensor
+    Type = TrackDict
+
 @comfytype(io_type="COMFY_MULTITYPED_V3")
 class MultiType:
     Type = Any
@@ -1815,7 +1822,7 @@ class NodeOutput(_NodeOutputInternal):
             ui = data["ui"]
         if "expand" in data:
             expand = data["expand"]
-        return cls(args=args, ui=ui, expand=expand)
+        return cls(*args, ui=ui, expand=expand)
 
     def __getitem__(self, index) -> Any:
         return self.args[index]
@@ -1894,6 +1901,7 @@ __all__ = [
     "SEGS",
     "AnyType",
     "MultiType",
+    "Tracks",
     # Dynamic Types
     "MatchType",
     # "DynamicCombo",

comfy_api/latest/_ui.py

@@ -5,7 +5,6 @@ import os
 import random
 import uuid
 from io import BytesIO
-from typing import Type
 
 import av
 import numpy as np
@@ -83,7 +82,7 @@ class ImageSaveHelper:
         return PILImage.fromarray(np.clip(255.0 * image_tensor.cpu().numpy(), 0, 255).astype(np.uint8))
 
     @staticmethod
-    def _create_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None:
+    def _create_png_metadata(cls: type[ComfyNode] | None) -> PngInfo | None:
         """Creates a PngInfo object with prompt and extra_pnginfo."""
         if args.disable_metadata or cls is None or not cls.hidden:
             return None
@@ -96,7 +95,7 @@ class ImageSaveHelper:
         return metadata
 
     @staticmethod
-    def _create_animated_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None:
+    def _create_animated_png_metadata(cls: type[ComfyNode] | None) -> PngInfo | None:
         """Creates a PngInfo object with prompt and extra_pnginfo for animated PNGs (APNG)."""
         if args.disable_metadata or cls is None or not cls.hidden:
             return None
@@ -121,7 +120,7 @@ class ImageSaveHelper:
         return metadata
 
     @staticmethod
-    def _create_webp_metadata(pil_image: PILImage.Image, cls: Type[ComfyNode] | None) -> PILImage.Exif:
+    def _create_webp_metadata(pil_image: PILImage.Image, cls: type[ComfyNode] | None) -> PILImage.Exif:
         """Creates EXIF metadata bytes for WebP images."""
         exif_data = pil_image.getexif()
         if args.disable_metadata or cls is None or cls.hidden is None:
@@ -137,7 +136,7 @@ class ImageSaveHelper:
 
     @staticmethod
     def save_images(
-        images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, compress_level = 4,
+        images, filename_prefix: str, folder_type: FolderType, cls: type[ComfyNode] | None, compress_level = 4,
     ) -> list[SavedResult]:
         """Saves a batch of images as individual PNG files."""
         full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
@@ -155,7 +154,7 @@ class ImageSaveHelper:
         return results
 
     @staticmethod
-    def get_save_images_ui(images, filename_prefix: str, cls: Type[ComfyNode] | None, compress_level=4) -> SavedImages:
+    def get_save_images_ui(images, filename_prefix: str, cls: type[ComfyNode] | None, compress_level=4) -> SavedImages:
         """Saves a batch of images and returns a UI object for the node output."""
         return SavedImages(
                 ImageSaveHelper.save_images(
@@ -169,7 +168,7 @@ class ImageSaveHelper:
 
     @staticmethod
     def save_animated_png(
-        images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, fps: float, compress_level: int
+        images, filename_prefix: str, folder_type: FolderType, cls: type[ComfyNode] | None, fps: float, compress_level: int
     ) -> SavedResult:
         """Saves a batch of images as a single animated PNG."""
         full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
@@ -191,7 +190,7 @@ class ImageSaveHelper:
 
     @staticmethod
     def get_save_animated_png_ui(
-        images, filename_prefix: str, cls: Type[ComfyNode] | None, fps: float, compress_level: int
+        images, filename_prefix: str, cls: type[ComfyNode] | None, fps: float, compress_level: int
     ) -> SavedImages:
         """Saves an animated PNG and returns a UI object for the node output."""
         result = ImageSaveHelper.save_animated_png(
@@ -209,7 +208,7 @@ class ImageSaveHelper:
         images,
         filename_prefix: str,
         folder_type: FolderType,
-        cls: Type[ComfyNode] | None,
+        cls: type[ComfyNode] | None,
         fps: float,
         lossless: bool,
         quality: int,
@@ -238,7 +237,7 @@ class ImageSaveHelper:
     def get_save_animated_webp_ui(
         images,
         filename_prefix: str,
-        cls: Type[ComfyNode] | None,
+        cls: type[ComfyNode] | None,
         fps: float,
         lossless: bool,
         quality: int,
@@ -267,7 +266,7 @@ class AudioSaveHelper:
         audio: dict,
         filename_prefix: str,
         folder_type: FolderType,
-        cls: Type[ComfyNode] | None,
+        cls: type[ComfyNode] | None,
         format: str = "flac",
         quality: str = "128k",
     ) -> list[SavedResult]:
@@ -372,7 +371,7 @@ class AudioSaveHelper:
 
     @staticmethod
     def get_save_audio_ui(
-        audio, filename_prefix: str, cls: Type[ComfyNode] | None, format: str = "flac", quality: str = "128k",
+        audio, filename_prefix: str, cls: type[ComfyNode] | None, format: str = "flac", quality: str = "128k",
     ) -> SavedAudios:
         """Save and instantly wrap for UI."""
         return SavedAudios(
@@ -388,7 +387,7 @@ class AudioSaveHelper:
 
 
 class PreviewImage(_UIOutput):
-    def __init__(self, image: Image.Type, animated: bool = False, cls: Type[ComfyNode] = None, **kwargs):
+    def __init__(self, image: Image.Type, animated: bool = False, cls: type[ComfyNode] = None, **kwargs):
         self.values = ImageSaveHelper.save_images(
             image,
             filename_prefix="ComfyUI_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5)),
@@ -412,7 +411,7 @@ class PreviewMask(PreviewImage):
 
 
 class PreviewAudio(_UIOutput):
-    def __init__(self, audio: dict, cls: Type[ComfyNode] = None, **kwargs):
+    def __init__(self, audio: dict, cls: type[ComfyNode] = None, **kwargs):
         self.values = AudioSaveHelper.save_audio(
             audio,
             filename_prefix="ComfyUI_temp_" + "".join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5)),

comfy_api/version_list.py

@@ -2,9 +2,8 @@ from comfy_api.latest import ComfyAPI_latest
 from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2
 from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1
 from comfy_api.internal import ComfyAPIBase
-from typing import List, Type
 
-supported_versions: List[Type[ComfyAPIBase]] = [
+supported_versions: list[type[ComfyAPIBase]] = [
     ComfyAPI_latest,
     ComfyAPIAdapter_v0_0_2,
     ComfyAPIAdapter_v0_0_1,

comfy_api_nodes/apis/kling_api.py

@@ -51,25 +51,25 @@ class TaskStatusImageResult(BaseModel):
     url: str = Field(..., description="URL for generated image")
 
 
-class OmniTaskStatusResults(BaseModel):
+class TaskStatusResults(BaseModel):
     videos: list[TaskStatusVideoResult] | None = Field(None)
     images: list[TaskStatusImageResult] | None = Field(None)
 
 
-class OmniTaskStatusResponseData(BaseModel):
+class TaskStatusResponseData(BaseModel):
     created_at: int | None = Field(None, description="Task creation time")
     updated_at: int | None = Field(None, description="Task update time")
     task_status: str | None = None
     task_status_msg: str | None = Field(None, description="Additional failure reason. Only for polling endpoint.")
     task_id: str | None = Field(None, description="Task ID")
-    task_result: OmniTaskStatusResults | None = Field(None)
+    task_result: TaskStatusResults | None = Field(None)
 
 
-class OmniTaskStatusResponse(BaseModel):
+class TaskStatusResponse(BaseModel):
     code: int | None = Field(None, description="Error code")
     message: str | None = Field(None, description="Error message")
     request_id: str | None = Field(None, description="Request ID")
-    data: OmniTaskStatusResponseData | None = Field(None)
+    data: TaskStatusResponseData | None = Field(None)
 
 
 class OmniImageParamImage(BaseModel):
@@ -84,3 +84,21 @@ class OmniProImageRequest(BaseModel):
     mode: str = Field("pro")
     n: int | None = Field(1, le=9)
     image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
+
+
+class TextToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(..., description="kling-v2-6")
+    aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")
+
+
+class ImageToVideoWithAudioRequest(BaseModel):
+    model_name: str = Field(..., description="kling-v2-6")
+    image: str = Field(...)
+    duration: str = Field(..., description="'5' or '10'")
+    prompt: str = Field(...)
+    mode: str = Field("pro")
+    sound: str = Field(..., description="'on' or 'off'")

comfy_api_nodes/apis/pika_api.py

@@ -1,100 +0,0 @@
-from typing import Optional
-from enum import Enum
-from pydantic import BaseModel, Field
-
-
-class Pikaffect(str, Enum):
-    Cake_ify = "Cake-ify"
-    Crumble = "Crumble"
-    Crush = "Crush"
-    Decapitate = "Decapitate"
-    Deflate = "Deflate"
-    Dissolve = "Dissolve"
-    Explode = "Explode"
-    Eye_pop = "Eye-pop"
-    Inflate = "Inflate"
-    Levitate = "Levitate"
-    Melt = "Melt"
-    Peel = "Peel"
-    Poke = "Poke"
-    Squish = "Squish"
-    Ta_da = "Ta-da"
-    Tear = "Tear"
-
-
-class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel):
-    aspectRatio: Optional[float] = Field(None, description='Aspect ratio (width / height)')
-    duration: Optional[int] = Field(5)
-    ingredientsMode: str = Field(...)
-    negativePrompt: Optional[str] = Field(None)
-    promptText: Optional[str] = Field(None)
-    resolution: Optional[str] = Field('1080p')
-    seed: Optional[int] = Field(None)
-
-
-class PikaGenerateResponse(BaseModel):
-    video_id: str = Field(...)
-
-
-class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel):
-    duration: Optional[int] = 5
-    negativePrompt: Optional[str] = Field(None)
-    promptText: Optional[str] = Field(None)
-    resolution: Optional[str] = '1080p'
-    seed: Optional[int] = Field(None)
-
-
-class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel):
-    duration: Optional[int] = Field(None, ge=5, le=10)
-    negativePrompt: Optional[str] = Field(None)
-    promptText: str = Field(...)
-    resolution: Optional[str] = '1080p'
-    seed: Optional[int] = Field(None)
-
-
-class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel):
-    aspectRatio: Optional[float] = Field(
-        1.7777777777777777,
-        description='Aspect ratio (width / height)',
-        ge=0.4,
-        le=2.5,
-    )
-    duration: Optional[int] = 5
-    negativePrompt: Optional[str] = Field(None)
-    promptText: str = Field(...)
-    resolution: Optional[str] = '1080p'
-    seed: Optional[int] = Field(None)
-
-
-class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel):
-    negativePrompt: Optional[str] = Field(None)
-    promptText: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-
-
-class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel):
-    negativePrompt: Optional[str] = Field(None)
-    pikaffect: Optional[str] = None
-    promptText: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-
-
-class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel):
-    negativePrompt: Optional[str] = Field(None)
-    promptText: Optional[str] = Field(None)
-    seed: Optional[int] = Field(None)
-    modifyRegionRoi: Optional[str] = Field(None)
-
-
-class PikaStatusEnum(str, Enum):
-    queued = "queued"
-    started = "started"
-    finished = "finished"
-    failed = "failed"
-
-
-class PikaVideoResponse(BaseModel):
-    id: str = Field(...)
-    progress: Optional[int] = Field(None)
-    status: PikaStatusEnum
-    url: Optional[str] = Field(None)

comfy_api_nodes/nodes_kling.py

@@ -50,6 +50,7 @@ from comfy_api_nodes.apis import (
     KlingSingleImageEffectModelName,
 )
 from comfy_api_nodes.apis.kling_api import (
+    ImageToVideoWithAudioRequest,
     OmniImageParamImage,
     OmniParamImage,
     OmniParamVideo,
@@ -57,7 +58,8 @@ from comfy_api_nodes.apis.kling_api import (
     OmniProImageRequest,
     OmniProReferences2VideoRequest,
     OmniProText2VideoRequest,
-    OmniTaskStatusResponse,
+    TaskStatusResponse,
+    TextToVideoWithAudioRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -103,10 +105,6 @@ AVERAGE_DURATION_VIDEO_EXTEND = 320
 
 
 MODE_TEXT2VIDEO = {
-    "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
-    "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"),
-    "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
-    "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"),
     "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"),
     "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"),
     "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"),
@@ -127,8 +125,6 @@ See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document
 
 
 MODE_START_END_FRAME = {
-    "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
-    "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
     "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"),
     "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"),
     "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"),
@@ -242,7 +238,7 @@ def normalize_omni_prompt_references(prompt: str) -> str:
     return re.sub(r"(?<!\w)@video(?P<idx>\d*)(?!\w)", _video_repl, prompt)
 
 
-async def finish_omni_video_task(cls: type[IO.ComfyNode], response: OmniTaskStatusResponse) -> IO.NodeOutput:
+async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusResponse) -> IO.NodeOutput:
     if response.code:
         raise RuntimeError(
             f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
@@ -250,7 +246,7 @@ async def finish_omni_video_task(cls: type[IO.ComfyNode], response: OmniTaskStat
     final_response = await poll_op(
         cls,
         ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
-        response_model=OmniTaskStatusResponse,
+        response_model=TaskStatusResponse,
         status_extractor=lambda r: (r.data.task_status if r.data else None),
         max_poll_attempts=160,
     )
@@ -483,12 +479,12 @@ async def execute_image2video(
     task_id = task_creation_response.data.task_id
 
     final_response = await poll_op(
-            cls,
-            ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
-            response_model=KlingImage2VideoResponse,
-            estimated_duration=AVERAGE_DURATION_I2V,
-            status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
-        )
+        cls,
+        ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
+        response_model=KlingImage2VideoResponse,
+        estimated_duration=AVERAGE_DURATION_I2V,
+        status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
+    )
     validate_video_result_response(final_response)
 
     video = get_video_from_response(final_response)
@@ -752,7 +748,7 @@ class KlingTextToVideoNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "mode",
                     options=modes,
-                    default=modes[4],
+                    default=modes[8],
                     tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
                 ),
             ],
@@ -834,7 +830,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProText2VideoRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -929,7 +925,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProFirstLastFrameRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -997,7 +993,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProReferences2VideoRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -1081,7 +1077,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProReferences2VideoRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -1162,7 +1158,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProReferences2VideoRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -1237,7 +1233,7 @@ class OmniProImageNode(IO.ComfyNode):
         response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/kling/v1/images/omni-image", method="POST"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             data=OmniProImageRequest(
                 model_name=model_name,
                 prompt=prompt,
@@ -1253,7 +1249,7 @@ class OmniProImageNode(IO.ComfyNode):
         final_response = await poll_op(
             cls,
             ApiEndpoint(path=f"/proxy/kling/v1/images/omni-image/{response.data.task_id}"),
-            response_model=OmniTaskStatusResponse,
+            response_model=TaskStatusResponse,
             status_extractor=lambda r: (r.data.task_status if r.data else None),
         )
         return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.task_result.images[0].url))
@@ -1328,9 +1324,8 @@ class KlingImage2VideoNode(IO.ComfyNode):
     def define_schema(cls) -> IO.Schema:
         return IO.Schema(
             node_id="KlingImage2VideoNode",
-            display_name="Kling Image to Video",
+            display_name="Kling Image(First Frame) to Video",
             category="api node/video/Kling",
-            description="Kling Image to Video Node",
             inputs=[
                 IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -1488,7 +1483,7 @@ class KlingStartEndFrameNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "mode",
                     options=modes,
-                    default=modes[8],
+                    default=modes[6],
                     tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
                 ),
             ],
@@ -1951,7 +1946,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
                 IO.Combo.Input(
                     "model_name",
                     options=[i.value for i in KlingImageGenModelName],
-                    default="kling-v1",
+                    default="kling-v2",
                 ),
                 IO.Combo.Input(
                     "aspect_ratio",
@@ -2034,6 +2029,136 @@ class KlingImageGenerationNode(IO.ComfyNode):
         return IO.NodeOutput(await image_result_to_node_output(images))
 
 
+class TextToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingTextToVideoWithAudio",
+            display_name="Kling Text to Video with Audio",
+            category="api node/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        prompt: str,
+        mode: str,
+        aspect_ratio: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/text2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=TextToVideoWithAudioRequest(
+                model_name=model_name,
+                prompt=prompt,
+                mode=mode,
+                aspect_ratio=aspect_ratio,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/text2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
+class ImageToVideoWithAudio(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingImageToVideoWithAudio",
+            display_name="Kling Image(First Frame) to Video with Audio",
+            category="api node/video/Kling",
+            inputs=[
+                IO.Combo.Input("model_name", options=["kling-v2-6"]),
+                IO.Image.Input("start_frame"),
+                IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
+                IO.Combo.Input("mode", options=["pro"]),
+                IO.Combo.Input("duration", options=[5, 10]),
+                IO.Boolean.Input("generate_audio", default=True),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_name: str,
+        start_frame: Input.Image,
+        prompt: str,
+        mode: str,
+        duration: int,
+        generate_audio: bool,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=2500)
+        validate_image_dimensions(start_frame, min_width=300, min_height=300)
+        validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=ImageToVideoWithAudioRequest(
+                model_name=model_name,
+                image=(await upload_images_to_comfyapi(cls, start_frame))[0],
+                prompt=prompt,
+                mode=mode,
+                duration=str(duration),
+                sound="on" if generate_audio else "off",
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 class KlingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -2056,7 +2181,9 @@ class KlingExtension(ComfyExtension):
             OmniProImageToVideoNode,
             OmniProVideoToVideoNode,
             OmniProEditVideoNode,
-            # OmniProImageNode,  # need support from backend
+            OmniProImageNode,
+            TextToVideoWithAudio,
+            ImageToVideoWithAudio,
         ]
 
 

comfy_api_nodes/nodes_pika.py

@@ -1,575 +0,0 @@
-"""
-Pika x ComfyUI API Nodes
-
-Pika API docs: https://pika-827374fb.mintlify.app/api-reference
-"""
-from __future__ import annotations
-
-from io import BytesIO
-import logging
-from typing import Optional
-
-import torch
-
-from typing_extensions import override
-from comfy_api.latest import ComfyExtension, IO
-from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
-from comfy_api_nodes.apis import pika_api as pika_defs
-from comfy_api_nodes.util import (
-    validate_string,
-    download_url_to_video_output,
-    tensor_to_bytesio,
-    ApiEndpoint,
-    sync_op,
-    poll_op,
-)
-
-
-PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions"
-PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps"
-PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects"
-
-PIKA_API_VERSION = "2.2"
-PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v"
-PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v"
-PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes"
-PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes"
-
-PATH_VIDEO_GET = "/proxy/pika/videos"
-
-
-async def execute_task(
-    task_id: str,
-    cls: type[IO.ComfyNode],
-) -> IO.NodeOutput:
-    final_response: pika_defs.PikaVideoResponse = await poll_op(
-        cls,
-        ApiEndpoint(path=f"{PATH_VIDEO_GET}/{task_id}"),
-        response_model=pika_defs.PikaVideoResponse,
-        status_extractor=lambda response: (response.status.value if response.status else None),
-        progress_extractor=lambda response: (response.progress if hasattr(response, "progress") else None),
-        estimated_duration=60,
-        max_poll_attempts=240,
-    )
-    if not final_response.url:
-        error_msg = f"Pika task {task_id} succeeded but no video data found in response:\n{final_response}"
-        logging.error(error_msg)
-        raise Exception(error_msg)
-    video_url = final_response.url
-    logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url)
-    return IO.NodeOutput(await download_url_to_video_output(video_url))
-
-
-def get_base_inputs_types() -> list[IO.Input]:
-    """Get the base required inputs types common to all Pika nodes."""
-    return [
-        IO.String.Input("prompt_text", multiline=True),
-        IO.String.Input("negative_prompt", multiline=True),
-        IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True),
-        IO.Combo.Input("resolution", options=["1080p", "720p"], default="1080p"),
-        IO.Combo.Input("duration", options=[5, 10], default=5),
-    ]
-
-
-class PikaImageToVideo(IO.ComfyNode):
-    """Pika 2.2 Image to Video Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="PikaImageToVideoNode2_2",
-            display_name="Pika Image to Video",
-            description="Sends an image and prompt to the Pika API v2.2 to generate a video.",
-            category="api node/video/Pika",
-            inputs=[
-                IO.Image.Input("image", tooltip="The image to convert to video"),
-                *get_base_inputs_types(),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: torch.Tensor,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-        resolution: str,
-        duration: int,
-    ) -> IO.NodeOutput:
-        image_bytes_io = tensor_to_bytesio(image)
-        pika_files = {"image": ("image.png", image_bytes_io, "image/png")}
-        pika_request_data = pika_defs.PikaBodyGenerate22I2vGenerate22I2vPost(
-            promptText=prompt_text,
-            negativePrompt=negative_prompt,
-            seed=seed,
-            resolution=resolution,
-            duration=duration,
-        )
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_request_data,
-            files=pika_files,
-            content_type="multipart/form-data",
-        )
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaTextToVideoNode(IO.ComfyNode):
-    """Pika Text2Video v2.2 Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="PikaTextToVideoNode2_2",
-            display_name="Pika Text to Video",
-            description="Sends a text prompt to the Pika API v2.2 to generate a video.",
-            category="api node/video/Pika",
-            inputs=[
-                *get_base_inputs_types(),
-                IO.Float.Input(
-                    "aspect_ratio",
-                    step=0.001,
-                    min=0.4,
-                    max=2.5,
-                    default=1.7777777777777777,
-                    tooltip="Aspect ratio (width / height)",
-                )
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-        resolution: str,
-        duration: int,
-        aspect_ratio: float,
-    ) -> IO.NodeOutput:
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_TEXT_TO_VIDEO, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost(
-                promptText=prompt_text,
-                negativePrompt=negative_prompt,
-                seed=seed,
-                resolution=resolution,
-                duration=duration,
-                aspectRatio=aspect_ratio,
-            ),
-            content_type="application/x-www-form-urlencoded",
-        )
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaScenes(IO.ComfyNode):
-    """PikaScenes v2.2 Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="PikaScenesV2_2",
-            display_name="Pika Scenes (Video Image Composition)",
-            description="Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them.",
-            category="api node/video/Pika",
-            inputs=[
-                *get_base_inputs_types(),
-                IO.Combo.Input(
-                    "ingredients_mode",
-                    options=["creative", "precise"],
-                    default="creative",
-                ),
-                IO.Float.Input(
-                    "aspect_ratio",
-                    step=0.001,
-                    min=0.4,
-                    max=2.5,
-                    default=1.7777777777777777,
-                    tooltip="Aspect ratio (width / height)",
-                ),
-                IO.Image.Input(
-                    "image_ingredient_1",
-                    optional=True,
-                    tooltip="Image that will be used as ingredient to create a video.",
-                ),
-                IO.Image.Input(
-                    "image_ingredient_2",
-                    optional=True,
-                    tooltip="Image that will be used as ingredient to create a video.",
-                ),
-                IO.Image.Input(
-                    "image_ingredient_3",
-                    optional=True,
-                    tooltip="Image that will be used as ingredient to create a video.",
-                ),
-                IO.Image.Input(
-                    "image_ingredient_4",
-                    optional=True,
-                    tooltip="Image that will be used as ingredient to create a video.",
-                ),
-                IO.Image.Input(
-                    "image_ingredient_5",
-                    optional=True,
-                    tooltip="Image that will be used as ingredient to create a video.",
-                ),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-        resolution: str,
-        duration: int,
-        ingredients_mode: str,
-        aspect_ratio: float,
-        image_ingredient_1: Optional[torch.Tensor] = None,
-        image_ingredient_2: Optional[torch.Tensor] = None,
-        image_ingredient_3: Optional[torch.Tensor] = None,
-        image_ingredient_4: Optional[torch.Tensor] = None,
-        image_ingredient_5: Optional[torch.Tensor] = None,
-    ) -> IO.NodeOutput:
-        all_image_bytes_io = []
-        for image in [
-            image_ingredient_1,
-            image_ingredient_2,
-            image_ingredient_3,
-            image_ingredient_4,
-            image_ingredient_5,
-        ]:
-            if image is not None:
-                all_image_bytes_io.append(tensor_to_bytesio(image))
-
-        pika_files = [
-            ("images", (f"image_{i}.png", image_bytes_io, "image/png"))
-            for i, image_bytes_io in enumerate(all_image_bytes_io)
-        ]
-
-        pika_request_data = pika_defs.PikaBodyGenerate22C2vGenerate22PikascenesPost(
-            ingredientsMode=ingredients_mode,
-            promptText=prompt_text,
-            negativePrompt=negative_prompt,
-            seed=seed,
-            resolution=resolution,
-            duration=duration,
-            aspectRatio=aspect_ratio,
-        )
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_PIKASCENES, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_request_data,
-            files=pika_files,
-            content_type="multipart/form-data",
-        )
-
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikAdditionsNode(IO.ComfyNode):
-    """Pika Pikadditions Node. Add an image into a video."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="Pikadditions",
-            display_name="Pikadditions (Video Object Insertion)",
-            description="Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result.",
-            category="api node/video/Pika",
-            inputs=[
-                IO.Video.Input("video", tooltip="The video to add an image to."),
-                IO.Image.Input("image", tooltip="The image to add to the video."),
-                IO.String.Input("prompt_text", multiline=True),
-                IO.String.Input("negative_prompt", multiline=True),
-                IO.Int.Input(
-                    "seed",
-                    min=0,
-                    max=0xFFFFFFFF,
-                    control_after_generate=True,
-                ),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        video: VideoInput,
-        image: torch.Tensor,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-    ) -> IO.NodeOutput:
-        video_bytes_io = BytesIO()
-        video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
-        video_bytes_io.seek(0)
-
-        image_bytes_io = tensor_to_bytesio(image)
-        pika_files = {
-            "video": ("video.mp4", video_bytes_io, "video/mp4"),
-            "image": ("image.png", image_bytes_io, "image/png"),
-        }
-        pika_request_data = pika_defs.PikaBodyGeneratePikadditionsGeneratePikadditionsPost(
-            promptText=prompt_text,
-            negativePrompt=negative_prompt,
-            seed=seed,
-        )
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_PIKADDITIONS, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_request_data,
-            files=pika_files,
-            content_type="multipart/form-data",
-        )
-
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaSwapsNode(IO.ComfyNode):
-    """Pika Pikaswaps Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="Pikaswaps",
-            display_name="Pika Swaps (Video Object Replacement)",
-            description="Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates.",
-            category="api node/video/Pika",
-            inputs=[
-                IO.Video.Input("video", tooltip="The video to swap an object in."),
-                IO.Image.Input(
-                    "image",
-                    tooltip="The image used to replace the masked object in the video.",
-                    optional=True,
-                ),
-                IO.Mask.Input(
-                    "mask",
-                    tooltip="Use the mask to define areas in the video to replace.",
-                    optional=True,
-                ),
-                IO.String.Input("prompt_text", multiline=True, optional=True),
-                IO.String.Input("negative_prompt", multiline=True, optional=True),
-                IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True, optional=True),
-                IO.String.Input(
-                    "region_to_modify",
-                    multiline=True,
-                    optional=True,
-                    tooltip="Plaintext description of the object / region to modify.",
-                ),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        video: VideoInput,
-        image: Optional[torch.Tensor] = None,
-        mask: Optional[torch.Tensor] = None,
-        prompt_text: str = "",
-        negative_prompt: str = "",
-        seed: int = 0,
-        region_to_modify: str = "",
-    ) -> IO.NodeOutput:
-        video_bytes_io = BytesIO()
-        video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
-        video_bytes_io.seek(0)
-        pika_files = {
-            "video": ("video.mp4", video_bytes_io, "video/mp4"),
-        }
-        if mask is not None:
-            pika_files["modifyRegionMask"] = ("mask.png", tensor_to_bytesio(mask), "image/png")
-        if image is not None:
-            pika_files["image"] = ("image.png", tensor_to_bytesio(image), "image/png")
-
-        pika_request_data = pika_defs.PikaBodyGeneratePikaswapsGeneratePikaswapsPost(
-            promptText=prompt_text,
-            negativePrompt=negative_prompt,
-            seed=seed,
-            modifyRegionRoi=region_to_modify if region_to_modify else None,
-        )
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_PIKASWAPS, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_request_data,
-            files=pika_files,
-            content_type="multipart/form-data",
-        )
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaffectsNode(IO.ComfyNode):
-    """Pika Pikaffects Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="Pikaffects",
-            display_name="Pikaffects (Video Effects)",
-            description="Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear",
-            category="api node/video/Pika",
-            inputs=[
-                IO.Image.Input("image", tooltip="The reference image to apply the Pikaffect to."),
-                IO.Combo.Input(
-                    "pikaffect", options=pika_defs.Pikaffect, default="Cake-ify"
-                ),
-                IO.String.Input("prompt_text", multiline=True),
-                IO.String.Input("negative_prompt", multiline=True),
-                IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: torch.Tensor,
-        pikaffect: str,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-    ) -> IO.NodeOutput:
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_PIKAFFECTS, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost(
-                pikaffect=pikaffect,
-                promptText=prompt_text,
-                negativePrompt=negative_prompt,
-                seed=seed,
-            ),
-            files={"image": ("image.png", tensor_to_bytesio(image), "image/png")},
-            content_type="multipart/form-data",
-        )
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaStartEndFrameNode(IO.ComfyNode):
-    """PikaFrames v2.2 Node."""
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="PikaStartEndFrameNode2_2",
-            display_name="Pika Start and End Frame to Video",
-            description="Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them.",
-            category="api node/video/Pika",
-            inputs=[
-                IO.Image.Input("image_start", tooltip="The first image to combine."),
-                IO.Image.Input("image_end", tooltip="The last image to combine."),
-                *get_base_inputs_types(),
-            ],
-            outputs=[IO.Video.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            is_deprecated=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image_start: torch.Tensor,
-        image_end: torch.Tensor,
-        prompt_text: str,
-        negative_prompt: str,
-        seed: int,
-        resolution: str,
-        duration: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt_text, field_name="prompt_text", min_length=1)
-        pika_files = [
-            ("keyFrames", ("image_start.png", tensor_to_bytesio(image_start), "image/png")),
-            ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")),
-        ]
-        initial_operation = await sync_op(
-            cls,
-            ApiEndpoint(path=PATH_PIKAFRAMES, method="POST"),
-            response_model=pika_defs.PikaGenerateResponse,
-            data=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost(
-                promptText=prompt_text,
-                negativePrompt=negative_prompt,
-                seed=seed,
-                resolution=resolution,
-                duration=duration,
-            ),
-            files=pika_files,
-            content_type="multipart/form-data",
-        )
-        return await execute_task(initial_operation.video_id, cls)
-
-
-class PikaApiNodesExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            PikaImageToVideo,
-            PikaTextToVideoNode,
-            PikaScenes,
-            PikAdditionsNode,
-            PikaSwapsNode,
-            PikaffectsNode,
-            PikaStartEndFrameNode,
-        ]
-
-
-async def comfy_entrypoint() -> PikaApiNodesExtension:
-    return PikaApiNodesExtension()

comfy_extras/nodes_custom_sampler.py

@@ -659,6 +659,31 @@ class SamplerSASolver(io.ComfyNode):
     get_sampler = execute
 
 
+class SamplerSEEDS2(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SamplerSEEDS2",
+            category="sampling/custom_sampling/samplers",
+            inputs=[
+                io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
+                io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength"),
+                io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
+                io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, solver_type, eta, s_noise, r) -> io.NodeOutput:
+        sampler_name = "seeds_2"
+        sampler = comfy.samplers.ksampler(
+            sampler_name,
+            {"eta": eta, "s_noise": s_noise, "r": r, "solver_type": solver_type},
+        )
+        return io.NodeOutput(sampler)
+
+
 class Noise_EmptyNoise:
     def __init__(self):
         self.seed = 0
@@ -996,6 +1021,7 @@ class CustomSamplersExtension(ComfyExtension):
             SamplerDPMAdaptative,
             SamplerER_SDE,
             SamplerSASolver,
+            SamplerSEEDS2,
             SplitSigmas,
             SplitSigmasDenoise,
             FlipSigmas,

comfy_extras/nodes_flux.py

@@ -154,12 +154,13 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="FluxKontextMultiReferenceLatentMethod",
+            display_name="Edit Model Reference Method",
             category="advanced/conditioning/flux",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Combo.Input(
                     "reference_latents_method",
-                    options=["offset", "index", "uxo/uno"],
+                    options=["offset", "index", "uxo/uno", "index_timestep_zero"],
                 ),
             ],
             outputs=[

comfy_extras/nodes_model_patch.py

@@ -243,7 +243,13 @@ class ModelPatchLoader:
             model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
         elif 'control_all_x_embedder.2-1.weight' in sd: # alipai z image fun controlnet
             sd = z_image_convert(sd)
-            model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+            config = {}
+            if 'control_layers.14.adaLN_modulation.0.weight' in sd:
+                config['n_control_layers'] = 15
+                config['additional_in_dim'] = 17
+                config['refiner_control'] = True
+                config['broken'] = True
+            model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast, **config)
 
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -297,56 +303,86 @@ class DiffSynthCnetPatch:
         return [self.model_patch]
 
 class ZImageControlPatch:
-    def __init__(self, model_patch, vae, image, strength):
+    def __init__(self, model_patch, vae, image, strength, inpaint_image=None, mask=None):
         self.model_patch = model_patch
         self.vae = vae
         self.image = image
+        self.inpaint_image = inpaint_image
+        self.mask = mask
         self.strength = strength
         self.encoded_image = self.encode_latent_cond(image)
         self.encoded_image_size = (image.shape[1], image.shape[2])
         self.temp_data = None
 
-    def encode_latent_cond(self, image):
-        latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(image))
-        return latent_image
+    def encode_latent_cond(self, control_image, inpaint_image=None):
+        latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(control_image))
+        if self.model_patch.model.additional_in_dim > 0:
+            if self.mask is None:
+                mask_ = torch.zeros_like(latent_image)[:, :1]
+            else:
+                mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none")
+            if inpaint_image is None:
+                inpaint_image = torch.ones_like(control_image) * 0.5
+
+            inpaint_image_latent = comfy.latent_formats.Flux().process_in(self.vae.encode(inpaint_image))
+
+            return torch.cat([latent_image, mask_, inpaint_image_latent], dim=1)
+        else:
+            return latent_image
 
     def __call__(self, kwargs):
         x = kwargs.get("x")
         img = kwargs.get("img")
+        img_input = kwargs.get("img_input")
         txt = kwargs.get("txt")
         pe = kwargs.get("pe")
         vec = kwargs.get("vec")
         block_index = kwargs.get("block_index")
+        block_type = kwargs.get("block_type", "")
         spacial_compression = self.vae.spacial_compression_encode()
         if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
             image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
+            inpaint_scaled = None
+            if self.inpaint_image is not None:
+                inpaint_scaled = comfy.utils.common_upscale(self.inpaint_image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center").movedim(1, -1)
             loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
-            self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1))
+            self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1), inpaint_scaled)
             self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
             comfy.model_management.load_models_gpu(loaded_models)
 
-        cnet_index = (block_index // 5)
-        cnet_index_float = (block_index / 5)
+        cnet_blocks = self.model_patch.model.n_control_layers
+        div = round(30 / cnet_blocks)
+
+        cnet_index = (block_index // div)
+        cnet_index_float = (block_index / div)
 
         kwargs.pop("img")  # we do ops in place
         kwargs.pop("txt")
 
-        cnet_blocks = self.model_patch.model.n_control_layers
         if cnet_index_float > (cnet_blocks - 1):
             self.temp_data = None
             return kwargs
 
         if self.temp_data is None or self.temp_data[0] > cnet_index:
-            self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+            if block_type == "noise_refiner":
+                self.temp_data = (-3, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+            else:
+                self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
 
-        while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+        if block_type == "noise_refiner":
             next_layer = self.temp_data[0] + 1
-            self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+            self.temp_data = (next_layer, self.model_patch.model.forward_noise_refiner_block(block_index, self.temp_data[1][1], img_input[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+            if self.temp_data[1][0] is not None:
+                img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+        else:
+            while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+                next_layer = self.temp_data[0] + 1
+                self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img_input[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
 
-        if cnet_index_float == self.temp_data[0]:
-            img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
-            if cnet_blocks == self.temp_data[0] + 1:
-                self.temp_data = None
+            if cnet_index_float == self.temp_data[0]:
+                img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+                if cnet_blocks == self.temp_data[0] + 1:
+                    self.temp_data = None
 
         return kwargs
 
@@ -386,7 +422,9 @@ class QwenImageDiffsynthControlnet:
             mask = 1.0 - mask
 
         if isinstance(model_patch.model, comfy.ldm.lumina.controlnet.ZImage_Control):
-            model_patched.set_model_double_block_patch(ZImageControlPatch(model_patch, vae, image, strength))
+            patch = ZImageControlPatch(model_patch, vae, image, strength, mask=mask)
+            model_patched.set_model_noise_refiner_patch(patch)
+            model_patched.set_model_double_block_patch(patch)
         else:
             model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
         return (model_patched,)

comfy_extras/nodes_torch_compile.py

@@ -2,6 +2,8 @@ from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 from comfy_api.torch_helpers import set_torch_compile_wrapper
 
+def skip_torch_compile_dict(guard_entries):
+    return [("transformer_options" not in entry.name) for entry in guard_entries]
 
 class TorchCompileModel(io.ComfyNode):
     @classmethod
@@ -23,7 +25,7 @@ class TorchCompileModel(io.ComfyNode):
     @classmethod
     def execute(cls, model, backend) -> io.NodeOutput:
         m = model.clone()
-        set_torch_compile_wrapper(model=m, backend=backend)
+        set_torch_compile_wrapper(model=m, backend=backend, options={"guard_filter_fn": skip_torch_compile_dict})
         return io.NodeOutput(m)
 
 

comfy_extras/nodes_wanmove.py

@@ -0,0 +1,535 @@
+import nodes
+import node_helpers
+import torch
+import torchvision.transforms.functional as TF
+import comfy.model_management
+import comfy.utils
+import numpy as np
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.nodes_wan import parse_json_tracks
+
+# https://github.com/ali-vilab/Wan-Move/blob/main/wan/modules/trajectory.py
+from PIL import Image, ImageDraw
+
+SKIP_ZERO = False
+
+def get_pos_emb(
+    pos_k: torch.Tensor, # A 1D tensor containing positions for which to generate embeddings.
+    pos_emb_dim: int,
+    theta_func: callable = lambda i, d: torch.pow(10000, torch.mul(2, torch.div(i.to(torch.float32), d))), #Function to compute thetas based on position and embedding dimensions.
+    device: torch.device = torch.device("cpu"),
+    dtype: torch.dtype = torch.float32,
+) -> torch.Tensor: # The position embeddings (batch_size, pos_emb_dim)
+
+    assert pos_emb_dim % 2 == 0, "The dimension of position embeddings must be even."
+    pos_k = pos_k.to(device, dtype)
+    if SKIP_ZERO:
+        pos_k = pos_k + 1
+    batch_size = pos_k.size(0)
+
+    denominator = torch.arange(0, pos_emb_dim // 2, device=device, dtype=dtype)
+    # Expand denominator to match the shape needed for broadcasting
+    denominator_expanded = denominator.view(1, -1).expand(batch_size, -1)
+
+    thetas = theta_func(denominator_expanded, pos_emb_dim)
+
+    # Ensure pos_k is in the correct shape for broadcasting
+    pos_k_expanded = pos_k.view(-1, 1).to(dtype)
+    sin_thetas = torch.sin(torch.div(pos_k_expanded, thetas))
+    cos_thetas = torch.cos(torch.div(pos_k_expanded, thetas))
+
+    # Concatenate sine and cosine embeddings along the last dimension
+    pos_emb = torch.cat([sin_thetas, cos_thetas], dim=-1)
+
+    return pos_emb
+
+def create_pos_embeddings(
+    pred_tracks: torch.Tensor, # the predicted tracks, [T, N, 2]
+    pred_visibility: torch.Tensor, # the predicted visibility [T, N]
+    downsample_ratios: list[int], # the ratios for downsampling time, height, and width
+    height: int, # the height of the feature map
+    width: int, # the width of the feature map
+    track_num: int = -1, # the number of tracks to use
+    t_down_strategy: str = "sample", # the strategy for downsampling time dimension
+):
+    assert t_down_strategy in ["sample", "average"], "Invalid strategy for downsampling time dimension."
+
+    t, n, _ = pred_tracks.shape
+    t_down, h_down, w_down = downsample_ratios
+    track_pos = - torch.ones(n, (t-1) // t_down + 1, 2, dtype=torch.long)
+
+    if track_num == -1:
+        track_num = n
+
+    tracks_idx = torch.randperm(n)[:track_num]
+    tracks = pred_tracks[:, tracks_idx]
+    visibility = pred_visibility[:, tracks_idx]
+
+    for t_idx in range(0, t, t_down):
+        if t_down_strategy == "sample" or t_idx == 0:
+            cur_tracks = tracks[t_idx] # [N, 2]
+            cur_visibility = visibility[t_idx] # [N]
+        else:
+            cur_tracks = tracks[t_idx:t_idx+t_down].mean(dim=0)
+            cur_visibility = torch.any(visibility[t_idx:t_idx+t_down], dim=0)
+
+        for i in range(track_num):
+            if not cur_visibility[i] or cur_tracks[i][0] < 0 or cur_tracks[i][1] < 0 or cur_tracks[i][0] >= width or cur_tracks[i][1] >= height:
+                continue
+            x, y = cur_tracks[i]
+            x, y = int(x // w_down), int(y // h_down)
+            track_pos[i, t_idx // t_down, 0], track_pos[i, t_idx // t_down, 1] = y, x
+
+    return track_pos # the position embeddings, [N, T', 2], 2 = height, width
+
+def replace_feature(
+    vae_feature: torch.Tensor,  # [B, C', T', H', W']
+    track_pos: torch.Tensor,    # [B, N, T', 2]
+    strength: float = 1.0
+) -> torch.Tensor:
+    b, _, t, h, w = vae_feature.shape
+    assert b == track_pos.shape[0], "Batch size mismatch."
+    n = track_pos.shape[1]
+
+    # Shuffle the trajectory order
+    track_pos = track_pos[:, torch.randperm(n), :, :]
+
+    # Extract coordinates at time steps ≥ 1 and generate a valid mask
+    current_pos = track_pos[:, :, 1:, :]  # [B, N, T-1, 2]
+    mask = (current_pos[..., 0] >= 0) & (current_pos[..., 1] >= 0)  # [B, N, T-1]
+
+    # Get all valid indices
+    valid_indices = mask.nonzero(as_tuple=False)  # [num_valid, 3]
+    num_valid = valid_indices.shape[0]
+
+    if num_valid == 0:
+        return vae_feature
+
+    # Decompose valid indices into each dimension
+    batch_idx = valid_indices[:, 0]
+    track_idx = valid_indices[:, 1]
+    t_rel = valid_indices[:, 2]
+    t_target = t_rel + 1  # Convert to original time step indices
+
+    # Extract target position coordinates
+    h_target = current_pos[batch_idx, track_idx, t_rel, 0].long()  # Ensure integer indices
+    w_target = current_pos[batch_idx, track_idx, t_rel, 1].long()
+
+    # Extract source position coordinates (t=0)
+    h_source = track_pos[batch_idx, track_idx, 0, 0].long()
+    w_source = track_pos[batch_idx, track_idx, 0, 1].long()
+
+    # Get source features and assign to target positions
+    src_features = vae_feature[batch_idx, :, 0, h_source, w_source]
+    dst_features = vae_feature[batch_idx, :, t_target, h_target, w_target]
+
+    vae_feature[batch_idx, :, t_target, h_target, w_target] = dst_features + (src_features - dst_features) * strength
+
+
+    return vae_feature
+
+# Visualize functions
+
+def _draw_gradient_polyline_on_overlay(overlay, line_width, points, start_color, opacity=1.0):
+    draw = ImageDraw.Draw(overlay, 'RGBA')
+    points = points[::-1]
+
+    # Compute total length
+    total_length = 0
+    segment_lengths = []
+    for i in range(len(points) - 1):
+        dx = points[i + 1][0] - points[i][0]
+        dy = points[i + 1][1] - points[i][1]
+        length = (dx * dx + dy * dy) ** 0.5
+        segment_lengths.append(length)
+        total_length += length
+
+    if total_length == 0:
+        return
+
+    accumulated_length = 0
+
+    # Draw the gradient polyline
+    for idx, (start_point, end_point) in enumerate(zip(points[:-1], points[1:])):
+        segment_length = segment_lengths[idx]
+        steps = max(int(segment_length), 1)
+
+        for i in range(steps):
+            current_length = accumulated_length + (i / steps) * segment_length
+            ratio = current_length / total_length
+
+            alpha = int(255 * (1 - ratio) * opacity)
+            color = (*start_color, alpha)
+
+            x = int(start_point[0] + (end_point[0] - start_point[0]) * i / steps)
+            y = int(start_point[1] + (end_point[1] - start_point[1]) * i / steps)
+
+            dynamic_line_width = max(int(line_width * (1 - ratio)), 1)
+            draw.line([(x, y), (x + 1, y)], fill=color, width=dynamic_line_width)
+
+        accumulated_length += segment_length
+
+
+def add_weighted(rgb, track):
+    rgb = np.array(rgb) # [H, W, C] "RGB"
+    track = np.array(track) # [H, W, C] "RGBA"
+
+    alpha = track[:, :, 3] / 255.0
+    alpha = np.stack([alpha] * 3, axis=-1)
+    blend_img = track[:, :, :3] * alpha + rgb * (1 - alpha)
+
+    return Image.fromarray(blend_img.astype(np.uint8))
+
+def draw_tracks_on_video(video, tracks, visibility=None, track_frame=24, circle_size=12, opacity=0.5, line_width=16):
+    color_map = [(102, 153, 255), (0, 255, 255), (255, 255, 0), (255, 102, 204), (0, 255, 0)]
+
+    video = video.byte().cpu().numpy()  # (81, 480, 832, 3)
+    tracks = tracks[0].long().detach().cpu().numpy()
+    if visibility is not None:
+        visibility = visibility[0].detach().cpu().numpy()
+
+    num_frames, height, width = video.shape[:3]
+    num_tracks = tracks.shape[1]
+    alpha_opacity = int(255 * opacity)
+
+    output_frames = []
+    for t in range(num_frames):
+        frame_rgb = video[t].astype(np.float32)
+
+        # Create a single RGBA overlay for all tracks in this frame
+        overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+        draw_overlay = ImageDraw.Draw(overlay)
+
+        polyline_data = []
+
+        # Draw all circles on a single overlay
+        for n in range(num_tracks):
+            if visibility is not None and visibility[t, n] == 0:
+                continue
+
+            track_coord = tracks[t, n]
+            color = color_map[n % len(color_map)]
+            circle_color = color + (alpha_opacity,)
+
+            draw_overlay.ellipse((track_coord[0] - circle_size, track_coord[1] - circle_size, track_coord[0] + circle_size, track_coord[1] + circle_size),
+                fill=circle_color
+            )
+
+            # Store polyline data for batch processing
+            tracks_coord = tracks[max(t - track_frame, 0):t + 1, n]
+            if len(tracks_coord) > 1:
+                polyline_data.append((tracks_coord, color))
+
+        # Blend circles overlay once
+        overlay_np = np.array(overlay)
+        alpha = overlay_np[:, :, 3:4] / 255.0
+        frame_rgb = overlay_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
+
+        # Draw all polylines on a single overlay
+        if polyline_data:
+            polyline_overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
+            for tracks_coord, color in polyline_data:
+                _draw_gradient_polyline_on_overlay(polyline_overlay, line_width, tracks_coord, color, opacity)
+
+            # Blend polylines overlay once
+            polyline_np = np.array(polyline_overlay)
+            alpha = polyline_np[:, :, 3:4] / 255.0
+            frame_rgb = polyline_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
+
+        output_frames.append(Image.fromarray(frame_rgb.astype(np.uint8)))
+
+    return output_frames
+
+
+class WanMoveVisualizeTracks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveVisualizeTracks",
+            category="conditioning/video_models",
+            inputs=[
+                io.Image.Input("images"),
+                io.Tracks.Input("tracks", optional=True),
+                io.Int.Input("line_resolution", default=24, min=1, max=1024),
+                io.Int.Input("circle_size", default=12, min=1, max=128),
+                io.Float.Input("opacity", default=0.75, min=0.0, max=1.0, step=0.01),
+                io.Int.Input("line_width", default=16, min=1, max=128),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, images, line_resolution, circle_size, opacity, line_width, tracks=None) -> io.NodeOutput:
+        if tracks is None:
+            return io.NodeOutput(images)
+
+        track_path = tracks["track_path"].unsqueeze(0)
+        track_visibility = tracks["track_visibility"].unsqueeze(0)
+        images_in = images * 255.0
+        if images_in.shape[0] != track_path.shape[1]:
+            repeat_count = track_path.shape[1] // images.shape[0]
+            images_in = images_in.repeat(repeat_count, 1, 1, 1)
+        track_video = draw_tracks_on_video(images_in, track_path, track_visibility, track_frame=line_resolution, circle_size=circle_size, opacity=opacity, line_width=line_width)
+        track_video = torch.stack([TF.to_tensor(frame) for frame in track_video], dim=0).movedim(1, -1).float()
+
+        return io.NodeOutput(track_video.to(comfy.model_management.intermediate_device()))
+
+
+class WanMoveTracksFromCoords(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveTracksFromCoords",
+            category="conditioning/video_models",
+            inputs=[
+                io.String.Input("track_coords", force_input=True, default="[]", optional=True),
+                io.Mask.Input("track_mask", optional=True),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+                io.Int.Output(display_name="track_length"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, track_coords, track_mask=None) -> io.NodeOutput:
+        device=comfy.model_management.intermediate_device()
+
+        tracks_data = parse_json_tracks(track_coords)
+        track_length = len(tracks_data[0])
+
+        track_list = [
+                [[track[frame]['x'], track[frame]['y']] for track in tracks_data]
+                for frame in range(len(tracks_data[0]))
+            ]
+        tracks = torch.tensor(track_list, dtype=torch.float32, device=device)  # [frames, num_tracks, 2]
+
+        num_tracks = tracks.shape[-2]
+        if track_mask is None:
+            track_visibility = torch.ones((track_length, num_tracks), dtype=torch.bool, device=device)
+        else:
+            track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks
+        out_track_info["track_visibility"] = track_visibility
+        return io.NodeOutput(out_track_info, track_length)
+
+
+class GenerateTracks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GenerateTracks",
+            category="conditioning/video_models",
+            inputs=[
+                io.Int.Input("width", default=832, min=16, max=4096, step=16),
+                io.Int.Input("height", default=480, min=16, max=4096, step=16),
+                io.Float.Input("start_x", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized X coordinate (0-1) for start position."),
+                io.Float.Input("start_y", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y coordinate (0-1) for start position."),
+                io.Float.Input("end_x", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized X coordinate (0-1) for end position."),
+                io.Float.Input("end_y", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y coordinate (0-1) for end position."),
+                io.Int.Input("num_frames", default=81, min=1, max=1024),
+                io.Int.Input("num_tracks", default=5, min=1, max=100),
+                io.Float.Input("track_spread", default=0.025, min=0.0, max=1.0, step=0.001, tooltip="Normalized distance between tracks. Tracks are spread perpendicular to the motion direction."),
+                io.Boolean.Input("bezier", default=False, tooltip="Enable Bezier curve path using the mid point as control point."),
+                io.Float.Input("mid_x", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Normalized X control point for Bezier curve. Only used when 'bezier' is enabled."),
+                io.Float.Input("mid_y", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Normalized Y control point for Bezier curve. Only used when 'bezier' is enabled."),
+                io.Combo.Input(
+                    "interpolation",
+                    options=["linear", "ease_in", "ease_out", "ease_in_out", "constant"],
+                    tooltip="Controls the timing/speed of movement along the path.",
+                ),
+                io.Mask.Input("track_mask", optional=True, tooltip="Optional mask to indicate visible frames."),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+                io.Int.Output(display_name="track_length"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, start_x, start_y, mid_x, mid_y, end_x, end_y, num_frames, num_tracks,
+                track_spread, bezier=False, interpolation="linear", track_mask=None) -> io.NodeOutput:
+        device = comfy.model_management.intermediate_device()
+        track_length = num_frames
+
+        # normalized coordinates to pixel coordinates
+        start_x_px = start_x * width
+        start_y_px = start_y * height
+        mid_x_px = mid_x * width
+        mid_y_px = mid_y * height
+        end_x_px = end_x * width
+        end_y_px = end_y * height
+
+        track_spread_px = track_spread * (width + height) / 2 # Use average of width/height for spread to keep it proportional
+
+        t = torch.linspace(0, 1, num_frames, device=device)
+        if interpolation == "constant": # All points stay at start position
+            interp_values = torch.zeros_like(t)
+        elif interpolation == "linear":
+            interp_values = t
+        elif interpolation == "ease_in":
+            interp_values = t ** 2
+        elif interpolation == "ease_out":
+            interp_values = 1 - (1 - t) ** 2
+        elif interpolation == "ease_in_out":
+            interp_values = t * t * (3 - 2 * t)
+
+        if bezier: # apply interpolation to t for timing control along the bezier path
+            t_interp = interp_values
+            one_minus_t = 1 - t_interp
+            x_positions = one_minus_t ** 2 * start_x_px + 2 * one_minus_t * t_interp * mid_x_px + t_interp ** 2 * end_x_px
+            y_positions = one_minus_t ** 2 * start_y_px + 2 * one_minus_t * t_interp * mid_y_px + t_interp ** 2 * end_y_px
+            tangent_x = 2 * one_minus_t * (mid_x_px - start_x_px) + 2 * t_interp * (end_x_px - mid_x_px)
+            tangent_y = 2 * one_minus_t * (mid_y_px - start_y_px) + 2 * t_interp * (end_y_px - mid_y_px)
+        else: # calculate base x and y positions for each frame (center track)
+            x_positions = start_x_px + (end_x_px - start_x_px) * interp_values
+            y_positions = start_y_px + (end_y_px - start_y_px) * interp_values
+            # For non-bezier, tangent is constant (direction from start to end)
+            tangent_x = torch.full_like(t, end_x_px - start_x_px)
+            tangent_y = torch.full_like(t, end_y_px - start_y_px)
+
+        track_list = []
+        for frame_idx in range(num_frames):
+            # Calculate perpendicular direction at this frame
+            tx = tangent_x[frame_idx].item()
+            ty = tangent_y[frame_idx].item()
+            length = (tx ** 2 + ty ** 2) ** 0.5
+
+            if length > 0: # Perpendicular unit vector (rotate 90 degrees)
+                perp_x = -ty / length
+                perp_y = tx / length
+            else: # If tangent is zero, spread horizontally
+                perp_x = 1.0
+                perp_y = 0.0
+
+            frame_tracks = []
+            for track_idx in range(num_tracks): # center tracks around the main path offset ranges from -(num_tracks-1)/2 to +(num_tracks-1)/2
+                offset = (track_idx - (num_tracks - 1) / 2) * track_spread_px
+                track_x = x_positions[frame_idx].item() + perp_x * offset
+                track_y = y_positions[frame_idx].item() + perp_y * offset
+                frame_tracks.append([track_x, track_y])
+            track_list.append(frame_tracks)
+
+        tracks = torch.tensor(track_list, dtype=torch.float32, device=device)  # [frames, num_tracks, 2]
+
+        if track_mask is None:
+            track_visibility = torch.ones((track_length, num_tracks), dtype=torch.bool, device=device)
+        else:
+            track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks
+        out_track_info["track_visibility"] = track_visibility
+        return io.NodeOutput(out_track_info, track_length)
+
+
+class WanMoveConcatTrack(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveConcatTrack",
+            category="conditioning/video_models",
+            inputs=[
+                io.Tracks.Input("tracks_1"),
+                io.Tracks.Input("tracks_2", optional=True),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, tracks_1=None, tracks_2=None) -> io.NodeOutput:
+        if tracks_2 is None:
+            return io.NodeOutput(tracks_1)
+
+        tracks_out = torch.cat([tracks_1["track_path"], tracks_2["track_path"]], dim=1)  # Concatenate along the track dimension
+        mask_out = torch.cat([tracks_1["track_visibility"], tracks_2["track_visibility"]], dim=-1)
+
+        out_track_info = {}
+        out_track_info["track_path"] = tracks_out
+        out_track_info["track_visibility"] = mask_out
+        return io.NodeOutput(out_track_info)
+
+
+class WanMoveTrackToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanMoveTrackToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Tracks.Input("tracks", optional=True),
+                io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01, tooltip="Strength of the track conditioning."),
+                io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image"),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, strength, tracks=None, start_image=None, clip_vision_output=None) -> io.NodeOutput:
+        device=comfy.model_management.intermediate_device()
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=device)
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5
+            image[:start_image.shape[0]] = start_image
+
+            concat_latent_image = vae.encode(image[:, :, :, :3])
+            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            if tracks is not None and strength > 0.0:
+                tracks_path = tracks["track_path"][:length]  # [T, N, 2]
+                num_tracks = tracks_path.shape[-2]
+
+                track_visibility = tracks.get("track_visibility", torch.ones((length, num_tracks), dtype=torch.bool, device=device))
+
+                track_pos = create_pos_embeddings(tracks_path, track_visibility, [4, 8, 8], height, width, track_num=num_tracks)
+                track_pos = comfy.utils.resize_to_batch_size(track_pos.unsqueeze(0), batch_size)
+                concat_latent_image_pos = replace_feature(concat_latent_image, track_pos, strength)
+            else:
+                concat_latent_image_pos = concat_latent_image
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image_pos, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+
+class WanMoveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            WanMoveTrackToVideo,
+            WanMoveTracksFromCoords,
+            WanMoveConcatTrack,
+            WanMoveVisualizeTracks,
+            GenerateTracks,
+        ]
+
+async def comfy_entrypoint() -> WanMoveExtension:
+    return WanMoveExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.76"
+__version__ = "0.4.0"

execution.py

@@ -13,6 +13,7 @@ import asyncio
 import torch
 
 import comfy.model_management
+from latent_preview import set_preview_method
 import nodes
 from comfy_execution.caching import (
     BasicCache,
@@ -669,6 +670,8 @@ class PromptExecutor:
         asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
 
     async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
+        set_preview_method(extra_data.get("preview_method"))
+
         nodes.interrupt_processing(False)
 
         if "client_id" in extra_data:

latent_preview.py

@@ -8,6 +8,8 @@ import folder_paths
 import comfy.utils
 import logging
 
+default_preview_method = args.preview_method
+
 MAX_PREVIEW_RESOLUTION = args.preview_size
 VIDEO_TAES = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5"]
 
@@ -125,3 +127,11 @@ def prepare_callback(model, steps, x0_output_dict=None):
         pbar.update_absolute(step + 1, total_steps, preview_bytes)
     return callback
 
+def set_preview_method(override: str = None):
+    if override and override != "default":
+        method = LatentPreviewMethod.from_string(override)
+        if method is not None:
+            args.preview_method = method
+            return
+    args.preview_method = default_preview_method
+

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.0.3b4
+comfyui_manager==4.0.3b5

nodes.py

@@ -2358,6 +2358,7 @@ async def init_builtin_extra_nodes():
         "nodes_logic.py",
         "nodes_nop.py",
         "nodes_kandinsky5.py",
+        "nodes_wanmove.py",
     ]
 
     import_failed = []
@@ -2383,7 +2384,6 @@ async def init_builtin_api_nodes():
         "nodes_recraft.py",
         "nodes_pixverse.py",
         "nodes_stability.py",
-        "nodes_pika.py",
         "nodes_runway.py",
         "nodes_sora.py",
         "nodes_topaz.py",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.76"
+version = "0.4.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.33.13
-comfyui-workflow-templates==0.7.54
+comfyui-frontend-package==1.34.8
+comfyui-workflow-templates==0.7.59
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

tests-unit/execution_test/preview_method_override_test.py

@@ -0,0 +1,352 @@
+"""
+Unit tests for Queue-specific Preview Method Override feature.
+
+Tests the preview method override functionality:
+- LatentPreviewMethod.from_string() method
+- set_preview_method() function in latent_preview.py
+- default_preview_method variable
+- Integration with args.preview_method
+"""
+import pytest
+from comfy.cli_args import args, LatentPreviewMethod
+from latent_preview import set_preview_method, default_preview_method
+
+
+class TestLatentPreviewMethodFromString:
+    """Test LatentPreviewMethod.from_string() classmethod."""
+
+    @pytest.mark.parametrize("value,expected", [
+        ("auto", LatentPreviewMethod.Auto),
+        ("latent2rgb", LatentPreviewMethod.Latent2RGB),
+        ("taesd", LatentPreviewMethod.TAESD),
+        ("none", LatentPreviewMethod.NoPreviews),
+    ])
+    def test_valid_values_return_enum(self, value, expected):
+        """Valid string values should return corresponding enum."""
+        assert LatentPreviewMethod.from_string(value) == expected
+
+    @pytest.mark.parametrize("invalid", [
+        "invalid",
+        "TAESD",      # Case sensitive
+        "AUTO",       # Case sensitive
+        "Latent2RGB", # Case sensitive
+        "latent",
+        "",
+        "default",    # default is special, not a method
+    ])
+    def test_invalid_values_return_none(self, invalid):
+        """Invalid string values should return None."""
+        assert LatentPreviewMethod.from_string(invalid) is None
+
+
+class TestLatentPreviewMethodEnumValues:
+    """Test LatentPreviewMethod enum has expected values."""
+
+    def test_enum_values(self):
+        """Verify enum values match expected strings."""
+        assert LatentPreviewMethod.NoPreviews.value == "none"
+        assert LatentPreviewMethod.Auto.value == "auto"
+        assert LatentPreviewMethod.Latent2RGB.value == "latent2rgb"
+        assert LatentPreviewMethod.TAESD.value == "taesd"
+
+    def test_enum_count(self):
+        """Verify exactly 4 preview methods exist."""
+        assert len(LatentPreviewMethod) == 4
+
+
+class TestSetPreviewMethod:
+    """Test set_preview_method() function from latent_preview.py."""
+
+    def setup_method(self):
+        """Store original value before each test."""
+        self.original = args.preview_method
+
+    def teardown_method(self):
+        """Restore original value after each test."""
+        args.preview_method = self.original
+
+    def test_override_with_taesd(self):
+        """'taesd' should set args.preview_method to TAESD."""
+        set_preview_method("taesd")
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+    def test_override_with_latent2rgb(self):
+        """'latent2rgb' should set args.preview_method to Latent2RGB."""
+        set_preview_method("latent2rgb")
+        assert args.preview_method == LatentPreviewMethod.Latent2RGB
+
+    def test_override_with_auto(self):
+        """'auto' should set args.preview_method to Auto."""
+        set_preview_method("auto")
+        assert args.preview_method == LatentPreviewMethod.Auto
+
+    def test_override_with_none_value(self):
+        """'none' should set args.preview_method to NoPreviews."""
+        set_preview_method("none")
+        assert args.preview_method == LatentPreviewMethod.NoPreviews
+
+    def test_default_restores_original(self):
+        """'default' should restore to default_preview_method."""
+        # First override to something else
+        set_preview_method("taesd")
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Then use 'default' to restore
+        set_preview_method("default")
+        assert args.preview_method == default_preview_method
+
+    def test_none_param_restores_original(self):
+        """None parameter should restore to default_preview_method."""
+        # First override to something else
+        set_preview_method("taesd")
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Then use None to restore
+        set_preview_method(None)
+        assert args.preview_method == default_preview_method
+
+    def test_empty_string_restores_original(self):
+        """Empty string should restore to default_preview_method."""
+        set_preview_method("taesd")
+        set_preview_method("")
+        assert args.preview_method == default_preview_method
+
+    def test_invalid_value_restores_original(self):
+        """Invalid value should restore to default_preview_method."""
+        set_preview_method("taesd")
+        set_preview_method("invalid_method")
+        assert args.preview_method == default_preview_method
+
+    def test_case_sensitive_invalid_restores(self):
+        """Case-mismatched values should restore to default."""
+        set_preview_method("taesd")
+        set_preview_method("TAESD")  # Wrong case
+        assert args.preview_method == default_preview_method
+
+
+class TestDefaultPreviewMethod:
+    """Test default_preview_method module variable."""
+
+    def test_default_is_not_none(self):
+        """default_preview_method should not be None."""
+        assert default_preview_method is not None
+
+    def test_default_is_enum_member(self):
+        """default_preview_method should be a LatentPreviewMethod enum."""
+        assert isinstance(default_preview_method, LatentPreviewMethod)
+
+    def test_default_matches_args_initial(self):
+        """default_preview_method should match CLI default or user setting."""
+        # This tests that default_preview_method was captured at module load
+        # After set_preview_method(None), args should equal default
+        original = args.preview_method
+        set_preview_method("taesd")
+        set_preview_method(None)
+        assert args.preview_method == default_preview_method
+        args.preview_method = original
+
+
+class TestArgsPreviewMethodModification:
+    """Test args.preview_method can be modified correctly."""
+
+    def setup_method(self):
+        """Store original value before each test."""
+        self.original = args.preview_method
+
+    def teardown_method(self):
+        """Restore original value after each test."""
+        args.preview_method = self.original
+
+    def test_args_accepts_all_enum_values(self):
+        """args.preview_method should accept all LatentPreviewMethod values."""
+        for method in LatentPreviewMethod:
+            args.preview_method = method
+            assert args.preview_method == method
+
+    def test_args_modification_and_restoration(self):
+        """args.preview_method should be modifiable and restorable."""
+        original = args.preview_method
+
+        args.preview_method = LatentPreviewMethod.TAESD
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        args.preview_method = original
+        assert args.preview_method == original
+
+
+class TestExecutionFlow:
+    """Test the execution flow pattern used in execution.py."""
+
+    def setup_method(self):
+        """Store original value before each test."""
+        self.original = args.preview_method
+
+    def teardown_method(self):
+        """Restore original value after each test."""
+        args.preview_method = self.original
+
+    def test_sequential_executions_with_different_methods(self):
+        """Simulate multiple queue executions with different preview methods."""
+        # Execution 1: taesd
+        set_preview_method("taesd")
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Execution 2: none
+        set_preview_method("none")
+        assert args.preview_method == LatentPreviewMethod.NoPreviews
+
+        # Execution 3: default (restore)
+        set_preview_method("default")
+        assert args.preview_method == default_preview_method
+
+        # Execution 4: auto
+        set_preview_method("auto")
+        assert args.preview_method == LatentPreviewMethod.Auto
+
+        # Execution 5: no override (None)
+        set_preview_method(None)
+        assert args.preview_method == default_preview_method
+
+    def test_override_then_default_pattern(self):
+        """Test the pattern: override -> execute -> next call restores."""
+        # First execution with override
+        set_preview_method("latent2rgb")
+        assert args.preview_method == LatentPreviewMethod.Latent2RGB
+
+        # Second execution without override restores default
+        set_preview_method(None)
+        assert args.preview_method == default_preview_method
+
+    def test_extra_data_simulation(self):
+        """Simulate extra_data.get('preview_method') patterns."""
+        # Simulate: extra_data = {"preview_method": "taesd"}
+        extra_data = {"preview_method": "taesd"}
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Simulate: extra_data = {}
+        extra_data = {}
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+        # Simulate: extra_data = {"preview_method": "default"}
+        extra_data = {"preview_method": "default"}
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+
+class TestRealWorldScenarios:
+    """Tests using real-world prompt data patterns."""
+
+    def setup_method(self):
+        """Store original value before each test."""
+        self.original = args.preview_method
+
+    def teardown_method(self):
+        """Restore original value after each test."""
+        args.preview_method = self.original
+
+    def test_captured_prompt_without_preview_method(self):
+        """
+        Test with captured prompt that has no preview_method.
+        Based on: tests-unit/execution_test/fixtures/default_prompt.json
+        """
+        # Real captured extra_data structure (preview_method absent)
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "271314f0dabd48e5aaa488ed7a4ceb0d",
+            "create_time": 1765416558179
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+    def test_captured_prompt_with_preview_method_taesd(self):
+        """Test captured prompt with preview_method: taesd."""
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "271314f0dabd48e5aaa488ed7a4ceb0d",
+            "preview_method": "taesd"
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+    def test_captured_prompt_with_preview_method_none(self):
+        """Test captured prompt with preview_method: none (disable preview)."""
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "test-client",
+            "preview_method": "none"
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.NoPreviews
+
+    def test_captured_prompt_with_preview_method_latent2rgb(self):
+        """Test captured prompt with preview_method: latent2rgb."""
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "test-client",
+            "preview_method": "latent2rgb"
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.Latent2RGB
+
+    def test_captured_prompt_with_preview_method_auto(self):
+        """Test captured prompt with preview_method: auto."""
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "test-client",
+            "preview_method": "auto"
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.Auto
+
+    def test_captured_prompt_with_preview_method_default(self):
+        """Test captured prompt with preview_method: default (use CLI setting)."""
+        # First set to something else
+        set_preview_method("taesd")
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Then simulate a prompt with "default"
+        extra_data = {
+            "extra_pnginfo": {"workflow": {}},
+            "client_id": "test-client",
+            "preview_method": "default"
+        }
+
+        set_preview_method(extra_data.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+    def test_sequential_queue_with_different_preview_methods(self):
+        """
+        Simulate real queue scenario: multiple prompts with different settings.
+        This tests the actual usage pattern in ComfyUI.
+        """
+        # Queue 1: User wants TAESD preview
+        extra_data_1 = {"client_id": "client-1", "preview_method": "taesd"}
+        set_preview_method(extra_data_1.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.TAESD
+
+        # Queue 2: User wants no preview (faster execution)
+        extra_data_2 = {"client_id": "client-2", "preview_method": "none"}
+        set_preview_method(extra_data_2.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.NoPreviews
+
+        # Queue 3: User doesn't specify (use server default)
+        extra_data_3 = {"client_id": "client-3"}
+        set_preview_method(extra_data_3.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+        # Queue 4: User explicitly wants default
+        extra_data_4 = {"client_id": "client-4", "preview_method": "default"}
+        set_preview_method(extra_data_4.get("preview_method"))
+        assert args.preview_method == default_preview_method
+
+        # Queue 5: User wants latent2rgb
+        extra_data_5 = {"client_id": "client-5", "preview_method": "latent2rgb"}
+        set_preview_method(extra_data_5.get("preview_method"))
+        assert args.preview_method == LatentPreviewMethod.Latent2RGB

tests/execution/test_preview_method.py

@@ -0,0 +1,358 @@
+"""
+E2E tests for Queue-specific Preview Method Override feature.
+
+Tests actual execution with different preview_method values.
+Requires a running ComfyUI server with models.
+
+Usage:
+    COMFYUI_SERVER=http://localhost:8988 pytest test_preview_method_e2e.py -v -m preview_method
+
+Note:
+    These tests execute actual image generation and wait for completion.
+    Tests verify preview image transmission based on preview_method setting.
+"""
+import os
+import json
+import pytest
+import uuid
+import time
+import random
+import websocket
+import urllib.request
+from pathlib import Path
+
+
+# Server configuration
+SERVER_URL = os.environ.get("COMFYUI_SERVER", "http://localhost:8988")
+SERVER_HOST = SERVER_URL.replace("http://", "").replace("https://", "")
+
+# Use existing inference graph fixture
+GRAPH_FILE = Path(__file__).parent.parent / "inference" / "graphs" / "default_graph_sdxl1_0.json"
+
+
+def is_server_running() -> bool:
+    """Check if ComfyUI server is running."""
+    try:
+        request = urllib.request.Request(f"{SERVER_URL}/system_stats")
+        with urllib.request.urlopen(request, timeout=2.0):
+            return True
+    except Exception:
+        return False
+
+
+def prepare_graph_for_test(graph: dict, steps: int = 5) -> dict:
+    """Prepare graph for testing: randomize seeds and reduce steps."""
+    adapted = json.loads(json.dumps(graph))  # Deep copy
+    for node_id, node in adapted.items():
+        inputs = node.get("inputs", {})
+        # Handle both "seed" and "noise_seed" (used by KSamplerAdvanced)
+        if "seed" in inputs:
+            inputs["seed"] = random.randint(0, 2**32 - 1)
+        if "noise_seed" in inputs:
+            inputs["noise_seed"] = random.randint(0, 2**32 - 1)
+        # Reduce steps for faster testing (default 20 -> 5)
+        if "steps" in inputs:
+            inputs["steps"] = steps
+    return adapted
+
+
+# Alias for backward compatibility
+randomize_seed = prepare_graph_for_test
+
+
+class PreviewMethodClient:
+    """Client for testing preview_method with WebSocket execution tracking."""
+
+    def __init__(self, server_address: str):
+        self.server_address = server_address
+        self.client_id = str(uuid.uuid4())
+        self.ws = None
+
+    def connect(self):
+        """Connect to WebSocket."""
+        self.ws = websocket.WebSocket()
+        self.ws.settimeout(120)  # 2 minute timeout for sampling
+        self.ws.connect(f"ws://{self.server_address}/ws?clientId={self.client_id}")
+
+    def close(self):
+        """Close WebSocket connection."""
+        if self.ws:
+            self.ws.close()
+
+    def queue_prompt(self, prompt: dict, extra_data: dict = None) -> dict:
+        """Queue a prompt and return response with prompt_id."""
+        data = {
+            "prompt": prompt,
+            "client_id": self.client_id,
+            "extra_data": extra_data or {}
+        }
+        req = urllib.request.Request(
+            f"http://{self.server_address}/prompt",
+            data=json.dumps(data).encode("utf-8"),
+            headers={"Content-Type": "application/json"}
+        )
+        return json.loads(urllib.request.urlopen(req).read())
+
+    def wait_for_execution(self, prompt_id: str, timeout: float = 120.0) -> dict:
+        """
+        Wait for execution to complete via WebSocket.
+
+        Returns:
+            dict with keys: completed, error, preview_count, execution_time
+        """
+        result = {
+            "completed": False,
+            "error": None,
+            "preview_count": 0,
+            "execution_time": 0.0
+        }
+
+        start_time = time.time()
+        self.ws.settimeout(timeout)
+
+        try:
+            while True:
+                out = self.ws.recv()
+                elapsed = time.time() - start_time
+
+                if isinstance(out, str):
+                    message = json.loads(out)
+                    msg_type = message.get("type")
+                    data = message.get("data", {})
+
+                    if data.get("prompt_id") != prompt_id:
+                        continue
+
+                    if msg_type == "executing":
+                        if data.get("node") is None:
+                            # Execution complete
+                            result["completed"] = True
+                            result["execution_time"] = elapsed
+                            break
+
+                    elif msg_type == "execution_error":
+                        result["error"] = data
+                        result["execution_time"] = elapsed
+                        break
+
+                    elif msg_type == "progress":
+                        # Progress update during sampling
+                        pass
+
+                elif isinstance(out, bytes):
+                    # Binary data = preview image
+                    result["preview_count"] += 1
+
+        except websocket.WebSocketTimeoutException:
+            result["error"] = "Timeout waiting for execution"
+            result["execution_time"] = time.time() - start_time
+
+        return result
+
+
+def load_graph() -> dict:
+    """Load the SDXL graph fixture with randomized seed."""
+    with open(GRAPH_FILE) as f:
+        graph = json.load(f)
+    return randomize_seed(graph)  # Avoid caching
+
+
+# Skip all tests if server is not running
+pytestmark = [
+    pytest.mark.skipif(
+        not is_server_running(),
+        reason=f"ComfyUI server not running at {SERVER_URL}"
+    ),
+    pytest.mark.preview_method,
+    pytest.mark.execution,
+]
+
+
+@pytest.fixture
+def client():
+    """Create and connect a test client."""
+    c = PreviewMethodClient(SERVER_HOST)
+    c.connect()
+    yield c
+    c.close()
+
+
+@pytest.fixture
+def graph():
+    """Load the test graph."""
+    return load_graph()
+
+
+class TestPreviewMethodExecution:
+    """Test actual execution with different preview methods."""
+
+    def test_execution_with_latent2rgb(self, client, graph):
+        """
+        Execute with preview_method=latent2rgb.
+        Should complete and potentially receive preview images.
+        """
+        extra_data = {"preview_method": "latent2rgb"}
+
+        response = client.queue_prompt(graph, extra_data)
+        assert "prompt_id" in response
+
+        result = client.wait_for_execution(response["prompt_id"])
+
+        # Should complete (may error if model missing, but that's separate)
+        assert result["completed"] or result["error"] is not None
+        # Execution should take some time (sampling)
+        if result["completed"]:
+            assert result["execution_time"] > 0.5, "Execution too fast - likely didn't run"
+            # latent2rgb should produce previews
+            print(f"latent2rgb: {result['preview_count']} previews in {result['execution_time']:.2f}s")  # noqa: T201
+
+    def test_execution_with_taesd(self, client, graph):
+        """
+        Execute with preview_method=taesd.
+        TAESD provides higher quality previews.
+        """
+        extra_data = {"preview_method": "taesd"}
+
+        response = client.queue_prompt(graph, extra_data)
+        assert "prompt_id" in response
+
+        result = client.wait_for_execution(response["prompt_id"])
+
+        assert result["completed"] or result["error"] is not None
+        if result["completed"]:
+            assert result["execution_time"] > 0.5
+            # taesd should also produce previews
+            print(f"taesd: {result['preview_count']} previews in {result['execution_time']:.2f}s")  # noqa: T201
+
+    def test_execution_with_none_preview(self, client, graph):
+        """
+        Execute with preview_method=none.
+        No preview images should be generated.
+        """
+        extra_data = {"preview_method": "none"}
+
+        response = client.queue_prompt(graph, extra_data)
+        assert "prompt_id" in response
+
+        result = client.wait_for_execution(response["prompt_id"])
+
+        assert result["completed"] or result["error"] is not None
+        if result["completed"]:
+            # With "none", should receive no preview images
+            assert result["preview_count"] == 0, \
+                f"Expected no previews with 'none', got {result['preview_count']}"
+            print(f"none: {result['preview_count']} previews in {result['execution_time']:.2f}s")  # noqa: T201
+
+    def test_execution_with_default(self, client, graph):
+        """
+        Execute with preview_method=default.
+        Should use server's CLI default setting.
+        """
+        extra_data = {"preview_method": "default"}
+
+        response = client.queue_prompt(graph, extra_data)
+        assert "prompt_id" in response
+
+        result = client.wait_for_execution(response["prompt_id"])
+
+        assert result["completed"] or result["error"] is not None
+        if result["completed"]:
+            print(f"default: {result['preview_count']} previews in {result['execution_time']:.2f}s")  # noqa: T201
+
+    def test_execution_without_preview_method(self, client, graph):
+        """
+        Execute without preview_method in extra_data.
+        Should use server's default preview method.
+        """
+        extra_data = {}  # No preview_method
+
+        response = client.queue_prompt(graph, extra_data)
+        assert "prompt_id" in response
+
+        result = client.wait_for_execution(response["prompt_id"])
+
+        assert result["completed"] or result["error"] is not None
+        if result["completed"]:
+            print(f"(no override): {result['preview_count']} previews in {result['execution_time']:.2f}s")  # noqa: T201
+
+
+class TestPreviewMethodComparison:
+    """Compare preview behavior between different methods."""
+
+    def test_none_vs_latent2rgb_preview_count(self, client, graph):
+        """
+        Compare preview counts: 'none' should have 0, others should have >0.
+        This is the key verification that preview_method actually works.
+        """
+        results = {}
+
+        # Run with none (randomize seed to avoid caching)
+        graph_none = randomize_seed(graph)
+        extra_data_none = {"preview_method": "none"}
+        response = client.queue_prompt(graph_none, extra_data_none)
+        results["none"] = client.wait_for_execution(response["prompt_id"])
+
+        # Run with latent2rgb (randomize seed again)
+        graph_rgb = randomize_seed(graph)
+        extra_data_rgb = {"preview_method": "latent2rgb"}
+        response = client.queue_prompt(graph_rgb, extra_data_rgb)
+        results["latent2rgb"] = client.wait_for_execution(response["prompt_id"])
+
+        # Verify both completed
+        assert results["none"]["completed"], f"'none' execution failed: {results['none']['error']}"
+        assert results["latent2rgb"]["completed"], f"'latent2rgb' execution failed: {results['latent2rgb']['error']}"
+
+        # Key assertion: 'none' should have 0 previews
+        assert results["none"]["preview_count"] == 0, \
+            f"'none' should have 0 previews, got {results['none']['preview_count']}"
+
+        # 'latent2rgb' should have at least 1 preview (depends on steps)
+        assert results["latent2rgb"]["preview_count"] > 0, \
+            f"'latent2rgb' should have >0 previews, got {results['latent2rgb']['preview_count']}"
+
+        print("\nPreview count comparison:")  # noqa: T201
+        print(f"  none: {results['none']['preview_count']} previews")  # noqa: T201
+        print(f"  latent2rgb: {results['latent2rgb']['preview_count']} previews")  # noqa: T201
+
+
+class TestPreviewMethodSequential:
+    """Test sequential execution with different preview methods."""
+
+    def test_sequential_different_methods(self, client, graph):
+        """
+        Execute multiple prompts sequentially with different preview methods.
+        Each should complete independently with correct preview behavior.
+        """
+        methods = ["latent2rgb", "none", "default"]
+        results = []
+
+        for method in methods:
+            # Randomize seed for each execution to avoid caching
+            graph_run = randomize_seed(graph)
+            extra_data = {"preview_method": method}
+            response = client.queue_prompt(graph_run, extra_data)
+
+            result = client.wait_for_execution(response["prompt_id"])
+            results.append({
+                "method": method,
+                "completed": result["completed"],
+                "preview_count": result["preview_count"],
+                "execution_time": result["execution_time"],
+                "error": result["error"]
+            })
+
+        # All should complete or have clear errors
+        for r in results:
+            assert r["completed"] or r["error"] is not None, \
+                f"Method {r['method']} neither completed nor errored"
+
+        # "none" should have zero previews if completed
+        none_result = next(r for r in results if r["method"] == "none")
+        if none_result["completed"]:
+            assert none_result["preview_count"] == 0, \
+                f"'none' should have 0 previews, got {none_result['preview_count']}"
+
+        print("\nSequential execution results:")  # noqa: T201
+        for r in results:
+            status = "✓" if r["completed"] else f"✗ ({r['error']})"
+            print(f"  {r['method']}: {status}, {r['preview_count']} previews, {r['execution_time']:.2f}s")  # noqa: T201