support additional solid texture outputs

We get a lot od bug reports without logs, especially for performance
issues.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
 
         ## Very Important
 
-        Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
+        Please make sure that you post ALL your ComfyUI logs in the bug report **even if there is no crash**. Just paste everything. The startup log (everything before "To see the GUI go to: ...") contains critical information to developers trying to help. For a performance issue or crash, paste everything from "got prompt" to the end, including the crash. More is better - always. A bug report without logs will likely be ignored.
   - type: checkboxes
     id: custom-nodes-test
     attributes:

README.md

@@ -189,8 +189,6 @@ The portable above currently comes with python 3.13 and pytorch cuda 13.0. Updat
 
 [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
-
 [Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -18,6 +18,8 @@ import comfy.patcher_extension
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
+from ..sdpose import HeatmapHead
+
 class TimestepBlock(nn.Module):
     """
     Any module where forward() takes timestep embeddings as a second argument.
@@ -441,6 +443,7 @@ class UNetModel(nn.Module):
         disable_temporal_crossattention=False,
         max_ddpm_temb_period=10000,
         attn_precision=None,
+        heatmap_head=False,
         device=None,
         operations=ops,
     ):
@@ -827,6 +830,9 @@ class UNetModel(nn.Module):
             #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
         )
 
+        if heatmap_head:
+            self.heatmap_head = HeatmapHead(device=device, dtype=self.dtype, operations=operations)
+
     def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
             self._forward,

comfy/ldm/modules/sdpose.py

@@ -0,0 +1,130 @@
+import torch
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+class HeatmapHead(torch.nn.Module):
+    def __init__(
+            self,
+            in_channels=640,
+            out_channels=133,
+            input_size=(768, 1024),
+            heatmap_scale=4,
+            deconv_out_channels=(640,),
+            deconv_kernel_sizes=(4,),
+            conv_out_channels=(640,),
+            conv_kernel_sizes=(1,),
+            final_layer_kernel_size=1,
+            device=None, dtype=None, operations=None
+        ):
+        super().__init__()
+
+        self.heatmap_size = (input_size[0] // heatmap_scale, input_size[1] // heatmap_scale)
+        self.scale_factor = ((np.array(input_size) - 1) / (np.array(self.heatmap_size) - 1)).astype(np.float32)
+
+        # Deconv layers
+        if deconv_out_channels:
+            deconv_layers = []
+            for out_ch, kernel_size in zip(deconv_out_channels, deconv_kernel_sizes):
+                if kernel_size == 4:
+                    padding, output_padding = 1, 0
+                elif kernel_size == 3:
+                    padding, output_padding = 1, 1
+                elif kernel_size == 2:
+                    padding, output_padding = 0, 0
+                else:
+                    raise ValueError(f'Unsupported kernel size {kernel_size}')
+
+                deconv_layers.extend([
+                    operations.ConvTranspose2d(in_channels, out_ch, kernel_size,
+                                     stride=2, padding=padding, output_padding=output_padding, bias=False, device=device, dtype=dtype),
+                    torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),
+                    torch.nn.SiLU(inplace=True)
+                ])
+                in_channels = out_ch
+            self.deconv_layers = torch.nn.Sequential(*deconv_layers)
+        else:
+            self.deconv_layers = torch.nn.Identity()
+
+        # Conv layers
+        if conv_out_channels:
+            conv_layers = []
+            for out_ch, kernel_size in zip(conv_out_channels, conv_kernel_sizes):
+                padding = (kernel_size - 1) // 2
+                conv_layers.extend([
+                    operations.Conv2d(in_channels, out_ch, kernel_size,
+                            stride=1, padding=padding, device=device, dtype=dtype),
+                    torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),
+                    torch.nn.SiLU(inplace=True)
+                ])
+                in_channels = out_ch
+            self.conv_layers = torch.nn.Sequential(*conv_layers)
+        else:
+            self.conv_layers = torch.nn.Identity()
+
+        self.final_layer = operations.Conv2d(in_channels, out_channels, kernel_size=final_layer_kernel_size, padding=final_layer_kernel_size // 2, device=device, dtype=dtype)
+
+    def forward(self, x): # Decode heatmaps to keypoints
+        heatmaps = self.final_layer(self.conv_layers(self.deconv_layers(x)))
+        heatmaps_np = heatmaps.float().cpu().numpy()  # (B, K, H, W)
+        B, K, H, W = heatmaps_np.shape
+
+        batch_keypoints = []
+        batch_scores = []
+
+        for b in range(B):
+            hm = heatmaps_np[b].copy()  # (K, H, W)
+
+            # --- vectorised argmax ---
+            flat = hm.reshape(K, -1)
+            idx = np.argmax(flat, axis=1)
+            scores = flat[np.arange(K), idx].copy()
+            y_locs, x_locs = np.unravel_index(idx, (H, W))
+            keypoints = np.stack([x_locs, y_locs], axis=-1).astype(np.float32)  # (K, 2) in heatmap space
+            invalid = scores <= 0.
+            keypoints[invalid] = -1
+
+            # --- DARK sub-pixel refinement (UDP) ---
+            # 1. Gaussian blur with max-preserving normalisation
+            border = 5  # (kernel-1)//2 for kernel=11
+            for k in range(K):
+                origin_max = np.max(hm[k])
+                dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32)
+                dr[border:-border, border:-border] = hm[k].copy()
+                dr = gaussian_filter(dr, sigma=2.0)
+                hm[k] = dr[border:-border, border:-border].copy()
+                cur_max = np.max(hm[k])
+                if cur_max > 0:
+                    hm[k] *= origin_max / cur_max
+            # 2. Log-space for Taylor expansion
+            np.clip(hm, 1e-3, 50., hm)
+            np.log(hm, hm)
+            # 3. Hessian-based Newton step
+            hm_pad = np.pad(hm, ((0, 0), (1, 1), (1, 1)), mode='edge').flatten()
+            index = keypoints[:, 0] + 1 + (keypoints[:, 1] + 1) * (W + 2)
+            index += (W + 2) * (H + 2) * np.arange(0, K)
+            index = index.astype(int).reshape(-1, 1)
+            i_       = hm_pad[index]
+            ix1      = hm_pad[index + 1]
+            iy1      = hm_pad[index + W + 2]
+            ix1y1    = hm_pad[index + W + 3]
+            ix1_y1_  = hm_pad[index - W - 3]
+            ix1_     = hm_pad[index - 1]
+            iy1_     = hm_pad[index - 2 - W]
+            dx = 0.5 * (ix1 - ix1_)
+            dy = 0.5 * (iy1 - iy1_)
+            derivative = np.concatenate([dx, dy], axis=1).reshape(K, 2, 1)
+            dxx = ix1  - 2 * i_ + ix1_
+            dyy = iy1  - 2 * i_ + iy1_
+            dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_)
+            hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1).reshape(K, 2, 2)
+            hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2))
+            keypoints -= np.einsum('imn,ink->imk', hessian, derivative).squeeze(axis=-1)
+
+            # --- restore to input image space ---
+            keypoints = keypoints * self.scale_factor
+            keypoints[invalid] = -1
+
+            batch_keypoints.append(keypoints)
+            batch_scores.append(scores)
+
+        return batch_keypoints, batch_scores

comfy/lora.py

@@ -337,6 +337,7 @@ def model_lora_keys_unet(model, key_map={}):
             if k.startswith("diffusion_model.decoder.") and k.endswith(".weight"):
                 key_lora = k[len("diffusion_model.decoder."):-len(".weight")]
                 key_map["base_model.model.{}".format(key_lora)] = k  # Official base model loras
+                key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k  # LyCORIS/LoKR format
 
     return key_map
 

comfy/model_detection.py

@@ -795,6 +795,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         unet_config["use_temporal_resblock"] = False
         unet_config["use_temporal_attention"] = False
 
+    heatmap_key = '{}heatmap_head.conv_layers.0.weight'.format(key_prefix)
+    if heatmap_key in state_dict_keys:
+        unet_config["heatmap_head"] = True
+
     return unet_config
 
 def model_config_from_unet_config(unet_config, state_dict=None):
@@ -1015,7 +1019,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
 
     LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4,
             'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
-            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8,
+            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64,
             'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
             'use_temporal_attention': False, 'use_temporal_resblock': False}
 

comfy/model_management.py

@@ -350,7 +350,7 @@ AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'
 
 try:
     if is_amd():
-        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0]
         if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
             if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':
                 torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
@@ -378,7 +378,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):

comfy/supported_models.py

@@ -525,7 +525,8 @@ class LotusD(SD20):
     }
 
     unet_extra_config = {
-        "num_classes": 'sequential'
+        "num_classes": 'sequential',
+        "num_head_channels": 64,
     }
 
     def get_model(self, state_dict, prefix="", device=None):

comfy_api/latest/_io.py

@@ -1224,9 +1224,10 @@ class BoundingBox(ComfyTypeIO):
 
     class Input(WidgetInput):
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: dict=None, component: str=None):
+                     socketless: bool=True, default: dict=None, component: str=None, force_input: bool=None):
             super().__init__(id, display_name, optional, tooltip, None, default, socketless)
             self.component = component
+            self.force_input = force_input
             if default is None:
                 self.default = {"x": 0, "y": 0, "width": 512, "height": 512}
 
@@ -1234,6 +1235,8 @@ class BoundingBox(ComfyTypeIO):
             d = super().as_dict()
             if self.component:
                 d["component"] = self.component
+            if self.force_input is not None:
+                d["forceInput"] = self.force_input
             return d
 
 

comfy_api_nodes/apis/gemini.py

@@ -127,9 +127,15 @@ class GeminiImageConfig(BaseModel):
     imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
+class GeminiThinkingConfig(BaseModel):
+    includeThoughts: bool | None = Field(None)
+    thinkingLevel: str = Field(...)
+
+
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
     responseModalities: list[str] | None = Field(None)
     imageConfig: GeminiImageConfig | None = Field(None)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
 
 
 class GeminiImageGenerateContentRequest(BaseModel):

comfy_api_nodes/nodes_bytedance.py

@@ -186,7 +186,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 5.0",
+            display_name="ByteDance Seedream 4.5 & 5.0",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[

comfy_api_nodes/nodes_gemini.py

@@ -29,6 +29,7 @@ from comfy_api_nodes.apis.gemini import (
     GeminiRole,
     GeminiSystemInstructionContent,
     GeminiTextPart,
+    GeminiThinkingConfig,
     Modality,
 )
 from comfy_api_nodes.util import (
@@ -55,6 +56,21 @@ GEMINI_IMAGE_SYS_PROMPT = (
     "Prioritize generating the visual representation above any text, formatting, or conversational requests."
 )
 
+GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge(
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "resolution"]),
+    expr="""
+    (
+      $m := widgets.model;
+      $r := widgets.resolution;
+      $isFlash := $contains($m, "nano banana 2");
+      $flashPrices := {"1k": 0.0696, "2k": 0.0696, "4k": 0.123};
+      $proPrices := {"1k": 0.134, "2k": 0.134, "4k": 0.24};
+      $prices := $isFlash ? $flashPrices : $proPrices;
+      {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+    )
+    """,
+)
+
 
 class GeminiModel(str, Enum):
     """
@@ -229,6 +245,10 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 120.0
+    elif response.modelVersion == "gemini-3.1-flash-image-preview":
+        input_tokens_price = 0.5
+        output_text_tokens_price = 3.0
+        output_image_tokens_price = 60.0
     else:
         return None
     final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@@ -686,7 +706,7 @@ class GeminiImage2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=["gemini-3-pro-image-preview"],
+                    options=["gemini-3-pro-image-preview", "Nano Banana 2 (Gemini 3.1 Flash Image)"],
                 ),
                 IO.Int.Input(
                     "seed",
@@ -750,19 +770,7 @@ class GeminiImage2(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
-                expr="""
-                (
-                  $r := widgets.resolution;
-                  ($contains($r,"1k") or $contains($r,"2k"))
-                    ? {"type":"usd","usd":0.134,"format":{"suffix":"/Image","approximate":true}}
-                    : $contains($r,"4k")
-                      ? {"type":"usd","usd":0.24,"format":{"suffix":"/Image","approximate":true}}
-                      : {"type":"text","text":"Token-based"}
-                )
-                """,
-            ),
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
         )
 
     @classmethod
@@ -779,6 +787,10 @@ class GeminiImage2(IO.ComfyNode):
         system_prompt: str = "",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image-preview"
+            if response_modalities == "IMAGE+TEXT":
+                raise ValueError("IMAGE+TEXT is not currently available for the Nano Banana 2 model.")
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -815,6 +827,168 @@ class GeminiImage2(IO.ComfyNode):
         return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
+class GeminiNanoBanana2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2",
+            display_name="Nano Banana 2",
+            category="api node/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.Combo.Input(
+                    "model",
+                    options=["Nano Banana 2 (Gemini 3.1 Flash Image)"],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=[
+                        "auto",
+                        "1:1",
+                        "2:3",
+                        "3:2",
+                        "3:4",
+                        "4:3",
+                        "4:5",
+                        "5:4",
+                        "9:16",
+                        "16:9",
+                        "21:9",
+                        # "1:4",
+                        # "4:1",
+                        # "8:1",
+                        # "1:8",
+                    ],
+                    default="auto",
+                    tooltip="If set to 'auto', matches your input image's aspect ratio; "
+                    "if no image is provided, a 16:9 square is usually generated.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        # "512px",
+                        "1K",
+                        "2K",
+                        "4K",
+                    ],
+                    tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE"],
+                    advanced=True,
+                ),
+                IO.Combo.Input(
+                    "thinking_level",
+                    options=["MINIMAL", "HIGH"],
+                ),
+                IO.Image.Input(
+                    "images",
+                    optional=True,
+                    tooltip="Optional reference image(s). "
+                    "To include multiple images, use the Batch Images node (up to 14).",
+                ),
+                IO.Custom("GEMINI_INPUT_FILES").Input(
+                    "files",
+                    optional=True,
+                    tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: str,
+        seed: int,
+        aspect_ratio: str,
+        resolution: str,
+        response_modalities: str,
+        thinking_level: str,
+        images: Input.Image | None = None,
+        files: list[GeminiPart] | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model = "gemini-3.1-flash-image-preview"
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images is not None:
+            if get_number_of_images(images) > 14:
+                raise ValueError("The current maximum number of supported images is 14.")
+            parts.extend(await create_image_parts(cls, images))
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=resolution)
+        if aspect_ratio != "auto":
+            image_config.aspectRatio = aspect_ratio
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=thinking_level),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -822,6 +996,7 @@ class GeminiExtension(ComfyExtension):
             GeminiNode,
             GeminiImage,
             GeminiImage2,
+            GeminiNanoBanana2,
             GeminiInputFiles,
         ]
 

comfy_api_nodes/nodes_hunyuan3d.py

@@ -1,3 +1,7 @@
+import zipfile
+from io import BytesIO
+
+import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, Types
@@ -15,6 +19,8 @@ from comfy_api_nodes.apis.hunyuan3d import (
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_to_bytesio,
     download_url_to_file_3d,
     download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
@@ -35,6 +41,68 @@ def _is_tencent_rate_limited(status: int, body: object) -> bool:
     )
 
 
+class ObjZipResult:
+    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
+
+    def __init__(
+        self,
+        obj: Types.File3D,
+        texture: Input.Image | None = None,
+        metallic: Input.Image | None = None,
+        normal: Input.Image | None = None,
+        roughness: Input.Image | None = None,
+    ):
+        self.obj = obj
+        self.texture = texture
+        self.metallic = metallic
+        self.normal = normal
+        self.roughness = roughness
+
+
+async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
+    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
+
+    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
+    identified by their filename suffixes.
+    """
+    data = BytesIO()
+    await download_url_to_bytesio(url, data)
+    data.seek(0)
+    if not zipfile.is_zipfile(data):
+        data.seek(0)
+        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
+    data.seek(0)
+    obj_bytes = None
+    textures: dict[str, Input.Image] = {}
+    with zipfile.ZipFile(data) as zf:
+        for name in zf.namelist():
+            lower = name.lower()
+            if lower.endswith(".obj"):
+                obj_bytes = zf.read(name)
+            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
+                stem = lower.rsplit(".", 1)[0]
+                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
+                matched_key = "texture"
+                for suffix, key in {
+                    "_metallic": "metallic",
+                    "_normal": "normal",
+                    "_roughness": "roughness",
+                }.items():
+                    if stem.endswith(suffix):
+                        matched_key = key
+                        break
+                textures[matched_key] = tensor
+    if obj_bytes is None:
+        raise ValueError("ZIP archive does not contain an OBJ file.")
+    return ObjZipResult(
+        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
+        texture=textures.get("texture"),
+        metallic=textures.get("metallic"),
+        normal=textures.get("normal"),
+        roughness=textures.get("roughness"),
+    )
+
+
 def get_file_from_response(
     response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
 ) -> ResultFile3D | None:
@@ -92,6 +160,7 @@ class TencentTextToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -150,14 +219,14 @@ class TencentTextToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
+        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            await download_url_to_file_3d(
-                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
-            ),
+            obj_result.obj,
+            obj_result.texture,
         )
 
 
@@ -210,6 +279,10 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+                IO.Image.Output(display_name="optional_metallic"),
+                IO.Image.Output(display_name="optional_normal"),
+                IO.Image.Output(display_name="optional_roughness"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -303,14 +376,17 @@ class TencentImageToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
+        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            await download_url_to_file_3d(
-                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
-            ),
+            obj_result.obj,
+            obj_result.texture,
+            obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
+            obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
+            obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
         )
 
 

comfy_extras/nodes_glsl.py

@@ -717,11 +717,11 @@ def _render_shader_batch(
         gl.glUseProgram(0)
 
         for tex in input_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         for tex in output_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         for tex in ping_pong_textures:
-            gl.glDeleteTextures(tex)
+            gl.glDeleteTextures(int(tex))
         if fbo is not None:
             gl.glDeleteFramebuffers(1, [fbo])
         for pp_fbo in ping_pong_fbos:
@@ -865,14 +865,15 @@ class GLSLShader(io.ComfyNode):
         cls, image_list: list[torch.Tensor], output_batch: torch.Tensor
     ) -> dict[str, list]:
         """Build UI output with input and output images for client-side shader execution."""
-        combined_inputs = torch.cat(image_list, dim=0)
-        input_images_ui = ui.ImageSaveHelper.save_images(
-            combined_inputs,
-            filename_prefix="GLSLShader_input",
-            folder_type=io.FolderType.temp,
-            cls=None,
-            compress_level=1,
-        )
+        input_images_ui = []
+        for img in image_list:
+            input_images_ui.extend(ui.ImageSaveHelper.save_images(
+                img,
+                filename_prefix="GLSLShader_input",
+                folder_type=io.FolderType.temp,
+                cls=None,
+                compress_level=1,
+            ))
 
         output_images_ui = ui.ImageSaveHelper.save_images(
             output_batch,

comfy_extras/nodes_post_processing.py

@@ -79,7 +79,6 @@ class Blur(io.ComfyNode):
             node_id="ImageBlur",
             display_name="Image Blur",
             category="image/postprocessing",
-            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
@@ -568,6 +567,7 @@ class BatchImagesNode(io.ComfyNode):
             node_id="BatchImagesNode",
             display_name="Batch Images",
             category="image",
+            essentials_category="Image Tools",
             search_aliases=["batch", "image batch", "batch images", "combine images", "merge images", "stack images"],
             inputs=[
                 io.Autogrow.Input("images", template=autogrow_template)

comfy_extras/nodes_sdpose.py

@@ -0,0 +1,740 @@
+import torch
+import comfy.utils
+import numpy as np
+import math
+import colorsys
+from tqdm import tqdm
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_extras.nodes_lotus import LotusConditioning
+
+
+def _preprocess_keypoints(kp_raw, sc_raw):
+    """Insert neck keypoint and remap from MMPose to OpenPose ordering.
+
+    Returns (kp, sc) where kp has shape (134, 2) and sc has shape (134,).
+    Layout:
+      0-17   body  (18 kp, OpenPose order)
+      18-23  feet  (6 kp)
+      24-91  face  (68 kp)
+      92-112 right hand (21 kp)
+      113-133 left hand (21 kp)
+    """
+    kp = np.array(kp_raw, dtype=np.float32)
+    sc = np.array(sc_raw, dtype=np.float32)
+    if len(kp) >= 17:
+        neck = (kp[5] + kp[6]) / 2
+        neck_score = min(sc[5], sc[6]) if sc[5] > 0.3 and sc[6] > 0.3 else 0
+        kp = np.insert(kp, 17, neck, axis=0)
+        sc = np.insert(sc, 17, neck_score)
+        mmpose_idx   = np.array([17, 6,  8, 10,  7,  9, 12, 14, 16, 13, 15, 2, 1, 4, 3])
+        openpose_idx = np.array([ 1, 2,  3,  4,  6,  7,  8,  9, 10, 12, 13, 14, 15, 16, 17])
+        tmp_kp, tmp_sc = kp.copy(), sc.copy()
+        tmp_kp[openpose_idx] = kp[mmpose_idx]
+        tmp_sc[openpose_idx] = sc[mmpose_idx]
+        kp, sc = tmp_kp, tmp_sc
+    return kp, sc
+
+
+def _to_openpose_frames(all_keypoints, all_scores, height, width):
+    """Convert raw keypoint lists to a list of OpenPose-style frame dicts.
+
+    Each frame dict contains:
+      canvas_width, canvas_height, people: list of person dicts with keys:
+        pose_keypoints_2d       - 18 body kp  as flat [x,y,score,...] (absolute pixels)
+        foot_keypoints_2d       -  6 foot kp  as flat [x,y,score,...] (absolute pixels)
+        face_keypoints_2d       - 70 face kp  as flat [x,y,score,...] (absolute pixels)
+                                   indices 0-67: 68 face landmarks
+                                   index  68:    right eye (body[14])
+                                   index  69:    left  eye (body[15])
+        hand_right_keypoints_2d - 21 right-hand kp (absolute pixels)
+        hand_left_keypoints_2d  - 21 left-hand  kp (absolute pixels)
+    """
+    def _flatten(kp_slice, sc_slice):
+        return np.stack([kp_slice[:, 0], kp_slice[:, 1], sc_slice], axis=1).flatten().tolist()
+
+    frames = []
+    for img_idx in range(len(all_keypoints)):
+        people = []
+        for kp_raw, sc_raw in zip(all_keypoints[img_idx], all_scores[img_idx]):
+            kp, sc = _preprocess_keypoints(kp_raw, sc_raw)
+            # 70 face kp = 68 face landmarks + REye (body[14]) + LEye (body[15])
+            face_kp = np.concatenate([kp[24:92], kp[[14, 15]]], axis=0)
+            face_sc = np.concatenate([sc[24:92], sc[[14, 15]]], axis=0)
+            people.append({
+                "pose_keypoints_2d":       _flatten(kp[0:18],   sc[0:18]),
+                "foot_keypoints_2d":       _flatten(kp[18:24],  sc[18:24]),
+                "face_keypoints_2d":       _flatten(face_kp,    face_sc),
+                "hand_right_keypoints_2d": _flatten(kp[92:113], sc[92:113]),
+                "hand_left_keypoints_2d":  _flatten(kp[113:134], sc[113:134]),
+            })
+        frames.append({"canvas_width": width, "canvas_height": height, "people": people})
+    return frames
+
+
+class KeypointDraw:
+    """
+    Pose keypoint drawing class that supports both numpy and cv2 backends.
+    """
+    def __init__(self):
+        try:
+            import cv2
+            self.draw = cv2
+        except ImportError:
+            self.draw = self
+
+        # Hand connections (same for both hands)
+        self.hand_edges = [
+            [0, 1], [1, 2], [2, 3], [3, 4],      # thumb
+            [0, 5], [5, 6], [6, 7], [7, 8],      # index
+            [0, 9], [9, 10], [10, 11], [11, 12], # middle
+            [0, 13], [13, 14], [14, 15], [15, 16], # ring
+            [0, 17], [17, 18], [18, 19], [19, 20], # pinky
+        ]
+
+        # Body connections - matching DWPose limbSeq (1-indexed, converted to 0-indexed)
+        self.body_limbSeq = [
+            [2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10],
+            [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17],
+            [1, 16], [16, 18]
+        ]
+
+        # Colors matching DWPose
+        self.colors = [
+            [255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0],
+            [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255],
+            [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
+            [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]
+        ]
+
+    @staticmethod
+    def circle(canvas_np, center, radius, color, **kwargs):
+        """Draw a filled circle using NumPy vectorized operations."""
+        cx, cy = center
+        h, w = canvas_np.shape[:2]
+
+        radius_int = int(np.ceil(radius))
+
+        y_min, y_max = max(0, cy - radius_int), min(h, cy + radius_int + 1)
+        x_min, x_max = max(0, cx - radius_int), min(w, cx + radius_int + 1)
+
+        if y_max <= y_min or x_max <= x_min:
+            return
+
+        y, x = np.ogrid[y_min:y_max, x_min:x_max]
+        mask = (x - cx)**2 + (y - cy)**2 <= radius**2
+        canvas_np[y_min:y_max, x_min:x_max][mask] = color
+
+    @staticmethod
+    def line(canvas_np, pt1, pt2, color, thickness=1, **kwargs):
+        """Draw line using Bresenham's algorithm with NumPy operations."""
+        x0, y0, x1, y1 = *pt1, *pt2
+        h, w = canvas_np.shape[:2]
+        dx, dy = abs(x1 - x0), abs(y1 - y0)
+        sx, sy = (1 if x0 < x1 else -1), (1 if y0 < y1 else -1)
+        err, x, y, line_points = dx - dy, x0, y0, []
+
+        while True:
+            line_points.append((x, y))
+            if x == x1 and y == y1:
+                break
+            e2 = 2 * err
+            if e2 > -dy:
+                err, x = err - dy, x + sx
+            if e2 < dx:
+                err, y = err + dx, y + sy
+
+        if thickness > 1:
+            radius, radius_int = (thickness / 2.0) + 0.5, int(np.ceil((thickness / 2.0) + 0.5))
+            for px, py in line_points:
+                y_min, y_max, x_min, x_max = max(0, py - radius_int), min(h, py + radius_int + 1), max(0, px - radius_int), min(w, px + radius_int + 1)
+                if y_max > y_min and x_max > x_min:
+                    yy, xx = np.ogrid[y_min:y_max, x_min:x_max]
+                    canvas_np[y_min:y_max, x_min:x_max][(xx - px)**2 + (yy - py)**2 <= radius**2] = color
+        else:
+            line_points = np.array(line_points)
+            valid = (line_points[:, 1] >= 0) & (line_points[:, 1] < h) & (line_points[:, 0] >= 0) & (line_points[:, 0] < w)
+            if (valid_points := line_points[valid]).size:
+                canvas_np[valid_points[:, 1], valid_points[:, 0]] = color
+
+    @staticmethod
+    def fillConvexPoly(canvas_np, pts, color, **kwargs):
+        """Fill polygon using vectorized scanline algorithm."""
+        if len(pts) < 3:
+            return
+        pts = np.array(pts, dtype=np.int32)
+        h, w = canvas_np.shape[:2]
+        y_min, y_max, x_min, x_max = max(0, pts[:, 1].min()), min(h, pts[:, 1].max() + 1), max(0, pts[:, 0].min()), min(w, pts[:, 0].max() + 1)
+        if y_max <= y_min or x_max <= x_min:
+            return
+        yy, xx = np.mgrid[y_min:y_max, x_min:x_max]
+        mask = np.zeros((y_max - y_min, x_max - x_min), dtype=bool)
+
+        for i in range(len(pts)):
+            p1, p2 = pts[i], pts[(i + 1) % len(pts)]
+            y1, y2 = p1[1], p2[1]
+            if y1 == y2:
+                continue
+            if y1 > y2:
+                p1, p2, y1, y2 = p2, p1, p2[1], p1[1]
+            if not (edge_mask := (yy >= y1) & (yy < y2)).any():
+                continue
+            mask ^= edge_mask & (xx >= p1[0] + (yy - y1) * (p2[0] - p1[0]) / (y2 - y1))
+
+        canvas_np[y_min:y_max, x_min:x_max][mask] = color
+
+    @staticmethod
+    def ellipse2Poly(center, axes, angle, arc_start, arc_end, delta=1, **kwargs):
+        """Python implementation of cv2.ellipse2Poly."""
+        axes = (axes[0] + 0.5, axes[1] + 0.5) # to better match cv2 output
+        angle = angle % 360
+        if arc_start > arc_end:
+            arc_start, arc_end = arc_end, arc_start
+        while arc_start < 0:
+            arc_start, arc_end = arc_start + 360, arc_end + 360
+        while arc_end > 360:
+            arc_end, arc_start = arc_end - 360, arc_start - 360
+        if arc_end - arc_start > 360:
+            arc_start, arc_end = 0, 360
+
+        angle_rad = math.radians(angle)
+        alpha, beta = math.cos(angle_rad), math.sin(angle_rad)
+        pts = []
+        for i in range(arc_start, arc_end + delta, delta):
+            theta_rad = math.radians(min(i, arc_end))
+            x, y = axes[0] * math.cos(theta_rad), axes[1] * math.sin(theta_rad)
+            pts.append([int(round(center[0] + x * alpha - y * beta)), int(round(center[1] + x * beta + y * alpha))])
+
+        unique_pts, prev_pt = [], (float('inf'), float('inf'))
+        for pt in pts:
+            if (pt_tuple := tuple(pt)) != prev_pt:
+                unique_pts.append(pt)
+                prev_pt = pt_tuple
+
+        return unique_pts if len(unique_pts) > 1 else [[center[0], center[1]], [center[0], center[1]]]
+
+    def draw_wholebody_keypoints(self, canvas, keypoints, scores=None, threshold=0.3,
+                                 draw_body=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3):
+        """
+        Draw wholebody keypoints (134 keypoints after processing) in DWPose style.
+
+        Expected keypoint format (after neck insertion and remapping):
+        - Body: 0-17 (18 keypoints in OpenPose format, neck at index 1)
+        - Foot: 18-23 (6 keypoints)
+        - Face: 24-91 (68 landmarks)
+        - Right hand: 92-112 (21 keypoints)
+        - Left hand: 113-133 (21 keypoints)
+
+        Args:
+            canvas: The canvas to draw on (numpy array)
+            keypoints: Array of keypoint coordinates
+            scores: Optional confidence scores for each keypoint
+            threshold: Minimum confidence threshold for drawing keypoints
+
+        Returns:
+            canvas: The canvas with keypoints drawn
+        """
+        H, W, C = canvas.shape
+
+        # Draw body limbs
+        if draw_body and len(keypoints) >= 18:
+            for i, limb in enumerate(self.body_limbSeq):
+                # Convert from 1-indexed to 0-indexed
+                idx1, idx2 = limb[0] - 1, limb[1] - 1
+
+                if idx1 >= 18 or idx2 >= 18:
+                    continue
+
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                Y = [keypoints[idx1][0], keypoints[idx2][0]]
+                X = [keypoints[idx1][1], keypoints[idx2][1]]
+                mX, mY = (X[0] + X[1]) / 2, (Y[0] + Y[1]) / 2
+                length = math.sqrt((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2)
+
+                if length < 1:
+                    continue
+
+                angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+
+                polygon = self.draw.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1)
+
+                self.draw.fillConvexPoly(canvas, polygon, self.colors[i % len(self.colors)])
+
+        # Draw body keypoints
+        if draw_body and len(keypoints) >= 18:
+            for i in range(18):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, self.colors[i % len(self.colors)], thickness=-1)
+
+        # Draw foot keypoints (18-23, 6 keypoints)
+        if draw_feet and len(keypoints) >= 24:
+            for i in range(18, 24):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, self.colors[i % len(self.colors)], thickness=-1)
+
+        # Draw right hand (92-112)
+        if draw_hands and len(keypoints) >= 113:
+            eps = 0.01
+            for ie, edge in enumerate(self.hand_edges):
+                idx1, idx2 = 92 + edge[0], 92 + edge[1]
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                x1, y1 = int(keypoints[idx1][0]), int(keypoints[idx1][1])
+                x2, y2 = int(keypoints[idx2][0]), int(keypoints[idx2][1])
+
+                if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+                    if 0 <= x1 < W and 0 <= y1 < H and 0 <= x2 < W and 0 <= y2 < H:
+                        # HSV to RGB conversion for rainbow colors
+                        r, g, b = colorsys.hsv_to_rgb(ie / float(len(self.hand_edges)), 1.0, 1.0)
+                        color = (int(r * 255), int(g * 255), int(b * 255))
+                        self.draw.line(canvas, (x1, y1), (x2, y2), color, thickness=2)
+
+            # Draw right hand keypoints
+            for i in range(92, 113):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+
+        # Draw left hand (113-133)
+        if draw_hands and len(keypoints) >= 134:
+            eps = 0.01
+            for ie, edge in enumerate(self.hand_edges):
+                idx1, idx2 = 113 + edge[0], 113 + edge[1]
+                if scores is not None:
+                    if scores[idx1] < threshold or scores[idx2] < threshold:
+                        continue
+
+                x1, y1 = int(keypoints[idx1][0]), int(keypoints[idx1][1])
+                x2, y2 = int(keypoints[idx2][0]), int(keypoints[idx2][1])
+
+                if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+                    if 0 <= x1 < W and 0 <= y1 < H and 0 <= x2 < W and 0 <= y2 < H:
+                        # HSV to RGB conversion for rainbow colors
+                        r, g, b = colorsys.hsv_to_rgb(ie / float(len(self.hand_edges)), 1.0, 1.0)
+                        color = (int(r * 255), int(g * 255), int(b * 255))
+                        self.draw.line(canvas, (x1, y1), (x2, y2), color, thickness=2)
+
+            # Draw left hand keypoints
+            for i in range(113, 134):
+                if scores is not None and i < len(scores) and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+
+        # Draw face keypoints (24-91) - white dots only, no lines
+        if draw_face and len(keypoints) >= 92:
+            eps = 0.01
+            for i in range(24, 92):
+                if scores is not None and scores[i] < threshold:
+                    continue
+                x, y = int(keypoints[i][0]), int(keypoints[i][1])
+                if x > eps and y > eps and 0 <= x < W and 0 <= y < H:
+                    self.draw.circle(canvas, (x, y), face_point_size, (255, 255, 255), thickness=-1)
+
+        return canvas
+
+class SDPoseDrawKeypoints(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseDrawKeypoints",
+            category="image/preprocessors",
+            search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "pose"],
+            inputs=[
+                io.Custom("POSE_KEYPOINT").Input("keypoints"),
+                io.Boolean.Input("draw_body", default=True),
+                io.Boolean.Input("draw_hands", default=True),
+                io.Boolean.Input("draw_face", default=True),
+                io.Boolean.Input("draw_feet", default=False),
+                io.Int.Input("stick_width", default=4, min=1, max=10, step=1),
+                io.Int.Input("face_point_size", default=3, min=1, max=10, step=1),
+                io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput:
+        if not keypoints:
+            return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32))
+        height = keypoints[0]["canvas_height"]
+        width  = keypoints[0]["canvas_width"]
+
+        def _parse(flat, n):
+            arr = np.array(flat, dtype=np.float32).reshape(n, 3)
+            return arr[:, :2], arr[:, 2]
+
+        def _zeros(n):
+            return np.zeros((n, 2), dtype=np.float32), np.zeros(n, dtype=np.float32)
+
+        pose_outputs = []
+        drawer = KeypointDraw()
+
+        for frame in tqdm(keypoints, desc="Drawing keypoints on frames"):
+            canvas = np.zeros((height, width, 3), dtype=np.uint8)
+            for person in frame["people"]:
+                body_kp,  body_sc  = _parse(person["pose_keypoints_2d"],       18)
+                foot_raw = person.get("foot_keypoints_2d")
+                foot_kp,  foot_sc  = _parse(foot_raw, 6) if foot_raw else _zeros(6)
+                face_kp,  face_sc  = _parse(person["face_keypoints_2d"],       70)
+                face_kp,  face_sc  = face_kp[:68], face_sc[:68]  # drop appended eye kp; body already draws them
+                rhand_kp, rhand_sc = _parse(person["hand_right_keypoints_2d"], 21)
+                lhand_kp, lhand_sc = _parse(person["hand_left_keypoints_2d"],  21)
+
+                kp = np.concatenate([body_kp, foot_kp, face_kp, rhand_kp, lhand_kp], axis=0)
+                sc = np.concatenate([body_sc, foot_sc, face_sc, rhand_sc, lhand_sc], axis=0)
+
+                canvas = drawer.draw_wholebody_keypoints(
+                    canvas, kp, sc,
+                    threshold=score_threshold,
+                    draw_body=draw_body, draw_feet=draw_feet,
+                    draw_face=draw_face, draw_hands=draw_hands,
+                    stick_width=stick_width, face_point_size=face_point_size,
+                )
+            pose_outputs.append(canvas)
+
+        pose_outputs_np = np.stack(pose_outputs) if len(pose_outputs) > 1 else np.expand_dims(pose_outputs[0], 0)
+        final_pose_output = torch.from_numpy(pose_outputs_np).float() / 255.0
+        return io.NodeOutput(final_pose_output)
+
+class SDPoseKeypointExtractor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseKeypointExtractor",
+            category="image/preprocessors",
+            search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "sdpose"],
+            description="Extract pose keypoints from images using the SDPose model: https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Int.Input("batch_size", default=16, min=1, max=10000, step=1),
+                io.BoundingBox.Input("bboxes", optional=True, force_input=True, tooltip="Optional bounding boxes for more accurate detections. Required for multi-person detection."),
+            ],
+            outputs=[
+                io.Custom("POSE_KEYPOINT").Output("keypoints", tooltip="Keypoints in OpenPose frame format (canvas_width, canvas_height, people)"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, image, batch_size, bboxes=None) -> io.NodeOutput:
+
+        height, width = image.shape[-3], image.shape[-2]
+        context = LotusConditioning().execute().result[0]
+
+        # Use output_block_patch to capture the last 640-channel feature
+        def output_patch(h, hsp, transformer_options):
+            nonlocal captured_feat
+            if h.shape[1] == 640:  # Capture the features for wholebody
+                captured_feat = h.clone()
+            return h, hsp
+
+        model_clone = model.clone()
+        model_clone.model_options["transformer_options"] = {"patches": {"output_block_patch": [output_patch]}}
+
+        if not hasattr(model.model.diffusion_model, 'heatmap_head'):
+            raise ValueError("The provided model does not have a heatmap_head. Please use SDPose model from here https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints.")
+
+        head = model.model.diffusion_model.heatmap_head
+        total_images = image.shape[0]
+        captured_feat = None
+
+        model_h = int(head.heatmap_size[0]) * 4   # e.g. 192 * 4 = 768
+        model_w = int(head.heatmap_size[1]) * 4   # e.g. 256 * 4 = 1024
+
+        def _run_on_latent(latent_batch):
+            """Run one forward pass and return (keypoints_list, scores_list) for the batch."""
+            nonlocal captured_feat
+            captured_feat = None
+            _ = comfy.sample.sample(
+                model_clone,
+                noise=torch.zeros_like(latent_batch),
+                steps=1, cfg=1.0,
+                sampler_name="euler", scheduler="simple",
+                positive=context, negative=context,
+                latent_image=latent_batch, disable_noise=True, disable_pbar=True,
+            )
+            return head(captured_feat)  # keypoints_batch, scores_batch
+
+        # all_keypoints / all_scores are lists-of-lists:
+        #   outer index = input image index
+        #   inner index = detected person (one per bbox, or one for full-image)
+        all_keypoints = []  # shape: [n_images][n_persons]
+        all_scores = []     # shape: [n_images][n_persons]
+        pbar = comfy.utils.ProgressBar(total_images)
+
+        if bboxes is not None:
+            if not isinstance(bboxes, list):
+                bboxes = [[bboxes]]
+            elif len(bboxes) == 0:
+                bboxes = [None] * total_images
+            # --- bbox-crop mode: one forward pass per crop -------------------------
+            for img_idx in tqdm(range(total_images), desc="Extracting keypoints from crops"):
+                img = image[img_idx:img_idx + 1]  # (1, H, W, C)
+                # Broadcasting: if fewer bbox lists than images, repeat the last one.
+                img_bboxes = bboxes[min(img_idx, len(bboxes) - 1)] if bboxes else None
+
+                img_keypoints = []
+                img_scores = []
+
+                if img_bboxes:
+                    for bbox in img_bboxes:
+                        x1 = max(0, int(bbox["x"]))
+                        y1 = max(0, int(bbox["y"]))
+                        x2 = min(width,  int(bbox["x"] + bbox["width"]))
+                        y2 = min(height, int(bbox["y"] + bbox["height"]))
+
+                        if x2 <= x1 or y2 <= y1:
+                            continue
+
+                        crop_h_px, crop_w_px = y2 - y1, x2 - x1
+                        crop = img[:, y1:y2, x1:x2, :]  # (1, crop_h, crop_w, C)
+
+                        # scale to fit inside (model_h, model_w) while preserving aspect ratio, then pad to exact model size.
+                        scale = min(model_h / crop_h_px, model_w / crop_w_px)
+                        scaled_h, scaled_w = int(round(crop_h_px * scale)), int(round(crop_w_px * scale))
+                        pad_top, pad_left  = (model_h - scaled_h) // 2, (model_w - scaled_w) // 2
+
+                        crop_chw = crop.permute(0, 3, 1, 2).float()  # BHWC → BCHW
+                        scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
+                        padded = torch.zeros(1, scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
+                        padded[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
+                        crop_resized = padded.permute(0, 2, 3, 1)  # BCHW → BHWC
+
+                        latent_crop = vae.encode(crop_resized)
+                        kp_batch, sc_batch = _run_on_latent(latent_crop)
+                        kp, sc = kp_batch[0], sc_batch[0]  # (K, 2), coords in model pixel space
+
+                        # remove padding offset, undo scale, offset to full-image coordinates.
+                        kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
+                        kp[..., 0] = (kp[..., 0] - pad_left) / scale + x1
+                        kp[..., 1] = (kp[..., 1] - pad_top)  / scale + y1
+
+                        img_keypoints.append(kp)
+                        img_scores.append(sc)
+                else:
+                    # No bboxes for this image – run on the full image
+                    latent_img = vae.encode(img)
+                    kp_batch, sc_batch = _run_on_latent(latent_img)
+                    img_keypoints.append(kp_batch[0])
+                    img_scores.append(sc_batch[0])
+
+                all_keypoints.append(img_keypoints)
+                all_scores.append(img_scores)
+                pbar.update(1)
+
+        else: # full-image mode, batched
+            tqdm_pbar = tqdm(total=total_images, desc="Extracting keypoints")
+            for batch_start in range(0, total_images, batch_size):
+                batch_end = min(batch_start + batch_size, total_images)
+                latent_batch = vae.encode(image[batch_start:batch_end])
+
+                kp_batch, sc_batch = _run_on_latent(latent_batch)
+
+                for kp, sc in zip(kp_batch, sc_batch):
+                    all_keypoints.append([kp])
+                    all_scores.append([sc])
+                    tqdm_pbar.update(1)
+
+                pbar.update(batch_end - batch_start)
+
+        openpose_frames = _to_openpose_frames(all_keypoints, all_scores, height, width)
+        return io.NodeOutput(openpose_frames)
+
+
+def get_face_bboxes(kp2ds, scale, image_shape):
+    h, w = image_shape
+    kp2ds_face = kp2ds.copy()[1:] * (w, h)
+
+    min_x, min_y = np.min(kp2ds_face, axis=0)
+    max_x, max_y = np.max(kp2ds_face, axis=0)
+
+    initial_width = max_x - min_x
+    initial_height = max_y - min_y
+
+    if initial_width <= 0 or initial_height <= 0:
+        return [0, 0, 0, 0]
+
+    initial_area = initial_width * initial_height
+
+    expanded_area = initial_area * scale
+
+    new_width = np.sqrt(expanded_area * (initial_width / initial_height))
+    new_height = np.sqrt(expanded_area * (initial_height / initial_width))
+
+    delta_width = (new_width - initial_width) / 2
+    delta_height = (new_height - initial_height) / 4
+
+    expanded_min_x = max(min_x - delta_width, 0)
+    expanded_max_x = min(max_x + delta_width, w)
+    expanded_min_y = max(min_y - 3 * delta_height, 0)
+    expanded_max_y = min(max_y + delta_height, h)
+
+    return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)]
+
+class SDPoseFaceBBoxes(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SDPoseFaceBBoxes",
+            category="image/preprocessors",
+            search_aliases=["face bbox", "face bounding box", "pose", "keypoints"],
+            inputs=[
+                io.Custom("POSE_KEYPOINT").Input("keypoints"),
+                io.Float.Input("scale", default=1.5, min=1.0, max=10.0, step=0.1, tooltip="Multiplier for the bounding box area around each detected face."),
+                io.Boolean.Input("force_square", default=True, tooltip="Expand the shorter bbox axis so the crop region is always square."),
+            ],
+            outputs=[
+                io.BoundingBox.Output("bboxes", tooltip="Face bounding boxes per frame, compatible with SDPoseKeypointExtractor bboxes input."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, keypoints, scale, force_square) -> io.NodeOutput:
+        all_bboxes = []
+        for frame in keypoints:
+            h = frame["canvas_height"]
+            w = frame["canvas_width"]
+            frame_bboxes = []
+            for person in frame["people"]:
+                face_flat = person.get("face_keypoints_2d", [])
+                if not face_flat:
+                    continue
+                # Parse absolute-pixel face keypoints (70 kp: 68 landmarks + REye + LEye)
+                face_arr = np.array(face_flat, dtype=np.float32).reshape(-1, 3)
+                face_xy  = face_arr[:, :2]  # (70, 2) in absolute pixels
+
+                kp_norm = face_xy / np.array([w, h], dtype=np.float32)
+                kp_padded = np.vstack([np.zeros((1, 2), dtype=np.float32), kp_norm])  # (71, 2)
+
+                x1, x2, y1, y2 = get_face_bboxes(kp_padded, scale, (h, w))
+                if x2 > x1 and y2 > y1:
+                    if force_square:
+                        bw, bh = x2 - x1, y2 - y1
+                        if bw != bh:
+                            side = max(bw, bh)
+                            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+                            half = side // 2
+                            x1 = max(0, cx - half)
+                            y1 = max(0, cy - half)
+                            x2 = min(w, x1 + side)
+                            y2 = min(h, y1 + side)
+                            # Re-anchor if clamped
+                            x1 = max(0, x2 - side)
+                            y1 = max(0, y2 - side)
+                    frame_bboxes.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1})
+
+            all_bboxes.append(frame_bboxes)
+
+        return io.NodeOutput(all_bboxes)
+
+
+class CropByBBoxes(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CropByBBoxes",
+            category="image/preprocessors",
+            search_aliases=["crop", "face crop", "bbox crop", "pose", "bounding box"],
+            description="Crop and resize regions from the input image batch based on provided bounding boxes.",
+            inputs=[
+                io.Image.Input("image"),
+                io.BoundingBox.Input("bboxes", force_input=True),
+                io.Int.Input("output_width",  default=512, min=64, max=4096, step=8, tooltip="Width each crop is resized to."),
+                io.Int.Input("output_height", default=512, min=64, max=4096, step=8, tooltip="Height each crop is resized to."),
+                io.Int.Input("padding", default=0, min=0, max=1024, step=1, tooltip="Extra padding in pixels added on each side of the bbox before cropping."),
+            ],
+            outputs=[
+                io.Image.Output(tooltip="All crops stacked into a single image batch."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, bboxes, output_width, output_height, padding) -> io.NodeOutput:
+        total_frames = image.shape[0]
+        img_h = image.shape[1]
+        img_w = image.shape[2]
+        num_ch = image.shape[3]
+
+        if not isinstance(bboxes, list):
+            bboxes = [[bboxes]]
+        elif len(bboxes) == 0:
+            return io.NodeOutput(image)
+
+        crops = []
+
+        for frame_idx in range(total_frames):
+            frame_bboxes = bboxes[min(frame_idx, len(bboxes) - 1)]
+            if not frame_bboxes:
+                continue
+
+            frame_chw = image[frame_idx].permute(2, 0, 1).unsqueeze(0)  # BHWC → BCHW (1, C, H, W)
+
+            # Union all bboxes for this frame into a single crop region
+            x1 = min(int(b["x"]) for b in frame_bboxes)
+            y1 = min(int(b["y"]) for b in frame_bboxes)
+            x2 = max(int(b["x"] + b["width"])  for b in frame_bboxes)
+            y2 = max(int(b["y"] + b["height"]) for b in frame_bboxes)
+
+            if padding > 0:
+                x1 = max(0, x1 - padding)
+                y1 = max(0, y1 - padding)
+                x2 = min(img_w, x2 + padding)
+                y2 = min(img_h, y2 + padding)
+
+            x1, x2 = max(0, x1), min(img_w, x2)
+            y1, y2 = max(0, y1), min(img_h, y2)
+
+            # Fallback for empty/degenerate crops
+            if x2 <= x1 or y2 <= y1:
+                fallback_size = int(min(img_h, img_w) * 0.3)
+                fb_x1 = max(0, (img_w - fallback_size) // 2)
+                fb_y1 = max(0, int(img_h * 0.1))
+                fb_x2 = min(img_w, fb_x1 + fallback_size)
+                fb_y2 = min(img_h, fb_y1 + fallback_size)
+                if fb_x2 <= fb_x1 or fb_y2 <= fb_y1:
+                    crops.append(torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device))
+                    continue
+                x1, y1, x2, y2 = fb_x1, fb_y1, fb_x2, fb_y2
+
+            crop_chw = frame_chw[:, :, y1:y2, x1:x2]  # (1, C, crop_h, crop_w)
+            resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
+            crops.append(resized)
+
+        if not crops:
+            return io.NodeOutput(image)
+
+        out_images = torch.cat(crops, dim=0).permute(0, 2, 3, 1)  # (N, H, W, C)
+        return io.NodeOutput(out_images)
+
+
+class SDPoseExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SDPoseKeypointExtractor,
+            SDPoseDrawKeypoints,
+            SDPoseFaceBBoxes,
+            CropByBBoxes,
+        ]
+
+async def comfy_entrypoint() -> SDPoseExtension:
+    return SDPoseExtension()

comfy_extras/nodes_video.py

@@ -147,7 +147,6 @@ class GetVideoComponents(io.ComfyNode):
             search_aliases=["extract frames", "split video", "video to images", "demux"],
             display_name="Get Video Components",
             category="image/video",
-            essentials_category="Video Tools",
             description="Extracts all components from a video: frames, audio, and framerate.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to extract components from."),
@@ -218,6 +217,7 @@ class VideoSlice(io.ComfyNode):
                 "start time",
             ],
             category="image/video",
+            essentials_category="Video Tools",
             inputs=[
                 io.Video.Input("video"),
                 io.Float.Input(

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.15.0"
+__version__ = "0.15.1"

main.py

@@ -16,6 +16,10 @@ from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    comfy_aimdo.control.init()
 
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
@@ -173,10 +177,6 @@ import gc
 if 'torch' in sys.modules:
     logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
 
-import comfy_aimdo.control
-
-if enables_dynamic_vram():
-    comfy_aimdo.control.init()
 
 import comfy.utils
 

nodes.py

@@ -1925,7 +1925,6 @@ class ImageInvert:
 
 class ImageBatch:
     SEARCH_ALIASES = ["combine images", "merge images", "stack images"]
-    ESSENTIALS_CATEGORY = "Image Tools"
 
     @classmethod
     def INPUT_TYPES(s):
@@ -2448,6 +2447,7 @@ async def init_builtin_extra_nodes():
         "nodes_toolkit.py",
         "nodes_replacements.py",
         "nodes_nag.py",
+        "nodes_sdpose.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.15.0"
+version = "0.15.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.3
+comfyui-workflow-templates==0.9.4
 comfyui-embedded-docs==0.4.3
 torch
 torchsde