Merge branch 'master' into worksplit-multigpu

README.md

@@ -110,7 +110,6 @@ ComfyUI follows a weekly release cycle every Friday, with three interconnected r
 
 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
    - Builds a new release using the latest stable core version
-   - Version numbers match the core release (e.g., Desktop v1.7.0 uses Core v1.7.0)
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
    - Weekly frontend updates are merged into the core repository
@@ -198,11 +197,11 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3```
 
-This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
 
 ### Intel GPUs (Windows and Linux)
 
@@ -302,7 +301,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt
 
 ### AMD ROCm Tips
 
-You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
+You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
 
 ```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
 

comfy/comfy_types/node_typing.py

@@ -235,7 +235,7 @@ class ComfyNodeABC(ABC):
     DEPRECATED: bool
     """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
     API_NODE: Optional[bool]
-    """Flags a node as an API node."""
+    """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
 
     @classmethod
     @abstractmethod

comfy/ldm/hunyuan_video/model.py

@@ -228,6 +228,7 @@ class HunyuanVideo(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         guiding_frame_index=None,
+        ref_latent=None,
         control=None,
         transformer_options={},
     ) -> Tensor:
@@ -238,6 +239,14 @@ class HunyuanVideo(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
 
+        if ref_latent is not None:
+            ref_latent_ids = self.img_ids(ref_latent)
+            ref_latent = self.img_in(ref_latent)
+            img = torch.cat([ref_latent, img], dim=-2)
+            ref_latent_ids[..., 0] = -1
+            ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1])
+            img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2)
+
         if guiding_frame_index is not None:
             token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))
             vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
@@ -313,6 +322,8 @@ class HunyuanVideo(nn.Module):
                         img[:, : img_len] += add
 
         img = img[:, : img_len]
+        if ref_latent is not None:
+            img = img[:, ref_latent.shape[1]:]
 
         img = self.final_layer(img, vec, modulation_dims=modulation_dims)  # (N, T, patch_size ** 2 * out_channels)
 
@@ -324,7 +335,7 @@ class HunyuanVideo(nn.Module):
         img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
-    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, control=None, transformer_options={}, **kwargs):
+    def img_ids(self, x):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
@@ -334,7 +345,11 @@ class HunyuanVideo(nn.Module):
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
         img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
-        img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+        return repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+        bs, c, t, h, w = x.shape
+        img_ids = self.img_ids(x)
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, control, transformer_options)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options)
         return out

comfy/ldm/wan/model.py

@@ -247,6 +247,60 @@ class VaceWanAttentionBlock(WanAttentionBlock):
         return c_skip, c
 
 
+class WanCamAdapter(nn.Module):
+    def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}):
+        super(WanCamAdapter, self).__init__()
+
+        # Pixel Unshuffle: reduce spatial dimensions by a factor of 8
+        self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
+
+        # Convolution: reduce spatial dimensions by a factor
+        #  of 2 (without overlap)
+        self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+        # Residual blocks for feature extraction
+        self.residual_blocks = nn.Sequential(
+            *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)]
+        )
+
+    def forward(self, x):
+        # Reshape to merge the frame dimension into batch
+        bs, c, f, h, w = x.size()
+        x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
+
+        # Pixel Unshuffle operation
+        x_unshuffled = self.pixel_unshuffle(x)
+
+        # Convolution operation
+        x_conv = self.conv(x_unshuffled)
+
+        # Feature extraction with residual blocks
+        out = self.residual_blocks(x_conv)
+
+        # Reshape to restore original bf dimension
+        out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
+
+        # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
+        out = out.permute(0, 2, 1, 3, 4)
+
+        return out
+
+
+class WanCamResidualBlock(nn.Module):
+    def __init__(self, dim, operation_settings={}):
+        super(WanCamResidualBlock, self).__init__()
+        self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x):
+        residual = x
+        out = self.relu(self.conv1(x))
+        out = self.conv2(out)
+        out += residual
+        return out
+
+
 class Head(nn.Module):
 
     def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
@@ -637,3 +691,92 @@ class VaceWanModel(WanModel):
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
+
+class CameraWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='camera',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 image_model=None,
+                 in_dim_control_adapter=24,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings)
+
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea=None,
+        freqs=None,
+        camera_conditions = None,
+        transformer_options={},
+        **kwargs,
+    ):
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        if self.control_adapter is not None and camera_conditions is not None:
+            x_camera = self.control_adapter(camera_conditions).to(x.dtype)
+            x = x + x_camera
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
+        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x

comfy/lora.py

@@ -286,6 +286,12 @@ def model_lora_keys_unet(model, key_map={}):
                     key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
                     key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format
 
+    if isinstance(model, comfy.model_base.ACEStep):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+
     return key_map
 
 

comfy/model_base.py

@@ -924,6 +924,10 @@ class HunyuanVideo(BaseModel):
         if guiding_frame_index is not None:
             out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index]))
 
+        ref_latent = kwargs.get("ref_latent", None)
+        if ref_latent is not None:
+            out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent))
+
         return out
 
     def scale_latent_inpaint(self, latent_image, **kwargs):
@@ -1075,6 +1079,17 @@ class WAN21_Vace(WAN21):
         out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
         return out
 
+class WAN21_Camera(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        camera_conditions = kwargs.get("camera_conditions", None)
+        if camera_conditions is not None:
+            out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
+        return out
 
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):

comfy/model_detection.py

@@ -361,6 +361,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "vace"
             dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]
             dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')
+        elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "camera"
         else:
             if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                 dit_config["model_type"] = "i2v"

comfy/rmsnorm.py

@@ -30,7 +30,7 @@ if RMSNorm is None:
         def __init__(
             self,
             normalized_shape,
-            eps=None,
+            eps=1e-6,
             elementwise_affine=True,
             device=None,
             dtype=None,

comfy/supported_models.py

@@ -992,6 +992,16 @@ class WAN21_FunControl2V(WAN21_T2V):
         out = model_base.WAN21(self, image_to_video=False, device=device)
         return out
 
+class WAN21_Camera(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "camera",
+        "in_dim": 32,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_Camera(self, image_to_video=False, device=device)
+        return out
 class WAN21_Vace(WAN21_T2V):
     unet_config = {
         "image_model": "wan2.1",
@@ -1129,6 +1139,6 @@ class ACEStep(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
 
 models += [SVD_img2vid]

comfy/utils.py

@@ -78,8 +78,6 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
-        if "global_step" in pl_sd:
-            logging.debug(f"Global Step: {pl_sd['global_step']}")
         if "state_dict" in pl_sd:
             sd = pl_sd["state_dict"]
         else:

comfy_api/input/video_types.py

@@ -43,3 +43,13 @@ class VideoInput(ABC):
         components = self.get_components()
         return components.images.shape[2], components.images.shape[1]
 
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        components = self.get_components()
+        frame_count = components.images.shape[0]
+        return float(frame_count / components.frame_rate)

comfy_api/input_impl/video_types.py

@@ -80,6 +80,38 @@ class VideoFromFile(VideoInput):
                     return stream.width, stream.height
         raise ValueError(f"No video stream found in file '{self.__file}'")
 
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode="r") as container:
+            if container.duration is not None:
+                return float(container.duration / av.time_base)
+
+            # Fallback: calculate from frame count and frame rate
+            video_stream = next(
+                (s for s in container.streams if s.type == "video"), None
+            )
+            if video_stream and video_stream.frames and video_stream.average_rate:
+                return float(video_stream.frames / video_stream.average_rate)
+
+            # Last resort: decode frames to count them
+            if video_stream and video_stream.average_rate:
+                frame_count = 0
+                container.seek(0)
+                for packet in container.demux(video_stream):
+                    for _ in packet.decode():
+                        frame_count += 1
+                if frame_count > 0:
+                    return float(frame_count / video_stream.average_rate)
+
+        raise ValueError(f"Could not determine duration for file '{self.__file}'")
+
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
         # Get video frames
         frames = []

comfy_api/torch_helpers/__init__.py

@@ -0,0 +1,5 @@
+from .torch_compile import set_torch_compile_wrapper
+
+__all__ = [
+    "set_torch_compile_wrapper",
+]

comfy_api/torch_helpers/torch_compile.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+import torch
+
+import comfy.utils
+from comfy.patcher_extension import WrappersMP
+from typing import TYPE_CHECKING, Callable, Optional
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+    from comfy.patcher_extension import WrapperExecutor
+
+
+COMPILE_KEY = "torch.compile"
+TORCH_COMPILE_KWARGS = "torch_compile_kwargs"
+
+
+def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable:
+    '''
+    Create a wrapper that will refer to the compiled_diffusion_model.
+    '''
+    def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs):
+        try:
+            orig_modules = {}
+            for key, value in compiled_module_dict.items():
+                orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key)
+                comfy.utils.set_attr(executor.class_obj, key, value)
+            return executor(*args, **kwargs)
+        finally:
+            for key, value in orig_modules.items():
+                comfy.utils.set_attr(executor.class_obj, key, value)
+    return apply_torch_compile_wrapper
+
+
+def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None,
+                              mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None,
+                              keys: list[str]=["diffusion_model"], *args, **kwargs):
+    '''
+    Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance.
+
+    When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model.
+    When a list of keys is provided, it will perform torch.compile on only the selected modules.
+    '''
+    # clear out any other torch.compile wrappers
+    model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY)
+    # if no keys, default to 'diffusion_model'
+    if not keys:
+        keys = ["diffusion_model"]
+    # create kwargs dict that can be referenced later
+    compile_kwargs = {
+        "backend": backend,
+        "options": options,
+        "mode": mode,
+        "fullgraph": fullgraph,
+        "dynamic": dynamic,
+    }
+    # get a dict of compiled keys
+    compiled_modules = {}
+    for key in keys:
+        compiled_modules[key] = torch.compile(
+                model=model.get_model_object(key),
+                **compile_kwargs,
+            )
+    # add torch.compile wrapper
+    wrapper_func = apply_torch_compile_factory(
+        compiled_module_dict=compiled_modules,
+    )
+    # store wrapper to run on BaseModel's apply_model function
+    model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func)
+    # keep compile kwargs for reference
+    model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs

comfy_api_nodes/apinode_utils.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 import io
 import logging
-from typing import Optional
+from typing import Optional, Union
 from comfy.utils import common_upscale
 from comfy_api.input_impl import VideoFromFile
 from comfy_api.util import VideoContainer, VideoCodec
@@ -15,6 +15,7 @@ from comfy_api_nodes.apis.client import (
     UploadRequest,
     UploadResponse,
 )
+from server import PromptServer
 
 
 import numpy as np
@@ -60,7 +61,9 @@ def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor:
     return s
 
 
-def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
+def validate_and_cast_response(
+    response, timeout: int = None, node_id: Union[str, None] = None
+) -> torch.Tensor:
     """Validates and casts a response to a torch.Tensor.
 
     Args:
@@ -94,6 +97,10 @@ def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
             img = Image.open(io.BytesIO(img_data))
 
         elif image_url:
+            if node_id:
+                PromptServer.instance.send_progress_text(
+                    f"Result URL: {image_url}", node_id
+                )
             img_response = requests.get(image_url, timeout=timeout)
             if img_response.status_code != 200:
                 raise ValueError("Failed to download the image")

comfy_api_nodes/apis/client.py

@@ -94,15 +94,19 @@ from __future__ import annotations
 import logging
 import time
 import io
-from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable
+import socket
+from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple
 from enum import Enum
 import json
 import requests
-from urllib.parse import urljoin
+from urllib.parse import urljoin, urlparse
 from pydantic import BaseModel, Field
+import uuid # For generating unique operation IDs
 
+from server import PromptServer
 from comfy.cli_args import args
 from comfy import utils
+from . import request_logger
 
 T = TypeVar("T", bound=BaseModel)
 R = TypeVar("R", bound=BaseModel)
@@ -111,6 +115,21 @@ P = TypeVar("P", bound=BaseModel)  # For poll response
 PROGRESS_BAR_MAX = 100
 
 
+class NetworkError(Exception):
+    """Base exception for network-related errors with diagnostic information."""
+    pass
+
+
+class LocalNetworkError(NetworkError):
+    """Exception raised when local network connectivity issues are detected."""
+    pass
+
+
+class ApiServerError(NetworkError):
+    """Exception raised when the API server is unreachable but internet is working."""
+    pass
+
+
 class EmptyRequest(BaseModel):
     """Base class for empty request bodies.
     For GET requests, fields will be sent as query parameters."""
@@ -141,7 +160,7 @@ class HttpMethod(str, Enum):
 
 class ApiClient:
     """
-    Client for making HTTP requests to an API with authentication and error handling.
+    Client for making HTTP requests to an API with authentication, error handling, and retry logic.
     """
 
     def __init__(
@@ -151,12 +170,26 @@ class ApiClient:
         comfy_api_key: Optional[str] = None,
         timeout: float = 3600.0,
         verify_ssl: bool = True,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
+        retry_status_codes: Optional[Tuple[int, ...]] = None,
     ):
         self.base_url = base_url
         self.auth_token = auth_token
         self.comfy_api_key = comfy_api_key
         self.timeout = timeout
         self.verify_ssl = verify_ssl
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+        # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests),
+        # 500, 502, 503, 504 (Server Errors)
+        self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504)
+
+    def _generate_operation_id(self, path: str) -> str:
+        """Generates a unique operation ID for logging."""
+        return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}"
 
     def _create_json_payload_args(
         self,
@@ -211,6 +244,56 @@ class ApiClient:
 
         return headers
 
+    def _check_connectivity(self, target_url: str) -> Dict[str, bool]:
+        """
+        Check connectivity to determine if network issues are local or server-related.
+
+        Args:
+            target_url: URL to check connectivity to
+
+        Returns:
+            Dictionary with connectivity status details
+        """
+        results = {
+            "internet_accessible": False,
+            "api_accessible": False,
+            "is_local_issue": False,
+            "is_api_issue": False
+        }
+
+        # First check basic internet connectivity using a reliable external site
+        try:
+            # Use a reliable external domain for checking basic connectivity
+            check_response = requests.get("https://www.google.com",
+                                         timeout=5.0,
+                                         verify=self.verify_ssl)
+            if check_response.status_code < 500:
+                results["internet_accessible"] = True
+        except (requests.RequestException, socket.error):
+            results["internet_accessible"] = False
+            results["is_local_issue"] = True
+            return results
+
+        # Now check API server connectivity
+        try:
+            # Extract domain from the target URL to do a simpler health check
+            parsed_url = urlparse(target_url)
+            api_base = f"{parsed_url.scheme}://{parsed_url.netloc}"
+
+            # Try to reach the API domain
+            api_response = requests.get(f"{api_base}/health", timeout=5.0, verify=self.verify_ssl)
+            if api_response.status_code < 500:
+                results["api_accessible"] = True
+            else:
+                results["api_accessible"] = False
+                results["is_api_issue"] = True
+        except requests.RequestException:
+            results["api_accessible"] = False
+            # If we can reach the internet but not the API, it's an API issue
+            results["is_api_issue"] = True
+
+        return results
+
     def request(
         self,
         method: str,
@@ -221,9 +304,10 @@ class ApiClient:
         headers: Optional[Dict[str, str]] = None,
         content_type: str = "application/json",
         multipart_parser: Callable = None,
+        retry_count: int = 0,  # Used internally for tracking retries
     ) -> Dict[str, Any]:
         """
-        Make an HTTP request to the API
+        Make an HTTP request to the API with automatic retries for transient errors.
 
         Args:
             method: HTTP method (GET, POST, etc.)
@@ -233,12 +317,15 @@ class ApiClient:
             files: Files to upload
             headers: Additional headers
             content_type: Content type of the request. Defaults to application/json.
+            retry_count: Internal parameter for tracking retries, do not set manually
 
         Returns:
             Parsed JSON response
 
         Raises:
-            requests.RequestException: If the request fails
+            LocalNetworkError: If local network connectivity issues are detected
+            ApiServerError: If the API server is unreachable but internet is working
+            Exception: For other request failures
         """
         url = urljoin(self.base_url, path)
         self.check_auth(self.auth_token, self.comfy_api_key)
@@ -265,6 +352,16 @@ class ApiClient:
         else:
             payload_args = self._create_json_payload_args(data, request_headers)
 
+        operation_id = self._generate_operation_id(path)
+        request_logger.log_request_response(
+            operation_id=operation_id,
+            request_method=method,
+            request_url=url,
+            request_headers=request_headers,
+            request_params=params,
+            request_data=data if content_type == "application/json" else "[form-data or other]"
+        )
+
         try:
             response = requests.request(
                 method=method,
@@ -275,50 +372,228 @@ class ApiClient:
                 **payload_args,
             )
 
-            # Raise exception for error status codes
+            # Check if we should retry based on status code
-            response.raise_for_status()
+            if (response.status_code in self.retry_status_codes and
-        except requests.ConnectionError:
+                retry_count < self.max_retries):
-            raise Exception(
+
-                f"Unable to connect to the API server at {self.base_url}. Please check your internet connection or verify the service is available."
+                # Calculate delay with exponential backoff
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+
+                logging.warning(
+                    f"Request failed with status {response.status_code}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
                 )
 
-        except requests.Timeout:
+                time.sleep(delay)
-            raise Exception(
+                return self.request(
-                f"Request timed out after {self.timeout} seconds. The server might be experiencing high load or the operation is taking longer than expected."
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
                 )
 
+            # Raise exception for error status codes
+            response.raise_for_status()
+
+            # Log successful response
+            response_content_to_log = response.content
+            try:
+                # Attempt to parse JSON for prettier logging, fallback to raw content
+                response_content_to_log = response.json()
+            except json.JSONDecodeError:
+                pass # Keep as bytes/str if not JSON
+
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method, # Pass request details again for context in log
+                request_url=url,
+                response_status_code=response.status_code,
+                response_headers=dict(response.headers),
+                response_content=response_content_to_log
+            )
+
+        except requests.ConnectionError as e:
+            error_message = f"ConnectionError: {str(e)}"
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method,
+                request_url=url,
+                error_message=error_message
+            )
+            # Only perform connectivity check if we've exhausted all retries
+            if retry_count >= self.max_retries:
+                # Check connectivity to determine if it's a local or API issue
+                connectivity = self._check_connectivity(self.base_url)
+
+                if connectivity["is_local_issue"]:
+                    raise LocalNetworkError(
+                        "Unable to connect to the API server due to local network issues. "
+                        "Please check your internet connection and try again."
+                    ) from e
+                elif connectivity["is_api_issue"]:
+                    raise ApiServerError(
+                        f"The API server at {self.base_url} is currently unreachable. "
+                        f"The service may be experiencing issues. Please try again later."
+                    ) from e
+
+            # If we haven't exhausted retries yet, retry the request
+            if retry_count < self.max_retries:
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"Connection error: {str(e)}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+
+            # If we've exhausted retries and didn't identify the specific issue,
+            # raise a generic exception
+            final_error_message = (
+                f"Unable to connect to the API server after {self.max_retries} attempts. "
+                f"Please check your internet connection or try again later."
+            )
+            request_logger.log_request_response( # Log final failure
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=final_error_message
+            )
+            raise Exception(final_error_message) from e
+
+        except requests.Timeout as e:
+            error_message = f"Timeout: {str(e)}"
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=error_message
+            )
+            # Retry timeouts if we haven't exhausted retries
+            if retry_count < self.max_retries:
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"Request timed out. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+            final_error_message = (
+                f"Request timed out after {self.timeout} seconds and {self.max_retries} retry attempts. "
+                f"The server might be experiencing high load or the operation is taking longer than expected."
+            )
+            request_logger.log_request_response( # Log final failure
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=final_error_message
+            )
+            raise Exception(final_error_message) from e
+
         except requests.HTTPError as e:
             status_code = e.response.status_code if hasattr(e, "response") else None
-            error_message = f"HTTP Error: {str(e)}"
+            original_error_message = f"HTTP Error: {str(e)}"
-
+            error_content_for_log = None
-            # Try to extract detailed error message from JSON response
+            if hasattr(e, "response") and e.response is not None:
+                error_content_for_log = e.response.content
                 try:
-                if hasattr(e, "response") and e.response.content:
+                    error_content_for_log = e.response.json()
+                except json.JSONDecodeError:
+                    pass
+
+
+            # Try to extract detailed error message from JSON response for user display
+            # but log the full error content.
+            user_display_error_message = original_error_message
+
+            try:
+                if hasattr(e, "response") and e.response is not None and e.response.content:
                     error_json = e.response.json()
                     if "error" in error_json and "message" in error_json["error"]:
-                        error_message = f"API Error: {error_json['error']['message']}"
+                        user_display_error_message = f"API Error: {error_json['error']['message']}"
                         if "type" in error_json["error"]:
-                            error_message += f" (Type: {error_json['error']['type']})"
+                            user_display_error_message += f" (Type: {error_json['error']['type']})"
+                    elif isinstance(error_json, dict): # Handle cases where error is just a JSON dict
+                        user_display_error_message = f"API Error: {json.dumps(error_json)}"
+                    else: # Non-dict JSON error
+                        user_display_error_message = f"API Error: {str(error_json)}"
+            except json.JSONDecodeError:
+                # If not JSON, use the raw content if it's not too long, or a summary
+                if hasattr(e, "response") and e.response is not None and e.response.content:
+                    raw_content = e.response.content.decode(errors='ignore')
+                    if len(raw_content) < 200: # Arbitrary limit for display
+                        user_display_error_message = f"API Error (raw): {raw_content}"
                     else:
-                        error_message = f"API Error: {error_json}"
+                        user_display_error_message = f"API Error (raw, status {status_code})"
-            except Exception as json_error:
+
-                # If we can't parse the JSON, fall back to the original error message
+            request_logger.log_request_response(
-                logging.debug(
+                operation_id=operation_id,
-                    f"[DEBUG] Failed to parse error response: {str(json_error)}"
+                request_method=method, request_url=url,
+                response_status_code=status_code,
+                response_headers=dict(e.response.headers) if hasattr(e, "response") and e.response is not None else None,
+                response_content=error_content_for_log,
+                error_message=original_error_message # Log the original exception string as error
             )
 
-            logging.debug(f"[DEBUG] API Error: {error_message} (Status: {status_code})")
+            logging.debug(f"[DEBUG] API Error: {user_display_error_message} (Status: {status_code})")
-            if hasattr(e, "response") and e.response.content:
+            if hasattr(e, "response") and e.response is not None and e.response.content:
                 logging.debug(f"[DEBUG] Response content: {e.response.content}")
+
+            # Retry if the status code is in our retry list and we haven't exhausted retries
+            if (status_code in self.retry_status_codes and
+                retry_count < self.max_retries):
+
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"HTTP error {status_code}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+
+            # Specific error messages for common status codes for user display
             if status_code == 401:
-                error_message = "Unauthorized: Please login first to use this node."
+                user_display_error_message = "Unauthorized: Please login first to use this node."
-            if status_code == 402:
+            elif status_code == 402:
-                error_message = "Payment Required: Please add credits to your account to use this node."
+                user_display_error_message = "Payment Required: Please add credits to your account to use this node."
-            if status_code == 409:
+            elif status_code == 409:
-                error_message = "There is a problem with your account. Please contact support@comfy.org. "
+                user_display_error_message = "There is a problem with your account. Please contact support@comfy.org."
-            if status_code == 429:
+            elif status_code == 429:
-                error_message = "Rate Limit Exceeded: Please try again later."
+                user_display_error_message = "Rate Limit Exceeded: Please try again later."
-            raise Exception(error_message)
+            # else, user_display_error_message remains as parsed from response or original HTTPError string
+
+            raise Exception(user_display_error_message) # Raise with the user-friendly message
 
         # Parse and return JSON response
         if response.content:
@@ -336,26 +611,126 @@ class ApiClient:
         upload_url: str,
         file: io.BytesIO | str,
         content_type: str | None = None,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
     ):
-        """Upload a file to the API. Make sure the file has a filename equal to what the url expects.
+        """Upload a file to the API with retry logic.
 
         Args:
             upload_url: The URL to upload to
             file: Either a file path string, BytesIO object, or tuple of (file_path, filename)
-            mime_type: Optional mime type to set for the upload
+            content_type: Optional mime type to set for the upload
+            max_retries: Maximum number of retry attempts
+            retry_delay: Initial delay between retries in seconds
+            retry_backoff_factor: Multiplier for the delay after each retry
         """
         headers = {}
         if content_type:
             headers["Content-Type"] = content_type
 
+        # Prepare the file data
         if isinstance(file, io.BytesIO):
             file.seek(0)  # Ensure we're at the start of the file
             data = file.read()
-            return requests.put(upload_url, data=data, headers=headers)
         elif isinstance(file, str):
             with open(file, "rb") as f:
                 data = f.read()
-                return requests.put(upload_url, data=data, headers=headers)
+        else:
+            raise ValueError("File must be either a BytesIO object or a file path string")
+
+        # Try the upload with retries
+        last_exception = None
+        operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}" # Simplified ID for uploads
+
+        # Log initial attempt (without full file data for brevity)
+        request_logger.log_request_response(
+            operation_id=operation_id,
+            request_method="PUT",
+            request_url=upload_url,
+            request_headers=headers,
+            request_data=f"[File data of type {content_type or 'unknown'}, size {len(data)} bytes]"
+        )
+
+        for retry_attempt in range(max_retries + 1):
+            try:
+                response = requests.put(upload_url, data=data, headers=headers)
+                response.raise_for_status()
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT", request_url=upload_url, # For context
+                    response_status_code=response.status_code,
+                    response_headers=dict(response.headers),
+                    response_content="File uploaded successfully." # Or response.text if available
+                )
+                return response
+
+            except (requests.ConnectionError, requests.Timeout, requests.HTTPError) as e:
+                last_exception = e
+                error_message_for_log = f"{type(e).__name__}: {str(e)}"
+                response_content_for_log = None
+                status_code_for_log = None
+                headers_for_log = None
+
+                if hasattr(e, 'response') and e.response is not None:
+                    status_code_for_log = e.response.status_code
+                    headers_for_log = dict(e.response.headers)
+                    try:
+                        response_content_for_log = e.response.json()
+                    except json.JSONDecodeError:
+                        response_content_for_log = e.response.content
+
+
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT", request_url=upload_url,
+                    response_status_code=status_code_for_log,
+                    response_headers=headers_for_log,
+                    response_content=response_content_for_log,
+                    error_message=error_message_for_log
+                )
+
+                if retry_attempt < max_retries:
+                    delay = retry_delay * (retry_backoff_factor ** retry_attempt)
+                    logging.warning(
+                        f"File upload failed: {str(e)}. "
+                        f"Retrying in {delay:.2f}s ({retry_attempt + 1}/{max_retries})"
+                    )
+                    time.sleep(delay)
+                else:
+                    break # Max retries reached
+
+        # If we've exhausted all retries, determine the final error type and raise
+        final_error_message = f"Failed to upload file after {max_retries + 1} attempts. Error: {str(last_exception)}"
+        try:
+            # Check basic internet connectivity
+            check_response = requests.get("https://www.google.com", timeout=5.0, verify=True) # Assuming verify=True is desired
+            if check_response.status_code >= 500: # Google itself has an issue (rare)
+                 final_error_message = (f"Failed to upload file. Internet connectivity check to Google failed "
+                                       f"(status {check_response.status_code}). Original error: {str(last_exception)}")
+                 # Not raising LocalNetworkError here as Google itself might be down.
+            # If Google is reachable, the issue is likely with the upload server or a more specific local problem
+            # not caught by a simple Google ping (e.g., DNS for the specific upload URL, firewall).
+            # The original last_exception is probably most relevant.
+
+        except (requests.RequestException, socket.error) as conn_check_exc:
+            # Could not reach Google, likely a local network issue
+            final_error_message = (f"Failed to upload file due to network connectivity issues "
+                                   f"(cannot reach Google: {str(conn_check_exc)}). "
+                                   f"Original upload error: {str(last_exception)}")
+            request_logger.log_request_response( # Log final failure reason
+                operation_id=operation_id,
+                request_method="PUT", request_url=upload_url,
+                error_message=final_error_message
+            )
+            raise LocalNetworkError(final_error_message) from last_exception
+
+        request_logger.log_request_response( # Log final failure reason if not LocalNetworkError
+            operation_id=operation_id,
+            request_method="PUT", request_url=upload_url,
+            error_message=final_error_message
+        )
+        raise Exception(final_error_message) from last_exception
 
 
 class ApiEndpoint(Generic[T, R]):
@@ -403,6 +778,9 @@ class SynchronousOperation(Generic[T, R]):
         verify_ssl: bool = True,
         content_type: str = "application/json",
         multipart_parser: Callable = None,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
     ):
         self.endpoint = endpoint
         self.request = request
@@ -419,8 +797,12 @@ class SynchronousOperation(Generic[T, R]):
         self.files = files
         self.content_type = content_type
         self.multipart_parser = multipart_parser
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+
     def execute(self, client: Optional[ApiClient] = None) -> R:
-        """Execute the API operation using the provided client or create one"""
+        """Execute the API operation using the provided client or create one with retry support"""
         try:
             # Create client if not provided
             if client is None:
@@ -430,6 +812,9 @@ class SynchronousOperation(Generic[T, R]):
                     comfy_api_key=self.comfy_api_key,
                     timeout=self.timeout,
                     verify_ssl=self.verify_ssl,
+                    max_retries=self.max_retries,
+                    retry_delay=self.retry_delay,
+                    retry_backoff_factor=self.retry_backoff_factor,
                 )
 
             # Convert request model to dict, but use None for EmptyRequest
@@ -443,11 +828,6 @@ class SynchronousOperation(Generic[T, R]):
                     if isinstance(value, Enum):
                         request_dict[key] = value.value
 
-            if request_dict:
-               for key, value in request_dict.items():
-                   if isinstance(value, Enum):
-                       request_dict[key] = value.value
-
             # Debug log for request
             logging.debug(
                 f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}"
@@ -455,7 +835,7 @@ class SynchronousOperation(Generic[T, R]):
             logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}")
             logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}")
 
-            # Make the request
+            # Make the request with built-in retry
             resp = client.request(
                 method=self.endpoint.method.value,
                 path=self.endpoint.path,
@@ -476,8 +856,18 @@ class SynchronousOperation(Generic[T, R]):
             # Parse and return the response
             return self._parse_response(resp)
 
+        except LocalNetworkError as e:
+            # Propagate specific network error types
+            logging.error(f"[ERROR] Local network error: {str(e)}")
+            raise
+
+        except ApiServerError as e:
+            # Propagate API server errors
+            logging.error(f"[ERROR] API server error: {str(e)}")
+            raise
+
         except Exception as e:
-            logging.error(f"[DEBUG] API Exception: {str(e)}")
+            logging.error(f"[ERROR] API Exception: {str(e)}")
             raise Exception(str(e))
 
     def _parse_response(self, resp):
@@ -511,12 +901,19 @@ class PollingOperation(Generic[T, R]):
         failed_statuses: list,
         status_extractor: Callable[[R], str],
         progress_extractor: Callable[[R], float] = None,
+        result_url_extractor: Callable[[R], str] = None,
         request: Optional[T] = None,
         api_base: str | None = None,
         auth_token: Optional[str] = None,
         comfy_api_key: Optional[str] = None,
         auth_kwargs: Optional[Dict[str,str]] = None,
         poll_interval: float = 5.0,
+        max_poll_attempts: int = 120,  # Default max polling attempts (10 minutes with 5s interval)
+        max_retries: int = 3,  # Max retries per individual API call
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
+        estimated_duration: Optional[float] = None,
+        node_id: Optional[str] = None,
     ):
         self.poll_endpoint = poll_endpoint
         self.request = request
@@ -527,12 +924,19 @@ class PollingOperation(Generic[T, R]):
             self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
             self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
         self.poll_interval = poll_interval
+        self.max_poll_attempts = max_poll_attempts
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+        self.estimated_duration = estimated_duration
 
         # Polling configuration
         self.status_extractor = status_extractor or (
             lambda x: getattr(x, "status", None)
         )
         self.progress_extractor = progress_extractor
+        self.result_url_extractor = result_url_extractor
+        self.node_id = node_id
         self.completed_statuses = completed_statuses
         self.failed_statuses = failed_statuses
 
@@ -548,11 +952,46 @@ class PollingOperation(Generic[T, R]):
                     base_url=self.api_base,
                     auth_token=self.auth_token,
                     comfy_api_key=self.comfy_api_key,
+                    max_retries=self.max_retries,
+                    retry_delay=self.retry_delay,
+                    retry_backoff_factor=self.retry_backoff_factor,
                 )
             return self._poll_until_complete(client)
+        except LocalNetworkError as e:
+            # Provide clear message for local network issues
+            raise Exception(
+                f"Polling failed due to local network issues. Please check your internet connection. "
+                f"Details: {str(e)}"
+            ) from e
+        except ApiServerError as e:
+            # Provide clear message for API server issues
+            raise Exception(
+                f"Polling failed due to API server issues. The service may be experiencing problems. "
+                f"Please try again later. Details: {str(e)}"
+            ) from e
         except Exception as e:
             raise Exception(f"Error during polling: {str(e)}")
 
+    def _display_text_on_node(self, text: str):
+        """Sends text to the client which will be displayed on the node in the UI"""
+        if not self.node_id:
+            return
+
+        PromptServer.instance.send_progress_text(text, self.node_id)
+
+    def _display_time_progress_on_node(self, time_completed: int):
+        if not self.node_id:
+            return
+
+        if self.estimated_duration is not None:
+            estimated_time_remaining = max(
+                0, int(self.estimated_duration) - int(time_completed)
+            )
+            message = f"Task in progress: {time_completed:.0f}s (~{estimated_time_remaining:.0f}s remaining)"
+        else:
+            message = f"Task in progress: {time_completed:.0f}s"
+        self._display_text_on_node(message)
+
     def _check_task_status(self, response: R) -> TaskStatus:
         """Check task status using the status extractor function"""
         try:
@@ -569,10 +1008,13 @@ class PollingOperation(Generic[T, R]):
     def _poll_until_complete(self, client: ApiClient) -> R:
         """Poll until the task is complete"""
         poll_count = 0
+        consecutive_errors = 0
+        max_consecutive_errors = min(5, self.max_retries * 2)  # Limit consecutive errors
+
         if self.progress_extractor:
             progress = utils.ProgressBar(PROGRESS_BAR_MAX)
 
-        while True:
+        while poll_count < self.max_poll_attempts:
             try:
                 poll_count += 1
                 logging.debug(f"[DEBUG] Polling attempt #{poll_count}")
@@ -599,8 +1041,12 @@ class PollingOperation(Generic[T, R]):
                     data=request_dict,
                 )
 
+                # Successfully got a response, reset consecutive error count
+                consecutive_errors = 0
+
                 # Parse response
                 response_obj = self.poll_endpoint.response_model.model_validate(resp)
+
                 # Check if task is complete
                 status = self._check_task_status(response_obj)
                 logging.debug(f"[DEBUG] Task Status: {status}")
@@ -612,7 +1058,15 @@ class PollingOperation(Generic[T, R]):
                         progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX)
 
                 if status == TaskStatus.COMPLETED:
-                    logging.debug("[DEBUG] Task completed successfully")
+                    message = "Task completed successfully"
+                    if self.result_url_extractor:
+                        result_url = self.result_url_extractor(response_obj)
+                        if result_url:
+                            message = f"Result URL: {result_url}"
+                    else:
+                        message = "Task completed successfully!"
+                    logging.debug(f"[DEBUG] {message}")
+                    self._display_text_on_node(message)
                     self.final_response = response_obj
                     if self.progress_extractor:
                         progress.update(100)
@@ -628,8 +1082,43 @@ class PollingOperation(Generic[T, R]):
                 logging.debug(
                     f"[DEBUG] Waiting {self.poll_interval} seconds before next poll"
                 )
+                for i in range(int(self.poll_interval)):
+                    time_completed = (poll_count * self.poll_interval) + i
+                    self._display_time_progress_on_node(time_completed)
+                    time.sleep(1)
+
+            except (LocalNetworkError, ApiServerError) as e:
+                # For network-related errors, increment error count and potentially abort
+                consecutive_errors += 1
+                if consecutive_errors >= max_consecutive_errors:
+                    raise Exception(
+                        f"Polling aborted after {consecutive_errors} consecutive network errors: {str(e)}"
+                    ) from e
+
+                # Log the error but continue polling
+                logging.warning(
+                    f"Network error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
+                    f"Will retry in {self.poll_interval} seconds."
+                )
                 time.sleep(self.poll_interval)
 
             except Exception as e:
+                # For other errors, increment count and potentially abort
+                consecutive_errors += 1
+                if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED:
+                    raise Exception(
+                        f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
+                    ) from e
+
                 logging.error(f"[DEBUG] Polling error: {str(e)}")
-                raise Exception(f"Error while polling: {str(e)}")
+                logging.warning(
+                    f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
+                    f"Will retry in {self.poll_interval} seconds."
+                )
+                time.sleep(self.poll_interval)
+
+        # If we've exhausted all polling attempts
+        raise Exception(
+            f"Polling timed out after {poll_count} attempts ({poll_count * self.poll_interval} seconds). "
+            f"The operation may still be running on the server but is taking longer than expected."
+        )

comfy_api_nodes/apis/request_logger.py

@@ -0,0 +1,125 @@
+import os
+import datetime
+import json
+import logging
+import folder_paths
+
+# Get the logger instance
+logger = logging.getLogger(__name__)
+
+def get_log_directory():
+    """
+    Ensures the API log directory exists within ComfyUI's temp directory
+    and returns its path.
+    """
+    base_temp_dir = folder_paths.get_temp_directory()
+    log_dir = os.path.join(base_temp_dir, "api_logs")
+    try:
+        os.makedirs(log_dir, exist_ok=True)
+    except Exception as e:
+        logger.error(f"Error creating API log directory {log_dir}: {e}")
+        # Fallback to base temp directory if sub-directory creation fails
+        return base_temp_dir
+    return log_dir
+
+def _format_data_for_logging(data):
+    """Helper to format data (dict, str, bytes) for logging."""
+    if isinstance(data, bytes):
+        try:
+            return data.decode('utf-8')  # Try to decode as text
+        except UnicodeDecodeError:
+            return f"[Binary data of length {len(data)} bytes]"
+    elif isinstance(data, (dict, list)):
+        try:
+            return json.dumps(data, indent=2, ensure_ascii=False)
+        except TypeError:
+            return str(data) # Fallback for non-serializable objects
+    return str(data)
+
+def log_request_response(
+    operation_id: str,
+    request_method: str,
+    request_url: str,
+    request_headers: dict | None = None,
+    request_params: dict | None = None,
+    request_data: any = None,
+    response_status_code: int | None = None,
+    response_headers: dict | None = None,
+    response_content: any = None,
+    error_message: str | None = None
+):
+    """
+    Logs API request and response details to a file in the temp/api_logs directory.
+    """
+    log_dir = get_log_directory()
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log"
+    filepath = os.path.join(log_dir, filename)
+
+    log_content = []
+
+    log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
+    log_content.append(f"Operation ID: {operation_id}")
+    log_content.append("-" * 30 + " REQUEST " + "-" * 30)
+    log_content.append(f"Method: {request_method}")
+    log_content.append(f"URL: {request_url}")
+    if request_headers:
+        log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+    if request_params:
+        log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
+    if request_data:
+        log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
+
+    log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
+    if response_status_code is not None:
+        log_content.append(f"Status Code: {response_status_code}")
+    if response_headers:
+        log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
+    if response_content:
+        log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
+    if error_message:
+        log_content.append(f"Error:\n{error_message}")
+
+    try:
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write("\n".join(log_content))
+        logger.debug(f"API log saved to: {filepath}")
+    except Exception as e:
+        logger.error(f"Error writing API log to {filepath}: {e}")
+
+if __name__ == '__main__':
+    # Example usage (for testing the logger directly)
+    logger.setLevel(logging.DEBUG)
+    # Mock folder_paths for direct execution if not running within ComfyUI full context
+    if not hasattr(folder_paths, 'get_temp_directory'):
+        class MockFolderPaths:
+            def get_temp_directory(self):
+                # Create a local temp dir for testing if needed
+                p = os.path.join(os.path.dirname(__file__), 'temp_test_logs')
+                os.makedirs(p, exist_ok=True)
+                return p
+        folder_paths = MockFolderPaths()
+
+    log_request_response(
+        operation_id="test_operation_get",
+        request_method="GET",
+        request_url="https://api.example.com/test",
+        request_headers={"Authorization": "Bearer testtoken"},
+        request_params={"param1": "value1"},
+        response_status_code=200,
+        response_content={"message": "Success!"}
+    )
+    log_request_response(
+        operation_id="test_operation_post_error",
+        request_method="POST",
+        request_url="https://api.example.com/submit",
+        request_data={"key": "value", "nested": {"num": 123}},
+        error_message="Connection timed out"
+    )
+    log_request_response(
+        operation_id="test_binary_response",
+        request_method="GET",
+        request_url="https://api.example.com/image.png",
+        response_status_code=200,
+        response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data
+    )

comfy_api_nodes/nodes_bfl.py

@@ -1,5 +1,6 @@
 import io
 from inspect import cleandoc
+from typing import Union
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api_nodes.apis.bfl_api import (
     BFLStatus,
@@ -30,6 +31,7 @@ import requests
 import torch
 import base64
 import time
+from server import PromptServer
 
 
 def convert_mask_to_image(mask: torch.Tensor):
@@ -42,14 +44,19 @@ def convert_mask_to_image(mask: torch.Tensor):
 
 
 def handle_bfl_synchronous_operation(
-    operation: SynchronousOperation, timeout_bfl_calls=360
+    operation: SynchronousOperation,
+    timeout_bfl_calls=360,
+    node_id: Union[str, None] = None,
 ):
     response_api: BFLFluxProGenerateResponse = operation.execute()
     return _poll_until_generated(
-        response_api.polling_url, timeout=timeout_bfl_calls
+        response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id
     )
 
-def _poll_until_generated(polling_url: str, timeout=360):
+
+def _poll_until_generated(
+    polling_url: str, timeout=360, node_id: Union[str, None] = None
+):
     # used bfl-comfy-nodes to verify code implementation:
     # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
     start_time = time.time()
@@ -61,11 +68,21 @@ def _poll_until_generated(polling_url: str, timeout=360):
     request = requests.Request(method=HttpMethod.GET, url=polling_url)
     # NOTE: should True loop be replaced with checking if workflow has been interrupted?
     while True:
+        if node_id:
+            time_elapsed = time.time() - start_time
+            PromptServer.instance.send_progress_text(
+                f"Generating ({time_elapsed:.0f}s)", node_id
+            )
+
         response = requests.Session().send(request.prepare())
         if response.status_code == 200:
             result = response.json()
             if result["status"] == BFLStatus.ready:
                 img_url = result["result"]["sample"]
+                if node_id:
+                    PromptServer.instance.send_progress_text(
+                        f"Result URL: {img_url}", node_id
+                    )
                 img_response = requests.get(img_url)
                 return process_image_response(img_response)
             elif result["status"] in [
@@ -180,6 +197,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -212,6 +230,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
         seed=0,
         image_prompt=None,
         image_prompt_strength=0.1,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         if image_prompt is None:
@@ -246,7 +265,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -320,6 +339,7 @@ class FluxProImageNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -338,6 +358,7 @@ class FluxProImageNode(ComfyNodeABC):
         seed=0,
         image_prompt=None,
         # image_prompt_strength=0.1,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         image_prompt = (
@@ -363,7 +384,7 @@ class FluxProImageNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -457,11 +478,11 @@ class FluxProExpandNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
+            "optional": {},
-            },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -483,6 +504,7 @@ class FluxProExpandNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         image = convert_image_to_base64(image)
@@ -508,7 +530,7 @@ class FluxProExpandNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -568,11 +590,11 @@ class FluxProFillNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
+            "optional": {},
-            },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -591,13 +613,14 @@ class FluxProFillNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         # prepare mask
         mask = resize_mask_to_image(mask, image)
         mask = convert_image_to_base64(convert_mask_to_image(mask))
         # make sure image will have alpha channel removed
-        image = convert_image_to_base64(image[:,:,:,:3])
+        image = convert_image_to_base64(image[:, :, :, :3])
 
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
@@ -617,7 +640,7 @@ class FluxProFillNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -702,11 +725,11 @@ class FluxProCannyNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
+            "optional": {},
-            },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -727,9 +750,10 @@ class FluxProCannyNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
-        control_image = convert_image_to_base64(control_image[:,:,:,:3])
+        control_image = convert_image_to_base64(control_image[:, :, :, :3])
         preprocessed_image = None
 
         # scale canny threshold between 0-500, to match BFL's API
@@ -765,7 +789,7 @@ class FluxProCannyNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -830,11 +854,11 @@ class FluxProDepthNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
+            "optional": {},
-            },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -853,6 +877,7 @@ class FluxProDepthNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         control_image = convert_image_to_base64(control_image[:,:,:,:3])
@@ -880,7 +905,7 @@ class FluxProDepthNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 

comfy_api_nodes/nodes_ideogram.py

@@ -23,6 +23,7 @@ from comfy_api_nodes.apinode_utils import (
     bytesio_to_image_tensor,
     resize_mask_to_image,
 )
+from server import PromptServer
 
 V1_V1_RES_MAP = {
   "Auto":"AUTO",
@@ -232,6 +233,19 @@ def download_and_process_images(image_urls):
     return stacked_tensors
 
 
+def display_image_urls_on_node(image_urls, node_id):
+    if node_id and image_urls:
+        if len(image_urls) == 1:
+            PromptServer.instance.send_progress_text(
+                f"Generated Image URL:\n{image_urls[0]}", node_id
+            )
+        else:
+            urls_text = "Generated Image URLs:\n" + "\n".join(
+                f"{i+1}. {url}" for i, url in enumerate(image_urls)
+            )
+            PromptServer.instance.send_progress_text(urls_text, node_id)
+
+
 class IdeogramV1(ComfyNodeABC):
     """
     Generates images using the Ideogram V1 model.
@@ -304,6 +318,7 @@ class IdeogramV1(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -322,6 +337,7 @@ class IdeogramV1(ComfyNodeABC):
         seed=0,
         negative_prompt="",
         num_images=1,
+        unique_id=None,
         **kwargs,
     ):
         # Determine the model based on turbo setting
@@ -361,6 +377,7 @@ class IdeogramV1(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 
@@ -460,6 +477,7 @@ class IdeogramV2(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -481,6 +499,7 @@ class IdeogramV2(ComfyNodeABC):
         negative_prompt="",
         num_images=1,
         color_palette="",
+        unique_id=None,
         **kwargs,
     ):
         aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
@@ -534,6 +553,7 @@ class IdeogramV2(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 class IdeogramV3(ComfyNodeABC):
@@ -623,6 +643,7 @@ class IdeogramV3(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -643,6 +664,7 @@ class IdeogramV3(ComfyNodeABC):
         seed=0,
         num_images=1,
         rendering_speed="BALANCED",
+        unique_id=None,
         **kwargs,
     ):
         # Check if both image and mask are provided for editing mode
@@ -762,6 +784,7 @@ class IdeogramV3(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 
@@ -776,4 +799,3 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "IdeogramV2": "Ideogram V2",
     "IdeogramV3": "Ideogram V3",
 }
-

comfy_api_nodes/nodes_kling.py

@@ -6,6 +6,7 @@ For source of truth on the allowed permutations of request fields, please refere
 
 from __future__ import annotations
 from typing import Optional, TypeVar, Any
+from collections.abc import Callable
 import math
 import logging
 
@@ -64,6 +65,12 @@ from comfy_api_nodes.apinode_utils import (
     download_url_to_image_tensor,
 )
 from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api_nodes.util.validation_utils import (
+    validate_image_dimensions,
+    validate_image_aspect_ratio,
+    validate_video_dimensions,
+    validate_video_duration,
+)
 from comfy_api.input.basic_types import AudioInput
 from comfy_api.input.video_types import VideoInput
 from comfy_api.input_impl import VideoFromFile
@@ -79,13 +86,20 @@ PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
 PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on"
 PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
 
-
 MAX_PROMPT_LENGTH_T2V = 2500
 MAX_PROMPT_LENGTH_I2V = 500
 MAX_PROMPT_LENGTH_IMAGE_GEN = 500
 MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
 MAX_PROMPT_LENGTH_LIP_SYNC = 120
 
+AVERAGE_DURATION_T2V = 319
+AVERAGE_DURATION_I2V = 164
+AVERAGE_DURATION_LIP_SYNC = 455
+AVERAGE_DURATION_VIRTUAL_TRY_ON = 19
+AVERAGE_DURATION_IMAGE_GEN = 32
+AVERAGE_DURATION_VIDEO_EFFECTS = 320
+AVERAGE_DURATION_VIDEO_EXTEND = 320
+
 R = TypeVar("R")
 
 
@@ -95,7 +109,13 @@ class KlingApiError(Exception):
     pass
 
 
-def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[Any, R]) -> R:
+def poll_until_finished(
+    auth_kwargs: dict[str, str],
+    api_endpoint: ApiEndpoint[Any, R],
+    result_url_extractor: Optional[Callable[[R], str]] = None,
+    estimated_duration: Optional[int] = None,
+    node_id: Optional[str] = None,
+) -> R:
     """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
     return PollingOperation(
         poll_endpoint=api_endpoint,
@@ -109,6 +129,9 @@ def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[An
             else None
         ),
         auth_kwargs=auth_kwargs,
+        result_url_extractor=result_url_extractor,
+        estimated_duration=estimated_duration,
+        node_id=node_id,
     ).execute()
 
 
@@ -192,23 +215,8 @@ def validate_input_image(image: torch.Tensor) -> None:
 
     See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
     """
-    if len(image.shape) == 4:
+    validate_image_dimensions(image, min_width=300, min_height=300)
-        height, width = image.shape[1], image.shape[2]
+    validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5)
-    elif len(image.shape) == 3:
-        height, width = image.shape[0], image.shape[1]
-    else:
-        raise ValueError("Invalid image tensor shape.")
-
-    # Ensure minimum resolution is met
-    if height < 300:
-        raise ValueError("Image height must be at least 300px")
-    if width < 300:
-        raise ValueError("Image width must be at least 300px")
-
-    # Ensure aspect ratio is within acceptable range
-    aspect_ratio = width / height
-    if aspect_ratio < 1 / 2.5 or aspect_ratio > 2.5:
-        raise ValueError("Image aspect ratio must be between 1:2.5 and 2.5:1")
 
 
 def get_camera_control_input_config(
@@ -227,7 +235,9 @@ def get_camera_control_input_config(
 
 
 def get_video_from_response(response) -> KlingVideoResult:
-    """Returns the first video object from the Kling video generation task result."""
+    """Returns the first video object from the Kling video generation task result.
+    Will raise an error if the response is not valid.
+    """
     video = response.data.task_result.videos[0]
     logging.info(
         "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url
@@ -235,12 +245,37 @@ def get_video_from_response(response) -> KlingVideoResult:
     return video
 
 
+def get_video_url_from_response(response) -> Optional[str]:
+    """Returns the first video url from the Kling video generation task result.
+    Will not raise an error if the response is not valid.
+    """
+    if response and is_valid_video_response(response):
+        return str(get_video_from_response(response).url)
+    else:
+        return None
+
+
 def get_images_from_response(response) -> list[KlingImageResult]:
+    """Returns the list of image objects from the Kling image generation task result.
+    Will raise an error if the response is not valid.
+    """
     images = response.data.task_result.images
     logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images)
     return images
 
 
+def get_images_urls_from_response(response) -> Optional[str]:
+    """Returns the list of image urls from the Kling image generation task result.
+    Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
+    """
+    if response and is_valid_image_response(response):
+        images = get_images_from_response(response)
+        image_urls = [str(image.url) for image in images]
+        return "\n".join(image_urls)
+    else:
+        return None
+
+
 def video_result_to_node_output(
     video: KlingVideoResult,
 ) -> tuple[VideoFromFile, str, str]:
@@ -312,6 +347,7 @@ class KlingCameraControls(KlingNodeBase):
     RETURN_TYPES = ("CAMERA_CONTROL",)
     RETURN_NAMES = ("camera_control",)
     FUNCTION = "main"
+    API_NODE = False  # This is just a helper node, it doesn't make an API call
 
     @classmethod
     def VALIDATE_INPUTS(
@@ -421,6 +457,7 @@ class KlingTextToVideoNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -428,7 +465,9 @@ class KlingTextToVideoNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Text to Video Node"
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingText2VideoResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingText2VideoResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -437,6 +476,9 @@ class KlingTextToVideoNode(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingText2VideoResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
+            node_id=node_id,
         )
 
     def api_call(
@@ -449,6 +491,7 @@ class KlingTextToVideoNode(KlingNodeBase):
         camera_control: Optional[KlingCameraControl] = None,
         model_name: Optional[str] = None,
         duration: Optional[str] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
@@ -478,7 +521,9 @@ class KlingTextToVideoNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
 
         task_id = task_creation_response.data.task_id
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -528,6 +573,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -540,6 +586,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         camera_control: Optional[KlingCameraControl] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -613,6 +660,7 @@ class KlingImage2VideoNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -620,7 +668,9 @@ class KlingImage2VideoNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Image to Video Node"
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingImage2VideoResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingImage2VideoResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -629,6 +679,9 @@ class KlingImage2VideoNode(KlingNodeBase):
                 request_model=KlingImage2VideoRequest,
                 response_model=KlingImage2VideoResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_I2V,
+            node_id=node_id,
         )
 
     def api_call(
@@ -643,6 +696,7 @@ class KlingImage2VideoNode(KlingNodeBase):
         duration: str,
         camera_control: Optional[KlingCameraControl] = None,
         end_frame: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
@@ -681,7 +735,9 @@ class KlingImage2VideoNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -734,6 +790,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -747,6 +804,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         camera_control: KlingCameraControl,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -759,6 +817,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
             prompt=prompt,
             negative_prompt=negative_prompt,
             camera_control=camera_control,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -830,6 +889,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -844,6 +904,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         mode: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
@@ -859,6 +920,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
             aspect_ratio=aspect_ratio,
             duration=duration,
             end_frame=end_frame,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -892,6 +954,7 @@ class KlingVideoExtendNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -899,7 +962,9 @@ class KlingVideoExtendNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoExtendResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingVideoExtendResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -908,6 +973,9 @@ class KlingVideoExtendNode(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingVideoExtendResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
+            node_id=node_id,
         )
 
     def api_call(
@@ -916,6 +984,7 @@ class KlingVideoExtendNode(KlingNodeBase):
         negative_prompt: str,
         cfg_scale: float,
         video_id: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
@@ -939,7 +1008,9 @@ class KlingVideoExtendNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -952,7 +1023,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
     RETURN_TYPES = ("VIDEO", "STRING", "STRING")
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoEffectsResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingVideoEffectsResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -961,6 +1034,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingVideoEffectsResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS,
+            node_id=node_id,
         )
 
     def api_call(
@@ -972,6 +1048,7 @@ class KlingVideoEffectsBase(KlingNodeBase):
         image_1: torch.Tensor,
         image_2: Optional[torch.Tensor] = None,
         mode: Optional[KlingVideoGenMode] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         if dual_character:
@@ -1009,7 +1086,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -1053,6 +1132,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -1068,6 +1148,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
         model_name: KlingCharacterEffectModelName,
         mode: KlingVideoGenMode,
         duration: KlingVideoGenDuration,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         video, _, duration = super().api_call(
@@ -1078,10 +1159,12 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
             duration=duration,
             image_1=image_left,
             image_2=image_right,
+            unique_id=unique_id,
             **kwargs,
         )
         return video, duration
 
+
 class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
     """Kling Single Image Video Effect Node"""
 
@@ -1117,6 +1200,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -1128,6 +1212,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
         effect_scene: KlingSingleImageEffectsScene,
         model_name: KlingSingleImageEffectModelName,
         duration: KlingVideoGenDuration,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -1136,6 +1221,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
             model_name=model_name,
             duration=duration,
             image_1=image,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1146,6 +1232,17 @@ class KlingLipSyncBase(KlingNodeBase):
     RETURN_TYPES = ("VIDEO", "STRING", "STRING")
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
 
+    def validate_lip_sync_video(self, video: VideoInput):
+        """
+        Validates the input video adheres to the expectations of the Kling Lip Sync API:
+        - Video length does not exceed 10s and is not shorter than 2s
+        - Length and width dimensions should both be between 720px and 1920px
+
+        See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip
+        """
+        validate_video_dimensions(video, 720, 1920)
+        validate_video_duration(video, 2, 10)
+
     def validate_text(self, text: str):
         if not text:
             raise ValueError("Text is required")
@@ -1154,7 +1251,9 @@ class KlingLipSyncBase(KlingNodeBase):
                 f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
             )
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingLipSyncResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingLipSyncResponse:
         """Polls the Kling API endpoint until the task reaches a terminal state."""
         return poll_until_finished(
             auth_kwargs,
@@ -1164,6 +1263,9 @@ class KlingLipSyncBase(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingLipSyncResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_LIP_SYNC,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1175,10 +1277,12 @@ class KlingLipSyncBase(KlingNodeBase):
         text: Optional[str] = None,
         voice_speed: Optional[float] = None,
         voice_id: Optional[str] = None,
-        **kwargs
+        unique_id: Optional[str] = None,
+        **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         if text:
             self.validate_text(text)
+        self.validate_lip_sync_video(video)
 
         # Upload video to Comfy API and get download URL
         video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
@@ -1217,7 +1321,9 @@ class KlingLipSyncBase(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -1243,16 +1349,18 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."
+    DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
 
     def api_call(
         self,
         video: VideoInput,
         audio: AudioInput,
         voice_language: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -1260,6 +1368,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
             audio=audio,
             voice_language=voice_language,
             mode="audio2video",
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1352,10 +1461,11 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."
+    DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
 
     def api_call(
         self,
@@ -1363,6 +1473,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
         text: str,
         voice: str,
         voice_speed: float,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
@@ -1373,6 +1484,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
             voice_id=voice_id,
             voice_speed=voice_speed,
             mode="text2video",
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1413,13 +1525,14 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human."
+    DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background."
 
     def get_response(
-        self, task_id: str, auth_kwargs: dict[str,str] = None
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
     ) -> KlingVirtualTryOnResponse:
         return poll_until_finished(
             auth_kwargs,
@@ -1429,6 +1542,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
                 request_model=EmptyRequest,
                 response_model=KlingVirtualTryOnResponse,
             ),
+            result_url_extractor=get_images_urls_from_response,
+            estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1436,6 +1552,7 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
         human_image: torch.Tensor,
         cloth_image: torch.Tensor,
         model_name: KlingVirtualTryOnModelName,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         initial_operation = SynchronousOperation(
@@ -1457,7 +1574,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_image_result_response(final_response)
 
         images = get_images_from_response(final_response)
@@ -1528,13 +1647,17 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
     DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."
 
     def get_response(
-        self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None
+        self,
+        task_id: str,
+        auth_kwargs: Optional[dict[str, str]],
+        node_id: Optional[str] = None,
     ) -> KlingImageGenerationsResponse:
         return poll_until_finished(
             auth_kwargs,
@@ -1544,6 +1667,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
                 request_model=EmptyRequest,
                 response_model=KlingImageGenerationsResponse,
             ),
+            result_url_extractor=get_images_urls_from_response,
+            estimated_duration=AVERAGE_DURATION_IMAGE_GEN,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1557,6 +1683,7 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
         n: int,
         aspect_ratio: KlingImageGenAspectRatio,
         image: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         self.validate_prompt(prompt, negative_prompt)
@@ -1589,7 +1716,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_image_result_response(final_response)
 
         images = get_images_from_response(final_response)

comfy_api_nodes/nodes_luma.py

@@ -36,11 +36,20 @@ from comfy_api_nodes.apinode_utils import (
     process_image_response,
     validate_string,
 )
+from server import PromptServer
 
 import requests
 import torch
 from io import BytesIO
 
+LUMA_T2V_AVERAGE_DURATION = 105
+LUMA_I2V_AVERAGE_DURATION = 100
+
+def image_result_url_extractor(response: LumaGeneration):
+    return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None
+
+def video_result_url_extractor(response: LumaGeneration):
+    return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None
 
 class LumaReferenceNode(ComfyNodeABC):
     """
@@ -204,6 +213,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -217,6 +227,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
         image_luma_ref: LumaReferenceChain = None,
         style_image: torch.Tensor = None,
         character_image: torch.Tensor = None,
+        unique_id: str = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=True, min_length=3)
@@ -271,6 +282,8 @@ class LumaImageGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=image_result_url_extractor,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -353,6 +366,7 @@ class LumaImageModifyNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -363,6 +377,7 @@ class LumaImageModifyNode(ComfyNodeABC):
         image: torch.Tensor,
         image_weight: float,
         seed,
+        unique_id: str = None,
         **kwargs,
     ):
         # first, upload image
@@ -399,6 +414,8 @@ class LumaImageModifyNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=image_result_url_extractor,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -473,6 +490,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -486,6 +504,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
         loop: bool,
         seed,
         luma_concepts: LumaConceptChain = None,
+        unique_id: str = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, min_length=3)
@@ -512,6 +531,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
         )
         response_api: LumaGeneration = operation.execute()
 
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
         operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
                 path=f"/proxy/luma/generations/{response_api.id}",
@@ -522,6 +544,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=video_result_url_extractor,
+            node_id=unique_id,
+            estimated_duration=LUMA_T2V_AVERAGE_DURATION,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -597,6 +622,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -611,6 +637,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
         first_image: torch.Tensor = None,
         last_image: torch.Tensor = None,
         luma_concepts: LumaConceptChain = None,
+        unique_id: str = None,
         **kwargs,
     ):
         if first_image is None and last_image is None:
@@ -642,6 +669,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
         )
         response_api: LumaGeneration = operation.execute()
 
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
         operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
                 path=f"/proxy/luma/generations/{response_api.id}",
@@ -652,6 +682,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=video_result_url_extractor,
+            node_id=unique_id,
+            estimated_duration=LUMA_I2V_AVERAGE_DURATION,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()

comfy_api_nodes/nodes_minimax.py

@@ -1,3 +1,7 @@
+from typing import Union
+import logging
+import torch
+
 from comfy.comfy_types.node_typing import IO
 from comfy_api.input_impl.video_types import VideoFromFile
 from comfy_api_nodes.apis import (
@@ -20,16 +24,19 @@ from comfy_api_nodes.apinode_utils import (
     upload_images_to_comfyapi,
     validate_string,
 )
+from server import PromptServer
 
-import torch
-import logging
 
+I2V_AVERAGE_DURATION = 114
+T2V_AVERAGE_DURATION = 234
 
 class MinimaxTextToVideoNode:
     """
     Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -68,6 +75,7 @@ class MinimaxTextToVideoNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -85,6 +93,7 @@ class MinimaxTextToVideoNode:
         model="T2V-01",
         image: torch.Tensor=None, # used for ImageToVideo
         subject: torch.Tensor=None, # used for SubjectToVideo
+        unique_id: Union[str, None]=None,
         **kwargs,
     ):
         '''
@@ -138,6 +147,8 @@ class MinimaxTextToVideoNode:
             completed_statuses=["Success"],
             failed_statuses=["Fail"],
             status_extractor=lambda x: x.status.value,
+            estimated_duration=self.AVERAGE_DURATION,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         task_result = video_generate_operation.execute()
@@ -164,6 +175,12 @@ class MinimaxTextToVideoNode:
                 f"No video was found in the response. Full response: {file_result.model_dump()}"
             )
         logging.info(f"Generated video URL: {file_url}")
+        if unique_id:
+            if hasattr(file_result.file, "backup_download_url"):
+                message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
+            else:
+                message = f"Result URL: {file_url}"
+            PromptServer.instance.send_progress_text(message, unique_id)
 
         video_io = download_url_to_bytesio(file_url)
         if video_io is None:
@@ -178,6 +195,8 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
     Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = I2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -223,6 +242,7 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -239,6 +259,8 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
     Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -282,6 +304,7 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 

comfy_api_nodes/nodes_openai.py

@@ -96,6 +96,7 @@ class OpenAIDalle2(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -113,6 +114,7 @@ class OpenAIDalle2(ComfyNodeABC):
         mask=None,
         n=1,
         size="1024x1024",
+        unique_id=None,
         **kwargs
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -176,7 +178,7 @@ class OpenAIDalle2(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 
@@ -242,6 +244,7 @@ class OpenAIDalle3(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -258,6 +261,7 @@ class OpenAIDalle3(ComfyNodeABC):
         style="natural",
         quality="standard",
         size="1024x1024",
+        unique_id=None,
         **kwargs
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -284,7 +288,7 @@ class OpenAIDalle3(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 
@@ -375,6 +379,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -394,6 +399,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
         mask=None,
         n=1,
         size="1024x1024",
+        unique_id=None,
         **kwargs
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -476,7 +482,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 

comfy_api_nodes/nodes_pika.py

@@ -121,7 +121,10 @@ class PikaNodeBase(ComfyNodeABC):
     RETURN_TYPES = ("VIDEO",)
 
     def poll_for_task_status(
-        self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None
+        self,
+        task_id: str,
+        auth_kwargs: Optional[dict[str, str]] = None,
+        node_id: Optional[str] = None,
     ) -> PikaGenerateResponse:
         polling_operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
@@ -141,13 +144,19 @@ class PikaNodeBase(ComfyNodeABC):
                 response.progress if hasattr(response, "progress") else None
             ),
             auth_kwargs=auth_kwargs,
+            result_url_extractor=lambda response: (
+                response.url if hasattr(response, "url") else None
+            ),
+            node_id=node_id,
+            estimated_duration=60
         )
         return polling_operation.execute()
 
     def execute_task(
         self,
         initial_operation: SynchronousOperation[R, PikaGenerateResponse],
-        auth_kwargs: Optional[dict[str,str]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
+        node_id: Optional[str] = None,
     ) -> tuple[VideoFromFile]:
         """Executes the initial operation then polls for the task status until it is completed.
 
@@ -208,7 +217,8 @@ class PikaImageToVideoV2_2(PikaNodeBase):
         seed: int,
         resolution: str,
         duration: int,
-        **kwargs
+        unique_id: str,
+        **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert image to BytesIO
         image_bytes_io = tensor_to_bytesio(image)
@@ -238,7 +248,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaTextToVideoNodeV2_2(PikaNodeBase):
@@ -262,6 +272,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -275,6 +286,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
         resolution: str,
         duration: int,
         aspect_ratio: float,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         initial_operation = SynchronousOperation(
@@ -296,7 +308,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
             content_type="application/x-www-form-urlencoded",
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaScenesV2_2(PikaNodeBase):
@@ -340,6 +352,7 @@ class PikaScenesV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -354,6 +367,7 @@ class PikaScenesV2_2(PikaNodeBase):
         duration: int,
         ingredients_mode: str,
         aspect_ratio: float,
+        unique_id: str,
         image_ingredient_1: Optional[torch.Tensor] = None,
         image_ingredient_2: Optional[torch.Tensor] = None,
         image_ingredient_3: Optional[torch.Tensor] = None,
@@ -403,7 +417,7 @@ class PikaScenesV2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikAdditionsNode(PikaNodeBase):
@@ -439,6 +453,7 @@ class PikAdditionsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -451,6 +466,7 @@ class PikAdditionsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert video to BytesIO
@@ -487,7 +503,7 @@ class PikAdditionsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaSwapsNode(PikaNodeBase):
@@ -532,6 +548,7 @@ class PikaSwapsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -546,6 +563,7 @@ class PikaSwapsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert video to BytesIO
@@ -592,7 +610,7 @@ class PikaSwapsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaffectsNode(PikaNodeBase):
@@ -637,6 +655,7 @@ class PikaffectsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -649,6 +668,7 @@ class PikaffectsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
 
@@ -670,7 +690,7 @@ class PikaffectsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaStartEndFrameNode2_2(PikaNodeBase):
@@ -689,6 +709,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -703,6 +724,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
         seed: int,
         resolution: str,
         duration: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
 
@@ -733,7 +755,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 NODE_CLASS_MAPPINGS = {

comfy_api_nodes/nodes_pixverse.py

@@ -1,5 +1,5 @@
 from inspect import cleandoc
-
+from typing import Optional
 from comfy_api_nodes.apis.pixverse_api import (
     PixverseTextVideoRequest,
     PixverseImageVideoRequest,
@@ -34,11 +34,22 @@ import requests
 from io import BytesIO
 
 
+AVERAGE_DURATION_T2V = 32
+AVERAGE_DURATION_I2V = 30
+AVERAGE_DURATION_T2T = 52
+
+
+def get_video_url_from_response(
+    response: PixverseGenerationStatusResponse,
+) -> Optional[str]:
+    if response.Resp is None or response.Resp.url is None:
+        return None
+    return str(response.Resp.url)
+
+
 def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
     # first, upload image to Pixverse and get image id to use in actual generation call
-    files = {
+    files = {"image": tensor_to_bytesio(image)}
-        "image": tensor_to_bytesio(image)
-    }
     operation = SynchronousOperation(
         endpoint=ApiEndpoint(
             path="/proxy/pixverse/image/upload",
@@ -54,7 +65,9 @@ def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
     response_upload: PixverseImageUploadResponse = operation.execute()
 
     if response_upload.Resp is None:
-        raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'")
+        raise Exception(
+            f"PixVerse image upload request failed: '{response_upload.ErrMsg}'"
+        )
 
     return response_upload.Resp.img_id
 
@@ -73,7 +86,7 @@ class PixverseTemplateNode:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "template": (list(pixverse_templates.keys()), ),
+                "template": (list(pixverse_templates.keys()),),
             }
         }
 
@@ -87,7 +100,7 @@ class PixverseTemplateNode:
 
 class PixverseTextToVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -108,9 +121,7 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                         "tooltip": "Prompt for the video generation",
                     },
                 ),
-                "aspect_ratio": (
+                "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],),
-                    [ratio.value for ratio in PixverseAspectRatio],
-                ),
                 "quality": (
                     [resolution.value for resolution in PixverseQuality],
                     {
@@ -143,12 +154,13 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                     PixverseIO.TEMPLATE,
                     {
                         "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    }
+                    },
-                )
+                ),
             },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -160,8 +172,9 @@ class PixverseTextToVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
+        negative_prompt: str = None,
-        pixverse_template: int=None,
+        pixverse_template: int = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -205,19 +218,27 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
         )
         response_poll = operation.execute()
 
         vid_response = requests.get(response_poll.Resp.url)
+
         return (VideoFromFile(BytesIO(vid_response.content)),)
 
 
 class PixverseImageToVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -230,9 +251,7 @@ class PixverseImageToVideoNode(ComfyNodeABC):
     def INPUT_TYPES(s):
         return {
             "required": {
-                "image": (
+                "image": (IO.IMAGE,),
-                    IO.IMAGE,
-                ),
                 "prompt": (
                     IO.STRING,
                     {
@@ -273,12 +292,13 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                     PixverseIO.TEMPLATE,
                     {
                         "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    }
+                    },
-                )
+                ),
             },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -290,8 +310,9 @@ class PixverseImageToVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
+        negative_prompt: str = None,
-        pixverse_template: int=None,
+        pixverse_template: int = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -337,9 +358,16 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_I2V,
         )
         response_poll = operation.execute()
 
@@ -349,7 +377,7 @@ class PixverseImageToVideoNode(ComfyNodeABC):
 
 class PixverseTransitionVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -362,12 +390,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
     def INPUT_TYPES(s):
         return {
             "required": {
-                "first_frame": (
+                "first_frame": (IO.IMAGE,),
-                    IO.IMAGE,
+                "last_frame": (IO.IMAGE,),
-                ),
-                "last_frame": (
-                    IO.IMAGE,
-                ),
                 "prompt": (
                     IO.STRING,
                     {
@@ -408,6 +432,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -420,7 +445,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
+        negative_prompt: str = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -467,9 +493,16 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
         )
         response_poll = operation.execute()
 

comfy_api_nodes/nodes_recraft.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from inspect import cleandoc
+from typing import Optional
 from comfy.utils import ProgressBar
 from comfy_extras.nodes_images import SVG # Added
 from comfy.comfy_types.node_typing import IO
@@ -29,6 +30,8 @@ from comfy_api_nodes.apinode_utils import (
     resize_mask_to_image,
     validate_string,
 )
+from server import PromptServer
+
 import torch
 from io import BytesIO
 from PIL import UnidentifiedImageError
@@ -388,6 +391,7 @@ class RecraftTextToImageNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -400,6 +404,7 @@ class RecraftTextToImageNode:
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, max_length=1000)
@@ -436,8 +441,15 @@ class RecraftTextToImageNode:
         )
         response: RecraftImageGenerationResponse = operation.execute()
         images = []
+        urls = []
         for data in response.data:
             with handle_recraft_image_output():
+                if unique_id and data.url:
+                    urls.append(data.url)
+                    urls_string = '\n'.join(urls)
+                    PromptServer.instance.send_progress_text(
+                        f"Result URL: {urls_string}", unique_id
+                    )
                 image = bytesio_to_image_tensor(
                     download_url_to_bytesio(data.url, timeout=1024)
                 )
@@ -763,6 +775,7 @@ class RecraftTextToVectorNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -775,6 +788,7 @@ class RecraftTextToVectorNode:
         seed,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, max_length=1000)
@@ -809,7 +823,14 @@ class RecraftTextToVectorNode:
         )
         response: RecraftImageGenerationResponse = operation.execute()
         svg_data = []
+        urls = []
         for data in response.data:
+            if unique_id and data.url:
+                urls.append(data.url)
+                # Print result on each iteration in case of error
+                PromptServer.instance.send_progress_text(
+                    f"Result URL: {' '.join(urls)}", unique_id
+                )
             svg_data.append(download_url_to_bytesio(data.url, timeout=1024))
 
         return (SVG(svg_data),)

comfy_api_nodes/nodes_veo2.py

@@ -3,6 +3,7 @@ import logging
 import base64
 import requests
 import torch
+from typing import Optional
 
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api.input_impl.video_types import VideoFromFile
@@ -24,6 +25,8 @@ from comfy_api_nodes.apinode_utils import (
     tensor_to_base64_string
 )
 
+AVERAGE_DURATION_VIDEO_GEN = 32
+
 def convert_image_to_base64(image: torch.Tensor):
     if image is None:
         return None
@@ -31,6 +34,22 @@ def convert_image_to_base64(image: torch.Tensor):
     scaled_image = downscale_image_tensor(image, total_pixels=2048*2048)
     return tensor_to_base64_string(scaled_image)
 
+
+def get_video_url_from_response(poll_response: Veo2GenVidPollResponse) -> Optional[str]:
+    if (
+        poll_response.response
+        and hasattr(poll_response.response, "videos")
+        and poll_response.response.videos
+        and len(poll_response.response.videos) > 0
+    ):
+        video = poll_response.response.videos[0]
+    else:
+        return None
+    if hasattr(video, "gcsUri") and video.gcsUri:
+        return str(video.gcsUri)
+    return None
+
+
 class VeoVideoGenerationNode(ComfyNodeABC):
     """
     Generates videos from text prompts using Google's Veo API.
@@ -115,6 +134,7 @@ class VeoVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -134,6 +154,7 @@ class VeoVideoGenerationNode(ComfyNodeABC):
         person_generation="ALLOW",
         seed=0,
         image=None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         # Prepare the instances for the request
@@ -215,7 +236,10 @@ class VeoVideoGenerationNode(ComfyNodeABC):
                 operationName=operation_name
             ),
             auth_kwargs=kwargs,
-            poll_interval=5.0
+            poll_interval=5.0,
+            result_url_extractor=get_video_url_from_response,
+            node_id=unique_id,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
         )
 
         # Execute the polling operation

comfy_api_nodes/util/validation_utils.py

@@ -0,0 +1,100 @@
+import logging
+from typing import Optional
+
+import torch
+from comfy_api.input.video_types import VideoInput
+
+
+def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
+    if len(image.shape) == 4:
+        return image.shape[1], image.shape[2]
+    elif len(image.shape) == 3:
+        return image.shape[0], image.shape[1]
+    else:
+        raise ValueError("Invalid image tensor shape.")
+
+
+def validate_image_dimensions(
+    image: torch.Tensor,
+    min_width: Optional[int] = None,
+    max_width: Optional[int] = None,
+    min_height: Optional[int] = None,
+    max_height: Optional[int] = None,
+):
+    height, width = get_image_dimensions(image)
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Image width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(
+            f"Image height must be at least {min_height}px, got {height}px"
+        )
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Image height must be at most {max_height}px, got {height}px")
+
+
+def validate_image_aspect_ratio(
+    image: torch.Tensor,
+    min_aspect_ratio: Optional[float] = None,
+    max_aspect_ratio: Optional[float] = None,
+):
+    width, height = get_image_dimensions(image)
+    aspect_ratio = width / height
+
+    if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio:
+        raise ValueError(
+            f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}"
+        )
+    if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio:
+        raise ValueError(
+            f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}"
+        )
+
+
+def validate_video_dimensions(
+    video: VideoInput,
+    min_width: Optional[int] = None,
+    max_width: Optional[int] = None,
+    min_height: Optional[int] = None,
+    max_height: Optional[int] = None,
+):
+    try:
+        width, height = video.get_dimensions()
+    except Exception as e:
+        logging.error("Error getting dimensions of video: %s", e)
+        return
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Video width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Video width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(
+            f"Video height must be at least {min_height}px, got {height}px"
+        )
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Video height must be at most {max_height}px, got {height}px")
+
+
+def validate_video_duration(
+    video: VideoInput,
+    min_duration: Optional[float] = None,
+    max_duration: Optional[float] = None,
+):
+    try:
+        duration = video.get_duration()
+    except Exception as e:
+        logging.error("Error getting duration of video: %s", e)
+        return
+
+    epsilon = 0.0001
+    if min_duration is not None and min_duration - epsilon > duration:
+        raise ValueError(
+            f"Video duration must be at least {min_duration}s, got {duration}s"
+        )
+    if max_duration is not None and duration > max_duration + epsilon:
+        raise ValueError(
+            f"Video duration must be at most {max_duration}s, got {duration}s"
+        )

comfy_extras/nodes_apg.py

@@ -0,0 +1,76 @@
+import torch
+
+def project(v0, v1):
+    v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+    v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    return v0_parallel, v0_orthogonal
+
+class APG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}),
+                "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}),
+                "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}),
+            }
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "sampling/custom_sampling"
+
+    def patch(self, model, eta, norm_threshold, momentum):
+        running_avg = 0
+        prev_sigma = None
+
+        def pre_cfg_function(args):
+            nonlocal running_avg, prev_sigma
+
+            if len(args["conds_out"]) == 1: return args["conds_out"]
+
+            cond = args["conds_out"][0]
+            uncond = args["conds_out"][1]
+            sigma = args["sigma"][0]
+            cond_scale = args["cond_scale"]
+
+            if prev_sigma is not None and sigma > prev_sigma:
+                running_avg = 0
+            prev_sigma = sigma
+
+            guidance = cond - uncond
+
+            if momentum != 0:
+                if not torch.is_tensor(running_avg):
+                    running_avg = guidance
+                else:
+                    running_avg = momentum * running_avg + guidance
+                guidance = running_avg
+
+            if norm_threshold > 0:
+                guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+                scale = torch.minimum(
+                    torch.ones_like(guidance_norm),
+                    norm_threshold / guidance_norm
+                )
+                guidance = guidance * scale
+
+            guidance_parallel, guidance_orthogonal = project(guidance, cond)
+            modified_guidance = guidance_orthogonal + eta * guidance_parallel
+
+            modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
+
+            return [modified_cond, uncond] + args["conds_out"][2:]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return (m,)
+
+NODE_CLASS_MAPPINGS = {
+    "APG": APG,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "APG": "Adaptive Projected Guidance",
+}

comfy_extras/nodes_camera_trajectory.py

@@ -0,0 +1,218 @@
+import nodes
+import torch
+import numpy as np
+from einops import rearrange
+import comfy.model_management
+
+
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+CAMERA_DICT = {
+    "base_T_norm": 1.5,
+    "base_angle": np.pi/3,
+    "Static": {     "angle":[0., 0., 0.],   "T":[0., 0., 0.]},
+    "Pan Up": {     "angle":[0., 0., 0.],   "T":[0., -1., 0.]},
+    "Pan Down": {   "angle":[0., 0., 0.],   "T":[0.,1.,0.]},
+    "Pan Left": {   "angle":[0., 0., 0.],   "T":[-1.,0.,0.]},
+    "Pan Right": {  "angle":[0., 0., 0.],   "T": [1.,0.,0.]},
+    "Zoom In": {    "angle":[0., 0., 0.],   "T": [0.,0.,2.]},
+    "Zoom Out": {   "angle":[0., 0., 0.],   "T": [0.,0.,-2.]},
+    "Anti Clockwise (ACW)": {        "angle": [0., 0., -1.],  "T":[0., 0., 0.]},
+    "ClockWise (CW)": {         "angle": [0., 0., 1.], "T":[0., 0., 0.]},
+}
+
+
+def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
+
+    def get_relative_pose(cam_params):
+        """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+        """
+        abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+        abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+        cam_to_origin = 0
+        target_cam_c2w = np.array([
+            [1, 0, 0, 0],
+            [0, 1, 0, -cam_to_origin],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1]
+        ])
+        abs2rel = target_cam_c2w @ abs_w2cs[0]
+        ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+        ret_poses = np.array(ret_poses, dtype=np.float32)
+        return ret_poses
+
+    """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+    sample_wh_ratio = width / height
+    pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed
+
+    if pose_wh_ratio > sample_wh_ratio:
+        resized_ori_w = height * pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fx = resized_ori_w * cam_param.fx / width
+    else:
+        resized_ori_h = width / pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fy = resized_ori_h * cam_param.fy / height
+
+    intrinsic = np.asarray([[cam_param.fx * width,
+                            cam_param.fy * height,
+                            cam_param.cx * width,
+                            cam_param.cy * height]
+                            for cam_param in cam_params], dtype=np.float32)
+
+    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+    c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
+    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+    plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
+    plucker_embedding = plucker_embedding[None]
+    plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
+    return plucker_embedding
+
+class Camera(object):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        c2w_mat = np.array(entry[7:]).reshape(4, 4)
+        self.c2w_mat = c2w_mat
+        self.w2c_mat = np.linalg.inv(c2w_mat)
+
+def ray_condition(K, c2w, H, W, device):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B = K.shape[0]
+
+    j, i = torch.meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+        indexing='ij'
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1
+
+    zs = torch.ones_like(i)  # [B, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
+    rays_o = c2w[..., :3, 3]  # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
+    # c2w @ dirctions
+    rays_dxo = torch.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+def get_camera_motion(angle, T, speed, n=81):
+    def compute_R_form_rad_angle(angles):
+        theta_x, theta_y, theta_z = angles
+        Rx = np.array([[1, 0, 0],
+                    [0, np.cos(theta_x), -np.sin(theta_x)],
+                    [0, np.sin(theta_x), np.cos(theta_x)]])
+
+        Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)],
+                    [0, 1, 0],
+                    [-np.sin(theta_y), 0, np.cos(theta_y)]])
+
+        Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0],
+                    [np.sin(theta_z), np.cos(theta_z), 0],
+                    [0, 0, 1]])
+
+        R = np.dot(Rz, np.dot(Ry, Rx))
+        return R
+    RT = []
+    for i in range(n):
+        _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle
+        R = compute_R_form_rad_angle(_angle)
+        _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1))
+        _RT = np.concatenate([R,_T], axis=1)
+        RT.append(_RT)
+    RT = np.stack(RT)
+    return RT
+
+class WanCameraEmbedding:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}),
+                "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+                "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+                "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}),
+            },
+            "optional":{
+                "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}),
+                "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+                "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+                "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+                "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+            }
+
+        }
+
+    RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT")
+    RETURN_NAMES = ("camera_embedding","width","height","length")
+    FUNCTION = "run"
+    CATEGORY = "camera"
+
+    def run(self, camera_pose, width, height, length, speed=1.0,  fx=0.5, fy=0.5, cx=0.5, cy=0.5):
+        """
+        Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
+        Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
+        """
+        motion_list = [camera_pose]
+        speed = speed
+        angle = np.array(CAMERA_DICT[motion_list[0]]["angle"])
+        T = np.array(CAMERA_DICT[motion_list[0]]["T"])
+        RT = get_camera_motion(angle, T, speed, length)
+
+        trajs=[]
+        for cp in RT.tolist():
+            traj=[fx,fy,cx,cy,0,0]
+            traj.extend(cp[0])
+            traj.extend(cp[1])
+            traj.extend(cp[2])
+            traj.extend([0,0,0,1])
+            trajs.append(traj)
+
+        cam_params = np.array([[float(x) for x in pose] for pose in trajs])
+        cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1)
+        control_camera_video = process_pose_params(cam_params, width=width, height=height)
+        control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device())
+
+        control_camera_video = torch.concat(
+            [
+                torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
+                control_camera_video[:, :, 1:]
+            ], dim=2
+        ).transpose(1, 2)
+
+        # Reshape, transpose, and view into desired shape
+        b, f, c, h, w = control_camera_video.shape
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
+
+        return (control_camera_video, width, height, length)
+
+
+NODE_CLASS_MAPPINGS = {
+    "WanCameraEmbedding": WanCameraEmbedding,
+}

comfy_extras/nodes_cond.py

@@ -31,6 +31,7 @@ class T5TokenizerOptions:
             }
         }
 
+    CATEGORY = "_for_testing/conditioning"
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "set_options"
 

comfy_extras/nodes_hunyuan.py

@@ -77,7 +77,7 @@ class HunyuanImageToVideo:
                              "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                              "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                             "guidance_type": (["v1 (concat)", "v2 (replace)"], )
+                             "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], )
                 },
                 "optional": {"start_image": ("IMAGE", ),
                 }}
@@ -101,10 +101,12 @@ class HunyuanImageToVideo:
 
             if guidance_type == "v1 (concat)":
                 cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
-            else:
+            elif guidance_type == "v2 (replace)":
                 cond = {'guiding_frame_index': 0}
                 latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
                 out_latent["noise_mask"] = mask
+            elif guidance_type == "custom":
+                cond = {"ref_latent": concat_latent_image}
 
             positive = node_helpers.conditioning_set_values(positive, cond)
 

comfy_extras/nodes_images.py

@@ -13,6 +13,7 @@ import os
 import re
 from io import BytesIO
 from inspect import cleandoc
+import torch
 
 from comfy.comfy_types import FileLocator
 
@@ -74,6 +75,24 @@ class ImageFromBatch:
         s = s_in[batch_index:batch_index + length].clone()
         return (s,)
 
+
+class ImageAddNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
+                              "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "image"
+
+    def repeat(self, image, seed, strength):
+        generator = torch.manual_seed(seed)
+        s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0)
+        return (s,)
+
 class SaveAnimatedWEBP:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -295,6 +314,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
     "RepeatImageBatch": RepeatImageBatch,
     "ImageFromBatch": ImageFromBatch,
+    "ImageAddNoise": ImageAddNoise,
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
     "SaveSVGNode": SaveSVGNode,

comfy_extras/nodes_string.py

@@ -8,7 +8,8 @@ class StringConcatenate():
         return {
             "required": {
                 "string_a": (IO.STRING, {"multiline": True}),
-                "string_b": (IO.STRING, {"multiline": True})
+                "string_b": (IO.STRING, {"multiline": True}),
+                "delimiter": (IO.STRING, {"multiline": False, "default": ""})
             }
         }
 
@@ -16,8 +17,8 @@ class StringConcatenate():
     FUNCTION = "execute"
     CATEGORY = "utils/string"
 
-    def execute(self, string_a, string_b, **kwargs):
+    def execute(self, string_a, string_b, delimiter, **kwargs):
-        return string_a + string_b,
+        return delimiter.join((string_a, string_b)),
 
 class StringSubstring():
     @classmethod

comfy_extras/nodes_torch_compile.py

@@ -1,4 +1,5 @@
-import torch
+from comfy_api.torch_helpers import set_torch_compile_wrapper
+
 
 class TorchCompileModel:
     @classmethod
@@ -14,7 +15,7 @@ class TorchCompileModel:
 
     def patch(self, model, backend):
         m = model.clone()
-        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend))
+        set_torch_compile_wrapper(model=m, backend=backend)
         return (m, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_wan.py

@@ -297,6 +297,52 @@ class TrimVideoLatent:
         samples_out["samples"] = s1[:, :, trim_amount:]
         return (samples_out,)
 
+class WanCameraImageToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "start_image": ("IMAGE", ),
+                             "camera_conditions": ("WAN_CAMERA_EMBEDDING", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
+
+        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)
+            concat_latent_image = vae.encode(start_image[:, :, :, :3])
+            concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent})
+
+        if camera_conditions is not None:
+            positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions})
+            negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions})
+
+        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 (positive, negative, out_latent)
 
 NODE_CLASS_MAPPINGS = {
     "WanImageToVideo": WanImageToVideo,
@@ -305,4 +351,5 @@ NODE_CLASS_MAPPINGS = {
     "WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
     "WanVaceToVideo": WanVaceToVideo,
     "TrimVideoLatent": TrimVideoLatent,
+    "WanCameraImageToVideo": WanCameraImageToVideo,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.34"
+__version__ = "0.3.35"

execution.py

@@ -909,7 +909,6 @@ class PromptQueue:
         self.currently_running = {}
         self.history = {}
         self.flags = {}
-        server.prompt_queue = self
 
     def put(self, item):
         with self.mutex:
@@ -954,6 +953,7 @@ class PromptQueue:
             self.history[prompt[1]].update(history_result)
             self.server.queue_updated()
 
+    # Note: slow
     def get_current_queue(self):
         with self.mutex:
             out = []
@@ -961,6 +961,13 @@ class PromptQueue:
                 out += [x]
             return (out, copy.deepcopy(self.queue))
 
+    # read-safe as long as queue items are immutable
+    def get_current_queue_volatile(self):
+        with self.mutex:
+            running = [x for x in self.currently_running.values()]
+            queued = copy.copy(self.queue)
+            return (running, queued)
+
     def get_tasks_remaining(self):
         with self.mutex:
             return len(self.queue) + len(self.currently_running)

fix_torch.py

@@ -1,28 +0,0 @@
-import importlib.util
-import shutil
-import os
-import ctypes
-import logging
-
-
-def fix_pytorch_libomp():
-    """
-    Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed.
-    """
-    torch_spec = importlib.util.find_spec("torch")
-    for folder in torch_spec.submodule_search_locations:
-        lib_folder = os.path.join(folder, "lib")
-        test_file = os.path.join(lib_folder, "fbgemm.dll")
-        dest = os.path.join(lib_folder, "libomp140.x86_64.dll")
-        if os.path.exists(dest):
-            break
-
-        with open(test_file, "rb") as f:
-            contents = f.read()
-            if b"libomp140.x86_64.dll" not in contents:
-                break
-        try:
-            ctypes.cdll.LoadLibrary(test_file)
-        except FileNotFoundError:
-            logging.warning("Detected pytorch version with libomp issue, patching.")
-            shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest)

main.py

@@ -125,13 +125,6 @@ if __name__ == "__main__":
 
     import cuda_malloc
 
-if args.windows_standalone_build:
-    try:
-        from fix_torch import fix_pytorch_libomp
-        fix_pytorch_libomp()
-    except:
-        pass
-
 import comfy.utils
 
 import execution
@@ -267,7 +260,6 @@ def start_comfyui(asyncio_loop=None):
         asyncio_loop = asyncio.new_event_loop()
         asyncio.set_event_loop(asyncio_loop)
     prompt_server = server.PromptServer(asyncio_loop)
-    q = execution.PromptQueue(prompt_server)
 
     hook_breaker_ac10a0.save_functions()
     nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes)
@@ -278,7 +270,7 @@ def start_comfyui(asyncio_loop=None):
     prompt_server.add_routes()
     hijack_progress(prompt_server)
 
-    threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start()
+    threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start()
 
     if args.quick_test_for_ci:
         exit(0)

nodes.py

@@ -1940,7 +1940,7 @@ class ImagePadForOutpaint:
 
         mask[top:top + d2, left:left + d3] = t
 
-        return (new_image, mask)
+        return (new_image, mask.unsqueeze(0))
 
 
 NODE_CLASS_MAPPINGS = {
@@ -2262,9 +2262,11 @@ def init_builtin_extra_nodes():
         "nodes_optimalsteps.py",
         "nodes_hidream.py",
         "nodes_fresca.py",
+        "nodes_apg.py",
         "nodes_preview_any.py",
         "nodes_ace.py",
         "nodes_string.py",
+        "nodes_camera_trajectory.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.34"
+version = "0.3.35"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.19.9
-comfyui-workflow-templates==0.1.14
+comfyui-workflow-templates==0.1.18
 torch
 torchsde
 torchvision

script_examples/basic_api_example.py

@@ -101,6 +101,14 @@ prompt_text = """
 
 def queue_prompt(prompt):
     p = {"prompt": prompt}
+
+    # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload.
+    # p["extra_data"] = {
+    #     "api_key_comfy_org": "comfyui-87d01e28d*******************************************************"  # replace with real key
+    # }
+    # See: https://docs.comfy.org/tutorials/api-nodes/overview
+    # Generate a key here: https://platform.comfy.org/login
+
     data = json.dumps(p).encode('utf-8')
     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
     request.urlopen(req)

server.py

@@ -29,6 +29,7 @@ import comfy.model_management
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager
+
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
 from app.custom_node_manager import CustomNodeManager
@@ -159,7 +160,7 @@ class PromptServer():
         self.custom_node_manager = CustomNodeManager()
         self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
-        self.prompt_queue = None
+        self.prompt_queue = execution.PromptQueue(self)
         self.loop = loop
         self.messages = asyncio.Queue()
         self.client_session:Optional[aiohttp.ClientSession] = None
@@ -226,7 +227,7 @@ class PromptServer():
             return response
 
         @routes.get("/embeddings")
-        def get_embeddings(self):
+        def get_embeddings(request):
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
 
@@ -282,7 +283,6 @@ class PromptServer():
                     a.update(f.read())
                     b.update(image.file.read())
                     image.file.seek(0)
-                    f.close()
                 return a.hexdigest() == b.hexdigest()
             return False
 
@@ -621,7 +621,7 @@ class PromptServer():
         @routes.get("/queue")
         async def get_queue(request):
             queue_info = {}
-            current_queue = self.prompt_queue.get_current_queue()
+            current_queue = self.prompt_queue.get_current_queue_volatile()
             queue_info['queue_running'] = current_queue[0]
             queue_info['queue_pending'] = current_queue[1]
             return web.json_response(queue_info)

tests-unit/comfy_api_test/video_types_test.py

@@ -0,0 +1,239 @@
+import pytest
+import torch
+import tempfile
+import os
+import av
+import io
+from fractions import Fraction
+from comfy_api.input_impl.video_types import VideoFromFile, VideoFromComponents
+from comfy_api.util.video_types import VideoComponents
+from comfy_api.input.basic_types import AudioInput
+from av.error import InvalidDataError
+
+EPSILON = 0.0001
+
+
+@pytest.fixture
+def sample_images():
+    """3-frame 2x2 RGB video tensor"""
+    return torch.rand(3, 2, 2, 3)
+
+
+@pytest.fixture
+def sample_audio():
+    """Stereo audio with 44.1kHz sample rate"""
+    return AudioInput(
+        {
+            "waveform": torch.rand(1, 2, 1000),
+            "sample_rate": 44100,
+        }
+    )
+
+
+@pytest.fixture
+def video_components(sample_images, sample_audio):
+    """VideoComponents with images, audio, and metadata"""
+    return VideoComponents(
+        images=sample_images,
+        audio=sample_audio,
+        frame_rate=Fraction(30),
+        metadata={"test": "metadata"},
+    )
+
+
+def create_test_video(width=4, height=4, frames=3, fps=30):
+    """Helper to create a temporary video file"""
+    tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    with av.open(tmp.name, mode="w") as container:
+        stream = container.add_stream("h264", rate=fps)
+        stream.width = width
+        stream.height = height
+        stream.pix_fmt = "yuv420p"
+
+        for i in range(frames):
+            frame = av.VideoFrame.from_ndarray(
+                torch.ones(height, width, 3, dtype=torch.uint8).numpy() * (i * 85),
+                format="rgb24",
+            )
+            frame = frame.reformat(format="yuv420p")
+            packet = stream.encode(frame)
+            container.mux(packet)
+
+        # Flush
+        packet = stream.encode(None)
+        container.mux(packet)
+
+    return tmp.name
+
+
+@pytest.fixture
+def simple_video_file():
+    """4x4 video with 3 frames at 30fps"""
+    file_path = create_test_video()
+    yield file_path
+    os.unlink(file_path)
+
+
+def test_video_from_components_get_duration(video_components):
+    """Duration calculated correctly from frame count and frame rate"""
+    video = VideoFromComponents(video_components)
+    duration = video.get_duration()
+
+    expected_duration = 3.0 / 30.0
+    assert duration == pytest.approx(expected_duration)
+
+
+def test_video_from_components_get_duration_different_frame_rates(sample_images):
+    """Duration correct for different frame rates including fractional"""
+    # Test with 60 fps
+    components_60fps = VideoComponents(images=sample_images, frame_rate=Fraction(60))
+    video_60fps = VideoFromComponents(components_60fps)
+    assert video_60fps.get_duration() == pytest.approx(3.0 / 60.0)
+
+    # Test with fractional frame rate (23.976fps)
+    components_frac = VideoComponents(
+        images=sample_images, frame_rate=Fraction(24000, 1001)
+    )
+    video_frac = VideoFromComponents(components_frac)
+    expected_frac = 3.0 / (24000.0 / 1001.0)
+    assert video_frac.get_duration() == pytest.approx(expected_frac)
+
+
+def test_video_from_components_get_duration_empty_video():
+    """Duration is zero for empty video"""
+    empty_components = VideoComponents(
+        images=torch.zeros(0, 2, 2, 3), frame_rate=Fraction(30)
+    )
+    video = VideoFromComponents(empty_components)
+    assert video.get_duration() == 0.0
+
+
+def test_video_from_components_get_dimensions(video_components):
+    """Dimensions returned correctly from image tensor shape"""
+    video = VideoFromComponents(video_components)
+    width, height = video.get_dimensions()
+    assert width == 2
+    assert height == 2
+
+
+def test_video_from_file_get_duration(simple_video_file):
+    """Duration extracted from file metadata"""
+    video = VideoFromFile(simple_video_file)
+    duration = video.get_duration()
+    assert duration == pytest.approx(0.1, abs=0.01)
+
+
+def test_video_from_file_get_dimensions(simple_video_file):
+    """Dimensions read from stream without decoding frames"""
+    video = VideoFromFile(simple_video_file)
+    width, height = video.get_dimensions()
+    assert width == 4
+    assert height == 4
+
+
+def test_video_from_file_bytesio_input():
+    """VideoFromFile works with BytesIO input"""
+    buffer = io.BytesIO()
+    with av.open(buffer, mode="w", format="mp4") as container:
+        stream = container.add_stream("h264", rate=30)
+        stream.width = 2
+        stream.height = 2
+        stream.pix_fmt = "yuv420p"
+
+        frame = av.VideoFrame.from_ndarray(
+            torch.zeros(2, 2, 3, dtype=torch.uint8).numpy(), format="rgb24"
+        )
+        frame = frame.reformat(format="yuv420p")
+        packet = stream.encode(frame)
+        container.mux(packet)
+        packet = stream.encode(None)
+        container.mux(packet)
+
+    buffer.seek(0)
+    video = VideoFromFile(buffer)
+
+    assert video.get_dimensions() == (2, 2)
+    assert video.get_duration() == pytest.approx(1 / 30, abs=0.01)
+
+
+def test_video_from_file_invalid_file_error():
+    """InvalidDataError raised for non-video files"""
+    with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as tmp:
+        tmp.write(b"not a video file")
+        tmp.flush()
+        tmp_name = tmp.name
+
+    try:
+        with pytest.raises(InvalidDataError):
+            video = VideoFromFile(tmp_name)
+            video.get_dimensions()
+    finally:
+        os.unlink(tmp_name)
+
+
+def test_video_from_file_audio_only_error():
+    """ValueError raised for audio-only files"""
+    with tempfile.NamedTemporaryFile(suffix=".m4a", delete=False) as tmp:
+        tmp_name = tmp.name
+
+    try:
+        with av.open(tmp_name, mode="w") as container:
+            stream = container.add_stream("aac", rate=44100)
+            stream.sample_rate = 44100
+            stream.format = "fltp"
+
+            audio_data = torch.zeros(1, 1024).numpy()
+            audio_frame = av.AudioFrame.from_ndarray(
+                audio_data, format="fltp", layout="mono"
+            )
+            audio_frame.sample_rate = 44100
+            audio_frame.pts = 0
+            packet = stream.encode(audio_frame)
+            container.mux(packet)
+
+            for packet in stream.encode(None):
+                container.mux(packet)
+
+        with pytest.raises(ValueError, match="No video stream found"):
+            video = VideoFromFile(tmp_name)
+            video.get_dimensions()
+    finally:
+        os.unlink(tmp_name)
+
+
+def test_single_frame_video():
+    """Single frame video has correct duration"""
+    components = VideoComponents(
+        images=torch.rand(1, 10, 10, 3), frame_rate=Fraction(1)
+    )
+    video = VideoFromComponents(components)
+    assert video.get_duration() == 1.0
+
+
+@pytest.mark.parametrize(
+    "frame_rate,expected_fps",
+    [
+        (Fraction(24000, 1001), 24000 / 1001),
+        (Fraction(30000, 1001), 30000 / 1001),
+        (Fraction(25, 1), 25.0),
+        (Fraction(50, 2), 25.0),
+    ],
+)
+def test_fractional_frame_rates(frame_rate, expected_fps):
+    """Duration calculated correctly for various fractional frame rates"""
+    components = VideoComponents(images=torch.rand(100, 4, 4, 3), frame_rate=frame_rate)
+    video = VideoFromComponents(components)
+    duration = video.get_duration()
+    expected_duration = 100.0 / expected_fps
+    assert duration == pytest.approx(expected_duration)
+
+
+def test_duration_consistency(video_components):
+    """get_duration() consistent with manual calculation from components"""
+    video = VideoFromComponents(video_components)
+
+    duration = video.get_duration()
+    components = video.get_components()
+    manual_duration = float(components.images.shape[0] / components.frame_rate)
+
+    assert duration == pytest.approx(manual_duration)