Merge branch 'master' into automation/comfyui-frontend-bump

comfy/sd.py

@@ -807,6 +807,7 @@ class VAE:
                     self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype))
                     self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
             elif "vocoder.resblocks.0.convs1.0.weight" in sd or "vocoder.vocoder.resblocks.0.convs1.0.weight" in sd: # LTX Audio
+                sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder."})
                 self.first_stage_model = comfy.ldm.lightricks.vae.audio_vae.AudioVAE(metadata=metadata)
                 self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype)
                 self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype)

comfy_api_nodes/apis/bytedance.py

@@ -158,10 +158,17 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
     ("Custom", None, None),
 ]
 
-# Seedance 2.0 reference video pixel count limits per model.
+# Seedance 2.0 reference video pixel count limits per model and output resolution.
 SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
-    "dreamina-seedance-2-0-260128": {"min": 409_600, "max": 927_408},
+    "dreamina-seedance-2-0-260128": {
-    "dreamina-seedance-2-0-fast-260128": {"min": 409_600, "max": 927_408},
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+        "1080p": {"min": 409_600, "max": 2_073_600},
+    },
+    "dreamina-seedance-2-0-fast-260128": {
+        "480p": {"min": 409_600, "max": 927_408},
+        "720p": {"min": 409_600, "max": 927_408},
+    },
 }
 
 # The time in this dictionary are given for 10 seconds duration.

comfy_api_nodes/nodes_bytedance.py

@@ -35,6 +35,7 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     image_tensor_pair_to_batch,
     poll_op,
+    resize_video_to_pixel_budget,
     sync_op,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
@@ -69,9 +70,12 @@ DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-2504
 logger = logging.getLogger(__name__)
 
 
-def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) -> None:
+def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: str, index: int) -> None:
-    """Validate reference video pixel count against Seedance 2.0 model limits."""
+    """Validate reference video pixel count against Seedance 2.0 model limits for the selected resolution."""
-    limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id)
+    model_limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id)
+    if not model_limits:
+        return
+    limits = model_limits.get(resolution)
     if not limits:
         return
     try:
@@ -1373,6 +1377,14 @@ def _seedance2_reference_inputs(resolutions: list[str]):
                 min=0,
             ),
         ),
+        IO.Boolean.Input(
+            "auto_downscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
+            "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
+        ),
     ]
 
 
@@ -1480,10 +1492,23 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
 
         model_id = SEEDANCE_MODELS[model["model"]]
         has_video_input = len(reference_videos) > 0
+
+        if model.get("auto_downscale") and reference_videos:
+            max_px = (
+                SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {})
+                .get(model["resolution"], {})
+                .get("max")
+            )
+            if max_px:
+                for key in reference_videos:
+                    reference_videos[key] = resize_video_to_pixel_budget(
+                        reference_videos[key], max_px
+                    )
+
         total_video_duration = 0.0
         for i, key in enumerate(reference_videos, 1):
             video = reference_videos[key]
-            _validate_ref_video_pixels(video, model_id, i)
+            _validate_ref_video_pixels(video, model_id, model["resolution"], i)
             try:
                 dur = video.get_duration()
                 if dur < 1.8:

comfy_api_nodes/nodes_veo2.py

@@ -24,8 +24,9 @@ from comfy_api_nodes.util import (
 AVERAGE_DURATION_VIDEO_GEN = 32
 MODELS_MAP = {
     "veo-2.0-generate-001": "veo-2.0-generate-001",
-    "veo-3.1-generate": "veo-3.1-generate-preview",
+    "veo-3.1-generate": "veo-3.1-generate-001",
-    "veo-3.1-fast-generate": "veo-3.1-fast-generate-preview",
+    "veo-3.1-fast-generate": "veo-3.1-fast-generate-001",
+    "veo-3.1-lite": "veo-3.1-lite-generate-001",
     "veo-3.0-generate-001": "veo-3.0-generate-001",
     "veo-3.0-fast-generate-001": "veo-3.0-fast-generate-001",
 }
@@ -247,17 +248,8 @@ class VeoVideoGenerationNode(IO.ComfyNode):
         raise Exception("Video generation completed but no video was returned")
 
 
-class Veo3VideoGenerationNode(VeoVideoGenerationNode):
+class Veo3VideoGenerationNode(IO.ComfyNode):
-    """
+    """Generates videos from text prompts using Google's Veo 3 API."""
-    Generates videos from text prompts using Google's Veo 3 API.
-
-    Supported models:
-    - veo-3.0-generate-001
-    - veo-3.0-fast-generate-001
-
-    This node extends the base Veo node with Veo 3 specific features including
-    audio generation and fixed 8-second duration.
-    """
 
     @classmethod
     def define_schema(cls):
@@ -279,6 +271,13 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     default="16:9",
                     tooltip="Aspect ratio of the output video",
                 ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p", "4k"],
+                    default="720p",
+                    tooltip="Output video resolution. 4K is not available for veo-3.1-lite and veo-3.0 models.",
+                    optional=True,
+                ),
                 IO.String.Input(
                     "negative_prompt",
                     multiline=True,
@@ -289,11 +288,11 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                 IO.Int.Input(
                     "duration_seconds",
                     default=8,
-                    min=8,
+                    min=4,
                     max=8,
-                    step=1,
+                    step=2,
                     display_mode=IO.NumberDisplay.number,
-                    tooltip="Duration of the output video in seconds (Veo 3 only supports 8 seconds)",
+                    tooltip="Duration of the output video in seconds",
                     optional=True,
                 ),
                 IO.Boolean.Input(
@@ -332,10 +331,10 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                     options=[
                         "veo-3.1-generate",
                         "veo-3.1-fast-generate",
+                        "veo-3.1-lite",
                         "veo-3.0-generate-001",
                         "veo-3.0-fast-generate-001",
                     ],
-                    default="veo-3.0-generate-001",
                     tooltip="Veo 3 model to use for video generation",
                     optional=True,
                 ),
@@ -356,21 +355,111 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "resolution", "duration_seconds"]),
                 expr="""
                 (
                   $m := widgets.model;
+                  $r := widgets.resolution;
                   $a := widgets.generate_audio;
-                  ($contains($m,"veo-3.0-fast-generate-001") or $contains($m,"veo-3.1-fast-generate"))
+                  $seconds := widgets.duration_seconds;
-                    ? {"type":"usd","usd": ($a ? 1.2 : 0.8)}
+                  $pps :=
-                    : ($contains($m,"veo-3.0-generate-001") or $contains($m,"veo-3.1-generate"))
+                    $contains($m, "lite")
-                      ? {"type":"usd","usd": ($a ? 3.2 : 1.6)}
+                      ? ($r = "1080p" ? ($a ? 0.08 : 0.05) : ($a ? 0.05 : 0.03))
-                      : {"type":"range_usd","min_usd":0.8,"max_usd":3.2}
+                    : $contains($m, "3.1-fast")
+                      ? ($r = "4k" ? ($a ? 0.30 : 0.25) : $r = "1080p" ? ($a ? 0.12 : 0.10) : ($a ? 0.10 : 0.08))
+                    : $contains($m, "3.1-generate")
+                      ? ($r = "4k" ? ($a ? 0.60 : 0.40) : ($a ? 0.40 : 0.20))
+                    : $contains($m, "3.0-fast")
+                      ? ($a ? 0.15 : 0.10)
+                    : ($a ? 0.40 : 0.20);
+                  {"type":"usd","usd": $pps * $seconds}
                 )
                 """,
             ),
         )
 
+    @classmethod
+    async def execute(
+        cls,
+        prompt,
+        aspect_ratio="16:9",
+        resolution="720p",
+        negative_prompt="",
+        duration_seconds=8,
+        enhance_prompt=True,
+        person_generation="ALLOW",
+        seed=0,
+        image=None,
+        model="veo-3.0-generate-001",
+        generate_audio=False,
+    ):
+        if "lite" in model and resolution == "4k":
+            raise Exception("4K resolution is not supported by the veo-3.1-lite model.")
+
+        model = MODELS_MAP[model]
+
+        instances = [{"prompt": prompt}]
+        if image is not None:
+            image_base64 = tensor_to_base64_string(image)
+            if image_base64:
+                instances[0]["image"] = {"bytesBase64Encoded": image_base64, "mimeType": "image/png"}
+
+        parameters = {
+            "aspectRatio": aspect_ratio,
+            "personGeneration": person_generation,
+            "durationSeconds": duration_seconds,
+            "enhancePrompt": True,
+            "generateAudio": generate_audio,
+        }
+        if negative_prompt:
+            parameters["negativePrompt"] = negative_prompt
+        if seed > 0:
+            parameters["seed"] = seed
+        if "veo-3.1" in model:
+            parameters["resolution"] = resolution
+
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"),
+            response_model=VeoGenVidResponse,
+            data=VeoGenVidRequest(
+                instances=instances,
+                parameters=parameters,
+            ),
+        )
+
+        poll_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"),
+            response_model=VeoGenVidPollResponse,
+            status_extractor=lambda r: "completed" if r.done else "pending",
+            data=VeoGenVidPollRequest(operationName=initial_response.name),
+            poll_interval=9.0,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+        )
+
+        if poll_response.error:
+            raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})")
+
+        response = poll_response.response
+        filtered_count = response.raiMediaFilteredCount
+        if filtered_count:
+            reasons = response.raiMediaFilteredReasons or []
+            reason_part = f": {reasons[0]}" if reasons else ""
+            raise Exception(
+                f"Content blocked by Google's Responsible AI filters{reason_part} "
+                f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)."
+            )
+
+        if response.videos:
+            video = response.videos[0]
+            if video.bytesBase64Encoded:
+                return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded))))
+            if video.gcsUri:
+                return IO.NodeOutput(await download_url_to_video_output(video.gcsUri))
+            raise Exception("Video returned but no data or URL was provided")
+        raise Exception("Video generation completed but no video was returned")
+
 
 class Veo3FirstLastFrameNode(IO.ComfyNode):
 
@@ -394,7 +483,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
                     default="",
                     tooltip="Negative text prompt to guide what to avoid in the video",
                 ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p", "4k"]),
                 IO.Combo.Input(
                     "aspect_ratio",
                     options=["16:9", "9:16"],
@@ -424,8 +513,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
                 IO.Image.Input("last_frame", tooltip="End frame"),
                 IO.Combo.Input(
                     "model",
-                    options=["veo-3.1-generate", "veo-3.1-fast-generate"],
+                    options=["veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.1-lite"],
-                    default="veo-3.1-fast-generate",
                 ),
                 IO.Boolean.Input(
                     "generate_audio",
@@ -443,26 +531,20 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration", "resolution"]),
                 expr="""
                 (
-                  $prices := {
-                    "veo-3.1-fast-generate": { "audio": 0.15, "no_audio": 0.10 },
-                    "veo-3.1-generate":      { "audio": 0.40, "no_audio": 0.20 }
-                  };
                   $m := widgets.model;
-                  $ga := (widgets.generate_audio = "true");
+                  $r := widgets.resolution;
+                  $ga := widgets.generate_audio;
                   $seconds := widgets.duration;
-                  $modelKey :=
+                  $pps :=
-                    $contains($m, "veo-3.1-fast-generate") ? "veo-3.1-fast-generate" :
+                    $contains($m, "lite")
-                    $contains($m, "veo-3.1-generate")      ? "veo-3.1-generate" :
+                      ? ($r = "1080p" ? ($ga ? 0.08 : 0.05) : ($ga ? 0.05 : 0.03))
-                    "";
+                    : $contains($m, "fast")
-                  $audioKey := $ga ? "audio" : "no_audio";
+                      ? ($r = "4k" ? ($ga ? 0.30 : 0.25) : $r = "1080p" ? ($ga ? 0.12 : 0.10) : ($ga ? 0.10 : 0.08))
-                  $modelPrices := $lookup($prices, $modelKey);
+                    : ($r = "4k" ? ($ga ? 0.60 : 0.40) : ($ga ? 0.40 : 0.20));
-                  $pps := $lookup($modelPrices, $audioKey);
+                  {"type":"usd","usd": $pps * $seconds}
-                  ($pps != null)
-                    ? {"type":"usd","usd": $pps * $seconds}
-                    : {"type":"range_usd","min_usd": 0.4, "max_usd": 3.2}
                 )
                 """,
             ),
@@ -482,6 +564,9 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
         model: str,
         generate_audio: bool,
     ):
+        if "lite" in model and resolution == "4k":
+            raise Exception("4K resolution is not supported by the veo-3.1-lite model.")
+
         model = MODELS_MAP[model]
         initial_response = await sync_op(
             cls,
@@ -519,7 +604,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
             data=VeoGenVidPollRequest(
                 operationName=initial_response.name,
             ),
-            poll_interval=5.0,
+            poll_interval=9.0,
             estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
         )
 

comfy_api_nodes/util/__init__.py

@@ -19,6 +19,7 @@ from .conversions import (
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
+    resize_video_to_pixel_budget,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
@@ -90,6 +91,7 @@ __all__ = [
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",
+    "resize_video_to_pixel_budget",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",

comfy_api_nodes/util/conversions.py

@@ -129,22 +129,38 @@ def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
     return img_byte_arr
 
 
+def _compute_downscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return downscaled (w, h) with even dims fitting ``total_pixels``, or None if already fits.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded down to even values (many  codecs require divisible-by-2).
+    """
+    pixels = src_w * src_h
+    if pixels <= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = max(2, int(src_w * scale))
+    new_h = max(2, int(src_h * scale))
+    new_w -= new_w % 2
+    new_h -= new_h % 2
+    return new_w, new_h
+
+
 def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor:
-    """Downscale input image tensor to roughly the specified total pixels."""
+    """Downscale input image tensor to roughly the specified total pixels.
+
+    Output dimensions are rounded down to even values so that the result is guaranteed to fit within ``total_pixels``
+    and is compatible with codecs that require even dimensions (e.g. yuv420p).
+    """
     samples = image.movedim(-1, 1)
-    total = int(total_pixels)
+    dims = _compute_downscale_dims(samples.shape[3], samples.shape[2], int(total_pixels))
-    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+    if dims is None:
-    if scale_by >= 1:
         return image
-    width = round(samples.shape[3] * scale_by)
+    new_w, new_h = dims
-    height = round(samples.shape[2] * scale_by)
+    return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1)
-
-    s = common_upscale(samples, width, height, "lanczos", "disabled")
-    s = s.movedim(1, -1)
-    return s
 
 
-def downscale_image_tensor_by_max_side(image: torch.Tensor, *,  max_side: int) -> torch.Tensor:
+def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor:
     """Downscale input image tensor so the largest dimension is at most max_side pixels."""
     samples = image.movedim(-1, 1)
     height, width = samples.shape[2], samples.shape[3]
@@ -399,6 +415,72 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
         raise RuntimeError(f"Failed to trim video: {str(e)}") from e
 
 
+def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
+    """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
+    Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input.Video:
+    """Re-encode ``video`` scaled to ``scale_dims`` with a single decode/encode pass."""
+    out_w, out_h = scale_dims
+    output_buffer = BytesIO()
+    input_container = None
+    output_container = None
+
+    try:
+        input_source = video.get_stream_source()
+        input_container = av.open(input_source, mode="r")
+        output_container = av.open(output_buffer, mode="w", format="mp4")
+
+        video_stream = output_container.add_stream("h264", rate=video.get_frame_rate())
+        video_stream.width = out_w
+        video_stream.height = out_h
+        video_stream.pix_fmt = "yuv420p"
+
+        audio_stream = None
+        for stream in input_container.streams:
+            if isinstance(stream, av.AudioStream):
+                audio_stream = output_container.add_stream("aac", rate=stream.sample_rate)
+                audio_stream.sample_rate = stream.sample_rate
+                audio_stream.layout = stream.layout
+                break
+
+        for frame in input_container.decode(video=0):
+            frame = frame.reformat(width=out_w, height=out_h, format="yuv420p")
+            for packet in video_stream.encode(frame):
+                output_container.mux(packet)
+        for packet in video_stream.encode():
+            output_container.mux(packet)
+
+        if audio_stream is not None:
+            input_container.seek(0)
+            for audio_frame in input_container.decode(audio=0):
+                for packet in audio_stream.encode(audio_frame):
+                    output_container.mux(packet)
+            for packet in audio_stream.encode():
+                output_container.mux(packet)
+
+        output_container.close()
+        input_container.close()
+        output_buffer.seek(0)
+        return InputImpl.VideoFromFile(output_buffer)
+
+    except Exception as e:
+        if input_container is not None:
+            input_container.close()
+        if output_container is not None:
+            output_container.close()
+        raise RuntimeError(f"Failed to resize video: {str(e)}") from e
+
+
 def _f32_pcm(wav: torch.Tensor) -> torch.Tensor:
     """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file."""
     if wav.dtype.is_floating_point: