Merge branch 'Comfy-Org:master' into fix/jobs-preview-prefer-media-over-text

AGENTS.md

@@ -0,0 +1,272 @@
+## Engineering Style
+
+- Keep changes small and direct. Most fixes should touch the narrowest code path
+  that explains the bug, performance issue, dtype issue, model-format issue, or
+  user-facing behavior.
+- Change the least amount of files possible. A change that touches many files is
+  more likely to be a bad change than a good one unless the broader scope is
+  directly required.
+- Prefer practical fixes over broad architecture work. Add abstractions only
+  when they remove real repeated logic or match an existing ComfyUI pattern.
+- Prefer fewer dependencies. Do not add new dependencies to ComfyUI unless they
+  are absolutely necessary.
+- Delete obsolete code aggressively when newer infrastructure makes it useless.
+  Remove dead fallbacks, migration paths, unused options, debug prints, and
+  compatibility branches that are no longer needed. Do not leave dead branches,
+  unreachable code, or functions that are never called. If code is not
+  necessary for the current behavior, remove it.
+- Revert or disable problematic behavior quickly when it breaks users. It is
+  better to remove a broken feature path than keep a complicated partial fix.
+- Preserve existing APIs, node names, model-loading behavior, file layout, and
+  workflow compatibility unless the change is explicitly about replacing them.
+- Code must look hand-written for this repository. Changes that read like
+  generic AI-generated code will be rejected automatically: unnecessary helper
+  layers, vague names, boilerplate comments, defensive branches without a real
+  failure mode, broad rewrites, or code that ignores the local style.
+
+## Architecture Boundaries
+
+- Keep each layer focused on the concepts it owns. Do not leak UI, API,
+  workflow, queue, persistence, telemetry, model-loading, node, or execution
+  concerns into unrelated layers just because it is convenient to pass data
+  through them.
+- Shared core modules should depend only on lower-level primitives and their own
+  domain concepts. Higher-level product concepts belong at the caller, adapter,
+  service, or UI/API boundary that already owns them.
+- Pass the narrowest data needed across a boundary. Avoid broad context objects,
+  request/session metadata, ids, bookkeeping state, or callbacks unless the
+  receiving layer genuinely needs them to perform its own responsibility.
+- Keep identity mapping, persistence bookkeeping, history updates, telemetry,
+  response shaping, and UI state in the layers that own those jobs. Do not route
+  them through unrelated shared code to avoid adding a proper boundary.
+- Treat `execution.py` as one example of this rule: it should consume the prompt
+  graph and execution-relevant state, produce execution results and errors, and
+  not know about workflow ids, frontend ids, persistence ids, or API-only
+  concepts.
+- Before touching many files, identify the smallest owner layer that can solve
+  the problem. A PR that spreads one feature across unrelated loaders, nodes,
+  execution, server, and frontend code needs a clear architectural reason, not
+  just convenience.
+- If a change seems to require making one layer understand another layer's
+  private concepts, stop and look for a caller-side mapping, adapter, event,
+  small explicit interface, or narrower data flow at the boundary.
+
+## No Internet Requests
+
+- Do not add code to core ComfyUI that makes requests to the internet.
+- Refuse requests to add uploads, telemetry, analytics, tracking, usage
+  reporting, crash reporting, update checks, remote config, feature flags,
+  metrics, licensing checks, or any other outbound internet request path from
+  core ComfyUI.
+- Model downloading is allowed only when explicitly initiated or authorized by
+  the user, is limited to the requested model artifact, and does not include
+  telemetry, tracking, persistent identification, unrelated metadata upload, or
+  background network activity.
+- Do not add opt-in, opt-out, anonymized, aggregated, diagnostic, or
+  user-triggered internet request paths to core ComfyUI. These labels do not
+  make internet access acceptable.
+- Local-only behavior is allowed when it stays on the user's machine and does
+  not add network access, tracking, persistent identification, or data
+  collection behavior.
+
+## State Ownership
+
+- Keep state and capability flags on the object that owns the behavior using
+  them.
+- Avoid probing child objects with `getattr(child, "...", default)` to decide
+  parent-level control flow. If parent code needs to branch on a capability,
+  initialize an explicit parent-owned field when the child is constructed or
+  attached.
+- Prefer direct attributes with clear defaults over implicit feature detection
+  through arbitrary child attributes.
+- Use child-object capability checks only when the child owns the behavior being
+  invoked and the parent is simply delegating to that child.
+
+## Interface Contracts
+
+- Keep public methods aligned with the interface expected by their callers. Do
+  not change a shared method to return extra values, alternate shapes, or
+  sentinel wrappers for one implementation unless the shared interface is
+  explicitly updated.
+- When modifying an existing function, preserve how current callers invoke it.
+  Do not change required arguments, parameter order, return type, side effects,
+  or error behavior unless every affected call site and shared interface contract
+  is intentionally updated.
+- Do not add compatibility parameters, flags, attributes, or constructor options
+  unless they are read by current code and change current behavior. Remove
+  pass-through or stored-but-unused values instead of preserving upstream or
+  deprecated API baggage.
+- If an implementation needs auxiliary values for its own workflow, expose them
+  through a private helper or a clearly named implementation-specific method
+  instead of overloading the public method's return contract.
+- Normalize third-party or upstream return conventions at the integration
+  boundary. Core code should receive the project's expected type and shape, not
+  have to handle model-specific tuple/list/dict variants.
+- Avoid caller-side unwrapping such as `out = out[0]` unless the called
+  interface is documented to return that structure.
+
+## Autograd and Model Freezing
+
+- Do not add `torch.no_grad`, `torch.inference_mode`, or inference-mode helper
+  wrappers in ComfyUI code. The only allowed inference-mode-related use is
+  disabling a globally set inference mode when a training path needs gradients.
+- Do not add freeze, unfreeze, or trainability toggles to model classes. ComfyUI
+  models are always treated as frozen for inference, so explicit freeze
+  functionality is redundant and should not be added.
+- Remove training-only behavior such as dropout from inference model code, but
+  preserve checkpoint and state-dict compatibility when doing so. If deleting a
+  module would change state-dict keys, module ordering, or checkpoint loading
+  behavior, replace it with a no-op such as `nn.Identity` instead of removing the
+  slot outright.
+
+## Python Style
+
+- Keep imports at module scope. Avoid inline imports unless they are already part
+  of an established optional-backend probe or are needed to avoid an import
+  cycle.
+- Do not add unnecessary `try`/`except` blocks. Use them for optional dependency,
+  platform, or backend capability detection only when the program has a useful
+  fallback. Prefer specific exception types when changing new code.
+- Remove any workarounds for PyTorch versions that ComfyUI no longer officially
+  supports. Deprecated workarounds include catching an exception and rerunning
+  the same op with the input cast to float. If a workaround does not have a
+  comment naming the exact PyTorch version or versions that still need it,
+  remove it.
+- Let unsupported model formats, invalid quantization metadata, and bad states
+  fail with clear errors instead of silently producing lower quality output.
+- Match the existing local style in the file you edit. This codebase tolerates
+  long lines, simple helper functions, module-level state, and direct tensor
+  operations when they make the code easier to follow.
+- Keep comments sparse and useful. Strip useless comments that restate the code
+  or describe obvious behavior. Short TODOs are fine when they name the concrete
+  missing follow-up.
+
+## Model, Device, and Memory Behavior
+
+- Treat dtype, device placement, VRAM usage, and offloading behavior as core
+  correctness concerns. Check CPU, CUDA, ROCm, MPS, DirectML, XPU, NPU, and low
+  VRAM implications when touching shared execution or loading code.
+- Prefer native ComfyUI formats and existing quantization/offload helpers over
+  adding parallel code paths. Use `comfy.quant_ops`, `comfy.model_management`,
+  `comfy.memory_management`, `comfy.pinned_memory`, `comfy_aimdo`, and
+  `comfy-kitchen` helpers where they already solve the problem.
+- Use optimized comfy-kitchen ops in places where they improve performance
+  without changing the expected dtype, device, memory, or interface behavior.
+- All models should use the optimized attention function selected by ComfyUI.
+  Treat optimized backend functions, dispatch helpers, and capability-selected
+  callables as opaque. Higher-level code must not inspect function identity,
+  names, modules, or implementation details to decide behavior.
+- Apply the same opacity rule to similar patterns beyond attention: callers
+  should depend on the documented interface and result contract, not on which
+  backend implementation was selected underneath.
+- Do not use custom inference ops that only duplicate an existing op while
+  upcasting to float32, such as custom RMSNorm variants. Use the generic ComfyUI
+  ops and/or native torch ops instead.
+- If a model class `__init__` has an `operations` parameter, assume
+  `operations` is never `None`. Do not add fallback branches or default torch
+  ops for a missing `operations` object.
+- Do not add unnecessary parameters to model, model block, or model ops related
+  classes. Constructor and forward signatures should carry only values that are
+  actually needed by that object for inference.
+- Reuse existing model classes, blocks, ops, and helper modules when appropriate.
+  Before implementing a new version of a model component, search the existing
+  model code for a class or helper that already provides the behavior.
+- Avoid adding `einops` usage in core inference code. Use native torch tensor
+  ops such as `reshape`, `view`, `permute`, `transpose`, `flatten`, `unflatten`,
+  `unsqueeze`, and `squeeze` instead.
+- Do not use tensors as general-purpose Python data structures. Keep metadata,
+  bookkeeping, counters, flags, shape math, padding math, index planning, memory
+  estimates, and control-flow decisions in plain Python values unless the data
+  must participate directly in tensor computation. Avoid creating temporary
+  tensors just to use tensor methods for scalar or structural calculations.
+- Avoid unnecessary casts and transfers. Preserve the intended compute dtype,
+  storage dtype, bias dtype, and original tensor shape metadata.
+- Assume inputs to the main model forward are already in the compute dtype by
+  default, except integer inputs such as some model timestep tensors. Do not add
+  defensive or convenience casts in model code; it is better for invalid dtype
+  plumbing to error clearly than to hide it with unnecessary casts.
+- Raw model parameters that are not owned by an op and may be initialized in a
+  dtype different from the compute dtype should be cast at use in forward or
+  inference code with `comfy.ops.cast_to_input` or
+  `comfy.model_management.cast_to` to avoid dtype mismatches.
+- Model code should not care what dtype it is initialized in, and model
+  `__init__` methods should not contain workarounds for specific dtypes. Dtype
+  workaround code, such as making a model work with fp16 compute, belongs in the
+  execution or model-management layer that owns compute policy.
+- Model code should not perform unnecessary device-to-CPU or CPU-to-device
+  transfers. New allocations must be created on the correct device and dtype;
+  never allocate on CPU and then move to GPU, or allocate in one dtype and then
+  convert to another.
+- Model code itself should not perform memory management. Loading, unloading,
+  offloading, device movement, VRAM policy, cache lifetime, and cleanup belong
+  in the relevant model-management and execution layers, not inside model
+  implementations.
+- Do not add global, module-level, class-level, singleton, or model-owned stores
+  for tensors or other large memory that persist across executions. Temporary
+  caches must be scoped to a single execution or forward/encode/decode call:
+  allocate them in the owning top-level call, pass them explicitly through the
+  call stack, and let them be discarded when that call returns.
+- Follow the Wan VAE temporal cache pattern for temporary caches: create a local
+  cache such as `feat_map` for the encode/decode operation, pass it into the
+  blocks that need it, and do not retain it on the model or in global state.
+- In model init code, prefer `torch.empty` for parameter/buffer placeholders
+  that are populated from the model state dict instead of zero-initializing with
+  `torch.zeros` or similar. If an allocation is not loaded from the state dict
+  and is useless for inference, do not include it.
+- `nn.Parameter` tensors that are stored in and populated from the model state
+  dict should be initialized with `torch.empty`, not with zero, random, or
+  otherwise meaningful initialization.
+- Model initialization should describe module structure, not fabricate
+  checkpoint-owned tensor contents. Parameters and buffers that are loaded from
+  the state dict must not be manually initialized, reassigned, or filled with
+  fallback values unless that value is actually used when no checkpoint key
+  exists.
+- When slicing large tensors, copy the slice if the sliced tensor's lifetime
+  exceeds the current function scope. Do not keep a long-lived view into a large
+  backing tensor when a smaller copy would release memory sooner.
+- Use fused or compound torch operations such as `addcmul` when they naturally
+  match the math. Reducing Python and torch dispatch overhead is a valid
+  optimization when it does not obscure the code or change dtype/device
+  behavior.
+- Avoid caches that persist across different executions as much as possible.
+  Persistent caches are acceptable only when they use a very minimal amount of
+  memory and have a clear ownership and invalidation story.
+- When optimizing, favor small measurable changes: fewer allocations, fewer
+  device transfers, less peak memory, better batching, or use of a faster
+  existing backend op.
+
+## Nodes and User-Facing Behavior
+
+- Follow existing node conventions: `INPUT_TYPES`, `RETURN_TYPES`, `FUNCTION`,
+  `CATEGORY`, and registration through the local mapping used by that file.
+- Keep node changes backward compatible by default. Add inputs with sensible
+  defaults and avoid changing output types unless the request requires it.
+- Model implementations should add the minimal number of ComfyUI nodes required
+  to run the model. Reuse existing nodes as much as possible; adapting the model
+  to work with existing nodes is strongly preferred over creating new nodes.
+- Node-level code must not patch model code directly. Any node behavior that
+  modifies, wraps, hooks, or changes model behavior must go through the model
+  patcher class instead of reaching into model internals.
+- The official mascot of ComfyUI is a very cute anime girl with massive fennec
+  ears, a big fluffy tail, long blonde wavy hair, and blue eyes. Feel free to
+  use her in ComfyUI materials, UI text, examples, tests, generated assets, or
+  comments, but do not disrespect her.
+- Warning and info messages should be short and actionable. Remove noisy or
+  misleading messages rather than adding more logging.
+- Documentation and README edits should be concise, factual, and tied to the
+  changed behavior.
+
+## Commit and Review Habits
+
+- If asked to write commit messages, use short direct subjects like the existing
+  history: `Fix ...`, `Add ...`, `Support ...`, `Remove ...`, `Update ...`,
+  `Make ...`, `Use ...`, `Disable ...`, `Bump ...`, or `Revert ...`.
+- Keep PR descriptions short and reviewable. State the problem, the behavioral
+  change, and the tests run; avoid long narrative explanations, implementation
+  diaries, or exhaustive file-by-file summaries unless the reviewer explicitly
+  needs that context.
+- Prefer one coherent behavioral change per commit. Dependency pins, tests, and
+  the code that needs them may be in the same commit when they are inseparable.
+- In reviews, prioritize real user impact: crashes, wrong dtype/device behavior,
+  memory regressions, broken model loading, workflow incompatibility, and noisy
+  or misleading user-facing output.

comfy/cli_args.py

@@ -240,6 +240,7 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
+parser.add_argument("--enable-asset-hashing", action="store_true", help="Compute blake3 content hashes when scanning assets. Hashing enables future asset-portability features (deduplication, cross-machine model resolution) but adds startup cost and per-output cost on large models directories. Off by default; enable to opt in.")
 parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
 parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 

comfy/text_encoders/qwen3vl.py

@@ -167,7 +167,7 @@ class Qwen3VLTokenizer(sd1_clip.SD1Tokenizer):
         embed_count = 0
         for r in tokens[key_name]:
             for i in range(len(r)):
-                if r[i][0] == 151655:  # <|image_pad|>
+                if isinstance(r[i][0], (int, float)) and r[i][0] == 151655:  # <|image_pad|>
                     if len(images) > embed_count:
                         r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
                         embed_count += 1

comfy_api_nodes/apis/gemini.py

@@ -121,6 +121,7 @@ class GeminiGenerationConfig(BaseModel):
     topK: int | None = Field(None, ge=1)
     topP: float | None = Field(None, ge=0.0, le=1.0)
     thinkingConfig: GeminiThinkingConfig | None = Field(None)
+    responseModalities: list[str] | None = Field(None)
 
 
 class GeminiImageOutputOptions(BaseModel):

comfy_api_nodes/apis/ideogram.py

@@ -33,53 +33,6 @@ class IdeogramColorPalette(
     )
 
 
-class ImageRequest(BaseModel):
-    aspect_ratio: Optional[str] = Field(
-        None,
-        description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
-    )
-    color_palette: Optional[Dict[str, Any]] = Field(
-        None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
-    )
-    magic_prompt_option: Optional[str] = Field(
-        None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
-    )
-    model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
-    negative_prompt: Optional[str] = Field(
-        None,
-        description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
-    )
-    num_images: Optional[int] = Field(
-        1,
-        description='Optional. Number of images to generate (1-8). Defaults to 1.',
-        ge=1,
-        le=8,
-    )
-    prompt: str = Field(
-        ..., description='Required. The prompt to use to generate the image.'
-    )
-    resolution: Optional[str] = Field(
-        None,
-        description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
-    )
-    seed: Optional[int] = Field(
-        None,
-        description='Optional. A number between 0 and 2147483647.',
-        ge=0,
-        le=2147483647,
-    )
-    style_type: Optional[str] = Field(
-        None,
-        description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
-    )
-
-
-class IdeogramGenerateRequest(BaseModel):
-    image_request: ImageRequest = Field(
-        ..., description='The image generation request parameters.'
-    )
-
-
 class Datum(BaseModel):
     is_image_safe: Optional[bool] = Field(
         None, description='Indicates whether the image is considered safe.'
@@ -113,20 +66,6 @@ class StyleCode(RootModel[str]):
     root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
 
 
-class Datum1(BaseModel):
-    is_image_safe: Optional[bool] = None
-    prompt: Optional[str] = None
-    resolution: Optional[str] = None
-    seed: Optional[int] = None
-    style_type: Optional[str] = None
-    url: Optional[str] = None
-
-
-class IdeogramV3IdeogramResponse(BaseModel):
-    created: Optional[datetime] = None
-    data: Optional[List[Datum1]] = None
-
-
 class RenderingSpeed1(str, Enum):
     TURBO = 'TURBO'
     DEFAULT = 'DEFAULT'

comfy_api_nodes/nodes_gemini.py

@@ -13,7 +13,7 @@ import torch
 from typing_extensions import override
 
 import folder_paths
-from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
 from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
@@ -37,6 +37,7 @@ from comfy_api_nodes.util import (
     audio_to_base64_string,
     bytesio_to_image_tensor,
     download_url_to_image_tensor,
+    download_url_to_video_output,
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
@@ -45,6 +46,7 @@ from comfy_api_nodes.util import (
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
     validate_string,
+    validate_video_duration,
     video_to_base64_string,
 )
 
@@ -229,10 +231,29 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
     return torch.cat(image_tensors, dim=0)
 
 
+async def get_video_from_response(
+    response: GeminiGenerateContentResponse, cls: type[IO.ComfyNode] | None = None
+) -> InputImpl.VideoFromFile:
+    parts = get_parts_by_type(response, "video/*")
+    for part in parts:
+        if part.inlineData and part.inlineData.data:
+            return InputImpl.VideoFromFile(BytesIO(base64.b64decode(part.inlineData.data)))
+        if part.fileData and part.fileData.fileUri:
+            return await download_url_to_video_output(part.fileData.fileUri, cls=cls)
+    model_message = get_text_from_response(response).strip()
+    if model_message:
+        raise ValueError(f"Gemini did not generate a video. Model response: {model_message}")
+    raise ValueError(
+        "Gemini did not generate a video. Try rephrasing your prompt, "
+        "shortening the requested duration, or reducing the number of input images/videos."
+    )
+
+
 def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None:
     if not response.modelVersion:
         return None
     # Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing
+    output_video_tokens_price = 0.0
     if response.modelVersion == "gemini-2.5-pro":
         input_tokens_price = 1.25
         output_text_tokens_price = 10.0
@@ -249,18 +270,27 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 0.0
-    elif response.modelVersion == "gemini-3.1-flash-lite-preview":
+    elif response.modelVersion in ("gemini-3.1-flash-lite-preview", "gemini-3.1-flash-lite"):
         input_tokens_price = 0.25
         output_text_tokens_price = 1.50
         output_image_tokens_price = 0.0
-    elif response.modelVersion == "gemini-3-pro-image-preview":
+    elif response.modelVersion in ("gemini-3-pro-image-preview", "gemini-3-pro-image"):
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 120.0
-    elif response.modelVersion == "gemini-3.1-flash-image-preview":
+    elif response.modelVersion in ("gemini-3.1-flash-image-preview", "gemini-3.1-flash-image"):
         input_tokens_price = 0.5
         output_text_tokens_price = 3.0
         output_image_tokens_price = 60.0
+    elif response.modelVersion == "gemini-3.1-flash-lite-image":
+        input_tokens_price = 0.25
+        output_text_tokens_price = 1.50
+        output_image_tokens_price = 30.0
+    elif response.modelVersion == "gemini-omni-flash-preview":
+        input_tokens_price = 2.145
+        output_text_tokens_price = 12.87
+        output_image_tokens_price = 0.0
+        output_video_tokens_price = 25.025
     else:
         return None
     final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@@ -268,6 +298,8 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         for i in response.usageMetadata.candidatesTokensDetails:
             if i.modality == Modality.IMAGE:
                 final_price += output_image_tokens_price * i.tokenCount  # for Nano Banana models
+            elif i.modality == Modality.VIDEO:
+                final_price += output_video_tokens_price * i.tokenCount  # for Omni Flash
             else:
                 final_price += output_text_tokens_price * i.tokenCount
     if response.usageMetadata.thoughtsTokenCount:
@@ -1302,7 +1334,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
         )
 
 
-def _nano_banana_2_v2_model_inputs():
+def _nano_banana_2_v2_model_inputs(resolutions: list[str]):
     return [
         IO.Combo.Input(
             "aspect_ratio",
@@ -1329,8 +1361,8 @@ def _nano_banana_2_v2_model_inputs():
         ),
         IO.Combo.Input(
             "resolution",
-            options=["1K", "2K", "4K"],
+            options=resolutions,
-            tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+            tooltip="Target output resolution.",
         ),
         IO.Combo.Input(
             "thinking_level",
@@ -1376,7 +1408,11 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Nano Banana 2 (Gemini 3.1 Flash Image)",
-                            _nano_banana_2_v2_model_inputs(),
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K", "2K", "4K"]),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 Lite",
+                            _nano_banana_2_v2_model_inputs(resolutions=["1K"]),
                         ),
                     ],
                 ),
@@ -1445,9 +1481,13 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
                 expr="""
                 (
-                  $r := $lookup(widgets, "model.resolution");
+                  $contains(widgets.model, "lite")
-                  $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                    ? {"type":"usd","usd": 0.034, "format":{"suffix":"/Image","approximate":true}}
-                  {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                    : (
+                        $r := $lookup(widgets, "model.resolution");
+                        $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                        {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                      )
                 )
                 """,
             ),
@@ -1468,6 +1508,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         model_choice = model["model"]
         if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
             model_id = "gemini-3.1-flash-image-preview"
+        elif model_choice == "Nano Banana 2 Lite":
+            model_id = "gemini-3.1-flash-lite-image"
         else:
             model_id = model_choice
 
@@ -1517,6 +1559,149 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         )
 
 
+OMNI_MAX_IMAGES = 14
+OMNI_MAX_VIDEOS = 3
+
+OMNI_MODELS: dict[str, str] = {
+    "Omni Flash": "gemini-omni-flash-preview",
+}
+
+
+def _omni_flash_inputs() -> list[Input]:
+    """Per-model inputs for the Omni video DynamicCombo (prompt + reference media + sampling)."""
+    return [
+        IO.String.Input(
+            "prompt",
+            multiline=True,
+            default="",
+            tooltip="Describe the video to generate. Specify the length and aspect ratio directly in the "
+            'prompt, e.g. "a 6-second clip in 16:9". Length may be 3-10 seconds; the aspect ratio must be '
+            "16:9 (landscape) or 9:16 (portrait). The output is 720p, 24 FPS, with audio.",
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
+        ),
+        IO.Autogrow.Input(
+            "videos",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
+                min=0,
+            ),
+            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
+            f"each up to 10 seconds long.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiVideoOmni(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiVideoOmni",
+            display_name="Google Gemini Omni (Video)",
+            category="partner/video/Gemini",
+            essentials_category="Video Generation",
+            description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash model. "
+            "Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
+            "length (3-10s) and aspect ratio (16:9 or 9:16) directly in the prompt.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Omni Flash", _omni_flash_inputs()),
+                    ],
+                    tooltip="The Gemini video model used to generate the video.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr='{"type":"usd","usd":0.146,"format":{"suffix":"/second","approximate":true}}'
+            ),
+        )
+
+    @classmethod
+    async def execute(cls, model: dict, seed: int) -> IO.NodeOutput:
+        prompt = model.get("prompt") or ""
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = OMNI_MODELS[model["model"]]
+
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        videos = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if sum(get_number_of_images(t) for t in images) > OMNI_MAX_IMAGES:
+            raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
+        if len(videos) > OMNI_MAX_VIDEOS:
+            raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
+        for video in videos:
+            validate_video_duration(video, max_duration=10)
+
+        parts: list[GeminiPart] = []
+        if images or videos:
+            parts.extend(await build_gemini_media_parts(cls, images, [], videos))
+        parts.append(GeminiPart(text=prompt))
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[GeminiContent(role=GeminiRole.user, parts=parts)],
+                generationConfig=GeminiGenerationConfig(
+                    responseModalities=["TEXT", "VIDEO"],
+                    temperature=model.get("temperature", 1.0),
+                    topP=model.get("top_p", 0.95),
+                ),
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(
+            await get_video_from_response(response, cls=cls),
+            get_text_from_response(response),
+        )
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1527,6 +1712,7 @@ class GeminiExtension(ComfyExtension):
             GeminiImage2,
             GeminiNanoBanana2,
             GeminiNanoBanana2V2,
+            GeminiVideoOmni,
             GeminiInputFiles,
         ]
 

comfy_api_nodes/nodes_ideogram.py

@@ -5,9 +5,7 @@ from PIL import Image
 import numpy as np
 import torch
 from comfy_api_nodes.apis.ideogram import (
-    IdeogramGenerateRequest,
     IdeogramGenerateResponse,
-    ImageRequest,
     IdeogramV3Request,
     IdeogramV3EditRequest,
     IdeogramV4Request,
@@ -21,101 +19,6 @@ from comfy_api_nodes.util import (
     validate_string,
 )
 
-V1_V1_RES_MAP = {
-  "Auto":"AUTO",
-  "512 x 1536":"RESOLUTION_512_1536",
-  "576 x 1408":"RESOLUTION_576_1408",
-  "576 x 1472":"RESOLUTION_576_1472",
-  "576 x 1536":"RESOLUTION_576_1536",
-  "640 x 1024":"RESOLUTION_640_1024",
-  "640 x 1344":"RESOLUTION_640_1344",
-  "640 x 1408":"RESOLUTION_640_1408",
-  "640 x 1472":"RESOLUTION_640_1472",
-  "640 x 1536":"RESOLUTION_640_1536",
-  "704 x 1152":"RESOLUTION_704_1152",
-  "704 x 1216":"RESOLUTION_704_1216",
-  "704 x 1280":"RESOLUTION_704_1280",
-  "704 x 1344":"RESOLUTION_704_1344",
-  "704 x 1408":"RESOLUTION_704_1408",
-  "704 x 1472":"RESOLUTION_704_1472",
-  "720 x 1280":"RESOLUTION_720_1280",
-  "736 x 1312":"RESOLUTION_736_1312",
-  "768 x 1024":"RESOLUTION_768_1024",
-  "768 x 1088":"RESOLUTION_768_1088",
-  "768 x 1152":"RESOLUTION_768_1152",
-  "768 x 1216":"RESOLUTION_768_1216",
-  "768 x 1232":"RESOLUTION_768_1232",
-  "768 x 1280":"RESOLUTION_768_1280",
-  "768 x 1344":"RESOLUTION_768_1344",
-  "832 x 960":"RESOLUTION_832_960",
-  "832 x 1024":"RESOLUTION_832_1024",
-  "832 x 1088":"RESOLUTION_832_1088",
-  "832 x 1152":"RESOLUTION_832_1152",
-  "832 x 1216":"RESOLUTION_832_1216",
-  "832 x 1248":"RESOLUTION_832_1248",
-  "864 x 1152":"RESOLUTION_864_1152",
-  "896 x 960":"RESOLUTION_896_960",
-  "896 x 1024":"RESOLUTION_896_1024",
-  "896 x 1088":"RESOLUTION_896_1088",
-  "896 x 1120":"RESOLUTION_896_1120",
-  "896 x 1152":"RESOLUTION_896_1152",
-  "960 x 832":"RESOLUTION_960_832",
-  "960 x 896":"RESOLUTION_960_896",
-  "960 x 1024":"RESOLUTION_960_1024",
-  "960 x 1088":"RESOLUTION_960_1088",
-  "1024 x 640":"RESOLUTION_1024_640",
-  "1024 x 768":"RESOLUTION_1024_768",
-  "1024 x 832":"RESOLUTION_1024_832",
-  "1024 x 896":"RESOLUTION_1024_896",
-  "1024 x 960":"RESOLUTION_1024_960",
-  "1024 x 1024":"RESOLUTION_1024_1024",
-  "1088 x 768":"RESOLUTION_1088_768",
-  "1088 x 832":"RESOLUTION_1088_832",
-  "1088 x 896":"RESOLUTION_1088_896",
-  "1088 x 960":"RESOLUTION_1088_960",
-  "1120 x 896":"RESOLUTION_1120_896",
-  "1152 x 704":"RESOLUTION_1152_704",
-  "1152 x 768":"RESOLUTION_1152_768",
-  "1152 x 832":"RESOLUTION_1152_832",
-  "1152 x 864":"RESOLUTION_1152_864",
-  "1152 x 896":"RESOLUTION_1152_896",
-  "1216 x 704":"RESOLUTION_1216_704",
-  "1216 x 768":"RESOLUTION_1216_768",
-  "1216 x 832":"RESOLUTION_1216_832",
-  "1232 x 768":"RESOLUTION_1232_768",
-  "1248 x 832":"RESOLUTION_1248_832",
-  "1280 x 704":"RESOLUTION_1280_704",
-  "1280 x 720":"RESOLUTION_1280_720",
-  "1280 x 768":"RESOLUTION_1280_768",
-  "1280 x 800":"RESOLUTION_1280_800",
-  "1312 x 736":"RESOLUTION_1312_736",
-  "1344 x 640":"RESOLUTION_1344_640",
-  "1344 x 704":"RESOLUTION_1344_704",
-  "1344 x 768":"RESOLUTION_1344_768",
-  "1408 x 576":"RESOLUTION_1408_576",
-  "1408 x 640":"RESOLUTION_1408_640",
-  "1408 x 704":"RESOLUTION_1408_704",
-  "1472 x 576":"RESOLUTION_1472_576",
-  "1472 x 640":"RESOLUTION_1472_640",
-  "1472 x 704":"RESOLUTION_1472_704",
-  "1536 x 512":"RESOLUTION_1536_512",
-  "1536 x 576":"RESOLUTION_1536_576",
-  "1536 x 640":"RESOLUTION_1536_640",
-}
-
-V1_V2_RATIO_MAP = {
-  "1:1":"ASPECT_1_1",
-  "4:3":"ASPECT_4_3",
-  "3:4":"ASPECT_3_4",
-  "16:9":"ASPECT_16_9",
-  "9:16":"ASPECT_9_16",
-  "2:1":"ASPECT_2_1",
-  "1:2":"ASPECT_1_2",
-  "3:2":"ASPECT_3_2",
-  "2:3":"ASPECT_2_3",
-  "4:5":"ASPECT_4_5",
-  "5:4":"ASPECT_5_4",
-}
 
 V3_RATIO_MAP = {
     "1:3":"1x3",
@@ -229,298 +132,6 @@ async def download_and_process_images(image_urls):
     return stacked_tensors
 
 
-class IdeogramV1(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="IdeogramV1",
-            display_name="Ideogram V1",
-            category="partner/image/Ideogram",
-            description="Generates images using the Ideogram V1 model.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Prompt for the image generation",
-                ),
-                IO.Boolean.Input(
-                    "turbo",
-                    default=False,
-                    tooltip="Whether to use turbo mode (faster generation, potentially lower quality)",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=list(V1_V2_RATIO_MAP.keys()),
-                    default="1:1",
-                    tooltip="The aspect ratio for image generation.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "magic_prompt_option",
-                    options=["AUTO", "ON", "OFF"],
-                    default="AUTO",
-                    tooltip="Determine if MagicPrompt should be used in generation",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    control_after_generate=True,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Description of what to exclude from the image",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "num_images",
-                    default=1,
-                    min=1,
-                    max=8,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["num_images", "turbo"]),
-                expr="""
-                (
-                  $n := widgets.num_images;
-                  $base := (widgets.turbo = true) ? 0.0286 : 0.0858;
-                  {"type":"usd","usd": $round($base * $n, 2)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt,
-        turbo=False,
-        aspect_ratio="1:1",
-        magic_prompt_option="AUTO",
-        seed=0,
-        negative_prompt="",
-        num_images=1,
-    ):
-        # Determine the model based on turbo setting
-        aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
-        model = "V_1_TURBO" if turbo else "V_1"
-
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
-            response_model=IdeogramGenerateResponse,
-            data=IdeogramGenerateRequest(
-                image_request=ImageRequest(
-                    prompt=prompt,
-                    model=model,
-                    num_images=num_images,
-                    seed=seed,
-                    aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
-                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
-                    negative_prompt=negative_prompt if negative_prompt else None,
-                )
-            ),
-            max_retries=1,
-        )
-
-        if not response.data or len(response.data) == 0:
-            raise Exception("No images were generated in the response")
-
-        image_urls = [image_data.url for image_data in response.data if image_data.url]
-        if not image_urls:
-            raise Exception("No image URLs were generated in the response")
-        return IO.NodeOutput(await download_and_process_images(image_urls))
-
-
-class IdeogramV2(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="IdeogramV2",
-            display_name="Ideogram V2",
-            category="partner/image/Ideogram",
-            description="Generates images using the Ideogram V2 model.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Prompt for the image generation",
-                ),
-                IO.Boolean.Input(
-                    "turbo",
-                    default=False,
-                    tooltip="Whether to use turbo mode (faster generation, potentially lower quality)",
-                ),
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=list(V1_V2_RATIO_MAP.keys()),
-                    default="1:1",
-                    tooltip="The aspect ratio for image generation. Ignored if resolution is not set to AUTO.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "resolution",
-                    options=list(V1_V1_RES_MAP.keys()),
-                    default="Auto",
-                    tooltip="The resolution for image generation. "
-                            "If not set to AUTO, this overrides the aspect_ratio setting.",
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "magic_prompt_option",
-                    options=["AUTO", "ON", "OFF"],
-                    default="AUTO",
-                    tooltip="Determine if MagicPrompt should be used in generation",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    control_after_generate=True,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                IO.Combo.Input(
-                    "style_type",
-                    options=["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"],
-                    default="NONE",
-                    tooltip="Style type for generation (V2 only)",
-                    optional=True,
-                    advanced=True,
-                ),
-                IO.String.Input(
-                    "negative_prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Description of what to exclude from the image",
-                    optional=True,
-                ),
-                IO.Int.Input(
-                    "num_images",
-                    default=1,
-                    min=1,
-                    max=8,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    optional=True,
-                ),
-                #"color_palette": (
-                #    IO.STRING,
-                #    {
-                #        "multiline": False,
-                #        "default": "",
-                #        "tooltip": "Color palette preset name or hex colors with weights",
-                #    },
-                #),
-            ],
-            outputs=[
-                IO.Image.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["num_images", "turbo"]),
-                expr="""
-                (
-                  $n := widgets.num_images;
-                  $base := (widgets.turbo = true) ? 0.0715 : 0.1144;
-                  {"type":"usd","usd": $round($base * $n, 2)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt,
-        turbo=False,
-        aspect_ratio="1:1",
-        resolution="Auto",
-        magic_prompt_option="AUTO",
-        seed=0,
-        style_type="NONE",
-        negative_prompt="",
-        num_images=1,
-        color_palette="",
-    ):
-        aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
-        resolution = V1_V1_RES_MAP.get(resolution, None)
-        # Determine the model based on turbo setting
-        model = "V_2_TURBO" if turbo else "V_2"
-
-        # Handle resolution vs aspect_ratio logic
-        # If resolution is not AUTO, it overrides aspect_ratio
-        final_resolution = None
-        final_aspect_ratio = None
-
-        if resolution != "AUTO":
-            final_resolution = resolution
-        else:
-            final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None
-
-        response = await sync_op(
-            cls,
-            endpoint=ApiEndpoint(path="/proxy/ideogram/generate", method="POST"),
-            response_model=IdeogramGenerateResponse,
-            data=IdeogramGenerateRequest(
-                image_request=ImageRequest(
-                    prompt=prompt,
-                    model=model,
-                    num_images=num_images,
-                    seed=seed,
-                    aspect_ratio=final_aspect_ratio,
-                    resolution=final_resolution,
-                    magic_prompt_option=(magic_prompt_option if magic_prompt_option != "AUTO" else None),
-                    style_type=style_type if style_type != "NONE" else None,
-                    negative_prompt=negative_prompt if negative_prompt else None,
-                    color_palette=color_palette if color_palette else None,
-                )
-            ),
-            max_retries=1,
-        )
-        if not response.data or len(response.data) == 0:
-            raise Exception("No images were generated in the response")
-
-        image_urls = [image_data.url for image_data in response.data if image_data.url]
-        if not image_urls:
-            raise Exception("No image URLs were generated in the response")
-        return IO.NodeOutput(await download_and_process_images(image_urls))
-
-
 class IdeogramV3(IO.ComfyNode):
 
     @classmethod
@@ -917,8 +528,6 @@ class IdeogramExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
-            IdeogramV1,
-            IdeogramV2,
             IdeogramV3,
             IdeogramV4,
         ]

comfy_extras/nodes_cond.py

@@ -8,7 +8,8 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CLIPTextEncodeControlnet",
-            category="experimental/conditioning",
+            display_name="CLIP Text Encode (Controlnet)",
+            category="model/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Conditioning.Input("conditioning"),
@@ -35,11 +36,12 @@ class T5TokenizerOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="T5TokenizerOptions",
-            category="experimental/conditioning",
+            display_name="T5 Tokenizer Options",
+            category="model/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
-                io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,

comfy_extras/nodes_custom_sampler.py

@@ -1070,7 +1070,7 @@ class AddNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="AddNoise",
-            category="experimental/custom_sampling/noise",
+            category="model/sampling/noise",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
@@ -1120,7 +1120,7 @@ class ManualSigmas(io.ComfyNode):
         return io.Schema(
             node_id="ManualSigmas",
             search_aliases=["custom noise schedule", "define sigmas"],
-            category="experimental/custom_sampling",
+            category="model/sampling/sigmas",
             is_experimental=True,
             inputs=[
                 io.String.Input("sigmas", default="1, 0.5", multiline=False)

comfy_extras/nodes_photomaker.py

@@ -123,7 +123,8 @@ class PhotoMakerLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerLoader",
-            category="experimental/photomaker",
+            display_name="Load PhotoMaker Model",
+            category="model/loaders",
             inputs=[
                 io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
             ],
@@ -149,7 +150,8 @@ class PhotoMakerEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerEncode",
-            category="experimental/photomaker",
+            display_name="PhotoMaker Encode",
+            category="model/conditioning/photomaker",
             inputs=[
                 io.Photomaker.Input("photomaker"),
                 io.Image.Input("image"),

comfy_extras/nodes_stable_cascade.py

@@ -119,7 +119,7 @@ class StableCascade_SuperResolutionControlnet(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_SuperResolutionControlnet",
-            category="experimental/stable_cascade",
+            category="experimental/stable cascade",
             is_experimental=True,
             inputs=[
                 io.Image.Input("image"),

comfy_extras/nodes_triposplat.py

@@ -143,7 +143,7 @@ class VAEDecodeTripoSplat(IO.ComfyNode):
         return IO.Schema(
             node_id="VAEDecodeTripoSplat",
             display_name="TripoSplat Decode",
-            category="3d/latent",
+            category="model/latent/triposplat",
             description="Decode the sampled TripoSplat latent into a 3D gaussian splat. "
                         "Modify the number of gaussians to vary the density.",
             inputs=[
@@ -188,7 +188,7 @@ class TripoSplatSamplingPreview(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoSplatSamplingPreview",
             display_name="TripoSplat Sampling Preview",
-            category="3d/latent",
+            category="model/latent/triposplat",
             description="Patch the TripoSplat model for the standard Ksampler node to show a live decoded "
                         "gaussian splat preview at each step.",
             inputs=[

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.26.0"
+__version__ = "0.27.0"

main.py

@@ -403,7 +403,7 @@ def prompt_worker(q, server_instance):
                 hook_breaker_ac10a0.restore_functions()
 
                 if not asset_seeder.is_disabled():
-                    asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=True)
+                    asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=args.enable_asset_hashing)
                 asset_seeder.resume()
 
 
@@ -458,7 +458,7 @@ def setup_database():
         if dependencies_available():
             init_db()
             if args.enable_assets:
-                if asset_seeder.start(roots=("models", "input", "output"), prune_first=True, compute_hashes=True):
+                if asset_seeder.start(roots=("models", "input", "output"), prune_first=True, compute_hashes=args.enable_asset_hashing):
                     logging.info("Background asset scan initiated for models, input, output")
     except Exception as e:
         if "database is locked" in str(e):

nodes.py

@@ -159,6 +159,29 @@ class ConditioningConcat:
 
         return (out, )
 
+class ConditioningMultiply:
+    SEARCH_ALIASES = ["scale conditioning", "scale prompt", "multiply conditioning", "multiply prompt"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                            "multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
+                            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION     = "multiply"
+    CATEGORY     = "model/conditioning/transform"
+
+    def multiply(self, conditioning, multiplier):
+        c = []
+        for t in conditioning:
+            values = {}
+            pooled_output = t[1].get("pooled_output", None)
+            if pooled_output is not None:
+                values["pooled_output"] = pooled_output * multiplier
+            scaled = node_helpers.conditioning_set_values([[t[0] * multiplier, t[1]]], values)[0]
+            c.append(scaled)
+        return (c,)
+
 class ConditioningSetArea:
     SEARCH_ALIASES = ["regional prompt", "area prompt", "spatial conditioning", "localized prompt"]
 
@@ -326,7 +349,7 @@ class VAEDecodeTiled:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "experimental"
+    CATEGORY = "model/latent"
 
     def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
         if tile_size < overlap * 4:
@@ -373,7 +396,7 @@ class VAEEncodeTiled:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "experimental"
+    CATEGORY = "model/latent"
 
     def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
         t = vae.encode_tiled(pixels, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
@@ -491,7 +514,7 @@ class SaveLatent:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "experimental"
+    CATEGORY = "model/latent"
 
     def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -536,7 +559,7 @@ class LoadLatent:
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
         return {"required": {"latent": [sorted(files), ]}, }
 
-    CATEGORY = "experimental"
+    CATEGORY = "model/latent"
 
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "load"
@@ -2050,6 +2073,7 @@ NODE_CLASS_MAPPINGS = {
     "ConditioningAverage": ConditioningAverage,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
+    "ConditioningMultiply": ConditioningMultiply,
     "ConditioningSetArea": ConditioningSetArea,
     "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
     "ConditioningSetAreaStrength": ConditioningSetAreaStrength,
@@ -2121,6 +2145,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningAverage ": "Conditioning (Average)",
     "ConditioningAverage": "Conditioning (Average)",
     "ConditioningConcat": "Conditioning (Concat)",
+    "ConditioningMultiply": "Conditioning (Multiply)",
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)",
@@ -2130,6 +2155,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GLIGENTextBoxApply": "Apply GLIGEN Text Box",
     "ConditioningZeroOut": "Conditioning Zero Out",
     # Latent
+    "LoadLatent": "Load Latent",
+    "SaveLatent": "Save Latent",
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
     "VAEDecode": "VAE Decode",
@@ -2164,7 +2191,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageSharpen": "Sharpen Image",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
-    # experimental
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
 }

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.26.0"
+version = "0.27.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.45.19
+comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.10.7
+comfyui-workflow-templates==0.11.1
 comfyui-embedded-docs==0.5.6
 torch
 torchsde
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.14
+comfy-kitchen==0.2.16
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0