diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 000000000..bd6a3e5e8 --- /dev/null +++ b/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. diff --git a/comfy/cli_args.py b/comfy/cli_args.py index e3099a230..4bef096fb 100644 --- a/comfy/cli_args.py +++ b/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.") diff --git a/comfy/text_encoders/qwen3vl.py b/comfy/text_encoders/qwen3vl.py index 59c9aae6d..2082c42e7 100644 --- a/comfy/text_encoders/qwen3vl.py +++ b/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 diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py index caaba8f36..7b2543270 100644 --- a/comfy_api_nodes/apis/gemini.py +++ b/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): diff --git a/comfy_api_nodes/apis/ideogram.py b/comfy_api_nodes/apis/ideogram.py index c5ad9559f..ee3256e96 100644 --- a/comfy_api_nodes/apis/ideogram.py +++ b/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' diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index a63625ada..aa992802d 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/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"], - tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.", + options=resolutions, + 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"); - $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154}; - {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}} + $contains(widgets.model, "lite") + ? {"type":"usd","usd": 0.034, "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, ] diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index 3b914a850..cc0467987 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/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, ] diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index b745a43af..c8091b7a4 100644 --- a/comfy_extras/nodes_cond.py +++ b/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_length", 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), ], outputs=[io.Clip.Output()], is_experimental=True, diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index c9d7e06fc..56ef5f526 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/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) diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py index 8a2248572..72fad1673 100644 --- a/comfy_extras/nodes_photomaker.py +++ b/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"), diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index 6a78ffb47..ddfb4f2b0 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/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"), diff --git a/comfy_extras/nodes_triposplat.py b/comfy_extras/nodes_triposplat.py index 7bf4703fe..c892213e4 100644 --- a/comfy_extras/nodes_triposplat.py +++ b/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=[ diff --git a/comfyui_version.py b/comfyui_version.py index f8db561ba..8e9967f1b 100644 --- a/comfyui_version.py +++ b/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" diff --git a/main.py b/main.py index aa4ee2adb..20ec83c9e 100644 --- a/main.py +++ b/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): diff --git a/nodes.py b/nodes.py index 028e58c77..9043a8d0a 100644 --- a/nodes.py +++ b/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)", } diff --git a/pyproject.toml b/pyproject.toml index 2e8a85d3f..8c17e410e 100644 --- a/pyproject.toml +++ b/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" diff --git a/requirements.txt b/requirements.txt index b09b12f29..1d9fe4137 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -comfyui-frontend-package==1.45.19 -comfyui-workflow-templates==0.10.7 +comfyui-frontend-package==1.45.20 +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