diff --git a/.spectral.yaml b/.spectral.yaml index 4bb4a4a94..a4b137628 100644 --- a/.spectral.yaml +++ b/.spectral.yaml @@ -89,3 +89,12 @@ rules: then: field: description function: truthy + +overrides: + # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a + # WebSocket upgrade, but not a 2xx, so operation-success-response fires + # as a false positive. OpenAPI 3.x has no native WebSocket support. + - files: + - "openapi.yaml#/paths/~1ws" + rules: + operation-success-response: off diff --git a/README.md b/README.md index 0fd317d0a..0eecd8a4b 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more... - ComfyUI natively supports the latest open-source state of the art models. - API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc. -- It is available on Windows, Linux, and macOS, locally with our desktop application or on our cloud. +- It is available on Windows, Linux, and macOS, locally with our [desktop application](https://www.comfy.org/download), our [portable install](#installing) or on our [cloud](https://www.comfy.org/cloud). - The most sophisticated workflows can be exposed through a simple UI thanks to App Mode. - It integrates seamlessly into production pipelines with our API endpoints. @@ -429,6 +429,8 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w See also: [https://www.comfy.org/](https://www.comfy.org/) +> _psst — we're hiring!_ Help build ComfyUI: [comfy.org/careers](https://www.comfy.org/careers) + ## Frontend Development As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 000000000..299b0067b --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,44 @@ +# Security Policy + +## Scope + +ComfyUI is designed to run locally. By default, the server binds to `127.0.0.1`, meaning only the user's own machine can reach it. Our threat model assumes: + +- The user installed ComfyUI through a supported channel: the desktop application, the portable build, or a manual install following the README. +- The user has not installed untrusted custom nodes. Custom nodes are arbitrary Python code and are trusted as much as any other software the user chooses to install. +- Anyone with access to the ComfyUI URL is trusted (a direct consequence of the localhost-only default). +- PyTorch and other dependencies are at the versions we ship or recommend in the README. + +A report is in scope only if it affects a user operating within this threat model. + +## What We Consider a Vulnerability + +We want to hear about issues where a **reasonable user** — someone who does not install random untrusted nodes and who reads UI prompts and warnings before clicking through them — can be harmed by ComfyUI itself. + +The clearest example: a workflow file that such a user might plausibly load and run, using only built-in nodes, that results in **untrusted code execution, arbitrary file read/write outside expected directories, or credential/data exfiltration**. + +When submitting a report, please include a clear description of *why this is a problem for a typical local ComfyUI user*. Reports without this context are difficult to act on. + +## What We Do Not Consider a Security Vulnerability + +Please report the following through our regular [GitHub issues](https://github.com/comfyanonymous/ComfyUI/issues) instead. Filing them as security reports will likely cause them to be deprioritized or closed. + +- **Issues requiring `--listen` or any non-default network exposure.** ComfyUI binds to localhost by default. If a remote attacker needs to reach the server for the attack to work, the user has chosen to expose it and is responsible for securing that deployment (firewall, reverse proxy, authentication, etc.). These are bugs, not vulnerabilities. +- **`torch.load` and related deserialization issues in old PyTorch versions.** These are upstream PyTorch issues. Our distributions ship with — and our documentation recommends — recent PyTorch versions where these are addressed. +- **Vulnerabilities that depend on outdated library versions** that we neither ship nor recommend (e.g., requiring PyTorch 2.6 or older). +- **Issues that require a specific custom node to be installed.** Custom nodes are third-party code. Report these to the maintainer of that node. +- **Crashes, hangs, or resource exhaustion from a loaded workflow.** Annoying, but not a security issue in our model. File a regular bug. +- **Social-engineering scenarios** where the user is expected to ignore an explicit UI warning or prompt. + +## Reporting + +If you believe you have found an issue that falls within the scope above, please report it privately via GitHub's [Report a vulnerability](https://github.com/comfyanonymous/ComfyUI/security/advisories/new) feature rather than opening a public issue. + +Please include: + +1. A description of the vulnerability and the affected component. +2. Reproduction steps, ideally with a minimal workflow file or proof-of-concept. +3. The ComfyUI version, install method (desktop / portable / manual), and OS. +4. An explanation of how this affects a typical local user as described in the threat model. + +We will acknowledge valid reports and coordinate a fix and disclosure timeline with you. diff --git a/app/frontend_management.py b/app/frontend_management.py index 7108bd35a..d0596b276 100644 --- a/app/frontend_management.py +++ b/app/frontend_management.py @@ -38,40 +38,54 @@ def is_valid_version(version: str) -> bool: pattern = r"^(\d+)\.(\d+)\.(\d+)$" return bool(re.match(pattern, version)) -def get_installed_frontend_version(): - """Get the currently installed frontend package version.""" - frontend_version_str = version("comfyui-frontend-package") - return frontend_version_str - - def get_required_frontend_version(): return get_required_packages_versions().get("comfyui-frontend-package", None) -def check_frontend_version(): - """Check if the frontend version is up to date.""" +COMFY_PACKAGE_VERSIONS = [] +def get_comfy_package_versions(): + """List installed/required versions for every comfy* package in requirements.txt.""" + if COMFY_PACKAGE_VERSIONS: + return COMFY_PACKAGE_VERSIONS.copy() + out = COMFY_PACKAGE_VERSIONS + for name, required in (get_required_packages_versions() or {}).items(): + if not name.startswith("comfy"): + continue + try: + installed = version(name) + except Exception: + installed = None + out.append({"name": name, "installed": installed, "required": required}) + return out.copy() - try: - frontend_version_str = get_installed_frontend_version() - frontend_version = parse_version(frontend_version_str) - required_frontend_str = get_required_frontend_version() - required_frontend = parse_version(required_frontend_str) - if frontend_version < required_frontend: + +def check_comfy_packages_versions(): + """Warn for every comfy* package whose installed version is below requirements.txt.""" + from packaging.version import InvalidVersion, parse as parse_pep440 + for pkg in get_comfy_package_versions(): + installed_str = pkg["installed"] + required_str = pkg["required"] + if not installed_str or not required_str: + continue + try: + outdated = parse_pep440(installed_str) < parse_pep440(required_str) + except InvalidVersion as e: + logging.error(f"Failed to check {pkg['name']} version: {e}") + continue + if outdated: app.logger.log_startup_warning( f""" ________________________________________________________________________ WARNING WARNING WARNING WARNING WARNING -Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. +Installed {pkg["name"]} version {installed_str} is lower than the recommended version {required_str}. -{frontend_install_warning_message()} +{get_missing_requirements_message()} ________________________________________________________________________ """.strip() ) else: - logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) - except Exception as e: - logging.error(f"Failed to check frontend version: {e}") + logging.info("{} version: {}".format(pkg["name"], installed_str)) REQUEST_TIMEOUT = 10 # seconds @@ -201,6 +215,11 @@ class FrontendManager: def get_required_templates_version(cls) -> str: return get_required_packages_versions().get("comfyui-workflow-templates", None) + @classmethod + def get_comfy_package_versions(cls): + """List installed/required versions for every comfy* package in requirements.txt.""" + return get_comfy_package_versions() + @classmethod def default_frontend_path(cls) -> str: try: @@ -341,7 +360,7 @@ comfyui-workflow-templates is not installed. main error source might be request timeout or invalid URL. """ if version_string == DEFAULT_VERSION_STRING: - check_frontend_version() + check_comfy_packages_versions() return cls.default_frontend_path() repo_owner, repo_name, version = cls.parse_version_string(version_string) @@ -403,7 +422,7 @@ comfyui-workflow-templates is not installed. except Exception as e: logging.error("Failed to initialize frontend: %s", e) logging.info("Falling back to the default frontend.") - check_frontend_version() + check_comfy_packages_versions() return cls.default_frontend_path() @classmethod def template_asset_handler(cls): diff --git a/blueprints/ControlNet (Z-Image-Turbo).json b/blueprints/ControlNet (Z-Image-Turbo).json new file mode 100644 index 000000000..fbec95a97 --- /dev/null +++ b/blueprints/ControlNet (Z-Image-Turbo).json @@ -0,0 +1,1412 @@ +{ + "revision": 0, + "last_node_id": 85, + "last_link_id": 0, + "nodes": [ + { + "id": 85, + "type": "d2e76ecf-6e84-4b8c-8913-48efc09ec1c4", + "pos": [ + 440, + 1220 + ], + "size": [ + 480, + 0 + ], + "flags": 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Tools", + "description": "Increases video frame rate by synthesizing intermediate frames with a frame interpolation model." + } + ] + }, + "extra": {} +} \ No newline at end of file diff --git a/blueprints/Get Any Video Frame.json b/blueprints/Get Any Video Frame.json new file mode 100644 index 000000000..9ff0f8e6e --- /dev/null +++ b/blueprints/Get Any Video Frame.json @@ -0,0 +1,485 @@ +{ + "revision": 0, + "last_node_id": 98, + "last_link_id": 0, + "nodes": [ + { + "id": 98, + "type": "dca6e78d-fb06-421e-97f7-6ce17a665260", + "pos": [ + -410, + -2230 + ], + "size": [ + 270, + 104 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "video", + "type": "VIDEO", + "link": null + }, + { + "label": "frame_index", + "name": "value", + "type": "INT", + "widget": { + "name": "value" + }, + "link": null + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [] + } + ], + "title": "Get Any Video Frame", + "properties": { + "proxyWidgets": [ + [ + 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b/blueprints/Image Edit (FireRed Image Edit 1.1).json index 14310353c..b82c7d18b 100644 --- a/blueprints/Image Edit (FireRed Image Edit 1.1).json +++ b/blueprints/Image Edit (FireRed Image Edit 1.1).json @@ -1,18 +1,18 @@ { "revision": 0, - "last_node_id": 172, + "last_node_id": 213, "last_link_id": 0, "nodes": [ { - "id": 172, - "type": "edf73971-14ee-4d39-b58e-46ce2a89d3d0", + "id": 213, + "type": "e35fbbeb-d7b1-46d1-a74e-959517d0fb1a", "pos": [ - 30, - 200 + -700, + -470 ], "size": [ 500, - 570 + 0 ], "flags": {}, "order": 2, @@ -105,44 +105,44 @@ "properties": { "proxyWidgets": [ [ - "118", + "208", "prompt" ], [ - "153", + "207", "value" ], [ - "130", + "210", "seed" ], [ - "128", + "205", "unet_name" ], [ - "115", + "203", "clip_name" ], [ - "116", + "202", "vae_name" ], [ - "151", + "204", "lora_name" ], [ - "130", + "210", "control_after_generate" ] ], + "cnr_id": "comfy-core", + "ver": "0.15.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.15.1", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -160,12 +160,12 @@ "definitions": { "subgraphs": [ { - "id": "edf73971-14ee-4d39-b58e-46ce2a89d3d0", + "id": "e35fbbeb-d7b1-46d1-a74e-959517d0fb1a", "version": 1, "state": { "lastGroupId": 8, - "lastNodeId": 174, - "lastLinkId": 376, + "lastNodeId": 213, + "lastLinkId": 378, "lastRerouteId": 0 }, "revision": 0, @@ -183,8 +183,8 @@ "outputNode": { "id": -20, "bounding": [ - 1147.5, - -1215, + 1860, + -1340, 120, 60 ] @@ -327,26 +327,26 @@ ], "localized_name": "IMAGE", "pos": [ - 1167.5, - -1195 + 1880, + -1320 ] } ], "widgets": [], "nodes": [ { - "id": 120, + "id": 193, "type": "ModelSamplingAuraFlow", "pos": [ - 1060, - -1760 + 1010, + -1680 ], "size": [ 290, 110 ], "flags": {}, - "order": 8, + "order": 4, "mode": 0, "inputs": [ { @@ -376,13 +376,13 @@ } ], "properties": { + "Node name for S&R": "ModelSamplingAuraFlow", + "cnr_id": "comfy-core", + "ver": "0.5.1", "ue_properties": { 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"ver": "0.15.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.15.1", - "Node name for S&R": "PrimitiveInt", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -521,18 +521,18 @@ ] }, { - "id": 123, + "id": 196, "type": "CFGNorm", "pos": [ - 1060, - -1590 + 1010, + -1510 ], "size": [ 290, 110 ], "flags": {}, - "order": 9, + "order": 6, "mode": 0, "inputs": [ { @@ -562,13 +562,13 @@ } ], "properties": { + "Node name for S&R": "CFGNorm", + "cnr_id": "comfy-core", + "ver": "0.5.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.5.1", - "Node name for S&R": "CFGNorm", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -582,7 +582,7 @@ ] }, { - "id": 164, + "id": 197, "type": "ComfySwitchNode", "pos": [ 680, @@ -593,7 +593,7 @@ 130 ], "flags": {}, - "order": 18, + "order": 7, "mode": 0, "inputs": [ { @@ -630,13 +630,13 @@ ], "title": "Switch (CFG)", "properties": { + "Node name for S&R": "ComfySwitchNode", + "cnr_id": "comfy-core", + "ver": "0.15.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.15.1", - "Node name for S&R": "ComfySwitchNode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -650,7 +650,7 @@ ] }, { - "id": 156, + "id": 198, "type": "PrimitiveInt", "pos": [ 190, @@ -686,13 +686,13 @@ ], "title": "Float (Steps)", "properties": { + "Node name for S&R": "PrimitiveInt", + "cnr_id": "comfy-core", + "ver": "0.15.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.15.1", - "Node name for S&R": "PrimitiveInt", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -707,7 +707,7 @@ ] }, { - "id": 162, + "id": 199, "type": "PrimitiveFloat", "pos": [ 190, @@ -743,13 +743,13 @@ ], "title": "Float (CFG)", "properties": { + "Node name for S&R": "PrimitiveFloat", 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for S&R": "ComfySwitchNode", + "cnr_id": "comfy-core", + "ver": "0.15.1", "ue_properties": { "widget_ue_connectable": {}, "input_ue_unconnectable": {} }, - "cnr_id": "comfy-core", - "ver": "0.15.1", - "Node name for S&R": "ComfySwitchNode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -887,11 +887,11 @@ ] }, { - "id": 116, + "id": 202, "type": "VAELoader", "pos": [ - -950, - -1040 + -960, + -1100 ], "size": [ 400, @@ -900,7 +900,7 @@ "flags": { "collapsed": false }, - "order": 5, + "order": 9, "mode": 0, "inputs": [ { @@ -928,45 +928,45 @@ } ], "properties": { - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {} - }, + "Node name for S&R": "VAELoader", "cnr_id": "comfy-core", "ver": "0.5.1", - "Node name for S&R": "VAELoader", - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - "secondTabOffset": 80, - "secondTabWidth": 65, "models": [ { "name": "qwen_image_vae.safetensors", "url": "https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0-ComfyUI/resolve/main/qwen_image_vae.safetensors", "directory": "vae" } - ] + ], + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {} + }, + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65 }, "widgets_values": [ "qwen_image_vae.safetensors" ] }, { - "id": 115, + "id": 203, "type": "CLIPLoader", "pos": [ -960, - -1370 + -1400 ], "size": [ 400, 150 ], "flags": {}, - "order": 4, + "order": 10, "mode": 0, "inputs": [ { @@ -1010,27 +1010,27 @@ } ], "properties": { - "ue_properties": { - "widget_ue_connectable": {}, - "input_ue_unconnectable": {} - }, + "Node name for S&R": "CLIPLoader", "cnr_id": "comfy-core", "ver": "0.5.1", - "Node name for S&R": "CLIPLoader", - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - 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image", - "description": "Generates images from text prompts using Flux.1 [dev], Black Forest Labs' 12B diffusion model." + "description": "Generates images from prompts using FLUX.1 [dev]: a 12B rectified-flow MMDiT with dual CLIP plus T5-XXL text encoders and guidance-distilled sampling for sharp prompt following versus classic DDPM diffusion." } ] }, diff --git a/blueprints/Text to Image (Flux.1 Krea Dev).json b/blueprints/Text to Image (Flux.1 Krea Dev).json index 30a78dca1..0d7fa03c4 100644 --- a/blueprints/Text to Image (Flux.1 Krea Dev).json +++ b/blueprints/Text to Image (Flux.1 Krea Dev).json @@ -1024,7 +1024,7 @@ "workflowRendererVersion": "LG" }, "category": "Image generation and editing/Text to image", - "description": "Generates images from text prompts using Flux.1 Krea Dev, a Black Forest Labs × Krea collaboration variant." + "description": "FLUX.1 Krea [dev] (Black Forest Labs × Krea): open-weight 12B rectified-flow text-to-image drop-in alongside FLUX.1 [dev], tuned 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a/blueprints/Text to Image (Z-Image-Turbo).json b/blueprints/Text to Image (Z-Image-Turbo).json index 6975151ea..2501486fa 100644 --- a/blueprints/Text to Image (Z-Image-Turbo).json +++ b/blueprints/Text to Image (Z-Image-Turbo).json @@ -1,22 +1,21 @@ { - "id": "1c3eaa76-5cfa-4dc7-8571-97a570324e01", "revision": 0, - "last_node_id": 34, - "last_link_id": 40, + "last_node_id": 57, + "last_link_id": 0, "nodes": [ { - "id": 5, - "type": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b", + "id": 57, + "type": "f2fdebf6-dfaf-43b6-9eb2-7f70613cfdc1", "pos": [ - -2.5766491043910378e-05, - 1229.999928629805 + 130, + 200 ], "size": [ 400, 470 ], "flags": {}, - "order": 0, + "order": 1, "mode": 0, "inputs": [ { @@ -44,6 +43,22 @@ }, "link": null }, + { + "name": "seed", + "type": "INT", + "widget": { + "name": "seed" + }, + "link": null + }, + { + "name": "steps", + "type": "INT", + "widget": { + "name": "steps" + }, + "link": null + }, { "name": "unet_name", "type": "COMBO", @@ -80,15 +95,15 @@ 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+164,17 @@ "inputNode": { "id": -10, "bounding": [ - -80, - 425, + -560, + 480, 120, - 160 + 200 ] }, "outputNode": { "id": -20, "bounding": [ - 1490, - 415, + 1670, + 320, 120, 60 ] @@ -178,8 +189,8 @@ ], "label": "prompt", "pos": [ - 20, - 445 + -460, + 500 ] }, { @@ -190,8 +201,8 @@ 35 ], "pos": [ - 20, - 465 + -460, + 520 ] }, { @@ -202,44 +213,68 @@ 36 ], "pos": [ - 20, - 485 + -460, + 540 ] }, { - "id": "23087d15-8412-4fbd-b71e-9b6d7ef76de1", + "id": "f77677f7-6bf6-4c19-a71f-c4a553d5981e", + "name": "seed", + "type": "INT", + "linkIds": [ + 71 + ], + "pos": [ + -460, + 560 + ] + }, + { + "id": "ef9a9fb1-5983-4bc9-a60b-cf5aec48bff1", + "name": "steps", + "type": "INT", + "linkIds": [ + 72 + ], + "pos": [ + -460, + 580 + ] + }, + { + "id": "a20a1b30-785f-4a04-bb6d-3d61adab9764", "name": "unet_name", "type": "COMBO", "linkIds": [ - 38 + 73 ], "pos": [ - 20, - 505 + -460, + 600 ] }, { - "id": "0677f5c3-2a3f-43d4-98ac-a4c56d5efdc0", + "id": "4af8fc2b-4655-4086-8240-45f8cb38c6f6", 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"CheckpointLoaderSimple", + "pos": [ + -1970, + -3310 + ], + "size": [ + 330, + 160 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "localized_name": "ckpt_name", + "name": "ckpt_name", + "type": "COMBO", + "widget": { + "name": "ckpt_name" + }, + "link": 273 + } + ], + "outputs": [ + { + "localized_name": "MODEL", + "name": "MODEL", + "type": "MODEL", + "links": [ + 237 + ] + }, + { + "localized_name": "CLIP", + "name": "CLIP", + "type": "CLIP", + "links": [ + 240 + ] + }, + { + "localized_name": "VAE", + "name": "VAE", + "type": "VAE", + "links": null + } + ], + "properties": { + "Node name for S&R": "CheckpointLoaderSimple", + "cnr_id": "comfy-core", + "ver": "0.19.3", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65, + "models": [ + { + "name": "sam3.1_multiplex_fp16.safetensors", + "url": "https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors", + "directory": "checkpoints" + } + ] + }, + "widgets_values": [ + "sam3.1_multiplex_fp16.safetensors" + ] + }, + { + "id": 128, + "type": "GetVideoComponents", + "pos": [ + -1910, + -3540 + ], + "size": [ + 230, + 120 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "localized_name": "video", + "name": "video", + "type": "VIDEO", + "link": 252 + } + ], + "outputs": [ + { + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "links": [ + 253 + ] + }, + { + "localized_name": "audio", + "name": "audio", + "type": "AUDIO", + "links": [ + 259 + ] + }, + { + "localized_name": "fps", + "name": "fps", + "type": "FLOAT", + "links": [ + 260 + ] + } + ], + "properties": { + "Node name for S&R": "GetVideoComponents", + "cnr_id": "comfy-core", + "ver": "0.19.3", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65 + } + }, + { + "id": 129, + "type": "Note", + "pos": [ + -1980, + -2790 + ], + "size": [ + 370, + 250 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Note: Prompt format", + "properties": {}, + "widgets_values": [ + "Max tokens for this model is only 32, to separately prompt multiple subjects you can separate prompts with comma, and set the max amount of objects detected for each prompt with :N\n\nFor example above test prompt finds 2 cakes, one apron, 4 window panels" + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "groups": [], + "links": [ + { + "id": 237, + "origin_id": 127, + "origin_slot": 0, + "target_id": 126, + "target_slot": 0, + "type": "MODEL" + }, + { + "id": 200, + "origin_id": 125, + "origin_slot": 0, + "target_id": 126, + "target_slot": 2, + "type": "CONDITIONING" + }, + { + "id": 240, + "origin_id": 127, + "origin_slot": 1, + "target_id": 125, + "target_slot": 0, + "type": "CLIP" + }, + { + "id": 231, + "origin_id": 126, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "MASK" + }, + { + "id": 232, + "origin_id": 126, + "origin_slot": 1, + "target_id": -20, + "target_slot": 1, + "type": "BOUNDING_BOX" + }, + { + "id": 252, + "origin_id": -10, + "origin_slot": 0, + "target_id": 128, + "target_slot": 0, + "type": "VIDEO" + }, + { + "id": 253, + "origin_id": 128, + "origin_slot": 0, + "target_id": 126, + "target_slot": 1, + "type": "IMAGE" + }, + { + "id": 254, + "origin_id": -10, + "origin_slot": 1, + "target_id": 125, + "target_slot": 1, + "type": "STRING" + }, + { + "id": 255, + "origin_id": -10, + "origin_slot": 2, + "target_id": 126, + "target_slot": 3, + "type": "BOUNDING_BOX" + }, + { + "id": 256, + "origin_id": -10, + "origin_slot": 3, + "target_id": 126, + "target_slot": 4, + "type": "STRING" + }, + { + "id": 257, + "origin_id": -10, + "origin_slot": 4, + "target_id": 126, + "target_slot": 5, + "type": "STRING" + }, + { + "id": 259, + "origin_id": 128, + "origin_slot": 1, + "target_id": -20, + "target_slot": 2, + "type": "AUDIO" + }, + { + "id": 260, + "origin_id": 128, + "origin_slot": 2, + "target_id": -20, + "target_slot": 3, + "type": "FLOAT" + }, + { + "id": 261, + "origin_id": -10, + "origin_slot": 5, + "target_id": 126, + "target_slot": 6, + "type": "FLOAT" + }, + { + "id": 262, + "origin_id": -10, + "origin_slot": 6, + "target_id": 126, + "target_slot": 7, + "type": "INT" + }, + { + "id": 263, + "origin_id": -10, + "origin_slot": 7, + "target_id": 126, + "target_slot": 8, + "type": "BOOLEAN" + }, + { + "id": 273, + "origin_id": -10, + "origin_slot": 8, + "target_id": 127, + "target_slot": 0, + "type": "COMBO" + } + ], + "extra": {}, + "category": "Video Tools", + "description": "Segments video into temporally consistent masks using Meta SAM3 from text or interactive prompts." + } + ] + }, + "extra": {} +} diff --git a/blueprints/Video Stitch.json b/blueprints/Video Stitch.json index 6eb0f0bbf..2ac78b328 100644 --- a/blueprints/Video Stitch.json +++ b/blueprints/Video Stitch.json @@ -1,21 +1,21 @@ { "revision": 0, - "last_node_id": 84, + "last_node_id": 85, "last_link_id": 0, "nodes": [ { - "id": 84, - "type": "8e8aa94a-647e-436d-8440-8ee4691864de", + "id": 85, + "type": "637913e7-0206-46ba-8ded-70ae3a7c2e19", "pos": [ - -6100, - 2620 + -880, + -2260 ], "size": [ 290, 160 ], "flags": {}, - "order": 0, + "order": 2, "mode": 0, "inputs": [ { @@ -76,31 +76,26 @@ "properties": { "proxyWidgets": [ [ - "-1", + "79", "direction" ], [ - "-1", + "79", "match_image_size" ], [ - "-1", + "79", "spacing_width" ], [ - "-1", + "79", "spacing_color" ] ], "cnr_id": "comfy-core", "ver": "0.13.0" }, - "widgets_values": [ - "right", - true, - 0, - "white" - ], + "widgets_values": [], "title": "Video Stitch" } ], @@ -109,12 +104,12 @@ "definitions": { "subgraphs": [ { - "id": "8e8aa94a-647e-436d-8440-8ee4691864de", + "id": "637913e7-0206-46ba-8ded-70ae3a7c2e19", "version": 1, "state": { "lastGroupId": 1, - "lastNodeId": 84, - "lastLinkId": 262, + "lastNodeId": 97, + "lastLinkId": 282, "lastRerouteId": 0 }, "revision": 0, @@ -123,8 +118,8 @@ "inputNode": { "id": -10, "bounding": [ - -6580, - 2649, + -6810, + 2580, 143.55859375, 160 ] @@ -132,8 +127,8 @@ "outputNode": { "id": -20, "bounding": [ - -5720, - 2659, + -4770, + 2600, 120, 60 ] @@ -149,8 +144,8 @@ "localized_name": "video", "label": "Before Video", "pos": [ - -6456.44140625, - 2669 + -6686.44140625, + 2600 ] }, { @@ -163,8 +158,8 @@ "localized_name": "video_1", "label": "After Video", "pos": [ - -6456.44140625, - 2689 + -6686.44140625, + 2620 ] }, { @@ -175,8 +170,8 @@ 259 ], "pos": [ - -6456.44140625, - 2709 + -6686.44140625, + 2640 ] }, { @@ -187,8 +182,8 @@ 260 ], "pos": [ - -6456.44140625, - 2729 + -6686.44140625, + 2660 ] }, { @@ -199,8 +194,8 @@ 261 ], "pos": [ - -6456.44140625, - 2749 + -6686.44140625, + 2680 ] }, { @@ -211,8 +206,8 @@ 262 ], "pos": [ - -6456.44140625, - 2769 + -6686.44140625, + 2700 ] } ], @@ -226,8 +221,8 @@ ], "localized_name": "VIDEO", "pos": [ - -5700, - 2679 + -4750, + 2620 ] } ], @@ -238,11 +233,11 @@ "type": "GetVideoComponents", "pos": [ -6390, - 2560 + 2600 ], "size": [ - 193.530859375, - 66 + 230, + 120 ], "flags": {}, "order": 1, @@ -278,9 +273,9 @@ } ], "properties": { + "Node name for S&R": "GetVideoComponents", "cnr_id": "comfy-core", - "ver": "0.13.0", - "Node name for S&R": "GetVideoComponents" + "ver": "0.13.0" } }, { @@ -291,8 +286,8 @@ 2420 ], "size": [ - 193.530859375, - 66 + 230, + 120 ], "flags": {}, "order": 0, @@ -332,21 +327,254 @@ } ], "properties": { + "Node name for S&R": "GetVideoComponents", "cnr_id": "comfy-core", - "ver": "0.13.0", - "Node name for S&R": "GetVideoComponents" + "ver": "0.13.0" } }, + { + "id": 90, + "type": "GetImageSize", + "pos": [ + -6390, + 3030 + ], + "size": [ + 230, + 120 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "localized_name": "image", + "name": "image", + "type": "IMAGE", + "link": 266 + } + ], + "outputs": [ + { + "localized_name": "width", + "name": "width", + "type": "INT", + "links": [ + 274 + ] + }, + { + "localized_name": "height", + "name": "height", + "type": "INT", + "links": [ + 276 + ] + }, + { + "localized_name": "batch_size", + "name": "batch_size", + "type": "INT", + "links": null + } + ], + "properties": { + "Node name for S&R": "GetImageSize" + } + }, + { + "id": 80, + "type": "CreateVideo", + "pos": [ + -5190, + 2420 + ], + "size": [ + 270, + 130 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "link": 282 + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": 251 + }, + { + "localized_name": "fps", + "name": "fps", + "type": "FLOAT", + "widget": { + "name": "fps" + }, + "link": 252 + } + ], + "outputs": [ + { + "localized_name": "VIDEO", + "name": "VIDEO", + "type": "VIDEO", + "links": [ + 255 + ] + } + ], + "properties": { + "Node name for S&R": "CreateVideo", + "cnr_id": "comfy-core", + "ver": "0.13.0" + }, + "widgets_values": [ + 30 + ] + }, + { + "id": 95, + "type": "ComfyMathExpression", + "pos": [ + -6040, + 3020 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "label": "a", + "localized_name": "values.a", + "name": "values.a", + "type": "FLOAT,INT", + "link": 274 + }, + { + "label": "b", + "localized_name": "values.b", + "name": "values.b", + "shape": 7, + "type": "FLOAT,INT", + "link": null + }, + { + "localized_name": "expression", + "name": "expression", + "type": "STRING", + "widget": { + "name": "expression" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "FLOAT", + "name": "FLOAT", + "type": "FLOAT", + "links": null + }, + { + "localized_name": "INT", + "name": "INT", + "type": "INT", + "links": [ + 279 + ] + } + ], + "properties": { + "Node name for S&R": "ComfyMathExpression" + }, + "widgets_values": [ + "a & ~1" + ] + }, + { + "id": 96, + "type": "ComfyMathExpression", + "pos": [ + -6040, + 3290 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [ + { + "label": "a", + "localized_name": "values.a", + "name": "values.a", + "type": "FLOAT,INT", + "link": 276 + }, + { + "label": "b", + "localized_name": "values.b", + "name": "values.b", + "shape": 7, + "type": "FLOAT,INT", + "link": null + }, + { + "localized_name": "expression", + "name": "expression", + "type": "STRING", + "widget": { + "name": "expression" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "FLOAT", + "name": "FLOAT", + "type": "FLOAT", + "links": null + }, + { + "localized_name": "INT", + "name": "INT", + "type": "INT", + "links": [ + 280 + ] + } + ], + "properties": { + "Node name for S&R": "ComfyMathExpression" + }, + "widgets_values": [ + "a & ~1" + ] + }, { "id": 79, "type": "ImageStitch", "pos": [ -6390, - 2700 + 2780 ], "size": [ 270, - 150 + 160 ], "flags": {}, "order": 2, @@ -408,14 +636,15 @@ "name": "IMAGE", "type": "IMAGE", "links": [ - 250 + 266, + 281 ] } ], "properties": { + "Node name for S&R": "ImageStitch", "cnr_id": "comfy-core", - "ver": "0.13.0", - "Node name for S&R": "ImageStitch" + "ver": "0.13.0" }, "widgets_values": [ "right", @@ -425,60 +654,91 @@ ] }, { - "id": 80, - "type": "CreateVideo", + "id": 97, + "type": "ResizeImageMaskNode", "pos": [ - -6040, - 2610 + -5560, + 2790 ], "size": [ 270, - 78 + 160 ], "flags": {}, - "order": 3, + "order": 7, "mode": 0, "inputs": [ { - "localized_name": "images", - "name": "images", - "type": "IMAGE", - "link": 250 + "localized_name": "input", + "name": "input", + "type": "IMAGE,MASK", + "link": 281 }, { - "localized_name": "audio", - "name": "audio", - "shape": 7, - "type": "AUDIO", - "link": 251 - }, - { - "localized_name": "fps", - "name": "fps", - "type": "FLOAT", + "localized_name": "resize_type", + "name": "resize_type", + "type": "COMFY_DYNAMICCOMBO_V3", "widget": { - "name": "fps" + "name": "resize_type" }, - "link": 252 + "link": null + }, + { + "localized_name": "width", + "name": "resize_type.width", + "type": "INT", + "widget": { + "name": "resize_type.width" + }, + "link": 279 + }, + { + "localized_name": "height", + "name": "resize_type.height", + "type": "INT", + "widget": { + "name": "resize_type.height" + }, + "link": 280 + }, + { + "localized_name": "crop", + "name": "resize_type.crop", + "type": "COMBO", + "widget": { + "name": "resize_type.crop" + }, + "link": null + }, + { + "localized_name": "scale_method", + "name": "scale_method", + "type": "COMBO", + "widget": { + "name": "scale_method" + }, + "link": null } ], "outputs": [ { - "localized_name": "VIDEO", - "name": "VIDEO", - "type": "VIDEO", + "localized_name": "resized", + "name": "resized", + "type": "*", "links": [ - 255 + 282 ] } ], "properties": { - "cnr_id": "comfy-core", - "ver": "0.13.0", - "Node name for S&R": "CreateVideo" + "Node name for S&R": "ResizeImageMaskNode" }, "widgets_values": [ - 30 + "scale dimensions", + 512, + 512, + "center", + "area" ] } ], @@ -500,14 +760,6 @@ "target_slot": 1, "type": "IMAGE" }, - { - "id": 250, - "origin_id": 79, - "origin_slot": 0, - "target_id": 80, - "target_slot": 0, - "type": "IMAGE" - }, { "id": 251, "origin_id": 77, @@ -579,6 +831,62 @@ "target_id": 79, "target_slot": 5, "type": "COMBO" + }, + { + "id": 266, + "origin_id": 79, + "origin_slot": 0, + "target_id": 90, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 274, + "origin_id": 90, + "origin_slot": 0, + "target_id": 95, + "target_slot": 0, + "type": "INT" + }, + { + "id": 276, + "origin_id": 90, + "origin_slot": 1, + "target_id": 96, + "target_slot": 0, + "type": "INT" + }, + { + "id": 279, + "origin_id": 95, + "origin_slot": 1, + "target_id": 97, + "target_slot": 2, + "type": "INT" + }, + { + "id": 280, + "origin_id": 96, + "origin_slot": 1, + "target_id": 97, + "target_slot": 3, + "type": "INT" + }, + { + "id": 281, + "origin_id": 79, + "origin_slot": 0, + "target_id": 97, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 282, + "origin_id": 97, + "origin_slot": 0, + "target_id": 80, + "target_slot": 0, + "type": "IMAGE" } ], "extra": { @@ -588,5 +896,6 @@ "description": "Stitches multiple video clips into a single sequential video file." } ] - } + }, + "extra": {} } \ No newline at end of file diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 9dadb0093..76faed3ad 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -141,8 +141,7 @@ manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", he vram_group = parser.add_mutually_exclusive_group() vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") -vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") -vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") +vram_group.add_argument("--lowvram", action="store_true", help="Doesn't do anything if dynamic vram is enabled. If dynamic vram isn't being used this option makes the text encoders run on the CPU.") vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py index 9b6dace9d..ee86f8309 100644 --- a/comfy/image_encoders/dino2.py +++ b/comfy/image_encoders/dino2.py @@ -106,6 +106,7 @@ class Dino2Encoder(torch.nn.Module): class Dino2PatchEmbeddings(torch.nn.Module): def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None): super().__init__() + self.patch_size = patch_size self.projection = operations.Conv2d( in_channels=num_channels, out_channels=dim, @@ -125,17 +126,37 @@ class Dino2Embeddings(torch.nn.Module): super().__init__() patch_size = 14 image_size = 518 + self.patch_size = patch_size self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) - self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) + self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key. self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) + def interpolate_pos_encoding(self, x, h_pixels, w_pixels): + pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32) + + class_pos = pos_embed[:, 0:1] + patch_pos = pos_embed[:, 1:] + N = patch_pos.shape[1] + M = int(N ** 0.5) + h0 = h_pixels // self.patch_size + w0 = w_pixels // self.patch_size + scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M) # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0). + + patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2) + patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False) + patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2) + return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype) + def forward(self, pixel_values): x = self.patch_embeddings(pixel_values) - # TODO: mask_token? x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) - x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) + if x.shape[1] - 1 == self.position_embeddings.shape[1] - 1: + x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) + else: + h, w = pixel_values.shape[-2:] + x = x + self.interpolate_pos_encoding(x, h, w) return x @@ -158,3 +179,21 @@ class Dinov2Model(torch.nn.Module): x = self.layernorm(x) pooled_output = x[:, 0, :] return x, i, pooled_output, None + + def get_intermediate_layers(self, pixel_values, indices, apply_norm=True): + x = self.embeddings(pixel_values) + optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) + n_layers = len(self.encoder.layer) + resolved = [(i if i >= 0 else n_layers + i) for i in indices] + target = set(resolved) + max_idx = max(resolved) + n_skip = 1 # skip cls token + cache = {} + for i, layer in enumerate(self.encoder.layer): + x = layer(x, optimized_attention) + if i in target: + normed = self.layernorm(x) if apply_norm else x + cache[i] = (normed[:, n_skip:], normed[:, 0]) + if i >= max_idx: + break + return [cache[i] for i in resolved] diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index c53ac4b2b..11db46d94 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -242,6 +242,7 @@ def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) @@ -373,6 +374,7 @@ def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) @@ -686,6 +688,7 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 lambda_fn = lambda sigma: ((1-sigma)/sigma).log() @@ -747,6 +750,7 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) @@ -832,6 +836,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) old_denoised = None h, h_last = None, None @@ -889,6 +894,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) denoised_1, denoised_2 = None, None h, h_1, h_2 = None, None, None @@ -1006,23 +1012,39 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) @torch.no_grad() -def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): +def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, s_noise=1.0, s_noise_end=None, noise_clip_std=0.0): + + # s_noise / s_noise_end: per-step noise multiplier, linearly interpolated across steps + # noise_clip_std: clamp injected noise to +/- N stddevs (0 disables). + extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): + n_steps = max(1, len(sigmas) - 1) + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + + s_start = float(s_noise) + s_end = s_start if s_noise_end is None else float(s_noise_end) + for i in trange(n_steps, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = denoised if sigmas[i + 1] > 0: - x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) + noise = noise_sampler(sigmas[i], sigmas[i + 1]) + if noise_clip_std > 0: + clip_val = noise_clip_std * noise.std() + noise = noise.clamp(min=-clip_val, max=clip_val) + t = (i / (n_steps - 1)) if n_steps > 1 else 0.0 + s_noise_i = s_start + (s_end - s_start) * t + if s_noise_i != 1.0: + noise = noise * s_noise_i + x = model_sampling.noise_scaling(sigmas[i + 1], noise, x) return x - @torch.no_grad() def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ @@ -1249,6 +1271,7 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) uncond_denoised = None @@ -1296,6 +1319,7 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) temp = [0] def post_cfg_function(args): @@ -1371,6 +1395,7 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() @@ -1504,6 +1529,7 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_noise = s_noise * getattr(model.inner_model.model_patcher.get_model_object('model_sampling'), "noise_scale", 1.0) s_in = x.new_ones([x.shape[0]]) def default_er_sde_noise_scaler(x): @@ -1574,9 +1600,10 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) - inject_noise = eta > 0 and s_noise > 0 model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) + inject_noise = eta > 0 and s_noise > 0 sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) @@ -1645,9 +1672,10 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) - inject_noise = eta > 0 and s_noise > 0 model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) + inject_noise = eta > 0 and s_noise > 0 sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) @@ -1713,6 +1741,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") + s_noise = s_noise * getattr(model_sampling, "noise_scale", 1.0) sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 91bebed3d..d527eec4a 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -792,6 +792,13 @@ class ZImagePixelSpace(ChromaRadiance): """ pass + +class HiDreamO1Pixel(ChromaRadiance): + """Pixel-space latent format for HiDream-O1. + No VAE — model patches/unpatches raw RGB internally with patch_size=32. + """ + pass + class CogVideoX(LatentFormat): """Latent format for CogVideoX-2b (THUDM/CogVideoX-2b). diff --git a/comfy/ldm/hidream_o1/attention.py b/comfy/ldm/hidream_o1/attention.py new file mode 100644 index 000000000..1b68f1771 --- /dev/null +++ b/comfy/ldm/hidream_o1/attention.py @@ -0,0 +1,41 @@ +"""HiDream-O1 two-pass attention: tokens [0, ar_len) are causal, [ar_len, T) +attend full K/V. Splitting Q at the boundary avoids the (B, 1, T, T) additive +mask the general-purpose path would build (~500 MB at T~16K) and lets the +gen half hit the user's preferred backend via optimized_attention. +""" + +import torch + +import comfy.ops +from comfy.ldm.modules.attention import optimized_attention + + +def make_two_pass_attention(ar_len: int, transformer_options=None): + """Build a two-pass attention callable. AR pass uses SDPA-causal directly, gen pass routes through optimized_attention. + The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes. + """ + + def two_pass_attention(q, k, v, heads, **kwargs): + B, H, T, D = q.shape + + if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call + out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options) + elif ar_len >= T: + out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True) + elif ar_len <= 0: + out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options) + else: + out_ar = comfy.ops.scaled_dot_product_attention( + q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len], + attn_mask=None, dropout_p=0.0, is_causal=True, + ) + out_gen = optimized_attention( + q[:, :, ar_len:], k, v, heads, + mask=None, skip_reshape=True, skip_output_reshape=True, + transformer_options=transformer_options, + ) + out = torch.cat([out_ar, out_gen], dim=2) + + return out.transpose(1, 2).reshape(B, T, H * D) + + return two_pass_attention diff --git a/comfy/ldm/hidream_o1/conditioning.py b/comfy/ldm/hidream_o1/conditioning.py new file mode 100644 index 000000000..7496f0035 --- /dev/null +++ b/comfy/ldm/hidream_o1/conditioning.py @@ -0,0 +1,230 @@ +"""HiDream-O1 conditioning prep — ref-image dual path + extra_conds assembly. + +Each ref image goes through two paths: a 32x32 patchified stream concatenated +to the noised target, and a Qwen3-VL ViT path producing tokens that scatter +into input_ids at <|image_pad|> positions. +""" + +from typing import List + +import torch + +import comfy.utils +from comfy.text_encoders.qwen_vl import process_qwen2vl_images + +from .utils import (PATCH_SIZE, calculate_dimensions, cond_image_size, ref_max_size, resize_tensor) + +# Qwen3-VL ViT preprocessing constants (preprocessor_config.json). +VIT_PATCH = 16 +VIT_MERGE = 2 +VIT_IMAGE_MEAN = [0.5, 0.5, 0.5] +VIT_IMAGE_STD = [0.5, 0.5, 0.5] + + +def prepare_ref_images( + ref_images: List[torch.Tensor], + target_h: int, + target_w: int, + device: torch.device, + dtype: torch.dtype, +): + """Build the dual-path tensors for K reference images at (target_h, target_w). + + Returns None for K=0, else a dict with ref_patches, ref_pixel_values, + ref_image_grid_thw, per_ref_vit_tokens, per_ref_patch_grids. + """ + K = len(ref_images) + if K == 0: + return None + max_size = ref_max_size(max(target_h, target_w), K) + cis = cond_image_size(K) + + refs_t = [img[0].clamp(0, 1).permute(2, 0, 1).unsqueeze(0).contiguous().float() for img in ref_images] + refs_t = [resize_tensor(t, max_size, PATCH_SIZE) for t in refs_t] + + # 32-patch path. + ref_patches_per = [] + per_ref_patch_grids = [] + for t in refs_t: + t_norm = (t.squeeze(0) - 0.5) / 0.5 # (3, H, W) in [-1, 1] + h_p, w_p = t_norm.shape[-2] // PATCH_SIZE, t_norm.shape[-1] // PATCH_SIZE + per_ref_patch_grids.append((h_p, w_p)) + patches = ( + t_norm.reshape(3, h_p, PATCH_SIZE, w_p, PATCH_SIZE) + .permute(1, 3, 0, 2, 4) + .reshape(h_p * w_p, 3 * PATCH_SIZE * PATCH_SIZE) + ) + ref_patches_per.append(patches) + ref_patches = torch.cat(ref_patches_per, dim=0).unsqueeze(0).to(device=device, dtype=dtype) + + # ViT path. + refs_vlm_t = [] + for t in refs_t: + _, _, h, w = t.shape + cond_w, cond_h = calculate_dimensions(cis, w / h) + cond_w = max(cond_w, VIT_PATCH * VIT_MERGE) + cond_h = max(cond_h, VIT_PATCH * VIT_MERGE) + refs_vlm_t.append(comfy.utils.common_upscale(t, cond_w, cond_h, "lanczos", "disabled")) + + pv_list, grid_list, per_ref_vit_tokens = [], [], [] + for t_v in refs_vlm_t: + pv, grid_thw = process_qwen2vl_images( + t_v.permute(0, 2, 3, 1), + min_pixels=0, max_pixels=10**12, + patch_size=VIT_PATCH, merge_size=VIT_MERGE, + image_mean=VIT_IMAGE_MEAN, image_std=VIT_IMAGE_STD, + ) + grid_thw = grid_thw[0] + pv_list.append(pv.to(device=device, dtype=dtype)) + grid_list.append(grid_thw.to(device=device)) + # Post-merge token count = number of <|image_pad|> tokens this image expands to in input_ids. + gh, gw = int(grid_thw[1].item()), int(grid_thw[2].item()) + per_ref_vit_tokens.append((gh // VIT_MERGE) * (gw // VIT_MERGE)) + + return { + "ref_patches": ref_patches, + "ref_pixel_values": torch.cat(pv_list, dim=0), + "ref_image_grid_thw": torch.stack(grid_list, dim=0), + "per_ref_vit_tokens": per_ref_vit_tokens, + "per_ref_patch_grids": per_ref_patch_grids, + } + + +def build_ref_input_ids( + text_input_ids: torch.Tensor, + per_ref_vit_tokens: List[int], + image_token_id: int, + vision_start_id: int, + vision_end_id: int, +): + """Splice [vision_start, image_pad*N, vision_end] blocks into input_ids + after the [im_start, user, \\n] prefix (matches original chat template). + """ + ids = text_input_ids[0].tolist() + inserted = [] + for n_pad in per_ref_vit_tokens: + inserted.extend([vision_start_id] + [image_token_id] * n_pad + [vision_end_id]) + new_ids = ids[:3] + inserted + ids[3:] # 3 = len([im_start, user, \n]) + return torch.tensor([new_ids], dtype=text_input_ids.dtype, device=text_input_ids.device) + + +def build_extra_conds( + text_input_ids: torch.Tensor, + noise: torch.Tensor, + ref_images: List[torch.Tensor] = None, + target_patch_size: int = 32, +): + """Assemble all conditioning tensors for HiDreamO1Transformer.forward: + input_ids (with ref-vision tokens spliced in for the edit/IP path), + position_ids (MRoPE), token_types, vinput_mask, plus the ref + dual-path tensors when refs are provided. + """ + from .utils import get_rope_index_fix_point + from comfy.text_encoders.hidream_o1 import ( + IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID, + ) + + if text_input_ids.dim() == 1: + text_input_ids = text_input_ids.unsqueeze(0) + text_input_ids = text_input_ids.long().to(noise.device) + B = noise.shape[0] + if text_input_ids.shape[0] == 1 and B > 1: + text_input_ids = text_input_ids.expand(B, -1) + + H, W = noise.shape[-2], noise.shape[-1] + h_p, w_p = H // target_patch_size, W // target_patch_size + image_len = h_p * w_p + image_grid_thw_tgt = torch.tensor( + [[1, h_p, w_p]], dtype=torch.long, device=text_input_ids.device, + ) + + out = {} + if ref_images: + ref = prepare_ref_images(ref_images, H, W, device=noise.device, dtype=noise.dtype) + text_input_ids = build_ref_input_ids( + text_input_ids, ref["per_ref_vit_tokens"], + IMAGE_TOKEN_ID, VISION_START_ID, VISION_END_ID, + ) + new_txt_len = text_input_ids.shape[1] + + # Each ref's patchified stream gets a [vision_start, image_pad*N-1] + # block in the position-id stream after the noised target. + ref_grid_lengths = [hp * wp for (hp, wp) in ref["per_ref_patch_grids"]] + tgt_vision = torch.full((1, image_len), IMAGE_TOKEN_ID, + dtype=text_input_ids.dtype, device=text_input_ids.device) + tgt_vision[:, 0] = VISION_START_ID + ref_vision_blocks = [] + for rl in ref_grid_lengths: + blk = torch.full((1, rl), IMAGE_TOKEN_ID, + dtype=text_input_ids.dtype, device=text_input_ids.device) + blk[:, 0] = VISION_START_ID + ref_vision_blocks.append(blk) + ref_vision_cat = torch.cat([tgt_vision] + ref_vision_blocks, dim=1) + input_ids_pad = torch.cat([text_input_ids, ref_vision_cat], dim=-1) + total_ref_patches_len = sum(ref_grid_lengths) + total_len = new_txt_len + image_len + total_ref_patches_len + + # K (ViT, post-merge) + 1 (target) + K (ref-patches) image grids. + K = len(ref_images) + igthw_cond = ref["ref_image_grid_thw"].clone() + igthw_cond[:, 1] //= 2 + igthw_cond[:, 2] //= 2 + image_grid_thw_ref = torch.tensor( + [[1, hp, wp] for (hp, wp) in ref["per_ref_patch_grids"]], + dtype=torch.long, device=text_input_ids.device, + ) + igthw_all = torch.cat([ + igthw_cond.to(text_input_ids.device), + image_grid_thw_tgt, + image_grid_thw_ref, + ], dim=0) + position_ids, _ = get_rope_index_fix_point( + spatial_merge_size=1, + image_token_id=IMAGE_TOKEN_ID, + vision_start_token_id=VISION_START_ID, + input_ids=input_ids_pad, image_grid_thw=igthw_all, + attention_mask=None, + skip_vision_start_token=[0] * K + [1] + [1] * K, + fix_point=4096, + ) + + # tms + target_image + ref_patches are all gen. + tms_pos = new_txt_len - 1 + ar_len = tms_pos + token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device) + token_types[:, tms_pos:] = 1 + vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device) + vinput_mask[:, new_txt_len:] = True + + # Leading batch dim sidesteps CONDRegular.process_cond's repeat_to_batch_size truncation + out["ref_pixel_values"] = ref["ref_pixel_values"].unsqueeze(0) + out["ref_image_grid_thw"] = ref["ref_image_grid_thw"].unsqueeze(0) + out["ref_patches"] = ref["ref_patches"] + else: + # T2I: text + noised target only, vision_start replaces the first image token + txt_len = text_input_ids.shape[1] + total_len = txt_len + image_len + vision_tokens = torch.full((B, image_len), IMAGE_TOKEN_ID, + dtype=text_input_ids.dtype, device=text_input_ids.device) + vision_tokens[:, 0] = VISION_START_ID + input_ids_pad = torch.cat([text_input_ids, vision_tokens], dim=-1) + position_ids, _ = get_rope_index_fix_point( + spatial_merge_size=1, + image_token_id=IMAGE_TOKEN_ID, + vision_start_token_id=VISION_START_ID, + input_ids=input_ids_pad, image_grid_thw=image_grid_thw_tgt, + attention_mask=None, + skip_vision_start_token=[1], + ) + ar_len = txt_len - 1 + token_types = torch.zeros(B, total_len, dtype=torch.long, device=noise.device) + token_types[:, ar_len:] = 1 + vinput_mask = torch.zeros(B, total_len, dtype=torch.bool, device=noise.device) + vinput_mask[:, txt_len:] = True + + out["input_ids"] = text_input_ids + out["position_ids"] = position_ids[:, 0].unsqueeze(0) # Collapse position_ids batch and add a leading dim so CONDRegular's batch-resize doesn't truncate the 3-axis MRoPE dim + out["token_types"] = token_types + out["vinput_mask"] = vinput_mask + out["ar_len"] = ar_len + return out diff --git a/comfy/ldm/hidream_o1/model.py b/comfy/ldm/hidream_o1/model.py new file mode 100644 index 000000000..a223e706f --- /dev/null +++ b/comfy/ldm/hidream_o1/model.py @@ -0,0 +1,306 @@ +"""HiDream-O1-Image transformer. + +Pixel-space DiT built on Qwen3-VL: the vision tower (Qwen35VisionModel) +encodes ref images, the Qwen3-VL-8B decoder (Llama2_ with interleaved MRoPE) +processes a unified text+image sequence, and 32x32 patch embed/unembed +shims map raw RGB in and out of LLM hidden space. The Qwen3-VL deepstack +mergers go unused — their weights are dropped at load. +""" + +from dataclasses import dataclass, field +from typing import List, Optional + +import einops +import torch +import torch.nn as nn + +import comfy.patcher_extension +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder +from comfy.text_encoders.llama import Llama2_ +from comfy.text_encoders.qwen35 import Qwen35VisionModel + +from .attention import make_two_pass_attention + + +IMAGE_TOKEN_ID = 151655 # Qwen3-VL <|image_pad|> +TMS_TOKEN_ID = 151673 # HiDream-O1 <|tms_token|> +PATCH_SIZE = 32 + + +@dataclass +class HiDreamO1TextConfig: + """Qwen3-VL-8B text-decoder dims (matches public Qwen3-VL-8B-Instruct).""" + vocab_size: int = 151936 + hidden_size: int = 4096 + intermediate_size: int = 12288 + num_hidden_layers: int = 36 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + head_dim: int = 128 + max_position_embeddings: int = 128000 + rms_norm_eps: float = 1e-6 + rope_theta: float = 5000000.0 + rope_scale: Optional[float] = None + rope_dims: List[int] = field(default_factory=lambda: [24, 20, 20]) + interleaved_mrope: bool = True + transformer_type: str = "llama" + rms_norm_add: bool = False + mlp_activation: str = "silu" + qkv_bias: bool = False + q_norm: str = "gemma3" + k_norm: str = "gemma3" + final_norm: bool = True + lm_head: bool = False + stop_tokens: List[int] = field(default_factory=lambda: [151643, 151645]) + + +QWEN3VL_VISION_DEFAULTS = dict( + hidden_size=1152, + num_heads=16, + intermediate_size=4304, + depth=27, + patch_size=16, + temporal_patch_size=2, + in_channels=3, + spatial_merge_size=2, + num_position_embeddings=2304, + deepstack_visual_indexes=(8, 16, 24), + out_hidden_size=4096, # final merger projects directly into LLM hidden +) + + +class BottleneckPatchEmbed(nn.Module): + # 3072 -> 1024 -> 4096 (raw 32x32 RGB patch -> bottleneck -> LLM hidden). + def __init__(self, patch_size=32, in_chans=3, pca_dim=1024, embed_dim=4096, bias=True, device=None, dtype=None, ops=None): + super().__init__() + self.proj1 = ops.Linear(patch_size * patch_size * in_chans, pca_dim, bias=False, device=device, dtype=dtype) + self.proj2 = ops.Linear(pca_dim, embed_dim, bias=bias, device=device, dtype=dtype) + + def forward(self, x): + return self.proj2(self.proj1(x)) + + +class FinalLayer(nn.Module): + # 4096 -> 3072 (LLM hidden -> flat pixel patch). + def __init__(self, hidden_size, patch_size=32, out_channels=3, device=None, dtype=None, ops=None): + super().__init__() + self.linear = ops.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, device=device, dtype=dtype) + + def forward(self, x): + return self.linear(x) + + +class HiDreamO1Transformer(nn.Module): + """HiDream-O1 unified pixel-level transformer.""" + + def __init__(self, image_model=None, dtype=None, device=None, operations=None, + text_config_overrides=None, vision_config_overrides=None, **kwargs): + super().__init__() + self.dtype = dtype + + text_cfg = HiDreamO1TextConfig(**(text_config_overrides or {})) + vision_cfg = dict(QWEN3VL_VISION_DEFAULTS) + if vision_config_overrides: + vision_cfg.update(vision_config_overrides) + vision_cfg["out_hidden_size"] = text_cfg.hidden_size + + self.text_config = text_cfg + self.vision_config = vision_cfg + self.hidden_size = text_cfg.hidden_size + self.patch_size = PATCH_SIZE + self.in_channels = 3 + self.tms_token_id = TMS_TOKEN_ID + + self.visual = Qwen35VisionModel(vision_cfg, device=device, dtype=dtype, ops=operations) + self.language_model = Llama2_(text_cfg, device=device, dtype=dtype, ops=operations) + self.t_embedder1 = TimestepEmbedder( + text_cfg.hidden_size, device=device, dtype=dtype, operations=operations, + ) + self.x_embedder = BottleneckPatchEmbed( + patch_size=self.patch_size, in_chans=self.in_channels, + pca_dim=text_cfg.hidden_size // 4, embed_dim=text_cfg.hidden_size, + bias=True, device=device, dtype=dtype, ops=operations, + ) + self.final_layer2 = FinalLayer( + text_cfg.hidden_size, patch_size=self.patch_size, + out_channels=self.in_channels, device=device, dtype=dtype, ops=operations, + ) + + self._visual_cache = None + self._kv_cache_entries = [] + + def clear_kv_cache(self): + self._kv_cache_entries = [] + self._visual_cache = None + + def forward(self, x, timesteps, context=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timesteps, context, transformer_options, **kwargs) + + def _forward(self, x, timesteps, context=None, transformer_options={}, input_ids=None, attention_mask=None, position_ids=None, + vinput_mask=None, ar_len=None, ref_pixel_values=None, ref_image_grid_thw=None, ref_patches=None, **kwargs): + """Returns flow-match velocity (x - x_pred) / sigma""" + + if input_ids is None or position_ids is None: + raise ValueError("HiDreamO1Transformer requires input_ids and position_ids in conditioning") + + B, _, H, W = x.shape + h_p, w_p = H // self.patch_size, W // self.patch_size + tgt_image_len = h_p * w_p + + z = einops.rearrange( + x, 'B C (H p1) (W p2) -> B (H W) (C p1 p2)', + p1=self.patch_size, p2=self.patch_size, + ) + vinputs = torch.cat([z, ref_patches.to(z.dtype)], dim=1) if ref_patches is not None else z + + inputs_embeds = self.language_model.embed_tokens(input_ids).to(x.dtype) + + if ref_pixel_values is not None and ref_image_grid_thw is not None: + # ViT output is constant across sampling steps within a generation + # identity-key by the input tensor so refs don't recompute every step. + cached = self._visual_cache + if cached is not None and cached[0] is ref_pixel_values: + image_embeds = cached[1] + else: + ref_pv = ref_pixel_values.to(inputs_embeds.device) + ref_grid = ref_image_grid_thw.to(inputs_embeds.device).long() + # extra_conds wraps with a leading batch dim; refs are model-level so [0] always recovers them. + if ref_pv.dim() == 3: + ref_pv = ref_pv[0] + if ref_grid.dim() == 3: + ref_grid = ref_grid[0] + image_embeds = self.visual(ref_pv, ref_grid).to(inputs_embeds.dtype) + self._visual_cache = (ref_pixel_values, image_embeds) + # image_pad positions identical across batch (input_ids shared cond/uncond). + image_idx = (input_ids[0] == IMAGE_TOKEN_ID).nonzero(as_tuple=True)[0] + if image_idx.shape[0] != image_embeds.shape[0]: + raise ValueError( + f"Image-token count {image_idx.shape[0]} != ViT output count " + f"{image_embeds.shape[0]}; check tokenizer/processor alignment." + ) + inputs_embeds[:, image_idx] = image_embeds.unsqueeze(0).expand(B, -1, -1) + + sigma = timesteps.float() / 1000.0 + t_pixeldit = 1.0 - sigma + t_emb = self.t_embedder1(t_pixeldit * 1000, inputs_embeds.dtype) + tms_mask_3d = (input_ids == self.tms_token_id).unsqueeze(-1).expand_as(inputs_embeds) + inputs_embeds = torch.where(tms_mask_3d, t_emb.unsqueeze(1).expand_as(inputs_embeds), inputs_embeds) + + vinputs_embedded = self.x_embedder(vinputs.to(inputs_embeds.dtype)) + inputs_embeds = torch.cat([inputs_embeds, vinputs_embedded], dim=1) + + # extra_conds stores position_ids as (1, 3, T); process_cond repeats dim 0 to B. Take row 0. + freqs_cis = self.language_model.compute_freqs_cis(position_ids[0].to(x.device), x.device) + freqs_cis = tuple(t.to(x.dtype) for t in freqs_cis) + + two_pass_attn = make_two_pass_attention(ar_len, transformer_options=transformer_options) + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.language_model.layers) + transformer_options["block_type"] = "double" + + # Cache prefix K/V across steps. Key includes input_ids (prompt), ref_id + # (refs scatter into inputs_embeds), and position_ids (RoPE baked into cached K). + can_cache = not blocks_replace and ar_len > 0 + cache_len = ar_len if can_cache else 0 + ref_id = id(ref_pixel_values) if ref_pixel_values is not None else None + pos_ids_key = position_ids[..., :cache_len] if can_cache else position_ids + cache_entries = self._kv_cache_entries + # Drop stale entries from a previous device (model was unloaded and reloaded). + if cache_entries and cache_entries[0]["input_ids"].device != input_ids.device: + cache_entries = [] + self._kv_cache_entries = [] + kv_cache = None + if can_cache: + for entry in cache_entries: + ck = entry["input_ids"] + ep = entry["position_ids"] + if (entry["cache_len"] == cache_len + and ck.shape == input_ids.shape and torch.equal(ck, input_ids) + and entry["ref_id"] == ref_id + and ep.shape == pos_ids_key.shape and torch.equal(ep, pos_ids_key)): + kv_cache = entry + break + + if kv_cache is not None: + # Hot path: project Q/K/V only for fresh positions; past_key_value prepends cached AR K/V. + hidden_states = inputs_embeds[:, cache_len:] + sliced_freqs = tuple(t[..., cache_len:, :] for t in freqs_cis) + for i, layer in enumerate(self.language_model.layers): + transformer_options["block_index"] = i + K_i, V_i = kv_cache["kv"][i] + hidden_states, _ = layer( + x=hidden_states, attention_mask=None, freqs_cis=sliced_freqs, optimized_attention=two_pass_attn, + past_key_value=(K_i, V_i, cache_len), + ) + else: + # Cold path: run full sequence; if cacheable, snapshot K/V at AR positions. + snapshots = [] if can_cache else None + past_kv_cold = () if can_cache else None + hidden_states = inputs_embeds + for i, layer in enumerate(self.language_model.layers): + transformer_options["block_index"] = i + if ("double_block", i) in blocks_replace: + def block_wrap(args, _layer=layer): + out = {} + out["x"], _ = _layer( + x=args["x"], attention_mask=args.get("attention_mask"), + freqs_cis=args["freqs_cis"], optimized_attention=args["optimized_attention"], + past_key_value=None, + ) + return out + out = blocks_replace[("double_block", i)]( + {"x": hidden_states, "attention_mask": None, + "freqs_cis": freqs_cis, "optimized_attention": two_pass_attn, + "transformer_options": transformer_options}, + {"original_block": block_wrap}, + ) + hidden_states = out["x"] + else: + hidden_states, present_kv = layer( + x=hidden_states, attention_mask=None, + freqs_cis=freqs_cis, optimized_attention=two_pass_attn, + past_key_value=past_kv_cold, + ) + if snapshots is not None: + K, V, _ = present_kv + snapshots.append((K[:, :, :cache_len].contiguous(), + V[:, :, :cache_len].contiguous())) + if snapshots is not None: + # Cap at 2 entries (cond + uncond). Multi-cond workflows LRU-evict. + new_entry = { + "input_ids": input_ids.clone(), + "cache_len": cache_len, + "kv": snapshots, + "ref_id": ref_id, + "position_ids": pos_ids_key.clone(), + } + self._kv_cache_entries = (cache_entries + [new_entry])[-2:] + + if self.language_model.norm is not None: + hidden_states = self.language_model.norm(hidden_states) + + # Slice target-image positions before the final projection so the Linear only runs on tgt_image_len tokens. + # In the hot path hidden_states starts at original position cache_len, so masks/indices shift by cache_len. + sliced_offset = cache_len if kv_cache is not None else 0 + if vinput_mask is not None: + vmask = vinput_mask.to(x.device).bool() + if sliced_offset > 0: + vmask = vmask[:, sliced_offset:] + target_hidden = hidden_states[vmask].view(B, -1, hidden_states.shape[-1])[:, :tgt_image_len] + else: + txt_seq_len = input_ids.shape[1] + start = txt_seq_len - sliced_offset + target_hidden = hidden_states[:, start:start + tgt_image_len] + x_pred_tgt = self.final_layer2(target_hidden) + + # fp32 final subtraction, bf16 here noticeably degrades samples. + x_pred_img = einops.rearrange( + x_pred_tgt, 'B (H W) (C p1 p2) -> B C (H p1) (W p2)', + H=h_p, W=w_p, p1=self.patch_size, p2=self.patch_size, + ) + return (x.float() - x_pred_img.float()) / sigma.view(B, 1, 1, 1).clamp_min(1e-3) diff --git a/comfy/ldm/hidream_o1/utils.py b/comfy/ldm/hidream_o1/utils.py new file mode 100644 index 000000000..5a1249c72 --- /dev/null +++ b/comfy/ldm/hidream_o1/utils.py @@ -0,0 +1,173 @@ +"""HiDream-O1 input-prep helpers: image/resolution math and unified-sequence +RoPE position-id assembly. The fix_point offset in get_rope_index_fix_point +lets the target image and patchified ref images share spatial RoPE positions +despite living at different sequence indices — same 2D image plane. +""" + +import math +from typing import Optional + +import torch + + +PATCH_SIZE = 32 +CONDITION_IMAGE_SIZE = 384 # ViT-side base size for ref images + + +def resize_tensor(img_t, image_size, patch_size=16): + """img_t: (1, 3, H, W) float [0, 1]. Fit to image_size**2 area, patch-aligned, center-cropped.""" + + while min(img_t.shape[-2], img_t.shape[-1]) >= 2 * image_size: # Pre-halves with 2x2 box averaging while the image is still very large + img_t = torch.nn.functional.avg_pool2d(img_t, kernel_size=2, stride=2) + + _, _, height, width = img_t.shape + m = patch_size + s_max = image_size * image_size + scale = math.sqrt(s_max / (width * height)) + + candidates = [ + (round(width * scale) // m * m, round(height * scale) // m * m), + (round(width * scale) // m * m, math.floor(height * scale) // m * m), + (math.floor(width * scale) // m * m, round(height * scale) // m * m), + (math.floor(width * scale) // m * m, math.floor(height * scale) // m * m), + ] + candidates = sorted(candidates, key=lambda x: x[0] * x[1], reverse=True) + new_size = candidates[-1] + for c in candidates: + if c[0] * c[1] <= s_max: + new_size = c + break + + new_w, new_h = new_size + s1 = width / new_w + s2 = height / new_h + if s1 < s2: + resize_w, resize_h = new_w, round(height / s1) + else: + resize_w, resize_h = round(width / s2), new_h + img_t = torch.nn.functional.interpolate(img_t, size=(resize_h, resize_w), mode="bicubic") + top = (resize_h - new_h) // 2 + left = (resize_w - new_w) // 2 + return img_t[..., top:top + new_h, left:left + new_w] + + +def calculate_dimensions(max_size, ratio): + """(W, H) for an aspect ratio fitting in max_size**2 area, 32-aligned.""" + width = math.sqrt(max_size * max_size * ratio) + height = width / ratio + width = int(width / 32) * 32 + height = int(height / 32) * 32 + return width, height + + +def ref_max_size(target_max_dim, k): + """K-dependent ref-image max dim before patchifying.""" + if k == 1: + return target_max_dim + if k == 2: + return target_max_dim * 48 // 64 + if k <= 4: + return target_max_dim // 2 + if k <= 8: + return target_max_dim * 24 // 64 + return target_max_dim // 4 + + +def cond_image_size(k): + """K-dependent ViT-side image size.""" + if k <= 4: + return CONDITION_IMAGE_SIZE + if k <= 8: + return CONDITION_IMAGE_SIZE * 48 // 64 + return CONDITION_IMAGE_SIZE // 2 + + +def get_rope_index_fix_point( + spatial_merge_size: int, + image_token_id: int, + vision_start_token_id: int, + input_ids: Optional[torch.LongTensor] = None, + image_grid_thw: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + skip_vision_start_token=None, + fix_point: int = 4096, +): + mrope_position_deltas = [] + if input_ids is not None and image_grid_thw is not None: + total_input_ids = input_ids + if attention_mask is None: + attention_mask = torch.ones_like(total_input_ids) + position_ids = torch.ones( + 3, input_ids.shape[0], input_ids.shape[1], + dtype=input_ids.dtype, device=input_ids.device, + ) + attention_mask = attention_mask.to(total_input_ids.device) + for i, input_ids_b in enumerate(total_input_ids): + fp = fix_point + image_index = 0 + input_ids_b = input_ids_b[attention_mask[i] == 1] + vision_start_indices = torch.argwhere(input_ids_b == vision_start_token_id).squeeze(1) + vision_tokens = input_ids_b[vision_start_indices + 1] + image_nums = (vision_tokens == image_token_id).sum() + input_tokens = input_ids_b.tolist() + llm_pos_ids_list = [] + st = 0 + remain_images = image_nums + for _ in range(image_nums): + if image_token_id in input_tokens and remain_images > 0: + ed = input_tokens.index(image_token_id, st) + else: + ed = len(input_tokens) + 1 + t = image_grid_thw[image_index][0] + h = image_grid_thw[image_index][1] + w = image_grid_thw[image_index][2] + image_index += 1 + remain_images -= 1 + llm_grid_t = t.item() + llm_grid_h = h.item() // spatial_merge_size + llm_grid_w = w.item() // spatial_merge_size + text_len = ed - st + text_len -= skip_vision_start_token[image_index - 1] + text_len = max(0, text_len) + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten() + h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() + w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() + + if skip_vision_start_token[image_index - 1]: + if fp > 0: + fp = fp - st_idx + llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + fp + st_idx) + fp = 0 + else: + llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx) + st = ed + llm_grid_t * llm_grid_h * llm_grid_w + + if st < len(input_tokens): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + text_len = len(input_tokens) - st + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i])) + mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) + return position_ids, mrope_position_deltas + + if attention_mask is not None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) + max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] + mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1] + else: + position_ids = ( + torch.arange(input_ids.shape[1], device=input_ids.device) + .view(1, 1, -1).expand(3, input_ids.shape[0], -1) + ) + mrope_position_deltas = torch.zeros( + [input_ids.shape[0], 1], device=input_ids.device, dtype=input_ids.dtype, + ) + return position_ids, mrope_position_deltas diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py index 3fb87b4a3..bc09fb77e 100644 --- a/comfy/ldm/lightricks/av_model.py +++ b/comfy/ldm/lightricks/av_model.py @@ -22,26 +22,25 @@ class CompressedTimestep: """Store video timestep embeddings in compressed form using per-frame indexing.""" __slots__ = ('data', 'batch_size', 'num_frames', 'patches_per_frame', 'feature_dim') - def __init__(self, tensor: torch.Tensor, patches_per_frame: int): + def __init__(self, tensor: torch.Tensor, patches_per_frame: int, per_frame: bool = False): """ - tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame - patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression + tensor: [batch, num_tokens, feature_dim] (per-token, default) or + [batch, num_frames, feature_dim] (per_frame=True, already compressed). + patches_per_frame: spatial patches per frame; pass None to disable compression. """ - self.batch_size, num_tokens, self.feature_dim = tensor.shape - - # Check if compression is valid (num_tokens must be divisible by patches_per_frame) - if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame: + self.batch_size, n, self.feature_dim = tensor.shape + if per_frame: self.patches_per_frame = patches_per_frame - self.num_frames = num_tokens // patches_per_frame - - # Reshape to [batch, frames, patches_per_frame, feature_dim] and store one value per frame - # All patches in a frame are identical, so we only keep the first one - reshaped = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim) - self.data = reshaped[:, :, 0, :].contiguous() # [batch, frames, feature_dim] + self.num_frames = n + self.data = tensor + elif patches_per_frame is not None and n >= patches_per_frame and n % patches_per_frame == 0: + self.patches_per_frame = patches_per_frame + self.num_frames = n // patches_per_frame + # All patches in a frame are identical — keep only the first. + self.data = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim)[:, :, 0, :].contiguous() else: - # Not divisible or too small - store directly without compression self.patches_per_frame = 1 - self.num_frames = num_tokens + self.num_frames = n self.data = tensor def expand(self): @@ -716,32 +715,35 @@ class LTXAVModel(LTXVModel): def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs): """Prepare timestep embeddings.""" - # TODO: some code reuse is needed here. grid_mask = kwargs.get("grid_mask", None) - if grid_mask is not None: - timestep = timestep[:, grid_mask] - - timestep_scaled = timestep * self.timestep_scale_multiplier - - v_timestep, v_embedded_timestep = self.adaln_single( - timestep_scaled.flatten(), - {"resolution": None, "aspect_ratio": None}, - batch_size=batch_size, - hidden_dtype=hidden_dtype, - ) - - # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width] - # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width orig_shape = kwargs.get("orig_shape") has_spatial_mask = kwargs.get("has_spatial_mask", None) v_patches_per_frame = None if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5: - # orig_shape[3] = height, orig_shape[4] = width (in latent space) v_patches_per_frame = orig_shape[3] * orig_shape[4] - # Reshape to [batch_size, num_tokens, dim] and compress for storage - v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame) - v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame) + # Used by compute_prompt_timestep and the audio cross-attention paths. + timestep_scaled = (timestep[:, grid_mask] if grid_mask is not None else timestep) * self.timestep_scale_multiplier + + # When patches in a frame share a timestep (no spatial mask), project one row per frame instead of one per token + per_frame_path = v_patches_per_frame is not None and (timestep.numel() // batch_size) % v_patches_per_frame == 0 + if per_frame_path: + per_frame = timestep.reshape(batch_size, -1, v_patches_per_frame)[:, :, 0] + if grid_mask is not None: + # All-or-nothing per frame when has_spatial_mask=False. + per_frame = per_frame[:, grid_mask[::v_patches_per_frame]] + ts_input = per_frame * self.timestep_scale_multiplier + else: + ts_input = timestep_scaled + + v_timestep, v_embedded_timestep = self.adaln_single( + ts_input.flatten(), + {"resolution": None, "aspect_ratio": None}, + batch_size=batch_size, + hidden_dtype=hidden_dtype, + ) + v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame, per_frame=per_frame_path) + v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame, per_frame=per_frame_path) v_prompt_timestep = compute_prompt_timestep( self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index bfbc08357..e0a4a0f9b 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -358,6 +358,61 @@ def apply_split_rotary_emb(input_tensor, cos, sin): return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output +class GuideAttentionMask: + """Holds the two per-group masks for LTXV guide self-attention. + _attention_with_guide_mask splits queries into noisy and tracked-guide + groups, so the largest mask is (1, 1, tracked_count, T). + """ + __slots__ = ("guide_start", "tracked_count", "noisy_mask", "tracked_mask") + + def __init__(self, total_tokens, guide_start, tracked_count, tracked_weights): + device = tracked_weights.device + dtype = tracked_weights.dtype + finfo = torch.finfo(dtype) + + pos = tracked_weights > 0 + log_w = torch.full_like(tracked_weights, finfo.min) + log_w[pos] = torch.log(tracked_weights[pos].clamp(min=finfo.tiny)) + + self.guide_start = guide_start + self.tracked_count = tracked_count + + self.noisy_mask = torch.zeros((1, 1, 1, total_tokens), device=device, dtype=dtype) + self.noisy_mask[:, :, :, guide_start:guide_start + tracked_count] = log_w.view(1, 1, 1, -1) + + self.tracked_mask = torch.zeros((1, 1, tracked_count, total_tokens), device=device, dtype=dtype) + self.tracked_mask[:, :, :, :guide_start] = log_w.view(1, 1, -1, 1) + + +def _attention_with_guide_mask(q, k, v, heads, guide_mask, attn_precision, transformer_options): + """Apply the guide mask by partitioning Q into noisy and tracked-guide + groups, so each group needs only its own sub-mask. Avoids materializing + the (1,1,T,T) dense mask. + """ + guide_start = guide_mask.guide_start + tracked_end = guide_start + guide_mask.tracked_count + + out = torch.empty_like(q) + + if guide_start > 0: # In practice currently guides are always after noise, guard for safety if this changes. + out[:, :guide_start, :] = comfy.ldm.modules.attention.optimized_attention( + q[:, :guide_start, :], k, v, heads, mask=guide_mask.noisy_mask, + attn_precision=attn_precision, transformer_options=transformer_options, + low_precision_attention=False, # sageattn mask support is unreliable + ) + out[:, guide_start:tracked_end, :] = comfy.ldm.modules.attention.optimized_attention( + q[:, guide_start:tracked_end, :], k, v, heads, mask=guide_mask.tracked_mask, + attn_precision=attn_precision, transformer_options=transformer_options, + low_precision_attention=False, + ) + if tracked_end < q.shape[1]: # Every guide token is tracked, and nothing comes after them, guard for safety if this changes. + out[:, tracked_end:, :] = comfy.ldm.modules.attention.optimized_attention( + q[:, tracked_end:, :], k, v, heads, + attn_precision=attn_precision, transformer_options=transformer_options, + ) + return out + + class CrossAttention(nn.Module): def __init__( self, @@ -412,8 +467,10 @@ class CrossAttention(nn.Module): if mask is None: out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) + elif isinstance(mask, GuideAttentionMask): + out = _attention_with_guide_mask(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) else: - out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) + out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, mask=mask, attn_precision=self.attn_precision, transformer_options=transformer_options) # Apply per-head gating if enabled if self.to_gate_logits is not None: @@ -1063,7 +1120,9 @@ class LTXVModel(LTXBaseModel): additional_args["resolved_guide_entries"] = resolved_entries keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :] - pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs + + if keyframe_idxs.shape[2] > 0: # Guard for the case of no keyframes surviving + pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs # Total surviving guide tokens (all guides) additional_args["num_guide_tokens"] = keyframe_idxs.shape[2] @@ -1099,12 +1158,12 @@ class LTXVModel(LTXBaseModel): if not resolved_entries: return None - # Check if any attenuation is actually needed - needs_attenuation = any( - e["strength"] < 1.0 or e.get("pixel_mask") is not None + # strength != 1.0 means we want to either attenuate (< 1) or amplify (> 1) guide attention. + needs_mask = any( + e["strength"] != 1.0 or e.get("pixel_mask") is not None for e in resolved_entries ) - if not needs_attenuation: + if not needs_mask: return None # Build per-guide-token weights for all tracked guide tokens. @@ -1159,16 +1218,11 @@ class LTXVModel(LTXBaseModel): # Concatenate per-token weights for all tracked guides tracked_weights = torch.cat(all_weights, dim=1) # (1, total_tracked) - # Check if any weight is actually < 1.0 (otherwise no attenuation needed) - if (tracked_weights >= 1.0).all(): + # Skip when every weight is exactly 1.0 (additive bias would be 0). + if (tracked_weights == 1.0).all(): return None - # Build the mask: guide tokens are at the end of the sequence. - # Tracked guides come first (in order), untracked follow. - return self._build_self_attention_mask( - total_tokens, num_guide_tokens, total_tracked, - tracked_weights, guide_start, device, dtype, - ) + return GuideAttentionMask(total_tokens, guide_start, total_tracked, tracked_weights) @staticmethod def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat): @@ -1234,45 +1288,6 @@ class LTXVModel(LTXBaseModel): return rearrange(latent_mask, "b 1 f h w -> b (f h w)") - @staticmethod - def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked_count, - tracked_weights, guide_start, device, dtype): - """Build a log-space additive self-attention bias mask. - - Attenuates attention between noisy tokens and tracked guide tokens. - Untracked guide tokens (at the end of the guide portion) keep full attention. - - Args: - total_tokens: Total sequence length. - num_guide_tokens: Total guide tokens (all guides) at end of sequence. - tracked_count: Number of tracked guide tokens (first in the guide portion). - tracked_weights: (1, tracked_count) tensor, values in [0, 1]. - guide_start: Index where guide tokens begin in the sequence. - device: Target device. - dtype: Target dtype. - - Returns: - (1, 1, total_tokens, total_tokens) additive bias mask. - 0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked. - """ - finfo = torch.finfo(dtype) - mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype) - tracked_end = guide_start + tracked_count - - # Convert weights to log-space bias - w = tracked_weights.to(device=device, dtype=dtype) # (1, tracked_count) - log_w = torch.full_like(w, finfo.min) - positive_mask = w > 0 - if positive_mask.any(): - log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny)) - - # noisy → tracked guides: each noisy row gets the same per-guide weight - mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1) - # tracked guides → noisy: each guide row broadcasts its weight across noisy cols - mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1) - - return mask - def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs): """Process transformer blocks for LTXV.""" patches_replace = transformer_options.get("patches_replace", {}) diff --git a/comfy/ldm/moge/geometry.py b/comfy/ldm/moge/geometry.py new file mode 100644 index 000000000..7fdc97871 --- /dev/null +++ b/comfy/ldm/moge/geometry.py @@ -0,0 +1,189 @@ +"""Pure-torch + scipy geometry helpers for MoGe inference and mesh export.""" + +from __future__ import annotations + +from typing import Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F + +from scipy.optimize import least_squares + +def normalized_view_plane_uv(width: int, height: int, aspect_ratio: Optional[float] = None, + dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> torch.Tensor: + """Normalized view-plane UV coordinates with corners at +/-(W, H)/diagonal.""" + if aspect_ratio is None: + aspect_ratio = width / height + span_x = aspect_ratio / (1 + aspect_ratio ** 2) ** 0.5 + span_y = 1.0 / (1 + aspect_ratio ** 2) ** 0.5 + u = torch.linspace(-span_x * (width - 1) / width, span_x * (width - 1) / width, width, dtype=dtype, device=device) + v = torch.linspace(-span_y * (height - 1) / height, span_y * (height - 1) / height, height, dtype=dtype, device=device) + u, v = torch.meshgrid(u, v, indexing="xy") + return torch.stack([u, v], dim=-1) + + +def intrinsics_from_focal_center(fx: torch.Tensor, fy: torch.Tensor, cx: torch.Tensor, cy: torch.Tensor) -> torch.Tensor: + """Assemble (..., 3, 3) intrinsics from broadcastable fx, fy, cx, cy.""" + fx, fy, cx, cy = [torch.as_tensor(v) for v in (fx, fy, cx, cy)] + fx, fy, cx, cy = torch.broadcast_tensors(fx, fy, cx, cy) + zero = torch.zeros_like(fx) + one = torch.ones_like(fx) + return torch.stack([ + torch.stack([fx, zero, cx], dim=-1), + torch.stack([zero, fy, cy], dim=-1), + torch.stack([zero, zero, one], dim=-1), + ], dim=-2) + + +def depth_map_to_point_map(depth: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor: + """Back-project a (..., H, W) depth map through K^-1 to (..., H, W, 3) camera-space points. + + Intrinsics use normalized image coords (x in [0, 1] left->right, y in [0, 1] top->bottom). + """ + H, W = depth.shape[-2:] + device, dtype = depth.device, depth.dtype + u = (torch.arange(W, dtype=dtype, device=device) + 0.5) / W + v = (torch.arange(H, dtype=dtype, device=device) + 0.5) / H + grid_v, grid_u = torch.meshgrid(v, u, indexing="ij") + pix = torch.stack([grid_u, grid_v, torch.ones_like(grid_u)], dim=-1) + K_inv = torch.linalg.inv(intrinsics) + rays = torch.einsum("...ij,hwj->...hwi", K_inv, pix) + return rays * depth.unsqueeze(-1) + + +def _solve_optimal_shift(uv: np.ndarray, xyz: np.ndarray, + focal: Optional[float] = None) -> Tuple[float, float]: + """LM-solve for z-shift; when focal is None, also recovers the optimal focal.""" + uv = uv.reshape(-1, 2) + xy = xyz[..., :2].reshape(-1, 2) + z = xyz[..., 2].reshape(-1) + + def fn(shift): + xy_proj = xy / (z + shift)[:, None] + f = focal if focal is not None else (xy_proj * uv).sum() / np.square(xy_proj).sum() + return (f * xy_proj - uv).ravel() + + sol = least_squares(fn, x0=0.0, ftol=1e-3, method="lm") + shift = float(np.asarray(sol["x"]).squeeze()) + if focal is None: + xy_proj = xy / (z + shift)[:, None] + focal = float((xy_proj * uv).sum() / np.square(xy_proj).sum()) + return shift, focal + + +def recover_focal_shift(points: torch.Tensor, mask: Optional[torch.Tensor] = None, + focal: Optional[torch.Tensor] = None, downsample_size: Tuple[int, int] = (64, 64) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Recover the focal length and z-shift that turn points into a metric point map. + + Optical center is at the image center; returned focal is relative to half the image diagonal. + Returns (focal, shift) on the same device/dtype as points. + """ + shape = points.shape + H, W = shape[-3], shape[-2] + points_b = points.reshape(-1, H, W, 3) + mask_b = None if mask is None else mask.reshape(-1, H, W) + focal_b = None if focal is None else focal.reshape(-1) + + uv = normalized_view_plane_uv(W, H, dtype=points.dtype, device=points.device) + + points_lr = F.interpolate(points_b.permute(0, 3, 1, 2), downsample_size, mode="nearest").permute(0, 2, 3, 1) + uv_lr = F.interpolate(uv.unsqueeze(0).permute(0, 3, 1, 2), downsample_size, mode="nearest").squeeze(0).permute(1, 2, 0) + mask_lr = None + if mask_b is not None: + mask_lr = F.interpolate(mask_b.to(torch.float32).unsqueeze(1), downsample_size, mode="nearest").squeeze(1) > 0 + + uv_np = uv_lr.detach().cpu().numpy() + points_np = points_lr.detach().cpu().numpy() + mask_np = None if mask_lr is None else mask_lr.detach().cpu().numpy() + focal_np = None if focal_b is None else focal_b.detach().cpu().numpy() + + out_focal: list = [] + out_shift: list = [] + for i in range(points_b.shape[0]): + if mask_np is None: + xyz_i = points_np[i].reshape(-1, 3) + uv_i = uv_np.reshape(-1, 2) + else: + sel = mask_np[i] + if sel.sum() < 2: + out_focal.append(1.0) + out_shift.append(0.0) + continue + xyz_i = points_np[i][sel] + uv_i = uv_np[sel] + if focal_np is None: + shift_i, focal_i = _solve_optimal_shift(uv_i, xyz_i) + out_focal.append(focal_i) + else: + shift_i, _ = _solve_optimal_shift(uv_i, xyz_i, focal=float(focal_np[i])) + out_shift.append(shift_i) + + shift_t = torch.tensor(out_shift, device=points.device, dtype=points.dtype).reshape(shape[:-3]) + if focal is None: + focal_t = torch.tensor(out_focal, device=points.device, dtype=points.dtype).reshape(shape[:-3]) + else: + focal_t = focal.reshape(shape[:-3]) + return focal_t, shift_t + + +def depth_map_edge(depth: torch.Tensor, atol: Optional[float] = None, rtol: Optional[float] = None, kernel_size: int = 3) -> torch.Tensor: + """Per-pixel boolean: True where the local depth window's max-min span exceeds atol or rtol*depth.""" + shape = depth.shape + d = depth.reshape(-1, 1, *shape[-2:]) + pad = kernel_size // 2 + diff = F.max_pool2d(d, kernel_size, stride=1, padding=pad) + F.max_pool2d(-d, kernel_size, stride=1, padding=pad) + edge = torch.zeros_like(d, dtype=torch.bool) + if atol is not None: + edge |= diff > atol + if rtol is not None: + edge |= (diff / d.clamp_min(1e-6)).nan_to_num_() > rtol + return edge.reshape(*shape) + + +def triangulate_grid_mesh(points: torch.Tensor, mask: Optional[torch.Tensor] = None, decimation: int = 1, discontinuity_threshold: float = 0.04, + depth: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Triangulate a (H, W, 3) point map into (vertices, faces, uvs) on CPU. + + Vertices: pixels with finite coords (passing optional mask). Quads with four valid corners + become two triangles. depth overrides the scalar used for the rtol edge check; pass radial + depth for panoramas (the default points[..., 2] goes negative below the equator). + """ + points = points.detach().cpu() + finite = torch.isfinite(points).all(dim=-1) + if mask is None: + mask = finite + else: + mask = mask.detach().cpu().to(torch.bool) & finite + + if discontinuity_threshold > 0: + d = depth.detach().cpu() if depth is not None else points[..., 2] + # Replace inf with 0 so max-pool doesn't poison neighbourhoods (mask above already excludes those pixels). + d_finite = torch.where(finite, d, torch.zeros_like(d)) + edge = depth_map_edge(d_finite, rtol=discontinuity_threshold) + mask = mask & ~edge + + if decimation > 1: + points = points[::decimation, ::decimation].contiguous() + mask = mask[::decimation, ::decimation].contiguous() + H, W = points.shape[:2] + + flat_mask = mask.reshape(-1) + idx = torch.full((H * W,), -1, dtype=torch.long) + n_valid = int(flat_mask.sum().item()) + idx[flat_mask] = torch.arange(n_valid, dtype=torch.long) + idx = idx.reshape(H, W) + + vertices = points.reshape(-1, 3)[flat_mask].contiguous() + + yy, xx = torch.meshgrid(torch.arange(H), torch.arange(W), indexing="ij") + u = xx.float() / max(W - 1, 1) + v = yy.float() / max(H - 1, 1) + uvs = torch.stack([u, v], dim=-1).reshape(-1, 2)[flat_mask].contiguous() + + a, b, c, d = idx[:-1, :-1], idx[:-1, 1:], idx[1:, 1:], idx[1:, :-1] + quad_ok = (a >= 0) & (b >= 0) & (c >= 0) & (d >= 0) + a, b, c, d = a[quad_ok], b[quad_ok], c[quad_ok], d[quad_ok] + faces = torch.cat([torch.stack([a, b, c], dim=-1), torch.stack([a, c, d], dim=-1)], dim=0).contiguous() + return vertices, faces, uvs diff --git a/comfy/ldm/moge/model.py b/comfy/ldm/moge/model.py new file mode 100644 index 000000000..6876c4af2 --- /dev/null +++ b/comfy/ldm/moge/model.py @@ -0,0 +1,347 @@ +"""MoGe v1 / v2 inference modules and a state-dict-driven builder. + +V1: DINOv2 backbone + multi-output head (points, mask). +V2: DINOv2 encoder + neck + per-output heads (points, mask, normal, optional metric-scale MLP). +""" + +from __future__ import annotations + +from numbers import Number +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops +import comfy.model_management +import comfy.model_patcher + +from comfy.image_encoders.dino2 import Dinov2Model + +from .geometry import depth_map_to_point_map, intrinsics_from_focal_center, recover_focal_shift +from .modules import ConvStack, DINOv2Encoder, HeadV1, MLP, _view_plane_uv_grid + + +def _remap_points(points: torch.Tensor) -> torch.Tensor: + """Apply the exp remap: z -> exp(z), xy stays linear and gets scaled by the new z.""" + xy, z = points.split([2, 1], dim=-1) + z = torch.exp(z) + return torch.cat([xy * z, z], dim=-1) + + +def _detect_dinov2(sd: dict, prefix: str) -> Dict[str, Any]: + # All shipped MoGe checkpoints use plain DINOv2 + hidden = sd[prefix + "embeddings.cls_token"].shape[-1] + layer_prefix = prefix + "encoder.layer." + depth = 1 + max(int(k[len(layer_prefix):].split(".")[0]) for k in sd if k.startswith(layer_prefix)) + return { + "hidden_size": hidden, + "num_attention_heads": hidden // 64, + "num_hidden_layers": depth, + "layer_norm_eps": 1e-6, + "use_swiglu_ffn": False, + } + + +class MoGeModelV1(nn.Module): + """MoGe v1: DINOv2 backbone + HeadV1 (points, mask).""" + + image_mean: torch.Tensor + image_std: torch.Tensor + + intermediate_layers = 4 + num_tokens_range: Tuple[Number, Number] = (1200, 2500) + mask_threshold = 0.5 + + def __init__(self, backbone: Dict[str, Any], dim_upsample: List[int] = (256, 128, 128), + num_res_blocks: int = 1, dim_times_res_block_hidden: int = 1, + dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + self.backbone = Dinov2Model(backbone, dtype, device, operations) + self.head = HeadV1(dim_in=backbone["hidden_size"], dim_upsample=list(dim_upsample), + num_res_blocks=num_res_blocks, dim_times_res_block_hidden=dim_times_res_block_hidden, + dtype=dtype, device=device, operations=operations) + self.register_buffer("image_mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + self.register_buffer("image_std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + + def forward(self, image: torch.Tensor, num_tokens: int) -> Dict[str, torch.Tensor]: + H, W = image.shape[-2:] + resize = ((num_tokens * 14 ** 2) / (H * W)) ** 0.5 + rh, rw = int(H * resize), int(W * resize) + x = F.interpolate(image, (rh, rw), mode="bicubic", align_corners=False, antialias=True) + x = (x - self.image_mean) / self.image_std + x14 = F.interpolate(x, (rh // 14 * 14, rw // 14 * 14), mode="bilinear", align_corners=False, antialias=True) + + n_layers = len(self.backbone.encoder.layer) + indices = list(range(n_layers - self.intermediate_layers, n_layers)) + feats = self.backbone.get_intermediate_layers(x14, indices, apply_norm=True) + + points, mask = self.head(feats, x) + points = F.interpolate(points.float(), (H, W), mode="bilinear", align_corners=False) + points = _remap_points(points.permute(0, 2, 3, 1)) + + mask = F.interpolate(mask.float(), (H, W), mode="bilinear", align_corners=False).squeeze(1) + + return {"points": points, "mask": mask} + + @classmethod + def from_state_dict(cls, sd, dtype=None, device=None, operations=comfy.ops.manual_cast): + """Detect the v1 head config from sd, build a model, and load weights.""" + n_up = 1 + max(int(k.split(".")[2]) for k in sd if k.startswith("head.upsample_blocks.")) + dim_upsample = [sd[f"head.upsample_blocks.{i}.0.0.weight"].shape[1] for i in range(n_up)] + # Each upsample stage is Sequential[upsampler, *res_blocks]; count res blocks at level 0. + num_res_blocks = max({int(k.split(".")[3]) for k in sd if k.startswith("head.upsample_blocks.0.")}) + hidden_out = sd["head.upsample_blocks.0.1.layers.2.weight"].shape[0] + dim_times = max(hidden_out // dim_upsample[0], 1) + model = cls(backbone=_detect_dinov2(sd, prefix="backbone."), + dim_upsample=dim_upsample, num_res_blocks=num_res_blocks, dim_times_res_block_hidden=dim_times, + dtype=dtype, device=device, operations=operations) + model.load_state_dict(sd, strict=True) + return model + + +class MoGeModelV2(nn.Module): + """MoGe v2: DINOv2 encoder + neck + per-output heads (points/mask/normal/metric-scale).""" + + intermediate_layers = 4 + num_tokens_range: Tuple[Number, Number] = (1200, 3600) + + def __init__(self, + encoder: Dict[str, Any], + neck: Dict[str, Any], + points_head: Dict[str, Any], + mask_head: Dict[str, Any], + scale_head: Dict[str, Any], + normal_head: Optional[Dict[str, Any]] = None, + dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + self.encoder = DINOv2Encoder(**encoder, dtype=dtype, device=device, operations=operations) + self.neck = ConvStack(**neck, dtype=dtype, device=device, operations=operations) + self.points_head = ConvStack(**points_head, dtype=dtype, device=device, operations=operations) + self.mask_head = ConvStack(**mask_head, dtype=dtype, device=device, operations=operations) + self.scale_head = MLP(**scale_head, dtype=dtype, device=device, operations=operations) + if normal_head is not None: + self.normal_head = ConvStack(**normal_head, dtype=dtype, device=device, operations=operations) + + def forward(self, image: torch.Tensor, num_tokens: int) -> Dict[str, torch.Tensor]: + B, _, H, W = image.shape + device, dtype = image.device, image.dtype + aspect_ratio = W / H + base_h = round((num_tokens / aspect_ratio) ** 0.5) + base_w = round((num_tokens * aspect_ratio) ** 0.5) + + feat_top, cls_token = self.encoder(image, base_h, base_w, return_class_token=True) + + # 5-level pyramid: feat at level 0 concatenated with UV, other levels UV-only. + levels = [_view_plane_uv_grid(B, base_h * (2 ** L), base_w * (2 ** L), aspect_ratio, dtype, device) + for L in range(5)] + levels[0] = torch.cat([feat_top, levels[0]], dim=1) + + feats = self.neck(levels) + + def _resize(v): + return F.interpolate(v, (H, W), mode="bilinear", align_corners=False) + + points = _remap_points(_resize(self.points_head(feats)[-1]).permute(0, 2, 3, 1)) + mask = _resize(self.mask_head(feats)[-1]).squeeze(1).sigmoid() + metric_scale = self.scale_head(cls_token).squeeze(1).exp() + + result = {"points": points, "mask": mask, "metric_scale": metric_scale} + if hasattr(self, "normal_head"): + normal = _resize(self.normal_head(feats)[-1]) + result["normal"] = F.normalize(normal.permute(0, 2, 3, 1), dim=-1) + return result + + @classmethod + def from_state_dict(cls, sd, dtype=None, device=None, operations=comfy.ops.manual_cast): + """Detect the v2 encoder/neck/heads config from sd, build a model, and load weights.""" + backbone = _detect_dinov2(sd, prefix="encoder.backbone.") + depth = backbone["num_hidden_layers"] + n = cls.intermediate_layers + encoder = { + "backbone": backbone, + "intermediate_layers": [(depth // n) * (i + 1) - 1 for i in range(n)], + "dim_out": sd["encoder.output_projections.0.weight"].shape[0], + } + # scale_head is an MLP: Sequential of [Linear, ReLU, ..., Linear]; Linear weight is (out, in). + scale_idxs = sorted({int(k.split(".")[1]) for k in sd if k.startswith("scale_head.")}) + scale_first = sd[f"scale_head.{scale_idxs[0]}.weight"] + cfg: Dict[str, Any] = { + "encoder": encoder, + "neck": cls._detect_convstack(sd, "neck."), + "points_head": cls._detect_convstack(sd, "points_head."), + "mask_head": cls._detect_convstack(sd, "mask_head."), + "scale_head": {"dims": [scale_first.shape[1]] + [sd[f"scale_head.{i}.weight"].shape[0] for i in scale_idxs]}, + } + if any(k.startswith("normal_head.") for k in sd): + cfg["normal_head"] = cls._detect_convstack(sd, "normal_head.") + model = cls(**cfg, dtype=dtype, device=device, operations=operations) + model.load_state_dict(sd, strict=True) + return model + + @staticmethod + def _detect_convstack(sd: dict, prefix: str) -> Dict[str, Any]: + """Reconstruct a ConvStack config from the keys under prefix""" + in_keys = [k for k in sd if k.startswith(f"{prefix}input_blocks.") and k.endswith(".weight")] + n = 1 + max(int(k[len(f"{prefix}input_blocks."):].split(".")[0]) for k in in_keys) + + in_shapes = [sd[f"{prefix}input_blocks.{i}.weight"].shape for i in range(n)] + has_out = lambda i: f"{prefix}output_blocks.{i}.weight" in sd + has_norm = f"{prefix}res_blocks.0.0.layers.0.weight" in sd + + def num_res_at(i): + rb_prefix = f"{prefix}res_blocks.{i}." + return len({int(k[len(rb_prefix):].split(".")[0]) for k in sd if k.startswith(rb_prefix)}) + + return { + "dim_in": [s[1] for s in in_shapes], + "dim_res_blocks": [s[0] for s in in_shapes], + "dim_out": [sd[f"{prefix}output_blocks.{i}.weight"].shape[0] if has_out(i) else None for i in range(n)], + "num_res_blocks": [num_res_at(i) for i in range(n)], + "resamplers": ["conv_transpose" if f"{prefix}resamplers.{i}.0.weight" in sd else "bilinear" + for i in range(n - 1)], + "res_block_in_norm": "layer_norm" if has_norm else "none", + "res_block_hidden_norm": "group_norm" if has_norm else "none", + } + + +# Translate the Meta-style DINOv2 keys MoGe ships to the naming ComfyUI DINOv2 port expects, +# and split each fused qkv tensor into Q/K/V. +_DINOV2_TOPLEVEL_RENAMES = { + "patch_embed.proj.weight": "embeddings.patch_embeddings.projection.weight", + "patch_embed.proj.bias": "embeddings.patch_embeddings.projection.bias", + "cls_token": "embeddings.cls_token", + "pos_embed": "embeddings.position_embeddings", + "register_tokens": "embeddings.register_tokens", + "mask_token": "embeddings.mask_token", + "norm.weight": "layernorm.weight", + "norm.bias": "layernorm.bias", +} +_DINOV2_BLOCK_RENAMES = [ + ("ls1.gamma", "layer_scale1.lambda1"), + ("ls2.gamma", "layer_scale2.lambda1"), + ("attn.proj.", "attention.output.dense."), + ("mlp.w12.", "mlp.weights_in."), + ("mlp.w3.", "mlp.weights_out."), +] + + +def _remap_state_dict(sd: dict) -> dict: + if "model" in sd and "model_config" in sd: + sd = sd["model"] + prefix = "encoder.backbone." if any(k.startswith("encoder.backbone.") for k in sd) else "backbone." + out: dict = {} + for k, v in sd.items(): + if not k.startswith(prefix): + out[k] = v + continue + rel = k[len(prefix):] + if rel in _DINOV2_TOPLEVEL_RENAMES: + out[prefix + _DINOV2_TOPLEVEL_RENAMES[rel]] = v + continue + if not rel.startswith("blocks."): + out[k] = v + continue + _, idx, sub = rel.split(".", 2) + if sub in ("attn.qkv.weight", "attn.qkv.bias"): + tail = sub.rsplit(".", 1)[1] + q, kw, vw = v.chunk(3, dim=0) + base = f"{prefix}encoder.layer.{idx}.attention.attention" + out[f"{base}.query.{tail}"] = q + out[f"{base}.key.{tail}"] = kw + out[f"{base}.value.{tail}"] = vw + continue + for old, new in _DINOV2_BLOCK_RENAMES: + sub = sub.replace(old, new) + out[f"{prefix}encoder.layer.{idx}.{sub}"] = v + return out + + +def build_from_state_dict(sd: dict, dtype=None, device=None, operations=comfy.ops.manual_cast) -> nn.Module: + """Dispatch to v1 or v2 based on the DINOv2 backbone prefix.""" + sd = _remap_state_dict(sd) + cls = MoGeModelV2 if any(k.startswith("encoder.backbone.") for k in sd) else MoGeModelV1 + return cls.from_state_dict(sd, dtype=dtype, device=device, operations=operations) + + +class MoGeModel: + """Loaded MoGe model + ComfyUI memory management.""" + + def __init__(self, state_dict: dict): + # text encoder dtype closest match + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) + + self.model = build_from_state_dict(state_dict, dtype=self.dtype, device=offload_device, operations=comfy.ops.manual_cast).eval() + self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + self.version = "v2" if hasattr(self.model, "encoder") else "v1" + self.mask_threshold = float(getattr(self.model, "mask_threshold", 0.5)) + nt = getattr(self.model, "num_tokens_range", (1200, 2500 if self.version == "v1" else 3600)) + self.num_tokens_range = (int(nt[0]), int(nt[1])) + + def infer(self, image: torch.Tensor, num_tokens: Optional[int] = None, + resolution_level: int = 9, fov_x: Optional[Union[Number, torch.Tensor]] = None, + force_projection: bool = True, apply_mask: bool = True, + apply_metric_scale: bool = True + ) -> Dict[str, torch.Tensor]: + """Run a single MoGe forward + post-process pass. image is (B, 3, H, W) in [0, 1].""" + comfy.model_management.load_model_gpu(self.patcher) + image = image.to(device=self.load_device, dtype=self.dtype) + H, W = image.shape[-2:] + aspect_ratio = W / H + + if num_tokens is None: + lo, hi = self.num_tokens_range + num_tokens = int(lo + (resolution_level / 9) * (hi - lo)) + + out = self.model.forward(image, num_tokens=num_tokens) + points = out["points"].float() # recover_focal_shift goes through scipy on CPU; needs fp32. + mask_binary = out["mask"] > self.mask_threshold + normal = out.get("normal") + metric_scale = out.get("metric_scale") + + diag = (1 + aspect_ratio ** 2) ** 0.5 + + def focal_from_fov_deg(deg): + fov = torch.as_tensor(deg, device=points.device, dtype=points.dtype) + return aspect_ratio / diag / torch.tan(torch.deg2rad(fov / 2)) + + if fov_x is None: + focal, shift = recover_focal_shift(points, mask_binary) + # Fall back to 60 deg FoV when the least-squares solver flips the focal sign. + bad = ~torch.isfinite(focal) | (focal <= 0) + if bool(bad.any()): + focal = torch.where(bad, focal_from_fov_deg(60.0), focal) + _, shift = recover_focal_shift(points, mask_binary, focal=focal) + else: + focal = focal_from_fov_deg(fov_x).expand(points.shape[0]) + _, shift = recover_focal_shift(points, mask_binary, focal=focal) + + f_diag = focal / 2 * diag + half = torch.tensor(0.5, device=points.device, dtype=points.dtype) + intrinsics = intrinsics_from_focal_center(f_diag / aspect_ratio, f_diag, half, half) + points[..., 2] = points[..., 2] + shift[..., None, None] + # v2 only: filter mask by depth>0 to drop metric-scale negative-depth artifacts. + if self.version == "v2": + mask_binary = mask_binary & (points[..., 2] > 0) + depth = points[..., 2].clone() + + if force_projection: + points = depth_map_to_point_map(depth, intrinsics=intrinsics) + + if apply_metric_scale and metric_scale is not None: + points = points * metric_scale[:, None, None, None] + depth = depth * metric_scale[:, None, None] + + if apply_mask: + points = torch.where(mask_binary[..., None], points, torch.full_like(points, float("inf"))) + depth = torch.where(mask_binary, depth, torch.full_like(depth, float("inf"))) + if normal is not None: + normal = torch.where(mask_binary[..., None], normal, torch.zeros_like(normal)) + + result = {"points": points, "depth": depth, "intrinsics": intrinsics, "mask": mask_binary} + if normal is not None: + result["normal"] = normal + return result diff --git a/comfy/ldm/moge/modules.py b/comfy/ldm/moge/modules.py new file mode 100644 index 000000000..235a59212 --- /dev/null +++ b/comfy/ldm/moge/modules.py @@ -0,0 +1,204 @@ +"""Building blocks for MoGe: residual conv stack, resamplers, MLP, DINOv2 encoder, v1 head.""" + +from __future__ import annotations + +from typing import List, Optional, Sequence, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops +from comfy.image_encoders.dino2 import Dinov2Model + +from .geometry import normalized_view_plane_uv + + +def _conv2d(operations, c_in: int, c_out: int, k: int = 3, *, dtype=None, device=None): + return operations.Conv2d(c_in, c_out, kernel_size=k, padding=k // 2, padding_mode="replicate", dtype=dtype, device=device) + + +def _view_plane_uv_grid(batch: int, height: int, width: int, aspect_ratio: float, dtype, device) -> torch.Tensor: + """Batched normalized view-plane UV grid as a (B, 2, H, W) tensor.""" + uv = normalized_view_plane_uv(width, height, aspect_ratio=aspect_ratio, dtype=dtype, device=device) + return uv.permute(2, 0, 1).unsqueeze(0).expand(batch, -1, -1, -1) + + +def _concat_view_plane_uv(x: torch.Tensor, aspect_ratio: float) -> torch.Tensor: + """Append a 2-channel normalized view-plane UV grid to x along the channel dim.""" + uv = _view_plane_uv_grid(x.shape[0], x.shape[-2], x.shape[-1], aspect_ratio, x.dtype, x.device) + return torch.cat([x, uv], dim=1) + + +class ResidualConvBlock(nn.Module): + def __init__(self, channels: int, hidden_channels: Optional[int] = None, in_norm: str = "layer_norm", hidden_norm: str = "group_norm", + dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + hidden_channels = hidden_channels if hidden_channels is not None else channels + + in_norm_layer = operations.GroupNorm(1, channels, dtype=dtype, device=device) if in_norm == "layer_norm" else nn.Identity() + hidden_norm_layer = (operations.GroupNorm(max(hidden_channels // 32, 1), hidden_channels, dtype=dtype, device=device) + if hidden_norm == "group_norm" else nn.Identity()) + + self.layers = nn.Sequential( + in_norm_layer, nn.ReLU(), _conv2d(operations, channels, hidden_channels, dtype=dtype, device=device), + hidden_norm_layer, nn.ReLU(), _conv2d(operations, hidden_channels, channels, dtype=dtype, device=device), + ) + + def forward(self, x): + return self.layers(x) + x + + +class Resampler(nn.Sequential): + """2x upsampler: ConvTranspose2d(2x2) or bilinear upsample, followed by a 3x3 conv.""" + + def __init__(self, in_channels: int, out_channels: int, type_: str, dtype=None, device=None, operations=comfy.ops.manual_cast): + if type_ == "conv_transpose": + up = operations.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2, dtype=dtype, device=device) + conv_in = out_channels + else: # "bilinear" + up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + conv_in = in_channels + super().__init__(up, _conv2d(operations, conv_in, out_channels, dtype=dtype, device=device)) + + +class MLP(nn.Sequential): + def __init__(self, dims: Sequence[int], dtype=None, device=None, operations=comfy.ops.manual_cast): + layers = [] + for d_in, d_out in zip(dims[:-2], dims[1:-1]): + layers.append(operations.Linear(d_in, d_out, dtype=dtype, device=device)) + layers.append(nn.ReLU(inplace=True)) + layers.append(operations.Linear(dims[-2], dims[-1], dtype=dtype, device=device)) + super().__init__(*layers) + + +class ConvStack(nn.Module): + def __init__(self, dim_in: List[Optional[int]], dim_res_blocks: List[int], dim_out: List[Optional[int]], resamplers: List[str], + num_res_blocks: List[int], dim_times_res_block_hidden: int = 1, res_block_in_norm: str = "layer_norm", res_block_hidden_norm: str = "group_norm", + dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + + self.input_blocks = nn.ModuleList([ + (_conv2d(operations, d_in, d_res, k=1, dtype=dtype, device=device) + if d_in is not None else nn.Identity()) + for d_in, d_res in zip(dim_in, dim_res_blocks) + ]) + + self.resamplers = nn.ModuleList([ + Resampler(prev, succ, type_=r, dtype=dtype, device=device, operations=operations) + for prev, succ, r in zip(dim_res_blocks[:-1], dim_res_blocks[1:], resamplers) + ]) + + self.res_blocks = nn.ModuleList([ + nn.Sequential(*[ + ResidualConvBlock(d_res, dim_times_res_block_hidden * d_res, in_norm=res_block_in_norm, hidden_norm=res_block_hidden_norm, dtype=dtype, device=device, operations=operations) + for _ in range(num_res_blocks[i]) + ]) + for i, d_res in enumerate(dim_res_blocks) + ]) + + self.output_blocks = nn.ModuleList([ + (_conv2d(operations, d_res, d_out, k=1, dtype=dtype, device=device) + if d_out is not None else nn.Identity()) + for d_out, d_res in zip(dim_out, dim_res_blocks) + ]) + + def forward(self, in_features: List[Optional[torch.Tensor]]): + out_features = [] + x = None + for i in range(len(self.res_blocks)): + feat = self.input_blocks[i](in_features[i]) if in_features[i] is not None else None + if i == 0: + x = feat + elif feat is not None: + x = x + feat + x = self.res_blocks[i](x) + out_features.append(self.output_blocks[i](x)) + if i < len(self.res_blocks) - 1: + x = self.resamplers[i](x) + return out_features + + +class DINOv2Encoder(nn.Module): + """Comfy DINOv2 backbone with per-layer 1x1 projection heads.""" + + def __init__(self, backbone: dict, intermediate_layers: List[int], dim_out: int, dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + self.intermediate_layers = list(intermediate_layers) + dim_features = backbone["hidden_size"] + self.backbone = Dinov2Model(backbone, dtype, device, operations) + self.output_projections = nn.ModuleList([ + _conv2d(operations, dim_features, dim_out, k=1, dtype=dtype, device=device) + for _ in range(len(self.intermediate_layers)) + ]) + self.register_buffer("image_mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + self.register_buffer("image_std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + + def forward(self, image: torch.Tensor, token_rows: int, token_cols: int, + return_class_token: bool = False) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + image_14 = F.interpolate(image, (token_rows * 14, token_cols * 14), mode="bilinear", align_corners=False, antialias=True) + image_14 = (image_14 - self.image_mean) / self.image_std + feats = self.backbone.get_intermediate_layers(image_14, self.intermediate_layers, apply_norm=True) + x = torch.stack([ + proj(feat.permute(0, 2, 1).unflatten(2, (token_rows, token_cols)).contiguous()) + for proj, (feat, _cls) in zip(self.output_projections, feats) + ], dim=1).sum(dim=1) + if return_class_token: + return x, feats[-1][1] + return x + + +class HeadV1(nn.Module): + """v1 head: 4 backbone-feature projections -> shared upsample stack -> per-target output convs (points, mask).""" + + NUM_FEATURES = 4 + DIM_PROJ = 512 + DIM_OUT = (3, 1) # 3 channels for points, 1 for mask + LAST_CONV_CHANNELS = 32 + + def __init__(self, dim_in: int, dim_upsample: List[int] = (256, 128, 128), num_res_blocks: int = 1, dim_times_res_block_hidden: int = 1, + dtype=None, device=None, operations=comfy.ops.manual_cast): + super().__init__() + self.projects = nn.ModuleList([ + _conv2d(operations, dim_in, self.DIM_PROJ, k=1, dtype=dtype, device=device) + for _ in range(self.NUM_FEATURES) + ]) + def upsampler(in_ch, out_ch): + return nn.Sequential( + operations.ConvTranspose2d(in_ch, out_ch, kernel_size=2, stride=2, dtype=dtype, device=device), + _conv2d(operations, out_ch, out_ch, dtype=dtype, device=device), + ) + + in_chs = [self.DIM_PROJ] + list(dim_upsample[:-1]) + self.upsample_blocks = nn.ModuleList([ + nn.Sequential( + upsampler(in_ch + 2, out_ch), + *(ResidualConvBlock(out_ch, dim_times_res_block_hidden * out_ch, dtype=dtype, device=device, operations=operations) + for _ in range(num_res_blocks)) + ) + for in_ch, out_ch in zip(in_chs, dim_upsample) + ]) + self.output_block = nn.ModuleList([ + nn.Sequential( + _conv2d(operations, dim_upsample[-1] + 2, self.LAST_CONV_CHANNELS, dtype=dtype, device=device), + nn.ReLU(inplace=True), + _conv2d(operations, self.LAST_CONV_CHANNELS, d_out, k=1, dtype=dtype, device=device), + ) + for d_out in self.DIM_OUT + ]) + + def forward(self, hidden_states, image: torch.Tensor): + img_h, img_w = image.shape[-2:] + patch_h, patch_w = img_h // 14, img_w // 14 + aspect = img_w / img_h + x = torch.stack([ + proj(feat.permute(0, 2, 1).unflatten(2, (patch_h, patch_w)).contiguous()) + for proj, (feat, _cls) in zip(self.projects, hidden_states) + ], dim=1).sum(dim=1) + + for block in self.upsample_blocks: + x = block(_concat_view_plane_uv(x, aspect)) + + x = F.interpolate(x, (img_h, img_w), mode="bilinear", align_corners=False) + x = _concat_view_plane_uv(x, aspect) + return [block(x) for block in self.output_block] diff --git a/comfy/ldm/moge/panorama.py b/comfy/ldm/moge/panorama.py new file mode 100644 index 000000000..de53ebe68 --- /dev/null +++ b/comfy/ldm/moge/panorama.py @@ -0,0 +1,313 @@ +"""Panorama (equirectangular) inference helpers for MoGe. + +Splits an equirect into 12 perspective views via an icosahedron camera rig, runs +the model per view, and stitches per-view distance maps back into a single +equirect distance map via a multi-scale Poisson + gradient sparse solve. +Image sampling uses F.grid_sample (GPU); the sparse solve uses lsmr (CPU). +""" + +from __future__ import annotations + +from typing import Callable, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F + +from scipy.ndimage import convolve, map_coordinates +from scipy.sparse import vstack, csr_array +from scipy.sparse.linalg import lsmr + + +def _icosahedron_directions() -> np.ndarray: + """12 icosahedron-vertex directions (non-normalised, matching upstream's vertex order).""" + A = (1.0 + np.sqrt(5.0)) / 2.0 + return np.array([ + [0, 1, A], [0, -1, A], [0, 1, -A], [0, -1, -A], + [1, A, 0], [-1, A, 0], [1, -A, 0], [-1, -A, 0], + [A, 0, 1], [A, 0, -1], [-A, 0, 1], [-A, 0, -1], + ], dtype=np.float32) + + +def _intrinsics_from_fov(fov_x_rad: float, fov_y_rad: float) -> np.ndarray: + """Normalised-image (unit-square) K matrix.""" + fx = 0.5 / np.tan(fov_x_rad / 2) + fy = 0.5 / np.tan(fov_y_rad / 2) + return np.array([[fx, 0, 0.5], [0, fy, 0.5], [0, 0, 1]], dtype=np.float32) + + +def _extrinsics_look_at(eye: np.ndarray, target: np.ndarray, up: np.ndarray) -> np.ndarray: + """OpenCV-convention world->camera extrinsics for an array of look-at targets (N, 4, 4).""" + eye = np.asarray(eye, dtype=np.float32) + target = np.asarray(target, dtype=np.float32) + up = np.asarray(up, dtype=np.float32) + if target.ndim == 1: + target = target[None] + + fwd = target - eye + fwd = fwd / np.linalg.norm(fwd, axis=-1, keepdims=True).clip(1e-12) + right = np.cross(fwd, up) + right_norm = np.linalg.norm(right, axis=-1, keepdims=True) + # Fall back to an arbitrary perpendicular if forward is parallel to up. + parallel = right_norm.squeeze(-1) < 1e-6 + if parallel.any(): + alt_up = np.array([1, 0, 0], dtype=np.float32) + right = np.where(parallel[:, None], np.cross(fwd, alt_up), right) + right_norm = np.linalg.norm(right, axis=-1, keepdims=True) + right = right / right_norm.clip(1e-12) + new_up = np.cross(fwd, right) + + R = np.stack([right, new_up, fwd], axis=-2) + t = -np.einsum("nij,j->ni", R, eye) + E = np.zeros((R.shape[0], 4, 4), dtype=np.float32) + E[:, :3, :3] = R + E[:, :3, 3] = t + E[:, 3, 3] = 1.0 + return E + + +def get_panorama_cameras() -> Tuple[np.ndarray, List[np.ndarray]]: + """Returns (extrinsics (12, 4, 4), [intrinsics] * 12) for icosahedron views at 90 deg FoV.""" + targets = _icosahedron_directions() + eye = np.zeros(3, dtype=np.float32) + up = np.array([0, 0, 1], dtype=np.float32) + extrinsics = _extrinsics_look_at(eye, targets, up) + K = _intrinsics_from_fov(np.deg2rad(90.0), np.deg2rad(90.0)) + return extrinsics, [K] * len(targets) + + +def spherical_uv_to_directions(uv: np.ndarray) -> np.ndarray: + """Equirect UV in [0, 1] -> 3D unit-direction (Z up).""" + theta = (1 - uv[..., 0]) * (2 * np.pi) + phi = uv[..., 1] * np.pi + return np.stack([ + np.sin(phi) * np.cos(theta), + np.sin(phi) * np.sin(theta), + np.cos(phi), + ], axis=-1).astype(np.float32) + + +def directions_to_spherical_uv(directions: np.ndarray) -> np.ndarray: + """3D direction -> equirect UV in [0, 1].""" + n = np.linalg.norm(directions, axis=-1, keepdims=True).clip(1e-12) + d = directions / n + u = 1 - np.arctan2(d[..., 1], d[..., 0]) / (2 * np.pi) % 1.0 + v = np.arccos(d[..., 2].clip(-1, 1)) / np.pi + return np.stack([u, v], axis=-1).astype(np.float32) + + +def _uv_grid(H: int, W: int) -> np.ndarray: + """Pixel-center UV grid in [0, 1]; (H, W, 2).""" + u = (np.arange(W, dtype=np.float32) + 0.5) / W + v = (np.arange(H, dtype=np.float32) + 0.5) / H + return np.stack(np.meshgrid(u, v, indexing="xy"), axis=-1) + + +def _unproject_cv(uv: np.ndarray, depth: np.ndarray, + extrinsics: np.ndarray, intrinsics: np.ndarray) -> np.ndarray: + """Back-project pixels into world coords (OpenCV convention).""" + pix = np.concatenate([uv, np.ones_like(uv[..., :1])], axis=-1) + K_inv = np.linalg.inv(intrinsics) + cam = pix @ K_inv.T * depth[..., None] + cam_h = np.concatenate([cam, np.ones_like(cam[..., :1])], axis=-1) + E_inv = np.linalg.inv(extrinsics) + return (cam_h @ E_inv.T)[..., :3] + + +def _project_cv(points: np.ndarray, extrinsics: np.ndarray, intrinsics: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """World coords -> (uv, depth) in the camera (OpenCV convention).""" + pts_h = np.concatenate([points, np.ones_like(points[..., :1])], axis=-1) + cam = pts_h @ extrinsics.T + cam_xyz = cam[..., :3] + depth = cam_xyz[..., 2] + proj = cam_xyz @ intrinsics.T + uv = proj[..., :2] / proj[..., 2:3].clip(1e-12) + return uv.astype(np.float32), depth.astype(np.float32) + + +def _grid_sample_uv(img_bchw: torch.Tensor, uv: torch.Tensor, mode: str = "bilinear") -> torch.Tensor: + """Sample img_bchw at UV-in-[0,1] coords uv of shape (B, H, W, 2); replicate-border.""" + grid = uv * 2.0 - 1.0 + return F.grid_sample(img_bchw, grid, mode=mode, padding_mode="border", align_corners=False) + + +def split_panorama_image(image: torch.Tensor, extrinsics: np.ndarray, intrinsics: List[np.ndarray], resolution: int) -> torch.Tensor: + """(3, Hp, Wp) equirect on any device -> (N, 3, R, R) perspective crops on the same device.""" + device = image.device + N = len(extrinsics) + uv = _uv_grid(resolution, resolution) + sample_uvs = [] + for i in range(N): + world = _unproject_cv(uv, np.ones(uv.shape[:-1], dtype=np.float32), extrinsics[i], intrinsics[i]) + sample_uvs.append(directions_to_spherical_uv(world)) + sample_uvs = np.stack(sample_uvs, axis=0) + + img_bchw = image.unsqueeze(0).expand(N, -1, -1, -1).contiguous() + sample_uvs_t = torch.from_numpy(sample_uvs).to(device=device, dtype=image.dtype) + return _grid_sample_uv(img_bchw, sample_uvs_t, mode="bilinear") + + +def _poisson_equation(W: int, H: int, wrap_x: bool = False, wrap_y: bool = False): + """Sparse Laplacian operator over the H x W grid.""" + grid_index = np.arange(H * W).reshape(H, W) + grid_index = np.pad(grid_index, ((0, 0), (1, 1)), mode="wrap" if wrap_x else "edge") + grid_index = np.pad(grid_index, ((1, 1), (0, 0)), mode="wrap" if wrap_y else "edge") + + data = np.array([[-4, 1, 1, 1, 1]], dtype=np.float32).repeat(H * W, axis=0).reshape(-1) + indices = np.stack([ + grid_index[1:-1, 1:-1], + grid_index[:-2, 1:-1], grid_index[2:, 1:-1], + grid_index[1:-1, :-2], grid_index[1:-1, 2:], + ], axis=-1).reshape(-1) + indptr = np.arange(0, H * W * 5 + 1, 5) + return csr_array((data, indices, indptr), shape=(H * W, H * W)) + + +def _grad_equation(W: int, H: int, wrap_x: bool = False, wrap_y: bool = False): + """Sparse forward-difference operator over the H x W grid.""" + grid_index = np.arange(W * H).reshape(H, W) + if wrap_x: + grid_index = np.pad(grid_index, ((0, 0), (0, 1)), mode="wrap") + if wrap_y: + grid_index = np.pad(grid_index, ((0, 1), (0, 0)), mode="wrap") + + data = np.concatenate([ + np.concatenate([ + np.ones((grid_index.shape[0], grid_index.shape[1] - 1), dtype=np.float32).reshape(-1, 1), + -np.ones((grid_index.shape[0], grid_index.shape[1] - 1), dtype=np.float32).reshape(-1, 1), + ], axis=1).reshape(-1), + np.concatenate([ + np.ones((grid_index.shape[0] - 1, grid_index.shape[1]), dtype=np.float32).reshape(-1, 1), + -np.ones((grid_index.shape[0] - 1, grid_index.shape[1]), dtype=np.float32).reshape(-1, 1), + ], axis=1).reshape(-1), + ]) + indices = np.concatenate([ + np.concatenate([grid_index[:, :-1].reshape(-1, 1), grid_index[:, 1:].reshape(-1, 1)], axis=1).reshape(-1), + np.concatenate([grid_index[:-1, :].reshape(-1, 1), grid_index[1:, :].reshape(-1, 1)], axis=1).reshape(-1), + ]) + nx = grid_index.shape[0] * (grid_index.shape[1] - 1) + ny = (grid_index.shape[0] - 1) * grid_index.shape[1] + indptr = np.arange(0, nx * 2 + ny * 2 + 1, 2) + return csr_array((data, indices, indptr), shape=(nx + ny, H * W)) + + +def _scipy_remap_bilinear(img: np.ndarray, sample_pixels: np.ndarray, mode: str = "bilinear") -> np.ndarray: + """Bilinear/nearest sampling at fractional pixel coords; out-of-range clamps to nearest border.""" + H, W = img.shape[:2] + yy = np.clip(sample_pixels[..., 1], 0, H - 1) + xx = np.clip(sample_pixels[..., 0], 0, W - 1) + order = 1 if mode == "bilinear" else 0 + if img.ndim == 2: + return map_coordinates(img, [yy, xx], order=order, mode="nearest").astype(img.dtype) + out = np.stack([ + map_coordinates(img[..., c], [yy, xx], order=order, mode="nearest") + for c in range(img.shape[-1]) + ], axis=-1) + return out.astype(img.dtype) + + +def merge_panorama_depth(width: int, height: int, + distance_maps: List[np.ndarray], pred_masks: List[np.ndarray], + extrinsics: List[np.ndarray], intrinsics: List[np.ndarray], + on_view: Optional[Callable[[], None]] = None, + on_solve_start: Optional[Callable[[int, int], None]] = None, + on_solve_end: Optional[Callable[[int, int], None]] = None, + ) -> Tuple[np.ndarray, np.ndarray]: + """Stitch per-view distance maps into a single equirect distance map. + + Recursive multi-scale solve: solves at half resolution first and uses that as the lsmr init + for the full-resolution solve. Optional callbacks fire per view processed and around each + lsmr solve so callers can drive a progress bar. + """ + + if max(width, height) > 256: + coarse_depth, _ = merge_panorama_depth(width // 2, height // 2, + distance_maps, pred_masks, extrinsics, intrinsics, + on_view=on_view, + on_solve_start=on_solve_start, + on_solve_end=on_solve_end) + t = torch.from_numpy(coarse_depth).unsqueeze(0).unsqueeze(0) + t = F.interpolate(t, size=(height, width), mode="bilinear", align_corners=False) + depth_init = t.squeeze().numpy().astype(np.float32) + else: + depth_init = None + + spherical_directions = spherical_uv_to_directions(_uv_grid(height, width)) + + pano_log_grad_maps, pano_grad_masks = [], [] + pano_log_lap_maps, pano_lap_masks = [], [] + pano_pred_masks: List[np.ndarray] = [] + + for i in range(len(distance_maps)): + proj_uv, proj_depth = _project_cv(spherical_directions, extrinsics[i], intrinsics[i]) + proj_valid = (proj_depth > 0) & (proj_uv > 0).all(axis=-1) & (proj_uv < 1).all(axis=-1) + + Hd, Wd = distance_maps[i].shape[:2] + proj_pixels = np.clip(proj_uv, 0, 1) * np.array([Wd - 1, Hd - 1], dtype=np.float32) + + log_dist = np.log(np.clip(distance_maps[i], 1e-6, None)) + sampled = _scipy_remap_bilinear(log_dist, proj_pixels, mode="bilinear") + pano_log = np.where(proj_valid, sampled, 0.0).astype(np.float32) + + sampled_mask = _scipy_remap_bilinear(pred_masks[i].astype(np.uint8), proj_pixels, mode="nearest") + pano_pred = proj_valid & (sampled_mask > 0) + + # Equirect wraps horizontally but not vertically: wrap pad along x, edge pad along y. + padded = np.pad(pano_log, ((0, 0), (0, 1)), mode="wrap") + gx, gy = padded[:, :-1] - padded[:, 1:], padded[:-1, :] - padded[1:, :] + padded_m = np.pad(pano_pred, ((0, 0), (0, 1)), mode="wrap") + mx, my = padded_m[:, :-1] & padded_m[:, 1:], padded_m[:-1, :] & padded_m[1:, :] + pano_log_grad_maps.append((gx, gy)) + pano_grad_masks.append((mx, my)) + + padded = np.pad(pano_log, ((1, 1), (0, 0)), mode="edge") + padded = np.pad(padded, ((0, 0), (1, 1)), mode="wrap") + lap_kernel = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=np.float32) + lap = convolve(padded, lap_kernel)[1:-1, 1:-1] + padded_m = np.pad(pano_pred, ((1, 1), (0, 0)), mode="edge") + padded_m = np.pad(padded_m, ((0, 0), (1, 1)), mode="wrap") + m_kernel = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=np.uint8) + lap_mask = convolve(padded_m.astype(np.uint8), m_kernel)[1:-1, 1:-1] == 5 + pano_log_lap_maps.append(lap) + pano_lap_masks.append(lap_mask) + pano_pred_masks.append(pano_pred) + + if on_view is not None: + on_view() + + gx = np.stack([m[0] for m in pano_log_grad_maps], axis=0) + gy = np.stack([m[1] for m in pano_log_grad_maps], axis=0) + mx = np.stack([m[0] for m in pano_grad_masks], axis=0) + my = np.stack([m[1] for m in pano_grad_masks], axis=0) + gx_avg = (gx * mx).sum(axis=0) / mx.sum(axis=0).clip(1e-3) + gy_avg = (gy * my).sum(axis=0) / my.sum(axis=0).clip(1e-3) + + laps = np.stack(pano_log_lap_maps, axis=0) + lap_masks = np.stack(pano_lap_masks, axis=0) + lap_avg = (laps * lap_masks).sum(axis=0) / lap_masks.sum(axis=0).clip(1e-3) + + grad_x_mask = mx.any(axis=0).reshape(-1) + grad_y_mask = my.any(axis=0).reshape(-1) + grad_mask = np.concatenate([grad_x_mask, grad_y_mask]) + lap_mask_flat = lap_masks.any(axis=0).reshape(-1) + + A = vstack([ + _grad_equation(width, height, wrap_x=True, wrap_y=False)[grad_mask], + _poisson_equation(width, height, wrap_x=True, wrap_y=False)[lap_mask_flat], + ]) + b = np.concatenate([ + gx_avg.reshape(-1)[grad_x_mask], + gy_avg.reshape(-1)[grad_y_mask], + lap_avg.reshape(-1)[lap_mask_flat], + ]) + x0 = np.log(np.clip(depth_init, 1e-6, None)).reshape(-1) if depth_init is not None else None + + if on_solve_start is not None: + on_solve_start(width, height) + x, *_ = lsmr(A, b, atol=1e-5, btol=1e-5, x0=x0, show=False) + if on_solve_end is not None: + on_solve_end(width, height) + + pano_depth = np.exp(x).reshape(height, width).astype(np.float32) + pano_mask = np.any(pano_pred_masks, axis=0) + return pano_depth, pano_mask diff --git a/comfy/lora.py b/comfy/lora.py index db8f16bcb..f11e26ec9 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -97,12 +97,14 @@ def load_lora(lora, to_load, log_missing=True): def model_lora_keys_clip(model, key_map={}): sdk = model.state_dict().keys() + prefix_set = set() for k in sdk: if k.endswith(".weight"): key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names tp = k.find(".transformer.") #also map without wrapper prefix for composite text encoder models if tp > 0 and not k.startswith("clip_"): key_map["text_encoders.{}".format(k[tp + 1:-len(".weight")])] = k + prefix_set.add(k.split('.')[0]) text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" clip_l_present = False @@ -163,6 +165,13 @@ def model_lora_keys_clip(model, key_map={}): lora_key = "lora_te1_{}".format(l_key.replace(".", "_")) key_map[lora_key] = k + if len(prefix_set) == 1: + full_prefix = "{}.transformer.model.".format(next(iter(prefix_set))) # kohya anima and maybe other single TE models that use a single llama arch based te + for k in sdk: + if k.endswith(".weight"): + if k.startswith(full_prefix): + l_key = k[len(full_prefix):-len(".weight")] + key_map["lora_te_{}".format(l_key.replace(".", "_"))] = k k = "clip_g.transformer.text_projection.weight" if k in sdk: diff --git a/comfy/model_base.py b/comfy/model_base.py index dbed239e5..c22705655 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -58,6 +58,8 @@ import comfy.ldm.cogvideo.model import comfy.ldm.rt_detr.rtdetr_v4 import comfy.ldm.ernie.model import comfy.ldm.sam3.detector +import comfy.ldm.hidream_o1.model +from comfy.ldm.hidream_o1.conditioning import build_extra_conds import comfy.model_management import comfy.patcher_extension @@ -1674,6 +1676,39 @@ class HiDream(BaseModel): out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out +class HiDreamO1(BaseModel): + """HiDream-O1-Image: pixel-space DiT (no VAE). Refs from HiDreamO1ReferenceImages and tokens from the stub TE flow through + extra_conds; the heavy preprocessing lives in comfy.ldm.hidream_o1.conditioning.""" + PATCH_SIZE = 32 + + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream_o1.model.HiDreamO1Transformer) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + text_input_ids = kwargs.get("text_input_ids", None) + noise = kwargs.get("noise", None) + if text_input_ids is None or noise is None: + return out + + # handle area conds + area = kwargs.get("area", None) + if area is not None: + crop_h = min(noise.shape[-2] - area[2], area[0]) + crop_w = min(noise.shape[-1] - area[3], area[1]) + noise = torch.empty((noise.shape[0], 3, crop_h, crop_w), dtype=noise.dtype, device=noise.device) + + conds = build_extra_conds( + text_input_ids, noise, + ref_images=kwargs.get("reference_latents", None), + target_patch_size=self.PATCH_SIZE, + ) + for k, v in conds.items(): + # ar_len is a Python int (precomputed to avoid a GPU sync in forward). + cls = comfy.conds.CONDConstant if k == "ar_len" else comfy.conds.CONDRegular + out[k] = cls(v) + return out + class Chroma(Flux): def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma): super().__init__(model_config, model_type, device=device, unet_model=unet_model) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 8ae456481..bc0b933bc 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -620,6 +620,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys return dit_config + if '{}t_embedder1.mlp.0.weight'.format(key_prefix) in state_dict_keys and '{}x_embedder.proj1.weight'.format(key_prefix) in state_dict_keys: # HiDream-O1 + return {"image_model": "hidream_o1"} + if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream dit_config = {} dit_config["image_model"] = "hidream" diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 33bdedfb1..2ea14bc2c 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -242,6 +242,37 @@ class LazyCastingParam(torch.nn.Parameter): return self.model.patch_weight_to_device(self.key, device_to=self.model.load_device, return_weight=True).to("cpu") +class LazyCastingQuantizedParam: + def __init__(self, model, key): + self.model = model + self.key = key + self.cpu_state_dict = None + + def state_dict_tensor(self, state_dict_key): + if self.cpu_state_dict is None: + weight = self.model.patch_weight_to_device(self.key, device_to=self.model.load_device, return_weight=True) + self.cpu_state_dict = {k: v.to("cpu") for k, v in weight.state_dict(self.key).items()} + return self.cpu_state_dict[state_dict_key] + + +class LazyCastingParamPiece(torch.nn.Parameter): + def __new__(cls, caster, state_dict_key, tensor): + return super().__new__(cls, tensor) + + def __init__(self, caster, state_dict_key, tensor): + self.caster = caster + self.state_dict_key = state_dict_key + + @property + def device(self): + return CustomTorchDevice + + def to(self, *args, **kwargs): + caster = self.caster + del self.caster + return caster.state_dict_tensor(self.state_dict_key) + + class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): self.size = size @@ -1463,20 +1494,37 @@ class ModelPatcher: self.clear_cached_hook_weights() def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): - unet_state_dict = self.model.diffusion_model.state_dict() - for k, v in unet_state_dict.items(): + original_state_dict = self.model.diffusion_model.state_dict() + unet_state_dict = {} + keys = list(original_state_dict) + while len(keys) > 0: + k = keys.pop(0) + v = original_state_dict[k] op_keys = k.rsplit('.', 1) if (len(op_keys) < 2) or op_keys[1] not in ["weight", "bias"]: + unet_state_dict[k] = v continue try: op = comfy.utils.get_attr(self.model.diffusion_model, op_keys[0]) except: + unet_state_dict[k] = v continue if not op or not hasattr(op, "comfy_cast_weights") or \ (hasattr(op, "comfy_patched_weights") and op.comfy_patched_weights == True): + unet_state_dict[k] = v continue key = "diffusion_model." + k - unet_state_dict[k] = LazyCastingParam(self, key, comfy.utils.get_attr(self.model, key)) + weight = comfy.utils.get_attr(self.model, key) + if isinstance(weight, QuantizedTensor) and k in original_state_dict: + qt_state_dict = weight.state_dict(k) + caster = LazyCastingQuantizedParam(self, key) + for group_key in (x for x in qt_state_dict if x in original_state_dict): + if group_key in keys: + keys.remove(group_key) + unet_state_dict.pop(group_key, "") + unet_state_dict[group_key] = LazyCastingParamPiece(caster, "diffusion_model." + group_key, original_state_dict[group_key]) + continue + unet_state_dict[k] = LazyCastingParam(self, key, weight) return self.model.state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) def __del__(self): diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index cf2b5db5f..5af336e76 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -93,7 +93,8 @@ class CONST: def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): sigma = reshape_sigma(sigma, noise.ndim) - return sigma * noise + (1.0 - sigma) * latent_image + s = getattr(self, "noise_scale", 1.0) + return sigma * (s * noise) + (1.0 - sigma) * latent_image def inverse_noise_scaling(self, sigma, latent): sigma = reshape_sigma(sigma, latent.ndim) @@ -288,7 +289,11 @@ class ModelSamplingDiscreteFlow(torch.nn.Module): else: sampling_settings = {} - self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) + self.set_noise_scale(sampling_settings.get("noise_scale", 1.0)) + self.set_parameters( + shift=sampling_settings.get("shift", 1.0), + multiplier=sampling_settings.get("multiplier", 1000), + ) def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): self.shift = shift @@ -296,6 +301,9 @@ class ModelSamplingDiscreteFlow(torch.nn.Module): ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) self.register_buffer('sigmas', ts) + def set_noise_scale(self, noise_scale): + self.noise_scale = float(noise_scale) + @property def sigma_min(self): return self.sigmas[0] diff --git a/comfy/ops.py b/comfy/ops.py index 77ad1d527..f9456854b 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -1285,7 +1285,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec if quant_format in ["float8_e4m3fn", "float8_e5m2"] and weight_key in state_dict: self.quant_format = quant_format qconfig = QUANT_ALGOS[quant_format] - layout_cls = get_layout_class(qconfig["comfy_tensor_layout"]) + self.layout_type = qconfig["comfy_tensor_layout"] + layout_cls = get_layout_class(self.layout_type) weight = state_dict.pop(weight_key) manually_loaded_keys = [weight_key] @@ -1375,6 +1376,7 @@ def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_ if not fp8_compute: disabled.add("float8_e4m3fn") disabled.add("float8_e5m2") + logging.info("Native ops: {} {}".format(", ".join(QUANT_ALGOS.keys() - disabled), ", emulated ops: {}".format(", ".join(disabled)) if len(disabled) > 0 else "")) return mixed_precision_ops(model_config.quant_config, compute_dtype, disabled=disabled) if ( diff --git a/comfy/sd.py b/comfy/sd.py index 749bdd710..1391dfad7 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -79,7 +79,7 @@ import comfy.latent_formats import comfy.ldm.flux.redux -def load_lora_for_models(model, clip, lora, strength_model, strength_clip): +def load_lora_for_models(model, clip, lora, strength_model, strength_clip, lora_metadata=None): key_map = {} if model is not None: key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) @@ -91,6 +91,8 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip): if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) + if lora_metadata: + new_modelpatcher.set_attachments("lora_metadata", lora_metadata) else: k = () new_modelpatcher = None @@ -98,6 +100,8 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip): if clip is not None: new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) + if lora_metadata: + new_clip.patcher.set_attachments("lora_metadata", lora_metadata) else: k1 = () new_clip = None @@ -239,7 +243,8 @@ class CLIP: model_management.archive_model_dtypes(self.cond_stage_model) self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher + te_disable_dynamic = disable_dynamic or getattr(self.cond_stage_model, "disable_offload", False) + ModelPatcher = comfy.model_patcher.ModelPatcher if te_disable_dynamic else comfy.model_patcher.CoreModelPatcher self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) #Match torch.float32 hardcode upcast in TE implemention self.patcher.set_model_compute_dtype(torch.float32) @@ -776,6 +781,7 @@ class VAE: self.latent_channels = 3 self.latent_dim = 2 self.output_channels = 3 + self.disable_offload = True elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE sample_rate = 16000 if sample_rate == 16000: diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 40417f922..1e4434fd5 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -28,6 +28,7 @@ import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.ernie import comfy.text_encoders.cogvideo +import comfy.text_encoders.hidream_o1 from . import supported_models_base from . import latent_formats @@ -1431,6 +1432,50 @@ class HiDream(supported_models_base.BASE): def clip_target(self, state_dict={}): return None # TODO +class HiDreamO1(supported_models_base.BASE): + unet_config = { + "image_model": "hidream_o1", + } + + sampling_settings = { + "shift": 3.0, + "noise_scale": 8.0, + } + + latent_format = latent_formats.HiDreamO1Pixel + memory_usage_factor = 0.033 + # fp16 not supported: LM MLP down_proj activations fp16 overflow, causing NaNs + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + optimizations = {"fp8": False} + + def get_model(self, state_dict, prefix="", device=None): + return model_base.HiDreamO1(self, device=device) + + def process_unet_state_dict(self, state_dict): + # Drop unused Qwen3-VL deepstack merger weights; upstream discards them at inference. + for key in list(state_dict.keys()): + if "visual.deepstack_merger_list" in key: + del state_dict[key] + return state_dict + + def process_vae_state_dict(self, state_dict): + # Pixel-space model: inject sentinel so VAE construction picks PixelspaceConversionVAE. + return {"pixel_space_vae": torch.tensor(1.0)} + + def process_clip_state_dict(self, state_dict): + # Tokenizer-only TE: inject sentinel so load_state_dict_guess_config triggers CLIP init. + return {"_hidream_o1_te_sentinel": torch.zeros(1)} + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget( + comfy.text_encoders.hidream_o1.HiDreamO1Tokenizer, + comfy.text_encoders.hidream_o1.HiDreamO1TE, + ) + class Chroma(supported_models_base.BASE): unet_config = { "image_model": "chroma", @@ -2018,6 +2063,7 @@ models = [ Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, + HiDreamO1, Chroma, ChromaRadiance, ACEStep, diff --git a/comfy/text_encoders/hidream_o1.py b/comfy/text_encoders/hidream_o1.py new file mode 100644 index 000000000..5d287b784 --- /dev/null +++ b/comfy/text_encoders/hidream_o1.py @@ -0,0 +1,119 @@ +"""HiDream-O1-Image tokenizer-only text encoder. + +The real Qwen3-VL backbone runs inside diffusion_model.* every step, so this +module just tokenizes the prompt into text_input_ids and emits them as +conditioning. Position ids / token_types / vinput_mask depend on target H/W +and are built later in model_base.HiDreamO1.extra_conds. +""" + +import os + +import torch +from transformers import Qwen2Tokenizer + +from comfy import sd1_clip + + +# Qwen3-VL special tokens +IM_START_ID = 151644 +IM_END_ID = 151645 +ASSISTANT_ID = 77091 +USER_ID = 872 +NEWLINE_ID = 198 +VISION_START_ID = 151652 +VISION_END_ID = 151653 +IMAGE_TOKEN_ID = 151655 +VIDEO_TOKEN_ID = 151656 +# HiDream-O1-specific tokens +BOI_TOKEN_ID = 151669 +BOR_TOKEN_ID = 151670 +EOR_TOKEN_ID = 151671 +BOT_TOKEN_ID = 151672 +TMS_TOKEN_ID = 151673 + + +class HiDreamO1QwenTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join( + os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer" + ) + super().__init__( + tokenizer_path, + pad_with_end=False, + embedding_size=4096, + embedding_key="hidream_o1", + tokenizer_class=Qwen2Tokenizer, + has_start_token=False, + has_end_token=False, + pad_to_max_length=False, + max_length=99999999, + min_length=1, + pad_token=151643, + tokenizer_data=tokenizer_data, + ) + + +class HiDreamO1Tokenizer(sd1_clip.SD1Tokenizer): + """Wraps prompt in the upstream chat template ending with boi/tms markers. + Image tokens get spliced in at sample time once target H/W is known. + """ + + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__( + embedding_directory=embedding_directory, + tokenizer_data=tokenizer_data, + name="hidream_o1", + tokenizer=HiDreamO1QwenTokenizer, + ) + + def tokenize_with_weights(self, text, return_word_ids=False, **kwargs): + text_tokens_dict = super().tokenize_with_weights( + text, return_word_ids=return_word_ids, disable_weights=True, **kwargs + ) + text_tuples = text_tokens_dict["hidream_o1"][0] + text_tuples = [t for t in text_tuples if int(t[0]) != 151643] # strip pad + + # <|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n<|boi|><|tms|> + def tok(tid): + return (tid, 1.0) if not return_word_ids else (tid, 1.0, 0) + + prefix = [tok(IM_START_ID), tok(USER_ID), tok(NEWLINE_ID)] + suffix = [ + tok(IM_END_ID), tok(NEWLINE_ID), + tok(IM_START_ID), tok(ASSISTANT_ID), tok(NEWLINE_ID), + tok(BOI_TOKEN_ID), tok(TMS_TOKEN_ID), + ] + full = prefix + list(text_tuples) + suffix + return {"hidream_o1": [full]} + + +class HiDreamO1TE(torch.nn.Module): + """Passthrough TE: emits int token ids; the Qwen3-VL backbone in diffusion_model does the actual encoding.""" + + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__() + self.dtypes = {torch.float32} + self.disable_offload = True # skips dynamic VRAM management for this zero-parameter module + self.device = torch.device("cpu") if device is None else torch.device(device) + + def encode_token_weights(self, token_weight_pairs): + tok_pairs = token_weight_pairs["hidream_o1"][0] + ids = [int(t[0]) for t in tok_pairs] + input_ids = torch.tensor([ids], dtype=torch.long) + # Surrogate keeps the cross_attn slot non-empty for CONDITIONING + # plumbing; the model reads text_input_ids out of `extra` instead. + cross_attn = input_ids.unsqueeze(-1).to(torch.float32) + extra = {"text_input_ids": input_ids} + return cross_attn, None, extra + + def load_sd(self, sd): + return [] + + def get_sd(self): + return {} + + def reset_clip_options(self): + pass + + def set_clip_options(self, options): + pass diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index a34c41144..5087228ca 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -397,7 +397,7 @@ class RMSNorm(nn.Module): -def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None): +def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None, interleaved_mrope=False): if not isinstance(theta, list): theta = [theta] @@ -415,16 +415,27 @@ def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_di inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() - sin = emb.sin() - if rope_dims is not None and position_ids.shape[0] > 1: - mrope_section = rope_dims * 2 - cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) - sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) + if rope_dims is not None and position_ids.shape[0] > 1 and interleaved_mrope: + # Qwen3-VL interleaved MRoPE: T-freqs by default, H/W replace every 3rd dim. + freqs_inter = freqs[0].clone() + for axis_idx, offset in ((1, 1), (2, 2)): + length = rope_dims[axis_idx] * 3 + idx = slice(offset, length, 3) + freqs_inter[..., idx] = freqs[axis_idx, ..., idx] + emb = torch.cat((freqs_inter, freqs_inter), dim=-1) + cos = emb.cos().unsqueeze(0) + sin = emb.sin().unsqueeze(0) else: - cos = cos.unsqueeze(1) - sin = sin.unsqueeze(1) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + if rope_dims is not None and position_ids.shape[0] > 1: + mrope_section = rope_dims * 2 + cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) + sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) + else: + cos = cos.unsqueeze(1) + sin = sin.unsqueeze(1) sin_split = sin.shape[-1] // 2 out.append((cos, sin[..., : sin_split], -sin[..., sin_split :])) @@ -689,6 +700,7 @@ class Llama2_(nn.Module): self.config.rope_theta, self.config.rope_scale, self.config.rope_dims, + interleaved_mrope=getattr(self.config, "interleaved_mrope", False), device=device) def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None): diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py index d8ed9cd32..416ce9d18 100644 --- a/comfy/text_encoders/qwen35.py +++ b/comfy/text_encoders/qwen35.py @@ -451,9 +451,8 @@ class Qwen35VisionPatchEmbed(nn.Module): self.proj = ops.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=True, device=device, dtype=dtype) def forward(self, x): - target_dtype = self.proj.weight.dtype x = x.view(-1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size) - return self.proj(x.to(target_dtype)).view(-1, self.embed_dim) + return self.proj(x).view(-1, self.embed_dim) class Qwen35VisionMLP(nn.Module): @@ -651,7 +650,7 @@ class Qwen35VisionModel(nn.Module): x = self.patch_embed(x) pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device) x = x + pos_embeds - rotary_pos_emb = self.rot_pos_emb(grid_thw) + rotary_pos_emb = self.rot_pos_emb(grid_thw).to(x.device) seq_len = x.shape[0] x = x.reshape(seq_len, -1) rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) @@ -761,7 +760,7 @@ class Qwen35ImageTokenizer(sd1_clip.SD1Tokenizer): def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs): image = kwargs.get("image", None) if image is not None and len(images) == 0: - images = [image] + images = [image[i:i + 1] for i in range(image.shape[0])] skip_template = False if text.startswith('<|im_start|>'): @@ -772,13 +771,16 @@ class Qwen35ImageTokenizer(sd1_clip.SD1Tokenizer): if skip_template: llama_text = text else: - if llama_template is None: - if len(images) > 0: - llama_text = self.llama_template_images.format(text) - else: - llama_text = self.llama_template.format(text) + if llama_template is not None: + template = llama_template + elif len(images) == 0: + template = self.llama_template else: - llama_text = llama_template.format(text) + template = self.llama_template_images + if len(images) > 1: + vision_block = "<|vision_start|><|image_pad|><|vision_end|>" + template = template.replace(vision_block, vision_block * len(images), 1) + llama_text = template.format(text) if not thinking: llama_text += "\n\n" diff --git a/comfy/utils.py b/comfy/utils.py index 91e1ba3d3..66682690a 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -1164,12 +1164,18 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am o = out o_d = out_div + ps_view = ps + mask_view = mask for d in range(dims): - o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) - o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + l = min(ps_view.shape[d + 2], o.shape[d + 2] - upscaled[d]) + o = o.narrow(d + 2, upscaled[d], l) + o_d = o_d.narrow(d + 2, upscaled[d], l) + if l < ps_view.shape[d + 2]: + ps_view = ps_view.narrow(d + 2, 0, l) + mask_view = mask_view.narrow(d + 2, 0, l) - o.add_(ps * mask) - o_d.add_(mask) + o.add_(ps_view * mask_view) + o_d.add_(mask_view) if pbar is not None: pbar.update(1) @@ -1196,7 +1202,7 @@ def model_trange(*args, **kwargs): pbar.i1_time = time.time() pbar.set_postfix_str(" Model Initialization complete! ") elif pbar._i == 2: - #bring forward the effective start time based the the diff between first and second iteration + #bring forward the effective start time based the diff between first and second iteration #to attempt to remove load overhead from the final step rate estimate. pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time) pbar.set_postfix_str("") diff --git a/comfy_api/latest/_util/geometry_types.py b/comfy_api/latest/_util/geometry_types.py index b586fceb3..cdde60b10 100644 --- a/comfy_api/latest/_util/geometry_types.py +++ b/comfy_api/latest/_util/geometry_types.py @@ -12,9 +12,24 @@ class VOXEL: class MESH: - def __init__(self, vertices: torch.Tensor, faces: torch.Tensor): - self.vertices = vertices - self.faces = faces + def __init__(self, vertices: torch.Tensor, faces: torch.Tensor, + uvs: torch.Tensor | None = None, + vertex_colors: torch.Tensor | None = None, + texture: torch.Tensor | None = None, + vertex_counts: torch.Tensor | None = None, + face_counts: torch.Tensor | None = None): + + assert (vertex_counts is None) == (face_counts is None), \ + "vertex_counts and face_counts must be provided together (both or neither)" + self.vertices = vertices # vertices: (B, N, 3) + self.faces = faces # faces: (B, M, 3) + self.uvs = uvs # uvs: (B, N, 2) + self.vertex_colors = vertex_colors # vertex_colors: (B, N, 3 or 4) + self.texture = texture # texture: (B, H, W, 3) + # When vertices/faces are zero-padded to a common N/M across the batch (variable-size mesh batch), + # these hold the real per-item lengths (B,). None means rows are uniform and no slicing is needed. + self.vertex_counts = vertex_counts + self.face_counts = face_counts class File3D: diff --git a/comfy_api_nodes/apis/anthropic.py b/comfy_api_nodes/apis/anthropic.py new file mode 100644 index 000000000..6cac537ea --- /dev/null +++ b/comfy_api_nodes/apis/anthropic.py @@ -0,0 +1,75 @@ +from enum import Enum +from typing import Literal + +from pydantic import BaseModel, Field + + +class AnthropicRole(str, Enum): + user = "user" + assistant = "assistant" + + +class AnthropicTextContent(BaseModel): + type: Literal["text"] = "text" + text: str = Field(...) + + +class AnthropicImageSourceBase64(BaseModel): + type: Literal["base64"] = "base64" + media_type: str = Field(..., description="MIME type of the image, e.g. image/png, image/jpeg") + data: str = Field(..., description="Base64-encoded image data") + + +class AnthropicImageSourceUrl(BaseModel): + type: Literal["url"] = "url" + url: str = Field(...) + + +class AnthropicImageContent(BaseModel): + type: Literal["image"] = "image" + source: AnthropicImageSourceBase64 | AnthropicImageSourceUrl = Field(...) + + +class AnthropicMessage(BaseModel): + role: AnthropicRole = Field(...) + content: list[AnthropicTextContent | AnthropicImageContent] = Field(...) + + +class AnthropicMessagesRequest(BaseModel): + model: str = Field(...) + messages: list[AnthropicMessage] = Field(...) + max_tokens: int = Field(..., ge=1) + system: str | None = Field(None, description="Top-level system prompt") + temperature: float | None = Field(None, ge=0.0, le=1.0) + top_p: float | None = Field(None, ge=0.0, le=1.0) + top_k: int | None = Field(None, ge=0) + stop_sequences: list[str] | None = Field(None) + + +class AnthropicResponseTextBlock(BaseModel): + type: Literal["text"] = "text" + text: str = Field(...) + + +class AnthropicCacheCreationUsage(BaseModel): + ephemeral_5m_input_tokens: int | None = Field(None) + ephemeral_1h_input_tokens: int | None = Field(None) + + +class AnthropicMessagesUsage(BaseModel): + input_tokens: int | None = Field(None) + output_tokens: int | None = Field(None) + cache_creation_input_tokens: int | None = Field(None) + cache_read_input_tokens: int | None = Field(None) + cache_creation: AnthropicCacheCreationUsage | None = Field(None) + + +class AnthropicMessagesResponse(BaseModel): + id: str | None = Field(None) + type: str | None = Field(None) + role: str | None = Field(None) + model: str | None = Field(None) + content: list[AnthropicResponseTextBlock] | None = Field(None) + stop_reason: str | None = Field(None) + stop_sequence: str | None = Field(None) + usage: AnthropicMessagesUsage | None = Field(None) diff --git a/comfy_api_nodes/apis/bria.py b/comfy_api_nodes/apis/bria.py index 8c496b56c..e08a519a8 100644 --- a/comfy_api_nodes/apis/bria.py +++ b/comfy_api_nodes/apis/bria.py @@ -23,7 +23,7 @@ class BriaEditImageRequest(BaseModel): None, description="Mask image (black and white). Black areas will be preserved, white areas will be edited. " "If omitted, the edit applies to the entire image. " - "The input image and the the input mask must be of the same size.", + "The input image and the input mask must be of the same size.", ) negative_prompt: str | None = Field(None) guidance_scale: float = Field(...) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index c05bd6893..03f4c445b 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -198,6 +198,62 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [ ("Custom", None, None), ] +_PRESETS_SEEDREAM_1K = [ + ("(1K) 1024x1024 (1:1)", 1024, 1024), + ("(1K) 864x1152 (3:4)", 864, 1152), + ("(1K) 1152x864 (4:3)", 1152, 864), + ("(1K) 1312x736 (16:9)", 1312, 736), + ("(1K) 736x1312 (9:16)", 736, 1312), + ("(1K) 832x1248 (2:3)", 832, 1248), + ("(1K) 1248x832 (3:2)", 1248, 832), + ("(1K) 1568x672 (21:9)", 1568, 672), +] + +_PRESETS_SEEDREAM_2K = [ + ("(2K) 2048x2048 (1:1)", 2048, 2048), + ("(2K) 1728x2304 (3:4)", 1728, 2304), + ("(2K) 2304x1728 (4:3)", 2304, 1728), + ("(2K) 2848x1600 (16:9)", 2848, 1600), + ("(2K) 1600x2848 (9:16)", 1600, 2848), + ("(2K) 1664x2496 (2:3)", 1664, 2496), + ("(2K) 2496x1664 (3:2)", 2496, 1664), + ("(2K) 3136x1344 (21:9)", 3136, 1344), +] + +_PRESETS_SEEDREAM_3K = [ + ("(3K) 3072x3072 (1:1)", 3072, 3072), + ("(3K) 2592x3456 (3:4)", 2592, 3456), + ("(3K) 3456x2592 (4:3)", 3456, 2592), + ("(3K) 4096x2304 (16:9)", 4096, 2304), + ("(3K) 2304x4096 (9:16)", 2304, 4096), + ("(3K) 2496x3744 (2:3)", 2496, 3744), + ("(3K) 3744x2496 (3:2)", 3744, 2496), + ("(3K) 4704x2016 (21:9)", 4704, 2016), +] + +_PRESETS_SEEDREAM_4K = [ + ("(4K) 4096x4096 (1:1)", 4096, 4096), + ("(4K) 3520x4704 (3:4)", 3520, 4704), + ("(4K) 4704x3520 (4:3)", 4704, 3520), + ("(4K) 5504x3040 (16:9)", 5504, 3040), + ("(4K) 3040x5504 (9:16)", 3040, 5504), + ("(4K) 3328x4992 (2:3)", 3328, 4992), + ("(4K) 4992x3328 (3:2)", 4992, 3328), + ("(4K) 6240x2656 (21:9)", 6240, 2656), +] + +_CUSTOM_PRESET = [("Custom", None, None)] + +RECOMMENDED_PRESETS_SEEDREAM_5_LITE = ( + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET +) +RECOMMENDED_PRESETS_SEEDREAM_4_5 = ( + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET +) +RECOMMENDED_PRESETS_SEEDREAM_4_0 = ( + _PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET +) + # Seedance 2.0 reference video pixel count limits per model and output resolution. SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = { "dreamina-seedance-2-0-260128": { diff --git a/comfy_api_nodes/nodes_anthropic.py b/comfy_api_nodes/nodes_anthropic.py new file mode 100644 index 000000000..28dd70d4e --- /dev/null +++ b/comfy_api_nodes/nodes_anthropic.py @@ -0,0 +1,245 @@ +"""API Nodes for Anthropic Claude (Messages API). See: https://docs.anthropic.com/en/api/messages""" + +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api_nodes.apis.anthropic import ( + AnthropicImageContent, + AnthropicImageSourceUrl, + AnthropicMessage, + AnthropicMessagesRequest, + AnthropicMessagesResponse, + AnthropicRole, + AnthropicTextContent, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + get_number_of_images, + sync_op, + upload_images_to_comfyapi, + validate_string, +) + +ANTHROPIC_MESSAGES_ENDPOINT = "/proxy/anthropic/v1/messages" +ANTHROPIC_IMAGE_MAX_PIXELS = 1568 * 1568 +CLAUDE_MAX_IMAGES = 20 + +CLAUDE_MODELS: dict[str, str] = { + "Opus 4.7": "claude-opus-4-7", + "Opus 4.6": "claude-opus-4-6", + "Sonnet 4.6": "claude-sonnet-4-6", + "Sonnet 4.5": "claude-sonnet-4-5-20250929", + "Haiku 4.5": "claude-haiku-4-5-20251001", +} + + +def _claude_model_inputs(): + return [ + IO.Int.Input( + "max_tokens", + default=16000, + min=32, + max=32000, + tooltip="Maximum number of tokens to generate before stopping.", + advanced=True, + ), + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=1.0, + step=0.01, + tooltip="Controls randomness. 0.0 is deterministic, 1.0 is most random. Ignored for Opus 4.7.", + advanced=True, + ), + ] + + +def _model_price_per_million(model: str) -> tuple[float, float] | None: + """Return (input_per_1M, output_per_1M) USD for a Claude model, or None if unknown.""" + if "opus-4-7" in model or "opus-4-6" in model or "opus-4-5" in model: + return 5.0, 25.0 + if "sonnet-4" in model: + return 3.0, 15.0 + if "haiku-4-5" in model: + return 1.0, 5.0 + return None + + +def calculate_tokens_price(response: AnthropicMessagesResponse) -> float | None: + """Compute approximate USD price from response usage. Server-side billing is authoritative.""" + if not response.usage or not response.model: + return None + rates = _model_price_per_million(response.model) + if rates is None: + return None + input_rate, output_rate = rates + input_tokens = response.usage.input_tokens or 0 + output_tokens = response.usage.output_tokens or 0 + cache_read = response.usage.cache_read_input_tokens or 0 + cache_5m = 0 + cache_1h = 0 + if response.usage.cache_creation: + cache_5m = response.usage.cache_creation.ephemeral_5m_input_tokens or 0 + cache_1h = response.usage.cache_creation.ephemeral_1h_input_tokens or 0 + total = ( + input_tokens * input_rate + + output_tokens * output_rate + + cache_read * input_rate * 0.1 + + cache_5m * input_rate * 1.25 + + cache_1h * input_rate * 2.0 + ) + return total / 1_000_000.0 + + +def _get_text_from_response(response: AnthropicMessagesResponse) -> str: + if not response.content: + return "" + return "\n".join(block.text for block in response.content if block.text) + + +async def _build_image_content_blocks( + cls: type[IO.ComfyNode], + image_tensors: list[Input.Image], +) -> list[AnthropicImageContent]: + urls = await upload_images_to_comfyapi( + cls, + image_tensors, + max_images=CLAUDE_MAX_IMAGES, + total_pixels=ANTHROPIC_IMAGE_MAX_PIXELS, + wait_label="Uploading reference images", + ) + return [AnthropicImageContent(source=AnthropicImageSourceUrl(url=url)) for url in urls] + + +class ClaudeNode(IO.ComfyNode): + """Generate text responses from an Anthropic Claude model.""" + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ClaudeNode", + display_name="Anthropic Claude", + category="api node/text/Anthropic", + essentials_category="Text Generation", + description="Generate text responses with Anthropic's Claude models. " + "Provide a text prompt and optionally one or more images for multimodal context.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text input to the model.", + ), + IO.DynamicCombo.Input( + "model", + options=[IO.DynamicCombo.Option(label, _claude_model_inputs()) for label in CLAUDE_MODELS], + tooltip="The Claude model used to generate the response.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, CLAUDE_MAX_IMAGES + 1)], + min=0, + ), + tooltip=f"Optional image(s) to use as context for the model. Up to {CLAUDE_MAX_IMAGES} images.", + ), + IO.String.Input( + "system_prompt", + multiline=True, + default="", + optional=True, + advanced=True, + tooltip="Foundational instructions that dictate the model's behavior.", + ), + ], + outputs=[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( + depends_on=IO.PriceBadgeDepends(widgets=["model"]), + expr=""" + ( + $m := widgets.model; + $contains($m, "opus") ? { + "type": "list_usd", + "usd": [0.005, 0.025], + "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } + } + : $contains($m, "sonnet") ? { + "type": "list_usd", + "usd": [0.003, 0.015], + "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } + } + : $contains($m, "haiku") ? { + "type": "list_usd", + "usd": [0.001, 0.005], + "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } + } + : {"type":"text", "text":"Token-based"} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + images: dict | None = None, + system_prompt: str = "", + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + model_label = model["model"] + max_tokens = model["max_tokens"] + temperature = None if model_label == "Opus 4.7" else model["temperature"] + + image_tensors: list[Input.Image] = [t for t in (images or {}).values() if t is not None] + if sum(get_number_of_images(t) for t in image_tensors) > CLAUDE_MAX_IMAGES: + raise ValueError(f"Up to {CLAUDE_MAX_IMAGES} images are supported per request.") + + content: list[AnthropicTextContent | AnthropicImageContent] = [] + if image_tensors: + content.extend(await _build_image_content_blocks(cls, image_tensors)) + content.append(AnthropicTextContent(text=prompt)) + + response = await sync_op( + cls, + ApiEndpoint(path=ANTHROPIC_MESSAGES_ENDPOINT, method="POST"), + response_model=AnthropicMessagesResponse, + data=AnthropicMessagesRequest( + model=CLAUDE_MODELS[model_label], + max_tokens=max_tokens, + messages=[AnthropicMessage(role=AnthropicRole.user, content=content)], + system=system_prompt or None, + temperature=temperature, + ), + price_extractor=calculate_tokens_price, + ) + return IO.NodeOutput(_get_text_from_response(response) or "Empty response from Claude model.") + + +class AnthropicExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ClaudeNode] + + +async def comfy_entrypoint() -> AnthropicExtension: + return AnthropicExtension() diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index 23590bf24..3f0ce29d8 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -596,6 +596,7 @@ class Flux2ProImageNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends(widgets=["width", "height"], inputs=["images"]), expr=cls.PRICE_BADGE_EXPR, ), + is_deprecated=True, ) @classmethod @@ -674,6 +675,175 @@ class Flux2MaxImageNode(Flux2ProImageNode): """ +_FLUX2_MODEL_ENDPOINTS = { + "Flux.2 [pro]": "/proxy/bfl/flux-2-pro/generate", + "Flux.2 [max]": "/proxy/bfl/flux-2-max/generate", +} + + +def _flux2_model_inputs(): + return [ + IO.Int.Input( + "width", + default=1024, + min=256, + max=2048, + step=32, + ), + IO.Int.Input( + "height", + default=768, + min=256, + max=2048, + step=32, + ), + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, 9)], + min=0, + ), + tooltip="Optional reference image(s) for image-to-image generation. Up to 8 images.", + ), + ] + + +class Flux2ImageNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Flux2ImageNode", + display_name="Flux.2 Image", + category="api node/image/BFL", + description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation or edit", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option("Flux.2 [pro]", _flux2_model_inputs()), + IO.DynamicCombo.Option("Flux.2 [max]", _flux2_model_inputs()), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + ], + 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=["model", "model.width", "model.height"], + input_groups=["model.images"], + ), + expr=""" + ( + $isMax := widgets.model = "flux.2 [max]"; + $MP := 1024 * 1024; + $w := $lookup(widgets, "model.width"); + $h := $lookup(widgets, "model.height"); + $outMP := $max([1, $floor((($w * $h) + $MP - 1) / $MP)]); + $outputCost := $isMax + ? (0.07 + 0.03 * ($outMP - 1)) + : (0.03 + 0.015 * ($outMP - 1)); + $refMin := $isMax ? 0.03 : 0.015; + $refMax := $isMax ? 0.24 : 0.12; + $hasRefs := $lookup(inputGroups, "model.images") > 0; + $hasRefs + ? { + "type": "range_usd", + "min_usd": $outputCost + $refMin, + "max_usd": $outputCost + $refMax, + "format": { "approximate": true } + } + : {"type": "usd", "usd": $outputCost} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + ) -> IO.NodeOutput: + model_choice = model["model"] + endpoint = _FLUX2_MODEL_ENDPOINTS[model_choice] + width = model["width"] + height = model["height"] + images_dict = model.get("images") or {} + + image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None] + n_images = sum(get_number_of_images(t) for t in image_tensors) + if n_images > 8: + raise ValueError("The current maximum number of supported images is 8.") + + flat_tensors: list[torch.Tensor] = [] + for tensor in image_tensors: + if len(tensor.shape) == 4: + flat_tensors.extend(tensor[i] for i in range(tensor.shape[0])) + else: + flat_tensors.append(tensor) + + reference_images: dict[str, str] = {} + for idx, tensor in enumerate(flat_tensors): + key_name = f"input_image_{idx + 1}" if idx else "input_image" + reference_images[key_name] = tensor_to_base64_string(tensor, total_pixels=2048 * 2048) + + initial_response = await sync_op( + cls, + ApiEndpoint(path=endpoint, method="POST"), + response_model=BFLFluxProGenerateResponse, + data=Flux2ProGenerateRequest( + prompt=prompt, + width=width, + height=height, + seed=seed, + **reference_images, + ), + ) + + def price_extractor(_r: BaseModel) -> float | None: + return None if initial_response.cost is None else initial_response.cost / 100 + + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + price_extractor=price_extractor, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + class BFLExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -685,6 +855,7 @@ class BFLExtension(ComfyExtension): FluxProFillNode, Flux2ProImageNode, Flux2MaxImageNode, + Flux2ImageNode, ] diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 5f74f4a14..d6b479336 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -10,6 +10,9 @@ from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.bytedance import ( RECOMMENDED_PRESETS, RECOMMENDED_PRESETS_SEEDREAM_4, + RECOMMENDED_PRESETS_SEEDREAM_4_0, + RECOMMENDED_PRESETS_SEEDREAM_4_5, + RECOMMENDED_PRESETS_SEEDREAM_5_LITE, SEEDANCE2_PRICE_PER_1K_TOKENS, SEEDANCE2_REF_VIDEO_PIXEL_LIMITS, VIDEO_TASKS_EXECUTION_TIME, @@ -68,6 +71,12 @@ SEEDREAM_MODELS = { "seedream-4-0-250828": "seedream-4-0-250828", } +SEEDREAM_PRESETS = { + "seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE, + "seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5, + "seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0, +} + # Long-running tasks endpoints(e.g., video) BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id} @@ -562,6 +571,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode): ) """, ), + is_deprecated=True, ) @classmethod @@ -651,6 +661,226 @@ class ByteDanceSeedreamNode(IO.ComfyNode): return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls])) +def _seedream_model_inputs(*, max_ref_images: int, presets: list): + return [ + IO.Combo.Input( + "size_preset", + options=[label for label, _, _ in presets], + tooltip="Pick a recommended size. Select Custom to use the width and height below.", + ), + IO.Int.Input( + "width", + default=2048, + min=1024, + max=6240, + step=2, + tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`", + ), + IO.Int.Input( + "height", + default=2048, + min=1024, + max=4992, + step=2, + tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`", + ), + IO.Int.Input( + "max_images", + default=1, + min=1, + max=max_ref_images, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Maximum number of images to generate. With 1, exactly one image is produced. " + "With >1, the model generates between 1 and max_images related images " + "(e.g., story scenes, character variations). " + "Total images (input + generated) cannot exceed 15.", + ), + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, max_ref_images + 1)], + min=0, + ), + tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. " + f"Up to {max_ref_images} images.", + ), + IO.Boolean.Input( + "fail_on_partial", + default=False, + tooltip="If enabled, abort execution if any requested images are missing or return an error.", + advanced=True, + ), + ] + + +class ByteDanceSeedreamNodeV2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceSeedreamNodeV2", + display_name="ByteDance Seedream 4.5 & 5.0", + category="api node/image/ByteDance", + description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for creating or editing an image.", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "seedream 5.0 lite", + _seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE), + ), + IO.DynamicCombo.Option( + "seedream-4-5-251128", + _seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_5), + ), + IO.DynamicCombo.Option( + "seedream-4-0-250828", + _seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_0), + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip='Whether to add an "AI generated" watermark to the image.', + advanced=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model"]), + expr=""" + ( + $price := $contains(widgets.model, "5.0 lite") ? 0.035 : + $contains(widgets.model, "4-5") ? 0.04 : 0.03; + { + "type":"usd", + "usd": $price, + "format": { "suffix":" x images/Run", "approximate": true } + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int = 0, + watermark: bool = False, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + model_id = SEEDREAM_MODELS[model["model"]] + presets = SEEDREAM_PRESETS[model_id] + + size_preset = model.get("size_preset", presets[0][0]) + width = model.get("width", 2048) + height = model.get("height", 2048) + max_images = model.get("max_images", 1) + sequential_image_generation = "disabled" if max_images == 1 else "auto" + images_dict = model.get("images") or {} + fail_on_partial = model.get("fail_on_partial", False) + + w = h = None + for label, tw, th in presets: + if label == size_preset: + w, h = tw, th + break + if w is None or h is None: + w, h = width, height + + out_num_pixels = w * h + mp_provided = out_num_pixels / 1_000_000.0 + if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3686400: + raise ValueError( + f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided." + ) + if "seedream-4-0" in model_id and out_num_pixels < 921600: + raise ValueError( + f"Minimum image resolution that the selected model can generate is 0.92MP, " + f"but {mp_provided:.2f}MP provided." + ) + if out_num_pixels > 16_777_216: + raise ValueError( + f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided." + ) + + image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None] + n_input_images = sum(get_number_of_images(t) for t in image_tensors) + max_num_of_images = 14 if model_id == "seedream-5-0-260128" else 10 + if n_input_images > max_num_of_images: + raise ValueError( + f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received." + ) + if sequential_image_generation == "auto" and n_input_images + max_images > 15: + raise ValueError( + "The maximum number of generated images plus the number of reference images cannot exceed 15." + ) + + reference_images_urls: list[str] = [] + if image_tensors: + for tensor in image_tensors: + validate_image_aspect_ratio(tensor, (1, 3), (3, 1)) + reference_images_urls = await upload_images_to_comfyapi( + cls, + image_tensors, + max_images=n_input_images, + mime_type="image/png", + wait_label="Uploading reference images", + ) + + response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"), + response_model=ImageTaskCreationResponse, + data=Seedream4TaskCreationRequest( + model=model_id, + prompt=prompt, + image=reference_images_urls, + size=f"{w}x{h}", + seed=seed, + sequential_image_generation=sequential_image_generation, + sequential_image_generation_options=Seedream4Options(max_images=max_images), + watermark=watermark, + ), + ) + if len(response.data) == 1: + return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response))) + urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d] + if fail_on_partial and len(urls) < len(response.data): + raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.") + return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls])) + + class ByteDanceTextToVideoNode(IO.ComfyNode): @classmethod @@ -2105,6 +2335,7 @@ class ByteDanceExtension(ComfyExtension): return [ ByteDanceImageNode, ByteDanceSeedreamNode, + ByteDanceSeedreamNodeV2, ByteDanceTextToVideoNode, ByteDanceImageToVideoNode, ByteDanceFirstLastFrameNode, diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py index dd5d7e249..a103f24ee 100644 --- a/comfy_api_nodes/nodes_grok.py +++ b/comfy_api_nodes/nodes_grok.py @@ -162,6 +162,61 @@ class GrokImageNode(IO.ComfyNode): ) +_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS = [ + "auto", + "1:1", + "2:3", + "3:2", + "3:4", + "4:3", + "9:16", + "16:9", + "9:19.5", + "19.5:9", + "9:20", + "20:9", + "1:2", + "2:1", +] + + +def _grok_image_edit_model_inputs(*, max_ref_images: int, with_aspect_ratio: bool): + inputs = [ + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, max_ref_images + 1)], + min=1, + ), + tooltip=( + "Reference image to edit." + if max_ref_images == 1 + else f"Reference image(s) to edit. Up to {max_ref_images} images." + ), + ), + IO.Combo.Input("resolution", options=["1K", "2K"]), + IO.Int.Input( + "number_of_images", + default=1, + min=1, + max=10, + step=1, + tooltip="Number of edited images to generate", + display_mode=IO.NumberDisplay.number, + ), + ] + if with_aspect_ratio: + inputs.append( + IO.Combo.Input( + "aspect_ratio", + options=_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS, + tooltip="Only allowed when multiple images are connected.", + ) + ) + return inputs + + class GrokImageEditNode(IO.ComfyNode): @classmethod @@ -256,6 +311,7 @@ class GrokImageEditNode(IO.ComfyNode): ) """, ), + is_deprecated=True, ) @classmethod @@ -303,6 +359,143 @@ class GrokImageEditNode(IO.ComfyNode): ) +class GrokImageEditNodeV2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GrokImageEditNodeV2", + display_name="Grok Image Edit", + category="api node/image/Grok", + description="Modify an existing image based on a text prompt", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="The text prompt used to generate the image", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "grok-imagine-image-quality", + _grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True), + ), + IO.DynamicCombo.Option( + "grok-imagine-image-pro", + _grok_image_edit_model_inputs(max_ref_images=1, with_aspect_ratio=False), + ), + IO.DynamicCombo.Option( + "grok-imagine-image", + _grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True), + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + 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=["model", "model.resolution", "model.number_of_images"], + ), + expr=""" + ( + $isQualityModel := widgets.model = "grok-imagine-image-quality"; + $isPro := $contains(widgets.model, "pro"); + $res := $lookup(widgets, "model.resolution"); + $n := $lookup(widgets, "model.number_of_images"); + $rate := $isQualityModel + ? ($res = "1k" ? 0.05 : 0.07) + : ($isPro ? 0.07 : 0.02); + $base := $isQualityModel ? 0.01 : 0.002; + $output := $rate * $n; + $isPro + ? {"type":"usd","usd": $base + $output} + : {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + model_id = model["model"] + resolution = model["resolution"] + number_of_images = model["number_of_images"] + images_dict = model.get("images") or {} + aspect_ratio = model.get("aspect_ratio", "auto") + + image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None] + n_images = sum(get_number_of_images(t) for t in image_tensors) + if n_images < 1: + raise ValueError("At least one image is required for editing.") + if model_id == "grok-imagine-image-pro" and n_images > 1: + raise ValueError("The pro model supports only 1 input image.") + if model_id != "grok-imagine-image-pro" and n_images > 3: + raise ValueError("A maximum of 3 input images is supported.") + if aspect_ratio != "auto" and n_images == 1: + raise ValueError( + "Custom aspect ratio is only allowed when multiple images are connected to the image input." + ) + + flat_tensors: list[torch.Tensor] = [] + for tensor in image_tensors: + if len(tensor.shape) == 4: + flat_tensors.extend(tensor[i] for i in range(tensor.shape[0])) + else: + flat_tensors.append(tensor) + + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"), + data=ImageEditRequest( + model=model_id, + images=[ + InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in flat_tensors + ], + prompt=prompt, + resolution=resolution.lower(), + n=number_of_images, + seed=seed, + aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio, + ), + response_model=ImageGenerationResponse, + price_extractor=_extract_grok_price, + ) + if len(response.data) == 1: + return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url)) + return IO.NodeOutput( + torch.cat( + [await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]], + ) + ) + + class GrokVideoNode(IO.ComfyNode): @classmethod @@ -737,6 +930,7 @@ class GrokExtension(ComfyExtension): return [ GrokImageNode, GrokImageEditNode, + GrokImageEditNodeV2, GrokVideoNode, GrokVideoReferenceNode, GrokVideoEditNode, diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index daed495da..a5a188634 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -27,6 +27,7 @@ from comfy_api_nodes.util import ( ApiEndpoint, download_url_to_bytesio, downscale_image_tensor, + get_number_of_images, poll_op, sync_op, tensor_to_base64_string, @@ -372,6 +373,7 @@ class OpenAIGPTImage1(IO.ComfyNode): display_name="OpenAI GPT Image 2", category="api node/image/OpenAI", description="Generates images synchronously via OpenAI's GPT Image endpoint.", + is_deprecated=True, inputs=[ IO.String.Input( "prompt", @@ -640,6 +642,316 @@ class OpenAIGPTImage1(IO.ComfyNode): return IO.NodeOutput(await validate_and_cast_response(response)) +def _gpt_image_shared_inputs(): + """Inputs shared by all GPT Image models (quality + reference images + mask).""" + return [ + IO.Combo.Input( + "quality", + default="low", + options=["low", "medium", "high"], + tooltip="Image quality, affects cost and generation time.", + ), + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, 17)], + min=0, + ), + tooltip="Optional reference image(s) for image editing. Up to 16 images.", + ), + IO.Mask.Input( + "mask", + optional=True, + tooltip="Optional mask for inpainting (white areas will be replaced). " + "Requires exactly one reference image.", + ), + ] + + +def _gpt_image_legacy_model_inputs(): + """Per-model widget set for legacy gpt-image-1 / gpt-image-1.5 (4 base sizes, transparent bg allowed).""" + return [ + IO.Combo.Input( + "size", + default="auto", + options=["auto", "1024x1024", "1024x1536", "1536x1024"], + tooltip="Image size.", + ), + IO.Combo.Input( + "background", + default="auto", + options=["auto", "opaque", "transparent"], + tooltip="Return image with or without background.", + ), + *_gpt_image_shared_inputs(), + ] + + +class OpenAIGPTImageNodeV2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="OpenAIGPTImageNodeV2", + display_name="OpenAI GPT Image 2", + category="api node/image/OpenAI", + description="Generates images via OpenAI's GPT Image endpoint.", + inputs=[ + IO.String.Input( + "prompt", + default="", + multiline=True, + tooltip="Text prompt for GPT Image", + ), + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "gpt-image-2", + [ + IO.Combo.Input( + "size", + default="auto", + options=[ + "auto", + "1024x1024", + "1024x1536", + "1536x1024", + "2048x2048", + "2048x1152", + "1152x2048", + "3840x2160", + "2160x3840", + "Custom", + ], + tooltip="Image size. Select 'Custom' to use the custom width and height.", + ), + IO.Int.Input( + "custom_width", + default=1024, + min=1024, + max=3840, + step=16, + tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.", + ), + IO.Int.Input( + "custom_height", + default=1024, + min=1024, + max=3840, + step=16, + tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.", + ), + IO.Combo.Input( + "background", + default="auto", + options=["auto", "opaque"], + tooltip="Return image with or without background.", + ), + *_gpt_image_shared_inputs(), + ], + ), + IO.DynamicCombo.Option("gpt-image-1.5", _gpt_image_legacy_model_inputs()), + IO.DynamicCombo.Option("gpt-image-1", _gpt_image_legacy_model_inputs()), + ], + ), + IO.Int.Input( + "n", + default=1, + min=1, + max=8, + step=1, + tooltip="How many images to generate", + display_mode=IO.NumberDisplay.number, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="not implemented yet in backend", + ), + ], + 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=["model", "model.quality", "n"]), + expr=""" + ( + $ranges := { + "gpt-image-1": { + "low": [0.011, 0.02], + "medium": [0.042, 0.07], + "high": [0.167, 0.25] + }, + "gpt-image-1.5": { + "low": [0.009, 0.02], + "medium": [0.034, 0.062], + "high": [0.133, 0.22] + }, + "gpt-image-2": { + "low": [0.0048, 0.019], + "medium": [0.041, 0.168], + "high": [0.165, 0.67] + } + }; + $range := $lookup($lookup($ranges, widgets.model), $lookup(widgets, "model.quality")); + $nRaw := widgets.n; + $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1; + ($n = 1) + ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}} + : { + "type":"range_usd", + "min_usd": $range[0] * $n, + "max_usd": $range[1] * $n, + "format": { "suffix": "/Run", "approximate": true } + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + n: int, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + + model_id = model["model"] + size = model["size"] + background = model["background"] + quality = model["quality"] + custom_width = model.get("custom_width", 1024) + custom_height = model.get("custom_height", 1024) + + images_dict = model.get("images") or {} + image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None] + n_images = sum(get_number_of_images(t) for t in image_tensors) + mask = model.get("mask") + + if mask is not None and n_images == 0: + raise ValueError("Cannot use a mask without an input image") + + if size == "Custom": + if custom_width % 16 != 0 or custom_height % 16 != 0: + raise ValueError( + f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}" + ) + if max(custom_width, custom_height) > 3840: + raise ValueError( + f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}" + ) + ratio = max(custom_width, custom_height) / min(custom_width, custom_height) + if ratio > 3: + raise ValueError( + f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}" + ) + total_pixels = custom_width * custom_height + if not 655_360 <= total_pixels <= 8_294_400: + raise ValueError( + f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}" + ) + size = f"{custom_width}x{custom_height}" + + if model_id == "gpt-image-1": + price_extractor = calculate_tokens_price_image_1 + elif model_id == "gpt-image-1.5": + price_extractor = calculate_tokens_price_image_1_5 + elif model_id == "gpt-image-2": + price_extractor = calculate_tokens_price_image_2_0 + else: + raise ValueError(f"Unknown model: {model_id}") + + if image_tensors: + flat: list[torch.Tensor] = [] + for tensor in image_tensors: + if len(tensor.shape) == 4: + flat.extend(tensor[i : i + 1] for i in range(tensor.shape[0])) + else: + flat.append(tensor.unsqueeze(0)) + + files = [] + for i, single_image in enumerate(flat): + scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze() + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + + if len(flat) == 1: + files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png"))) + else: + files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png"))) + + if mask is not None: + if len(flat) != 1: + raise Exception("Cannot use a mask with multiple image") + ref_image = flat[0] + if mask.shape[1:] != ref_image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + _, height, width = mask.shape + rgba_mask = torch.zeros(height, width, 4, device="cpu") + rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu() + scaled_mask = downscale_image_tensor( + rgba_mask.unsqueeze(0), total_pixels=2048 * 2048 + ).squeeze() + mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_img_byte_arr = BytesIO() + mask_img.save(mask_img_byte_arr, format="PNG") + mask_img_byte_arr.seek(0) + files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png"))) + + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/openai/images/edits", method="POST"), + response_model=OpenAIImageGenerationResponse, + data=OpenAIImageEditRequest( + model=model_id, + prompt=prompt, + quality=quality, + background=background, + n=n, + size=size, + moderation="low", + ), + content_type="multipart/form-data", + files=files, + price_extractor=price_extractor, + ) + else: + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/openai/images/generations", method="POST"), + response_model=OpenAIImageGenerationResponse, + data=OpenAIImageGenerationRequest( + model=model_id, + prompt=prompt, + quality=quality, + background=background, + n=n, + size=size, + moderation="low", + ), + price_extractor=price_extractor, + ) + return IO.NodeOutput(await validate_and_cast_response(response)) + + class OpenAIChatNode(IO.ComfyNode): """ Node to generate text responses from an OpenAI model. @@ -999,6 +1311,7 @@ class OpenAIExtension(ComfyExtension): OpenAIDalle2, OpenAIDalle3, OpenAIGPTImage1, + OpenAIGPTImageNodeV2, OpenAIChatNode, OpenAIInputFiles, OpenAIChatConfig, diff --git a/comfy_api_nodes/nodes_quiver.py b/comfy_api_nodes/nodes_quiver.py index 28862e368..3269c0afe 100644 --- a/comfy_api_nodes/nodes_quiver.py +++ b/comfy_api_nodes/nodes_quiver.py @@ -143,7 +143,7 @@ class QuiverTextToSVGNode(IO.ComfyNode): if reference_images: references = [] for key in reference_images: - url = await upload_image_to_comfyapi(cls, reference_images[key]) + url = await upload_image_to_comfyapi(cls, reference_images[key], mime_type="image/png") references.append(QuiverImageObject(url=url)) if len(references) > 4: raise ValueError("Maximum 4 reference images are allowed.") @@ -252,7 +252,7 @@ class QuiverImageToSVGNode(IO.ComfyNode): model: dict, seed: int, ) -> IO.NodeOutput: - image_url = await upload_image_to_comfyapi(cls, image) + image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png") response = await sync_op( cls, diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py index 7e8411fa4..567c37be0 100644 --- a/comfy_extras/nodes_advanced_samplers.py +++ b/comfy_extras/nodes_advanced_samplers.py @@ -86,6 +86,37 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No return x +class SamplerLCM(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="SamplerLCM", + category="sampling/samplers", + description=("LCM sampler with tunable per-step noise. s_noise is a multiplier on the model's training noise scale"), + inputs=[ + io.Float.Input("s_noise", default=1.0, min=0.0, max=64.0, step=0.01, + tooltip="Per-step noise multiplier at the first step (1.0 = match training)."), + io.Float.Input("s_noise_end", default=1.0, min=0.0, max=64.0, step=0.01, + tooltip="Per-step noise multiplier at the last step. Set equal to s_noise for a constant schedule."), + io.Float.Input("noise_clip_std", default=0.0, min=0.0, max=10.0, step=0.01, + tooltip="Clamp per-step noise to +/- N*std. 0 disables."), + ], + outputs=[io.Sampler.Output()], + ) + + @classmethod + def execute(cls, s_noise, s_noise_end, noise_clip_std) -> io.NodeOutput: + sampler = comfy.samplers.ksampler( + "lcm", + { + "s_noise": float(s_noise), + "s_noise_end": float(s_noise_end), + "noise_clip_std": float(noise_clip_std), + }, + ) + return io.NodeOutput(sampler) + + class SamplerEulerCFGpp(io.ComfyNode): @classmethod def define_schema(cls) -> io.Schema: @@ -114,6 +145,7 @@ class AdvancedSamplersExtension(ComfyExtension): async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ SamplerLCMUpscale, + SamplerLCM, SamplerEulerCFGpp, ] diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 5f514716f..fcc1c34d5 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -82,6 +82,8 @@ class VAEEncodeAudio(IO.ComfyNode): @classmethod def execute(cls, vae, audio) -> IO.NodeOutput: + if audio is None: + raise ValueError("VAEEncodeAudio: input audio is None (source video may have no audio track).") sample_rate = audio["sample_rate"] vae_sample_rate = getattr(vae, "audio_sample_rate", 44100) if vae_sample_rate != sample_rate: @@ -171,6 +173,8 @@ class SaveAudio(IO.ComfyNode): @classmethod def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> IO.NodeOutput: + if audio is None: + raise ValueError("SaveAudio: input audio is None (source video may have no audio track).") return IO.NodeOutput( ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format) ) @@ -198,6 +202,8 @@ class SaveAudioMP3(IO.ComfyNode): @classmethod def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="128k") -> IO.NodeOutput: + if audio is None: + raise ValueError("SaveAudioMP3: input audio is None (source video may have no audio track).") return IO.NodeOutput( ui=UI.AudioSaveHelper.get_save_audio_ui( audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality @@ -226,6 +232,8 @@ class SaveAudioOpus(IO.ComfyNode): @classmethod def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="V3") -> IO.NodeOutput: + if audio is None: + raise ValueError("SaveAudioOpus: input audio is None (source video may have no audio track).") return IO.NodeOutput( ui=UI.AudioSaveHelper.get_save_audio_ui( audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality @@ -252,6 +260,8 @@ class PreviewAudio(IO.ComfyNode): @classmethod def execute(cls, audio) -> IO.NodeOutput: + if audio is None: + raise ValueError("PreviewAudio: input audio is None (source video may have no audio track).") return IO.NodeOutput(ui=UI.PreviewAudio(audio, cls=cls)) save_flac = execute # TODO: remove @@ -297,6 +307,7 @@ class LoadAudio(IO.ComfyNode): @classmethod def define_schema(cls): input_dir = folder_paths.get_input_directory() + os.makedirs(input_dir, exist_ok=True) files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"]) return IO.Schema( node_id="LoadAudio", @@ -391,21 +402,26 @@ class TrimAudioDuration(IO.ComfyNode): @classmethod def execute(cls, audio, start_index, duration) -> IO.NodeOutput: + if audio is None: + return IO.NodeOutput(None) waveform = audio["waveform"] sample_rate = audio["sample_rate"] audio_length = waveform.shape[-1] + if audio_length == 0: + return IO.NodeOutput(audio) + if start_index < 0: start_frame = audio_length + int(round(start_index * sample_rate)) else: start_frame = int(round(start_index * sample_rate)) - start_frame = max(0, min(start_frame, audio_length - 1)) + start_frame = max(0, min(start_frame, audio_length)) end_frame = start_frame + int(round(duration * sample_rate)) end_frame = max(0, min(end_frame, audio_length)) if start_frame >= end_frame: - raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.") + raise ValueError("TrimAudioDuration: Start time must be less than end time and be within the audio length.") return IO.NodeOutput({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate}) @@ -432,11 +448,13 @@ class SplitAudioChannels(IO.ComfyNode): @classmethod def execute(cls, audio) -> IO.NodeOutput: + if audio is None: + return IO.NodeOutput(None, None) waveform = audio["waveform"] sample_rate = audio["sample_rate"] if waveform.shape[1] != 2: - raise ValueError("AudioSplit: Input audio has only one channel.") + raise ValueError(f"AudioSplit: Input audio must be stereo (2 channels), got {waveform.shape[1]} channel(s).") left_channel = waveform[..., 0:1, :] right_channel = waveform[..., 1:2, :] @@ -464,6 +482,12 @@ class JoinAudioChannels(IO.ComfyNode): @classmethod def execute(cls, audio_left, audio_right) -> IO.NodeOutput: + if audio_left is None and audio_right is None: + return IO.NodeOutput(None) + if audio_left is None: + return IO.NodeOutput(audio_right) + if audio_right is None: + return IO.NodeOutput(audio_left) waveform_left = audio_left["waveform"] sample_rate_left = audio_left["sample_rate"] waveform_right = audio_right["waveform"] @@ -537,6 +561,12 @@ class AudioConcat(IO.ComfyNode): @classmethod def execute(cls, audio1, audio2, direction) -> IO.NodeOutput: + if audio1 is None and audio2 is None: + return IO.NodeOutput(None) + if audio1 is None: + return IO.NodeOutput(audio2) + if audio2 is None: + return IO.NodeOutput(audio1) waveform_1 = audio1["waveform"] waveform_2 = audio2["waveform"] sample_rate_1 = audio1["sample_rate"] @@ -584,6 +614,12 @@ class AudioMerge(IO.ComfyNode): @classmethod def execute(cls, audio1, audio2, merge_method) -> IO.NodeOutput: + if audio1 is None and audio2 is None: + return IO.NodeOutput(None) + if audio1 is None: + return IO.NodeOutput(audio2) + if audio2 is None: + return IO.NodeOutput(audio1) waveform_1 = audio1["waveform"] waveform_2 = audio2["waveform"] sample_rate_1 = audio1["sample_rate"] @@ -594,6 +630,9 @@ class AudioMerge(IO.ComfyNode): length_1 = waveform_1.shape[-1] length_2 = waveform_2.shape[-1] + if length_1 == 0 or length_2 == 0: + return IO.NodeOutput({"waveform": waveform_1, "sample_rate": output_sample_rate}) + if length_2 > length_1: logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.") waveform_2 = waveform_2[..., :length_1] @@ -645,6 +684,8 @@ class AudioAdjustVolume(IO.ComfyNode): @classmethod def execute(cls, audio, volume) -> IO.NodeOutput: + if audio is None: + return IO.NodeOutput(None) if volume == 0: return IO.NodeOutput(audio) waveform = audio["waveform"] @@ -728,8 +769,14 @@ class AudioEqualizer3Band(IO.ComfyNode): @classmethod def execute(cls, audio, low_gain_dB, low_freq, mid_gain_dB, mid_freq, mid_q, high_gain_dB, high_freq) -> IO.NodeOutput: + if audio is None: + return IO.NodeOutput(None) waveform = audio["waveform"] sample_rate = audio["sample_rate"] + + if waveform.shape[-1] == 0: + return IO.NodeOutput(audio) + eq_waveform = waveform.clone() # 1. Apply Low Shelf (Bass) diff --git a/comfy_extras/nodes_hidream_o1.py b/comfy_extras/nodes_hidream_o1.py new file mode 100644 index 000000000..f393745f6 --- /dev/null +++ b/comfy_extras/nodes_hidream_o1.py @@ -0,0 +1,256 @@ +from typing_extensions import override + +import torch + +import comfy.model_management +import comfy.patcher_extension +import node_helpers +from comfy_api.latest import ComfyExtension, io + + +class EmptyHiDreamO1LatentImage(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="EmptyHiDreamO1LatentImage", + display_name="Empty HiDream-O1 Latent Image", + category="latent/image", + description=( + "Empty pixel-space latent for HiDream-O1-Image. The model was " + "trained at ~4 megapixels; lower resolutions go off-distribution " + "and quality regresses noticeably. Trained resolutions: " + "2048x2048, 2304x1728, 1728x2304, 2560x1440, 1440x2560, " + "2496x1664, 1664x2496, 3104x1312, 1312x3104, 2304x1792, 1792x2304." + ), + inputs=[ + io.Int.Input(id="width", default=2048, min=64, max=4096, step=32), + io.Int.Input(id="height", default=2048, min=64, max=4096, step=32), + io.Int.Input(id="batch_size", default=1, min=1, max=64), + ], + outputs=[io.Latent().Output()], + ) + + @classmethod + def execute(cls, *, width: int, height: int, batch_size: int = 1) -> io.NodeOutput: + latent = torch.zeros( + (batch_size, 3, height, width), + device=comfy.model_management.intermediate_device(), + ) + return io.NodeOutput({"samples": latent}) + + +class HiDreamO1ReferenceImages(io.ComfyNode): + """Attach reference images to both positive and negative conditioning.""" + + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="HiDreamO1ReferenceImages", + display_name="HiDream-O1 Reference Images", + category="conditioning/image", + description=( + "Attach 1-10 reference images to conditioning, one for edit instruction" + "or multiple for subject-driven personalization." + ), + inputs=[ + io.Conditioning.Input(id="positive"), + io.Conditioning.Input(id="negative"), + io.Autogrow.Input( + "images", + template=io.Autogrow.TemplateNames( + io.Image.Input("image"), + names=[f"image_{i}" for i in range(1, 11)], + min=1, + ), + tooltip=("Reference images. 1 image = instruction edit; 2-10 images = multi reference." + ), + ), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + ) + + @classmethod + def execute(cls, *, positive, negative, images: io.Autogrow.Type) -> io.NodeOutput: + refs = [images[f"image_{i}"] for i in range(1, 11) if f"image_{i}" in images] + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": refs}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": refs}, append=True) + return io.NodeOutput(positive, negative) + + +class HiDreamO1PatchSeamSmoothing(io.ComfyNode): + PATCH_SIZE = 32 + EDGE_FEATHER = 4 + + # Shift presets per (pattern, N). 8-pass = 4-quadrant + 4 quarter-patch offsets. + SHIFTS_BY_PATTERN = { + ("single_shift", 2): [(0, 0), (16, 16)], + ("single_shift", 4): [(0, 0), (16, 0), (0, 16), (16, 16)], + ("single_shift", 8): [(0, 0), (16, 0), (0, 16), (16, 16), + (8, 8), (24, 8), (8, 24), (24, 24)], + ("symmetric", 2): [(-8, -8), (8, 8)], + ("symmetric", 4): [(-8, -8), (8, -8), (-8, 8), (8, 8)], + ("symmetric", 8): [(-12, -12), (4, -12), (-12, 4), (4, 4), + (-4, -4), (12, -4), (-4, 12), (12, 12)], + } + RAMP_LEVELS = { + "2": [2], + "4": [4], + "ramp_2_4": [2, 4], + "ramp_2_4_8": [2, 4, 8], + } + + @staticmethod + def _hann_tile(cy: int, cx: int, size: int = 32) -> torch.Tensor: + """size x size Hann tile peaking at (cy, cx) within a patch.""" + half = size // 2 + yy = torch.arange(size).view(size, 1) + xx = torch.arange(size).view(1, size) + dy = ((yy - cy + half) % size) - half + dx = ((xx - cx + half) % size) - half + return 0.25 * (1 + torch.cos(torch.pi * dy / half)) * (1 + torch.cos(torch.pi * dx / half)) + + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="HiDreamO1PatchSeamSmoothing", + display_name="HiDream-O1 Patch Seam Smoothing", + category="advanced/model", + is_experimental=True, + description=( + "Average the model output across multiple shifted patch-grid " + "positions during the late portion of sampling. Cancels seams." + ), + inputs=[ + io.Model.Input(id="model"), + io.Float.Input(id="start_percent", default=0.8, min=0.0, max=1.0, step=0.01, + tooltip="Sampling progress (0=start, 1=end) at which the blend turns ON.", + ), + io.Float.Input(id="end_percent", default=1.0, min=0.0, max=1.0, step=0.01, + tooltip="Sampling progress at which the blend turns OFF.", + ), + io.Combo.Input( + id="pattern", + options=["single_shift", "symmetric"], + default="single_shift", + tooltip="Shift layout. single_shift: one pass at the natural patch grid + others offset. symmetric: all passes off-grid, shifts split around origin.", + ), + io.Combo.Input( + id="passes", + options=["2", "4", "ramp_2_4", "ramp_2_4_8"], + default="2", + tooltip="Number of passes per gated step. 2/4 = fixed. ramp_*: pass count increases as sampling approaches end (more smoothing where seams are most visible).", + ), + io.Combo.Input( + id="blend", + options=["average", "window", "median"], + default="average", + tooltip="average: equal-weight mean. window: Hann-windowed weighting favoring each pass away from its patch boundaries. median: per-pixel median, rejects wraparound-outlier passes.", + ), + io.Float.Input(id="strength", default=1.0, min=0.0, max=1.0, step=0.01, + tooltip="Interpolation between the natural-grid pred (0) and the averaged result (1).", + ), + ], + outputs=[io.Model.Output()], + ) + + @classmethod + def execute(cls, *, model, start_percent: float, end_percent: float, pattern: str, passes: str, blend: str, strength: float) -> io.NodeOutput: + if strength <= 0.0 or end_percent <= start_percent: + return io.NodeOutput(model) + + P = cls.PATCH_SIZE + half = P // 2 + shift_levels = [cls.SHIFTS_BY_PATTERN[(pattern, n)] for n in cls.RAMP_LEVELS[passes]] + + if blend == "window": + window_tile_levels = [ + torch.stack([cls._hann_tile((half - sy) % P, (half - sx) % P, P) for sy, sx in lst], dim=0) + for lst in shift_levels + ] + else: + window_tile_levels = [None] * len(shift_levels) + + m = model.clone() + model_sampling = m.get_model_object("model_sampling") + multiplier = float(model_sampling.multiplier) + start_t = float(model_sampling.percent_to_sigma(start_percent)) * multiplier + end_t = float(model_sampling.percent_to_sigma(end_percent)) * multiplier + + edge_ramp_cache: dict = {} + + def get_edge_ramp(H: int, W: int, device, dtype) -> torch.Tensor: + key = (H, W, device, dtype) + cached = edge_ramp_cache.get(key) + if cached is not None: + return cached + feather = cls.EDGE_FEATHER + ys = torch.minimum(torch.arange(H, device=device, dtype=torch.float32), + (H - 1) - torch.arange(H, device=device, dtype=torch.float32)) + xs = torch.minimum(torch.arange(W, device=device, dtype=torch.float32), + (W - 1) - torch.arange(W, device=device, dtype=torch.float32)) + y_mask = ((ys - P) / feather).clamp(0, 1) + x_mask = ((xs - P) / feather).clamp(0, 1) + ramp = (y_mask[:, None] * x_mask[None, :]).to(dtype) + edge_ramp_cache[key] = ramp + return ramp + + def smoothing_wrapper(executor, *args, **kwargs): + x = args[0] + t = float(args[1][0]) + pred = executor(*args, **kwargs) + if not (end_t <= t <= start_t): + return pred + # Pick shift-level by sigma phase across the gated range. + if len(shift_levels) == 1: + level_idx = 0 + else: + phase = (start_t - t) / max(start_t - end_t, 1e-8) + level_idx = min(int(phase * len(shift_levels)), len(shift_levels) - 1) + shifts = shift_levels[level_idx] + window_tiles = window_tile_levels[level_idx] + + preds = [] + for sy, sx in shifts: + if sy == 0 and sx == 0: + preds.append(pred) + continue + x_rolled = torch.roll(x, shifts=(sy, sx), dims=(-2, -1)) + pred_rolled = executor(x_rolled, *args[1:], **kwargs) + preds.append(torch.roll(pred_rolled, shifts=(-sy, -sx), dims=(-2, -1))) + stacked = torch.stack(preds, dim=0) # (N, B, C, H, W) + _, _, _, H, W = stacked.shape + if blend == "window": + N = stacked.shape[0] + tiles = window_tiles.to(device=stacked.device, dtype=stacked.dtype) + w = tiles.repeat(1, H // P, W // P)[:, :H, :W] + sum_w = w.sum(dim=0, keepdim=True) + w = torch.where(sum_w < 1e-3, torch.full_like(w, 1.0 / N), w / sum_w.clamp(min=1e-8)) + avg = (stacked * w[:, None, None, :, :]).sum(dim=0) + elif blend == "median": + avg = torch.median(stacked, dim=0).values + else: + avg = stacked.mean(dim=0) + + # Mask out the P-px wraparound contamination strip at each edge. + mask = get_edge_ramp(H, W, pred.device, pred.dtype) + return pred * (1.0 - mask * strength) + avg * (mask * strength) + + m.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "hidream_o1_patch_seam_smoothing", smoothing_wrapper) + return io.NodeOutput(m) + + +class HiDreamO1Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyHiDreamO1LatentImage, + HiDreamO1ReferenceImages, + HiDreamO1PatchSeamSmoothing, + ] + + +async def comfy_entrypoint() -> HiDreamO1Extension: + return HiDreamO1Extension() diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py index bf18ecb88..403eb855b 100644 --- a/comfy_extras/nodes_hunyuan3d.py +++ b/comfy_extras/nodes_hunyuan3d.py @@ -1,12 +1,7 @@ import torch -import os -import json -import struct -import numpy as np from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch -import folder_paths import comfy.model_management -from comfy.cli_args import args +from comfy_extras.nodes_save_3d import pack_variable_mesh_batch from typing_extensions import override from comfy_api.latest import ComfyExtension, IO, Types from comfy_api.latest._util import MESH, VOXEL # only for backward compatibility if someone import it from this file (will be removed later) # noqa @@ -444,7 +439,9 @@ class VoxelToMeshBasic(IO.ComfyNode): vertices.append(v) faces.append(f) - return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) + if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces): + return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) + return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces)) decode = execute # TODO: remove @@ -481,206 +478,13 @@ class VoxelToMesh(IO.ComfyNode): vertices.append(v) faces.append(f) - return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) + if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces): + return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces))) + return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces)) decode = execute # TODO: remove -def save_glb(vertices, faces, filepath, metadata=None): - """ - Save PyTorch tensor vertices and faces as a GLB file without external dependencies. - - Parameters: - vertices: torch.Tensor of shape (N, 3) - The vertex coordinates - faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces) - filepath: str - Output filepath (should end with .glb) - """ - - # Convert tensors to numpy arrays - vertices_np = vertices.cpu().numpy().astype(np.float32) - faces_np = faces.cpu().numpy().astype(np.uint32) - - vertices_buffer = vertices_np.tobytes() - indices_buffer = faces_np.tobytes() - - def pad_to_4_bytes(buffer): - padding_length = (4 - (len(buffer) % 4)) % 4 - return buffer + b'\x00' * padding_length - - vertices_buffer_padded = pad_to_4_bytes(vertices_buffer) - indices_buffer_padded = pad_to_4_bytes(indices_buffer) - - buffer_data = vertices_buffer_padded + indices_buffer_padded - - vertices_byte_length = len(vertices_buffer) - vertices_byte_offset = 0 - indices_byte_length = len(indices_buffer) - indices_byte_offset = len(vertices_buffer_padded) - - gltf = { - "asset": {"version": "2.0", "generator": "ComfyUI"}, - "buffers": [ - { - "byteLength": len(buffer_data) - } - ], - "bufferViews": [ - { - "buffer": 0, - "byteOffset": vertices_byte_offset, - "byteLength": vertices_byte_length, - "target": 34962 # ARRAY_BUFFER - }, - { - "buffer": 0, - "byteOffset": indices_byte_offset, - "byteLength": indices_byte_length, - "target": 34963 # ELEMENT_ARRAY_BUFFER - } - ], - "accessors": [ - { - "bufferView": 0, - "byteOffset": 0, - "componentType": 5126, # FLOAT - "count": len(vertices_np), - "type": "VEC3", - "max": vertices_np.max(axis=0).tolist(), - "min": vertices_np.min(axis=0).tolist() - }, - { - "bufferView": 1, - "byteOffset": 0, - "componentType": 5125, # UNSIGNED_INT - "count": faces_np.size, - "type": "SCALAR" - } - ], - "meshes": [ - { - "primitives": [ - { - "attributes": { - "POSITION": 0 - }, - "indices": 1, - "mode": 4 # TRIANGLES - } - ] - } - ], - "nodes": [ - { - "mesh": 0 - } - ], - "scenes": [ - { - "nodes": [0] - } - ], - "scene": 0 - } - - if metadata is not None: - gltf["asset"]["extras"] = metadata - - # Convert the JSON to bytes - gltf_json = json.dumps(gltf).encode('utf8') - - def pad_json_to_4_bytes(buffer): - padding_length = (4 - (len(buffer) % 4)) % 4 - return buffer + b' ' * padding_length - - gltf_json_padded = pad_json_to_4_bytes(gltf_json) - - # Create the GLB header - # Magic glTF - glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data)) - - # Create JSON chunk header (chunk type 0) - json_chunk_header = struct.pack('