comfyui: add native ComfyUI provider for image, video, and music generation
Adds three new BaseTool providers that delegate GPU work to a running ComfyUI server via its REST API. This avoids the need to install PyTorch/diffusers directly, which is critical on hardware where the ecosystem hasn't caught up (e.g. NVIDIA Blackwell / DGX Spark, aarch64 + CUDA 13.0). New files: - tools/_comfyui/client.py — shared REST client (submit/poll/download) - tools/_comfyui/workflows/ — 4 bundled workflow templates - tools/graphics/comfyui_image.py — FLUX 2 Dev NVFP4 text-to-image - tools/video/comfyui_video.py — WAN 2.2 14B t2v + i2v (4-step LightX2V) - tools/audio/comfyui_music.py — ACE-Step 3.5B music generation - tests/contracts/test_comfyui_tools.py — 41 contract tests - docs/comfyui-adapter-plan.md — design document Zero changes to existing tools, selectors, registry, or pipelines. Tools are auto-discovered and selectors pick them up via capability match. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
martimramos
committed by
Alastair Beal
Alastair Beal
parent
80e51fd618
commit
6ec2bbb090
@@ -0,0 +1 @@
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"""ComfyUI integration — shared client and bundled workflow templates."""
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@@ -0,0 +1,207 @@
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"""Thin REST client for a running ComfyUI server.
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Handles the full generation cycle: submit workflow, poll for completion,
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download artifacts. Used by comfyui_image, comfyui_video, and comfyui_music.
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"""
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from __future__ import annotations
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import copy
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import json
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import os
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import random
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import time
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from pathlib import Path
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from typing import Any
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import requests
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class ComfyUIError(Exception):
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"""Raised when ComfyUI returns an error or times out."""
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class ComfyUIClient:
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"""Client for the ComfyUI REST API.
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The protocol is simple and battle-tested:
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1. POST /prompt → queue a workflow, get a prompt_id
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2. GET /history/{id} → poll until outputs appear
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3. GET /view?filename=… → download the generated artifact
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4. POST /upload/image → stage a local image for I2V workflows
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"""
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def __init__(self, server_url: str | None = None) -> None:
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self.server_url = (
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server_url
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or os.environ.get("COMFYUI_SERVER_URL", "http://localhost:8188")
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).rstrip("/")
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# ------------------------------------------------------------------
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# Health
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# ------------------------------------------------------------------
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def is_available(self) -> bool:
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"""Return True if the ComfyUI server is reachable."""
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try:
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resp = requests.get(
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f"{self.server_url}/system_stats", timeout=5
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)
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return resp.status_code == 200
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except Exception:
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return False
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# ------------------------------------------------------------------
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# Core cycle
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# ------------------------------------------------------------------
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def submit(self, workflow: dict) -> str:
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"""Queue a workflow for execution. Returns the ``prompt_id``."""
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resp = requests.post(
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f"{self.server_url}/prompt",
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json={"prompt": workflow},
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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if data.get("node_errors"):
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raise ComfyUIError(f"Node errors: {json.dumps(data['node_errors'])}")
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prompt_id = data.get("prompt_id")
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if not prompt_id:
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raise ComfyUIError(f"No prompt_id in response: {data}")
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return prompt_id
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def poll(
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self,
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prompt_id: str,
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*,
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timeout: int = 600,
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interval: int = 5,
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) -> dict:
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"""Block until *prompt_id* finishes. Returns the history entry."""
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deadline = time.time() + timeout
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while time.time() < deadline:
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resp = requests.get(
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f"{self.server_url}/history/{prompt_id}", timeout=10
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)
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resp.raise_for_status()
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history = resp.json()
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if prompt_id in history:
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entry = history[prompt_id]
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status = entry.get("status", {})
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if status.get("status_str") == "error":
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msgs = status.get("messages", [])
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raise ComfyUIError(f"Execution error: {msgs}")
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return entry
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time.sleep(interval)
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raise ComfyUIError(
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f"Prompt {prompt_id} did not complete within {timeout}s"
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)
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def download(
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self,
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filename: str,
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subfolder: str,
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dest: Path,
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) -> Path:
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"""Download an output artifact from the ComfyUI server."""
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resp = requests.get(
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f"{self.server_url}/view",
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params={
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"filename": filename,
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"subfolder": subfolder,
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"type": "output",
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},
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timeout=120,
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)
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resp.raise_for_status()
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dest.parent.mkdir(parents=True, exist_ok=True)
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dest.write_bytes(resp.content)
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return dest
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def upload_image(self, local_path: Path, name: str) -> str:
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"""Upload a local image so it can be referenced by LoadImage nodes.
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Returns the server-side filename.
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"""
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with open(local_path, "rb") as f:
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resp = requests.post(
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f"{self.server_url}/upload/image",
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files={"image": (name, f, "image/png")},
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timeout=30,
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)
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resp.raise_for_status()
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return resp.json()["name"]
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# ------------------------------------------------------------------
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# High-level helper
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# ------------------------------------------------------------------
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def generate(
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self,
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workflow: dict,
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output_node: str,
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dest: Path,
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*,
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timeout: int = 600,
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interval: int = 5,
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) -> list[Path]:
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"""Submit → poll → download. Returns list of artifact paths."""
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prompt_id = self.submit(workflow)
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entry = self.poll(prompt_id, timeout=timeout, interval=interval)
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outputs = entry.get("outputs", {})
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node_output = outputs.get(output_node, {})
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# ComfyUI stores images and videos under the "images" key
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items = node_output.get("images", []) or node_output.get("gifs", [])
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if not items:
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raise ComfyUIError(
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f"No output artifacts on node {output_node}. "
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f"Available nodes: {list(outputs.keys())}"
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)
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paths: list[Path] = []
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for i, item in enumerate(items):
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suffix = Path(item["filename"]).suffix
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if len(items) == 1:
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target = dest
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else:
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target = dest.with_stem(f"{dest.stem}_{i:03d}").with_suffix(suffix)
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self.download(item["filename"], item.get("subfolder", ""), target)
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paths.append(target)
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return paths
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# ------------------------------------------------------------------
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# Workflow helpers
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# ------------------------------------------------------------------
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@staticmethod
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def load_workflow(path: Path) -> dict:
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"""Load a workflow JSON template from disk."""
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with open(path) as f:
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return json.load(f)
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@staticmethod
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def patch_workflow(
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workflow: dict, patches: dict[str, dict[str, Any]]
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) -> dict:
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"""Deep-copy *workflow* and apply *patches*.
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*patches* maps ``node_id`` → ``{input_name: value, ...}``.
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"""
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w = copy.deepcopy(workflow)
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for node_id, values in patches.items():
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if node_id not in w:
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raise ComfyUIError(
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f"Node {node_id!r} not found in workflow. "
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f"Available: {list(w.keys())}"
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)
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for key, val in values.items():
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w[node_id]["inputs"][key] = val
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return w
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@staticmethod
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def random_seed() -> int:
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"""Return a random seed suitable for ComfyUI noise nodes."""
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return random.randint(0, 2**32 - 1)
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@@ -0,0 +1,27 @@
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{
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"1": {
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"class_type": "AceStepModelLoader",
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"inputs": {
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"model": "ace_step_v1_3.5b.safetensors"
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}
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},
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"2": {
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"class_type": "AceStepSampler",
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"inputs": {
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"model": ["1", 0],
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"prompt": "",
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"lyrics": "",
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"duration": 30.0,
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"seed": 42,
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"steps": 60,
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"cfg": 3.0
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}
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},
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"3": {
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"class_type": "SaveAudio",
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"inputs": {
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"audio": ["2", 0],
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"filename_prefix": "openmontage_music"
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}
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}
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}
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@@ -0,0 +1,96 @@
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{
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"1": {
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"class_type": "UNETLoader",
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"inputs": {
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"unet_name": "flux2-dev-nvfp4.safetensors",
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"weight_dtype": "default"
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}
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},
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"2": {
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"class_type": "CLIPLoader",
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"inputs": {
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"clip_name": "mistral_3_small_flux2_fp4_mixed.safetensors",
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"type": "flux2",
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"device": "cpu"
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}
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},
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"3": {
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"class_type": "VAELoader",
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"inputs": {
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"vae_name": "flux2-vae.safetensors"
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}
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},
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"4": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": ["2", 0],
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"text": ""
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}
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},
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"5": {
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"class_type": "FluxGuidance",
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"inputs": {
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"conditioning": ["4", 0],
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"guidance": 3.5
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}
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},
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"6": {
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"class_type": "EmptyFlux2LatentImage",
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"inputs": {
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"width": 1024,
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"height": 1024,
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"batch_size": 1
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}
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},
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"7": {
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"class_type": "RandomNoise",
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"inputs": {
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"noise_seed": 42
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}
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},
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"8": {
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"class_type": "BasicGuider",
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"inputs": {
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"model": ["1", 0],
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"conditioning": ["5", 0]
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}
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},
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"9": {
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"class_type": "KSamplerSelect",
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"inputs": {
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"sampler_name": "euler"
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}
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},
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"10": {
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"class_type": "Flux2Scheduler",
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"inputs": {
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"steps": 20,
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"width": 1024,
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"height": 1024
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}
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},
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"11": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {
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"noise": ["7", 0],
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"guider": ["8", 0],
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"sampler": ["9", 0],
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"sigmas": ["10", 0],
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"latent_image": ["6", 0]
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}
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},
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"12": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": ["11", 0],
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"vae": ["3", 0]
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}
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},
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"13": {
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"class_type": "SaveImage",
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"inputs": {
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"images": ["12", 0],
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"filename_prefix": "openmontage"
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}
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}
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}
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@@ -0,0 +1,154 @@
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{
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"84": {
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"class_type": "CLIPLoader",
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"inputs": {
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"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
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"type": "wan",
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"device": "default"
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}
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},
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"89": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": ["84", 0],
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"text": "oversaturated, overexposed, static, blurry details, subtitles, style, artwork, painting, still frame, gray overall, worst quality, low quality, JPEG artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, deformed limbs, fused fingers, static frame, cluttered background, three legs, many people in background, walking backwards"
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}
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},
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"90": {
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"class_type": "VAELoader",
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"inputs": {
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"vae_name": "wan_2.1_vae.safetensors"
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}
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},
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"93": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": ["84", 0],
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"text": ""
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}
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},
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"95": {
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"class_type": "UNETLoader",
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"inputs": {
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"unet_name": "wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors",
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"weight_dtype": "default"
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}
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},
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"96": {
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"class_type": "UNETLoader",
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"inputs": {
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"unet_name": "wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors",
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"weight_dtype": "default"
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}
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},
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"97": {
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"class_type": "LoadImage",
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"inputs": {
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"image": ""
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}
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},
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"98": {
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"class_type": "WanImageToVideo",
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"inputs": {
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"width": 640,
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"height": 640,
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"length": 81,
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"batch_size": 1,
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"positive": ["93", 0],
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"negative": ["89", 0],
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"vae": ["90", 0],
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"start_image": ["97", 0]
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}
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},
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"101": {
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"class_type": "LoraLoaderModelOnly",
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"inputs": {
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"model": ["95", 0],
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"lora_name": "wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors",
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"strength_model": 1.0
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}
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},
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"102": {
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"class_type": "LoraLoaderModelOnly",
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"inputs": {
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"model": ["96", 0],
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"lora_name": "wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors",
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"strength_model": 1.0
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}
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},
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"103": {
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"class_type": "ModelSamplingSD3",
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"inputs": {
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"model": ["102", 0],
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"shift": 5.0
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}
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},
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"104": {
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"class_type": "ModelSamplingSD3",
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"inputs": {
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"model": ["101", 0],
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"shift": 5.0
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}
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},
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"86": {
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"class_type": "KSamplerAdvanced",
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"inputs": {
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"model": ["104", 0],
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"positive": ["98", 0],
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"negative": ["98", 1],
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"latent_image": ["98", 2],
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"add_noise": "enable",
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"noise_seed": 42,
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"control_after_generate": "randomize",
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"steps": 4,
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"cfg": 1.0,
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"sampler_name": "euler",
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"scheduler": "simple",
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"start_at_step": 0,
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"end_at_step": 2,
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"return_with_leftover_noise": "enable"
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}
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},
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"85": {
|
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"class_type": "KSamplerAdvanced",
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"inputs": {
|
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"model": ["103", 0],
|
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"positive": ["98", 0],
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"negative": ["98", 1],
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"latent_image": ["86", 0],
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"add_noise": "disable",
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"noise_seed": 0,
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"control_after_generate": "fixed",
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"steps": 4,
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"cfg": 1.0,
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"sampler_name": "euler",
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"scheduler": "simple",
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"start_at_step": 2,
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"end_at_step": 4,
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"return_with_leftover_noise": "disable"
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}
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},
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"87": {
|
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"class_type": "VAEDecode",
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"inputs": {
|
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"samples": ["85", 0],
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"vae": ["90", 0]
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}
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},
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"94": {
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"class_type": "CreateVideo",
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"inputs": {
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"images": ["87", 0],
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"fps": 16
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}
|
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},
|
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"108": {
|
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"class_type": "SaveVideo",
|
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"inputs": {
|
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"video": ["94", 0],
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"filename_prefix": "openmontage_i2v",
|
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"format": "auto",
|
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"codec": "auto"
|
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}
|
||||
}
|
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}
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@@ -0,0 +1,143 @@
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{
|
||||
"1": {
|
||||
"class_type": "CLIPLoader",
|
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"inputs": {
|
||||
"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
|
||||
"type": "wan",
|
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"device": "default"
|
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}
|
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},
|
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"2": {
|
||||
"class_type": "CLIPTextEncode",
|
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"inputs": {
|
||||
"clip": ["1", 0],
|
||||
"text": ""
|
||||
}
|
||||
},
|
||||
"3": {
|
||||
"class_type": "CLIPTextEncode",
|
||||
"inputs": {
|
||||
"clip": ["1", 0],
|
||||
"text": "oversaturated, overexposed, static, blurry details, subtitles, style, artwork, painting, still frame, gray overall, worst quality, low quality, JPEG artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, deformed limbs, fused fingers, static frame, cluttered background, three legs, many people in background, walking backwards"
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"class_type": "VAELoader",
|
||||
"inputs": {
|
||||
"vae_name": "wan2.2_vae.safetensors"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"class_type": "UNETLoader",
|
||||
"inputs": {
|
||||
"unet_name": "wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors",
|
||||
"weight_dtype": "default"
|
||||
}
|
||||
},
|
||||
"6": {
|
||||
"class_type": "UNETLoader",
|
||||
"inputs": {
|
||||
"unet_name": "wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors",
|
||||
"weight_dtype": "default"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"inputs": {
|
||||
"model": ["5", 0],
|
||||
"lora_name": "wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
|
||||
"strength_model": 1.0
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"inputs": {
|
||||
"model": ["6", 0],
|
||||
"lora_name": "wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors",
|
||||
"strength_model": 1.0
|
||||
}
|
||||
},
|
||||
"9": {
|
||||
"class_type": "ModelSamplingSD3",
|
||||
"inputs": {
|
||||
"model": ["7", 0],
|
||||
"shift": 5.0
|
||||
}
|
||||
},
|
||||
"10": {
|
||||
"class_type": "ModelSamplingSD3",
|
||||
"inputs": {
|
||||
"model": ["8", 0],
|
||||
"shift": 5.0
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"class_type": "EmptyLatentImage",
|
||||
"inputs": {
|
||||
"width": 832,
|
||||
"height": 480,
|
||||
"batch_size": 81
|
||||
}
|
||||
},
|
||||
"12": {
|
||||
"class_type": "KSamplerAdvanced",
|
||||
"inputs": {
|
||||
"model": ["9", 0],
|
||||
"positive": ["2", 0],
|
||||
"negative": ["3", 0],
|
||||
"latent_image": ["11", 0],
|
||||
"add_noise": "enable",
|
||||
"noise_seed": 42,
|
||||
"control_after_generate": "randomize",
|
||||
"steps": 4,
|
||||
"cfg": 1.0,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "simple",
|
||||
"start_at_step": 0,
|
||||
"end_at_step": 2,
|
||||
"return_with_leftover_noise": "enable"
|
||||
}
|
||||
},
|
||||
"13": {
|
||||
"class_type": "KSamplerAdvanced",
|
||||
"inputs": {
|
||||
"model": ["10", 0],
|
||||
"positive": ["2", 0],
|
||||
"negative": ["3", 0],
|
||||
"latent_image": ["12", 0],
|
||||
"add_noise": "disable",
|
||||
"noise_seed": 0,
|
||||
"control_after_generate": "fixed",
|
||||
"steps": 4,
|
||||
"cfg": 1.0,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "simple",
|
||||
"start_at_step": 2,
|
||||
"end_at_step": 4,
|
||||
"return_with_leftover_noise": "disable"
|
||||
}
|
||||
},
|
||||
"14": {
|
||||
"class_type": "VAEDecode",
|
||||
"inputs": {
|
||||
"samples": ["13", 0],
|
||||
"vae": ["4", 0]
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"class_type": "CreateVideo",
|
||||
"inputs": {
|
||||
"images": ["14", 0],
|
||||
"fps": 16
|
||||
}
|
||||
},
|
||||
"16": {
|
||||
"class_type": "SaveVideo",
|
||||
"inputs": {
|
||||
"video": ["15", 0],
|
||||
"filename_prefix": "openmontage_t2v",
|
||||
"format": "auto",
|
||||
"codec": "auto"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,192 @@
|
||||
"""ComfyUI music generation via ACE-Step model.
|
||||
|
||||
Generates background music and songs locally using the ACE-Step 3.5B
|
||||
model running inside a ComfyUI server. Custom workflows are accepted
|
||||
via the ``workflow_json`` input.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from tools.base_tool import (
|
||||
BaseTool,
|
||||
Determinism,
|
||||
ExecutionMode,
|
||||
ResourceProfile,
|
||||
RetryPolicy,
|
||||
ToolResult,
|
||||
ToolRuntime,
|
||||
ToolStability,
|
||||
ToolStatus,
|
||||
ToolTier,
|
||||
)
|
||||
from tools._comfyui.client import ComfyUIClient, ComfyUIError
|
||||
|
||||
_WORKFLOWS = Path(__file__).resolve().parent.parent / "_comfyui" / "workflows"
|
||||
|
||||
_OUTPUT_NODE = "3"
|
||||
|
||||
|
||||
class ComfyUIMusic(BaseTool):
|
||||
name = "comfyui_music"
|
||||
version = "0.1.0"
|
||||
tier = ToolTier.GENERATE
|
||||
capability = "music_generation"
|
||||
provider = "comfyui"
|
||||
stability = ToolStability.EXPERIMENTAL
|
||||
execution_mode = ExecutionMode.SYNC
|
||||
determinism = Determinism.SEEDED
|
||||
runtime = ToolRuntime.LOCAL_GPU
|
||||
|
||||
dependencies = []
|
||||
install_instructions = (
|
||||
"Start a ComfyUI server and set COMFYUI_SERVER_URL "
|
||||
"(default http://localhost:8188).\n"
|
||||
"Requires ACE-Step model (ace_step_v1_3.5b.safetensors) in "
|
||||
"ComfyUI's checkpoints directory and the ACE-Step custom node installed."
|
||||
)
|
||||
agent_skills = ["music"]
|
||||
|
||||
capabilities = [
|
||||
"generate_background_music",
|
||||
"generate_instrumental",
|
||||
"generate_song",
|
||||
"text_to_music",
|
||||
]
|
||||
supports = {
|
||||
"seed": True,
|
||||
"duration_control": True,
|
||||
"lyrics": True,
|
||||
"custom_workflow": True,
|
||||
"offline": True,
|
||||
}
|
||||
best_for = [
|
||||
"local music generation without API costs",
|
||||
"background music and instrumentals for video production",
|
||||
"song generation with lyrics",
|
||||
]
|
||||
not_good_for = [
|
||||
"setups without a running ComfyUI server",
|
||||
"highest quality commercial music (use Suno or ElevenLabs)",
|
||||
]
|
||||
fallback = "suno_music"
|
||||
fallback_tools = ["suno_music", "elevenlabs_music", "freesound_music"]
|
||||
|
||||
input_schema = {
|
||||
"type": "object",
|
||||
"required": ["prompt"],
|
||||
"properties": {
|
||||
"prompt": {
|
||||
"type": "string",
|
||||
"description": "Music style / mood description (e.g. 'upbeat corporate background music')",
|
||||
},
|
||||
"lyrics": {
|
||||
"type": "string",
|
||||
"default": "",
|
||||
"description": "Optional lyrics for song generation",
|
||||
},
|
||||
"duration": {
|
||||
"type": "number",
|
||||
"default": 30.0,
|
||||
"description": "Duration in seconds",
|
||||
},
|
||||
"steps": {"type": "integer", "default": 60},
|
||||
"cfg": {"type": "number", "default": 3.0},
|
||||
"seed": {"type": "integer", "description": "Random if omitted"},
|
||||
"output_path": {"type": "string", "description": "Where to save the audio"},
|
||||
"workflow_json": {
|
||||
"type": "string",
|
||||
"description": "Optional full ComfyUI workflow JSON (overrides default)",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
resource_profile = ResourceProfile(
|
||||
cpu_cores=2, ram_mb=8000, vram_mb=6000, disk_mb=500, network_required=False,
|
||||
)
|
||||
retry_policy = RetryPolicy(max_retries=1, retryable_errors=["timeout"])
|
||||
idempotency_key_fields = ["prompt", "lyrics", "duration", "steps", "seed"]
|
||||
side_effects = ["writes audio file to output_path"]
|
||||
user_visible_verification = ["Listen to generated audio for quality and mood match"]
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._client = ComfyUIClient()
|
||||
|
||||
def get_status(self) -> ToolStatus:
|
||||
if self._client.is_available():
|
||||
return ToolStatus.AVAILABLE
|
||||
return ToolStatus.UNAVAILABLE
|
||||
|
||||
def estimate_cost(self, inputs: dict[str, Any]) -> float:
|
||||
return 0.0
|
||||
|
||||
def estimate_runtime(self, inputs: dict[str, Any]) -> float:
|
||||
duration = inputs.get("duration", 30.0)
|
||||
return duration * 2.0 # rough: ~2x realtime
|
||||
|
||||
def execute(self, inputs: dict[str, Any]) -> ToolResult:
|
||||
if not self._client.is_available():
|
||||
return ToolResult(
|
||||
success=False,
|
||||
error="ComfyUI server not reachable. " + self.install_instructions,
|
||||
)
|
||||
|
||||
start = time.time()
|
||||
seed = inputs.get("seed") or ComfyUIClient.random_seed()
|
||||
duration = inputs.get("duration", 30.0)
|
||||
output_path = Path(
|
||||
inputs.get("output_path", f"comfyui_music_{seed}.wav")
|
||||
)
|
||||
|
||||
try:
|
||||
if inputs.get("workflow_json"):
|
||||
workflow = json.loads(inputs["workflow_json"])
|
||||
else:
|
||||
workflow = ComfyUIClient.load_workflow(
|
||||
_WORKFLOWS / "ace-step-music.json"
|
||||
)
|
||||
workflow = ComfyUIClient.patch_workflow(workflow, {
|
||||
"2": {
|
||||
"prompt": inputs["prompt"],
|
||||
"lyrics": inputs.get("lyrics", ""),
|
||||
"duration": duration,
|
||||
"seed": seed,
|
||||
"steps": inputs.get("steps", 60),
|
||||
"cfg": inputs.get("cfg", 3.0),
|
||||
},
|
||||
"3": {"filename_prefix": output_path.stem},
|
||||
})
|
||||
|
||||
paths = self._client.generate(
|
||||
workflow,
|
||||
output_node=_OUTPUT_NODE,
|
||||
dest=output_path,
|
||||
timeout=int(duration * 4), # generous timeout
|
||||
)
|
||||
|
||||
except ComfyUIError as exc:
|
||||
return ToolResult(success=False, error=str(exc))
|
||||
except Exception as exc:
|
||||
return ToolResult(success=False, error=f"ComfyUI music generation failed: {exc}")
|
||||
|
||||
return ToolResult(
|
||||
success=True,
|
||||
data={
|
||||
"provider": "comfyui",
|
||||
"model": "ace-step-v1-3.5b",
|
||||
"prompt": inputs["prompt"],
|
||||
"lyrics": inputs.get("lyrics", ""),
|
||||
"duration": duration,
|
||||
"output": str(paths[0]),
|
||||
"format": output_path.suffix.lstrip("."),
|
||||
},
|
||||
artifacts=[str(p) for p in paths],
|
||||
cost_usd=0.0,
|
||||
duration_seconds=round(time.time() - start, 2),
|
||||
seed=seed,
|
||||
model="ace-step-v1-3.5b",
|
||||
)
|
||||
@@ -0,0 +1,165 @@
|
||||
"""ComfyUI image generation via a local or remote ComfyUI server.
|
||||
|
||||
Default workflow: FLUX 2 Dev (NVFP4) with Mistral text encoder.
|
||||
Supports custom workflows via the ``workflow_json`` input.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from tools.base_tool import (
|
||||
BaseTool,
|
||||
Determinism,
|
||||
ExecutionMode,
|
||||
ResourceProfile,
|
||||
RetryPolicy,
|
||||
ToolResult,
|
||||
ToolRuntime,
|
||||
ToolStability,
|
||||
ToolStatus,
|
||||
ToolTier,
|
||||
)
|
||||
from tools._comfyui.client import ComfyUIClient, ComfyUIError
|
||||
|
||||
_WORKFLOWS = Path(__file__).resolve().parent.parent / "_comfyui" / "workflows"
|
||||
|
||||
|
||||
class ComfyUIImage(BaseTool):
|
||||
name = "comfyui_image"
|
||||
version = "0.1.0"
|
||||
tier = ToolTier.GENERATE
|
||||
capability = "image_generation"
|
||||
provider = "comfyui"
|
||||
stability = ToolStability.EXPERIMENTAL
|
||||
execution_mode = ExecutionMode.SYNC
|
||||
determinism = Determinism.SEEDED
|
||||
runtime = ToolRuntime.LOCAL_GPU
|
||||
|
||||
dependencies = [] # checked at runtime via server health
|
||||
install_instructions = (
|
||||
"Start a ComfyUI server and set COMFYUI_SERVER_URL "
|
||||
"(default http://localhost:8188).\n"
|
||||
"See https://github.com/comfyanonymous/ComfyUI for setup."
|
||||
)
|
||||
agent_skills = []
|
||||
|
||||
capabilities = ["text_to_image"]
|
||||
supports = {
|
||||
"seed": True,
|
||||
"custom_size": True,
|
||||
"custom_workflow": True,
|
||||
"offline": True,
|
||||
}
|
||||
best_for = [
|
||||
"local GPU generation without API costs",
|
||||
"Blackwell / DGX Spark hardware where diffusers is unsupported",
|
||||
"full control over sampling via custom ComfyUI workflows",
|
||||
]
|
||||
not_good_for = [
|
||||
"setups without a running ComfyUI server",
|
||||
"CPU-only machines",
|
||||
]
|
||||
fallback = "flux_image"
|
||||
fallback_tools = ["flux_image", "local_diffusion", "openai_image"]
|
||||
|
||||
input_schema = {
|
||||
"type": "object",
|
||||
"required": ["prompt"],
|
||||
"properties": {
|
||||
"prompt": {"type": "string", "description": "Text prompt for image generation"},
|
||||
"width": {"type": "integer", "default": 1024},
|
||||
"height": {"type": "integer", "default": 1024},
|
||||
"steps": {"type": "integer", "default": 20},
|
||||
"guidance": {"type": "number", "default": 3.5},
|
||||
"seed": {"type": "integer", "description": "Random if omitted"},
|
||||
"output_path": {"type": "string", "description": "Where to save the image"},
|
||||
"workflow_json": {
|
||||
"type": "string",
|
||||
"description": "Optional full ComfyUI workflow JSON (overrides default)",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
resource_profile = ResourceProfile(
|
||||
cpu_cores=2, ram_mb=8000, vram_mb=8000, disk_mb=500, network_required=False,
|
||||
)
|
||||
retry_policy = RetryPolicy(max_retries=1, retryable_errors=["timeout"])
|
||||
idempotency_key_fields = ["prompt", "width", "height", "steps", "seed"]
|
||||
side_effects = ["writes image file to output_path"]
|
||||
user_visible_verification = ["Inspect generated image for quality and prompt adherence"]
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._client = ComfyUIClient()
|
||||
|
||||
def get_status(self) -> ToolStatus:
|
||||
if self._client.is_available():
|
||||
return ToolStatus.AVAILABLE
|
||||
return ToolStatus.UNAVAILABLE
|
||||
|
||||
def estimate_cost(self, inputs: dict[str, Any]) -> float:
|
||||
return 0.0
|
||||
|
||||
def estimate_runtime(self, inputs: dict[str, Any]) -> float:
|
||||
return float(inputs.get("steps", 20)) * 1.5
|
||||
|
||||
def execute(self, inputs: dict[str, Any]) -> ToolResult:
|
||||
if not self._client.is_available():
|
||||
return ToolResult(
|
||||
success=False,
|
||||
error="ComfyUI server not reachable. " + self.install_instructions,
|
||||
)
|
||||
|
||||
start = time.time()
|
||||
seed = inputs.get("seed") or ComfyUIClient.random_seed()
|
||||
width = inputs.get("width", 1024)
|
||||
height = inputs.get("height", 1024)
|
||||
steps = inputs.get("steps", 20)
|
||||
guidance = inputs.get("guidance", 3.5)
|
||||
output_path = Path(inputs.get("output_path", f"comfyui_image_{seed}.png"))
|
||||
|
||||
try:
|
||||
if inputs.get("workflow_json"):
|
||||
workflow = json.loads(inputs["workflow_json"])
|
||||
else:
|
||||
workflow = ComfyUIClient.load_workflow(_WORKFLOWS / "flux2-txt2img.json")
|
||||
workflow = ComfyUIClient.patch_workflow(workflow, {
|
||||
"4": {"text": inputs["prompt"]},
|
||||
"5": {"guidance": guidance},
|
||||
"6": {"width": width, "height": height, "batch_size": 1},
|
||||
"7": {"noise_seed": seed},
|
||||
"10": {"steps": steps, "width": width, "height": height},
|
||||
"13": {"filename_prefix": output_path.stem},
|
||||
})
|
||||
|
||||
paths = self._client.generate(
|
||||
workflow, output_node="13", dest=output_path, timeout=600,
|
||||
)
|
||||
|
||||
except ComfyUIError as exc:
|
||||
return ToolResult(success=False, error=str(exc))
|
||||
except Exception as exc:
|
||||
return ToolResult(success=False, error=f"ComfyUI image generation failed: {exc}")
|
||||
|
||||
return ToolResult(
|
||||
success=True,
|
||||
data={
|
||||
"provider": "comfyui",
|
||||
"model": "flux2-dev-nvfp4",
|
||||
"prompt": inputs["prompt"],
|
||||
"width": width,
|
||||
"height": height,
|
||||
"steps": steps,
|
||||
"guidance": guidance,
|
||||
"output": str(paths[0]),
|
||||
"format": "png",
|
||||
},
|
||||
artifacts=[str(p) for p in paths],
|
||||
cost_usd=0.0,
|
||||
duration_seconds=round(time.time() - start, 2),
|
||||
seed=seed,
|
||||
model="flux2-dev-nvfp4",
|
||||
)
|
||||
@@ -0,0 +1,250 @@
|
||||
"""ComfyUI video generation via a local or remote ComfyUI server.
|
||||
|
||||
Supports text-to-video and image-to-video using WAN 2.2 14B with
|
||||
4-step LightX2V LoRA acceleration. Custom workflows are accepted
|
||||
via the ``workflow_json`` input.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import requests
|
||||
|
||||
from tools.base_tool import (
|
||||
BaseTool,
|
||||
Determinism,
|
||||
ExecutionMode,
|
||||
ResourceProfile,
|
||||
RetryPolicy,
|
||||
ToolResult,
|
||||
ToolRuntime,
|
||||
ToolStability,
|
||||
ToolStatus,
|
||||
ToolTier,
|
||||
)
|
||||
from tools._comfyui.client import ComfyUIClient, ComfyUIError
|
||||
|
||||
_WORKFLOWS = Path(__file__).resolve().parent.parent / "_comfyui" / "workflows"
|
||||
|
||||
# Output node IDs in the bundled workflows
|
||||
_T2V_OUTPUT_NODE = "16"
|
||||
_I2V_OUTPUT_NODE = "108"
|
||||
|
||||
|
||||
class ComfyUIVideo(BaseTool):
|
||||
name = "comfyui_video"
|
||||
version = "0.1.0"
|
||||
tier = ToolTier.GENERATE
|
||||
capability = "video_generation"
|
||||
provider = "comfyui"
|
||||
stability = ToolStability.EXPERIMENTAL
|
||||
execution_mode = ExecutionMode.SYNC
|
||||
determinism = Determinism.SEEDED
|
||||
runtime = ToolRuntime.LOCAL_GPU
|
||||
|
||||
dependencies = []
|
||||
install_instructions = (
|
||||
"Start a ComfyUI server and set COMFYUI_SERVER_URL "
|
||||
"(default http://localhost:8188).\n"
|
||||
"Requires WAN 2.2 models and LightX2V LoRAs in ComfyUI's model directory."
|
||||
)
|
||||
agent_skills = []
|
||||
|
||||
capabilities = ["text_to_video", "image_to_video"]
|
||||
supports = {
|
||||
"seed": True,
|
||||
"reference_image": True,
|
||||
"custom_workflow": True,
|
||||
"offline": True,
|
||||
}
|
||||
best_for = [
|
||||
"local GPU video generation without API costs",
|
||||
"Blackwell / DGX Spark hardware where diffusers is unsupported",
|
||||
"image-to-video with WAN 2.2 14B (4-step accelerated)",
|
||||
"text-to-video with WAN 2.2 14B (4-step accelerated)",
|
||||
]
|
||||
not_good_for = [
|
||||
"setups without a running ComfyUI server",
|
||||
"CPU-only machines",
|
||||
]
|
||||
fallback = "wan_video"
|
||||
fallback_tools = ["wan_video", "hunyuan_video", "ltx_video_local", "kling_video"]
|
||||
|
||||
input_schema = {
|
||||
"type": "object",
|
||||
"required": ["prompt"],
|
||||
"properties": {
|
||||
"prompt": {"type": "string", "description": "Text prompt for video generation"},
|
||||
"operation": {
|
||||
"type": "string",
|
||||
"enum": ["text_to_video", "image_to_video"],
|
||||
"default": "text_to_video",
|
||||
},
|
||||
"reference_image_path": {
|
||||
"type": "string",
|
||||
"description": "Local path to reference image (for image_to_video)",
|
||||
},
|
||||
"reference_image_url": {
|
||||
"type": "string",
|
||||
"description": "URL of reference image (for image_to_video, downloaded first)",
|
||||
},
|
||||
"width": {"type": "integer", "default": 832, "description": "T2V default 832, I2V default 640"},
|
||||
"height": {"type": "integer", "default": 480, "description": "T2V default 480, I2V default 640"},
|
||||
"num_frames": {"type": "integer", "default": 81, "description": "81 frames = 5s at 16fps"},
|
||||
"seed": {"type": "integer", "description": "Random if omitted"},
|
||||
"output_path": {"type": "string", "description": "Where to save the video"},
|
||||
"workflow_json": {
|
||||
"type": "string",
|
||||
"description": "Optional full ComfyUI workflow JSON (overrides default)",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
resource_profile = ResourceProfile(
|
||||
cpu_cores=2, ram_mb=32000, vram_mb=16000, disk_mb=2000, network_required=False,
|
||||
)
|
||||
retry_policy = RetryPolicy(max_retries=1, retryable_errors=["timeout"])
|
||||
idempotency_key_fields = ["prompt", "operation", "width", "height", "num_frames", "seed"]
|
||||
side_effects = ["writes video file to output_path"]
|
||||
user_visible_verification = ["Watch generated clip for motion coherence and artifacts"]
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._client = ComfyUIClient()
|
||||
|
||||
def get_status(self) -> ToolStatus:
|
||||
if self._client.is_available():
|
||||
return ToolStatus.AVAILABLE
|
||||
return ToolStatus.UNAVAILABLE
|
||||
|
||||
def estimate_cost(self, inputs: dict[str, Any]) -> float:
|
||||
return 0.0
|
||||
|
||||
def estimate_runtime(self, inputs: dict[str, Any]) -> float:
|
||||
operation = inputs.get("operation", "text_to_video")
|
||||
if operation == "image_to_video":
|
||||
return 210.0 # ~3.5 min
|
||||
return 240.0 # ~4 min
|
||||
|
||||
def execute(self, inputs: dict[str, Any]) -> ToolResult:
|
||||
if not self._client.is_available():
|
||||
return ToolResult(
|
||||
success=False,
|
||||
error="ComfyUI server not reachable. " + self.install_instructions,
|
||||
)
|
||||
|
||||
operation = inputs.get("operation", "text_to_video")
|
||||
start = time.time()
|
||||
seed = inputs.get("seed") or ComfyUIClient.random_seed()
|
||||
output_path = Path(
|
||||
inputs.get("output_path", f"comfyui_video_{operation}_{seed}.mp4")
|
||||
)
|
||||
|
||||
try:
|
||||
if inputs.get("workflow_json"):
|
||||
workflow = json.loads(inputs["workflow_json"])
|
||||
output_node = _T2V_OUTPUT_NODE
|
||||
elif operation == "image_to_video":
|
||||
workflow, output_node = self._build_i2v(inputs, seed, output_path)
|
||||
else:
|
||||
workflow, output_node = self._build_t2v(inputs, seed, output_path)
|
||||
|
||||
paths = self._client.generate(
|
||||
workflow,
|
||||
output_node=output_node,
|
||||
dest=output_path,
|
||||
timeout=900,
|
||||
interval=10,
|
||||
)
|
||||
|
||||
except ComfyUIError as exc:
|
||||
return ToolResult(success=False, error=str(exc))
|
||||
except Exception as exc:
|
||||
return ToolResult(success=False, error=f"ComfyUI video generation failed: {exc}")
|
||||
|
||||
width = inputs.get("width", 832 if operation == "text_to_video" else 640)
|
||||
height = inputs.get("height", 480 if operation == "text_to_video" else 640)
|
||||
num_frames = inputs.get("num_frames", 81)
|
||||
|
||||
return ToolResult(
|
||||
success=True,
|
||||
data={
|
||||
"provider": "comfyui",
|
||||
"model": "wan2.2-14b-fp8-4step",
|
||||
"prompt": inputs["prompt"],
|
||||
"operation": operation,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"num_frames": num_frames,
|
||||
"fps": 16,
|
||||
"duration_seconds": round(num_frames / 16, 2),
|
||||
"output": str(paths[0]),
|
||||
"format": "mp4",
|
||||
},
|
||||
artifacts=[str(p) for p in paths],
|
||||
cost_usd=0.0,
|
||||
duration_seconds=round(time.time() - start, 2),
|
||||
seed=seed,
|
||||
model="wan2.2-14b-fp8-4step",
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Workflow builders
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_t2v(
|
||||
self, inputs: dict[str, Any], seed: int, output_path: Path
|
||||
) -> tuple[dict, str]:
|
||||
width = inputs.get("width", 832)
|
||||
height = inputs.get("height", 480)
|
||||
num_frames = inputs.get("num_frames", 81)
|
||||
|
||||
workflow = ComfyUIClient.load_workflow(_WORKFLOWS / "wan22-t2v-4step.json")
|
||||
workflow = ComfyUIClient.patch_workflow(workflow, {
|
||||
"2": {"text": inputs["prompt"]},
|
||||
"11": {"width": width, "height": height, "batch_size": num_frames},
|
||||
"12": {"noise_seed": seed},
|
||||
"16": {"filename_prefix": output_path.stem},
|
||||
})
|
||||
return workflow, _T2V_OUTPUT_NODE
|
||||
|
||||
def _build_i2v(
|
||||
self, inputs: dict[str, Any], seed: int, output_path: Path
|
||||
) -> tuple[dict, str]:
|
||||
width = inputs.get("width", 640)
|
||||
height = inputs.get("height", 640)
|
||||
num_frames = inputs.get("num_frames", 81)
|
||||
|
||||
# Resolve reference image
|
||||
ref_path = inputs.get("reference_image_path")
|
||||
ref_url = inputs.get("reference_image_url")
|
||||
|
||||
if ref_url and not ref_path:
|
||||
# Download to a temp location
|
||||
resp = requests.get(ref_url, timeout=60)
|
||||
resp.raise_for_status()
|
||||
ref_path = str(output_path.with_suffix(".ref.png"))
|
||||
Path(ref_path).parent.mkdir(parents=True, exist_ok=True)
|
||||
Path(ref_path).write_bytes(resp.content)
|
||||
|
||||
if not ref_path:
|
||||
raise ComfyUIError(
|
||||
"image_to_video requires reference_image_path or reference_image_url"
|
||||
)
|
||||
|
||||
# Upload to ComfyUI
|
||||
upload_name = f"om_{output_path.stem}.png"
|
||||
server_name = self._client.upload_image(Path(ref_path), upload_name)
|
||||
|
||||
workflow = ComfyUIClient.load_workflow(_WORKFLOWS / "wan22-i2v-4step.json")
|
||||
workflow = ComfyUIClient.patch_workflow(workflow, {
|
||||
"93": {"text": inputs["prompt"]},
|
||||
"97": {"image": server_name},
|
||||
"98": {"width": width, "height": height, "length": num_frames},
|
||||
"86": {"noise_seed": seed},
|
||||
"108": {"filename_prefix": output_path.stem},
|
||||
})
|
||||
return workflow, _I2V_OUTPUT_NODE
|
||||
Reference in New Issue
Block a user