MeteorAndyandClaude
6f2aa13575
Rewrite each node to faithfully mirror the official comfy_api_nodes/
contract (apis/*.py + Comfy-Org/embedded-docs). The ComfyOrg proxy
forwards the body verbatim, so the official request/response models
ARE the real vendor API.
- Video nodes now return VIDEO (InputImpl.VideoFromFile), not file paths
- Audio nodes now return AUDIO ({waveform, sample_rate}), not file paths
- Request/response bodies match official pydantic models field-for-field
- Model ids, options, defaults, ranges match official + embedded docs
- Every node exposes api_key + base_url (overridable for relays/proxies)
- utils.py: add ComfyUI-native helpers — video_from_bytes,
download_video_output, audio_from_bytes, download_audio_output,
poll_until, validate_prompt, tensor_to_data_uri
- Rewrite nodes_minimax.py (submit -> poll -> retrieve -> download VIDEO)
- nodes_extra.py: base_url on all three, poll_until, validate_prompt
- Clean __init__.py (drop duplicate grok import/keys); 37 nodes total
- README: output-type alignment, node list, relay examples
Co-Authored-By: Claude <noreply@anthropic.com>
206 lines
8.8 KiB
Python
206 lines
8.8 KiB
Python
"""Additional BYOKey providers sharing one module file.
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- BYOKey_ByteDance_LLM : ByteDance Seed / Doubao LLM (Volcengine Ark,
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OpenAI-compatible chat) -> STRING
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- BYOKey_WaveSpeed_Upscale : WaveSpeed image upscale (submit+poll) -> IMAGE
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- BYOKey_Bria_Image : Bria text-to-image -> IMAGE
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All three expose ``api_key`` + ``base_url`` so they can be pointed at the real
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vendor host or a relay, consistent with the rest of the package.
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"""
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import asyncio
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import torch
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from .utils import (
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bytes_to_tensor,
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download_bytes,
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http_request,
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poll_until,
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run_async,
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tensor_to_data_uri,
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validate_prompt,
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)
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# Default vendor hosts (overridable via base_url for relays/mirrors).
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ARK_DEFAULT_BASE_URL = "https://ark.cn-beijing.volces.com/api/v3"
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WAVESPEED_DEFAULT_BASE_URL = "https://api.wavespeed.ai/api/v3"
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BRIA_DEFAULT_BASE_URL = "https://engine.prod.bria-api.com/v1"
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class BYOKey_ByteDance_LLM:
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"""ByteDance Seed / Doubao LLM via the Volcengine Ark API. Output: STRING.
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The Ark inference API is OpenAI-compatible (``{base}/chat/completions``),
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so this node mirrors the standard OpenAI chat request/response, including
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optional multimodal (vision) input sent as inline base64 data URIs.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"api_key": ("STRING", {"default": "", "tooltip": "Volcengine Ark API key (Bearer token)."}),
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"base_url": ("STRING", {
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"default": ARK_DEFAULT_BASE_URL,
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"tooltip": "Ark API base URL. International default; override for relays.",
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}),
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"model": ("STRING", {
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"default": "doubao-1-5-pro-32k-250115",
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"tooltip": "Ark model/endpoint id (e.g. doubao-1-5-pro-32k-250115, deepseek-v3.1).",
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}),
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"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Text input to the model."}),
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},
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"optional": {
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"system_prompt": ("STRING", {
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"multiline": True, "default": "",
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"tooltip": "Foundational instructions that dictate the model's behavior.",
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}),
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"images": ("IMAGE", {"tooltip": "Optional reference image(s) for vision-capable models."}),
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"max_tokens": ("INT", {"default": 4096, "min": 1, "max": 128000}),
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"temperature": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 2.0, "step": 0.05}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 2**31 - 1, "control_after_generate": True}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("output",)
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FUNCTION = "execute"
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CATEGORY = "api/byokey/text"
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DESCRIPTION = "Generate text with ByteDance Seed / Doubao via the Volcengine Ark API."
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def execute(self, api_key, base_url, model, prompt,
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system_prompt="", images=None, max_tokens=4096, temperature=0.7, seed=0):
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validate_prompt(prompt, min_length=1)
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url = f"{base_url.rstrip('/')}/chat/completions"
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if images is not None:
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content = [{"type": "text", "text": prompt}]
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for i in range(images.shape[0]):
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content.append({"type": "image_url", "image_url": {"url": tensor_to_data_uri(images[i].unsqueeze(0))}})
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messages.append({"role": "user", "content": content})
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else:
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messages.append({"role": "user", "content": prompt})
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data = {"model": model, "messages": messages, "max_tokens": max_tokens, "temperature": temperature}
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if seed and seed > 0:
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data["seed"] = seed
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resp = run_async(http_request("POST", url, headers, data))
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return (resp["choices"][0]["message"]["content"],)
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class BYOKey_WaveSpeed_Upscale:
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"""WaveSpeed image upscale (AuraSR). Output: IMAGE.
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Mirrors the official submit+poll flow: POST ``{base}/{model}`` -> data.id,
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poll ``{base}/predictions/{id}/result`` until data.status is completed.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"api_key": ("STRING", {"default": "", "tooltip": "WaveSpeed API key (Bearer token)."}),
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"base_url": ("STRING", {
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"default": WAVESPEED_DEFAULT_BASE_URL,
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"tooltip": "WaveSpeed API base URL. Override for relays.",
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}),
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"image": ("IMAGE", {"tooltip": "Image to upscale."}),
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},
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"optional": {
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"model": ("STRING", {"default": "wavespeed-ai/aura-sr", "tooltip": "WaveSpeed model id."}),
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"scale": ("INT", {"default": 4, "min": 2, "max": 8, "tooltip": "Upscaling factor."}),
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"poll_interval": ("INT", {"default": 3, "min": 1, "max": 30}),
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"max_wait": ("INT", {"default": 120, "min": 30, "max": 600}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "execute"
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CATEGORY = "api/byokey/image"
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DESCRIPTION = "Upscale an image with WaveSpeed (AuraSR)."
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def execute(self, api_key, base_url, image, model="wavespeed-ai/aura-sr",
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scale=4, poll_interval=3, max_wait=120):
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base = base_url.rstrip("/")
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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body = {"input": {"image": tensor_to_data_uri(image), "upscaling_factor": scale}}
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submit = run_async(http_request("POST", f"{base}/{model}", headers, body))
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request_id = (submit.get("data") or {}).get("id") or submit.get("id")
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if not request_id:
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raise RuntimeError(f"WaveSpeed returned no request id: {submit}")
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result = run_async(poll_until(
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lambda: http_request("GET", f"{base}/predictions/{request_id}/result", headers),
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status_of=lambda r: str((r.get("data") or {}).get("status", "")),
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done_statuses=["completed", "succeeded"],
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failed_statuses=["failed", "error"],
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interval=poll_interval, max_wait=max_wait,
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on_fail_message="WaveSpeed upscale failed",
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))
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outputs = (result.get("data") or {}).get("outputs") or []
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if not outputs:
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raise RuntimeError(f"WaveSpeed completed with no output: {result}")
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img_bytes = run_async(download_bytes(outputs[0]))
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return (bytes_to_tensor(img_bytes),)
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class BYOKey_Bria_Image:
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"""Bria text-to-image. Output: IMAGE.
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Calls Bria's ``{base}/text-to-image/{model}`` endpoint (header ``api_token``)
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and concatenates all returned image URLs along the batch dimension.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"api_key": ("STRING", {"default": "", "tooltip": "Bria API key (sent as the api_token header)."}),
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"base_url": ("STRING", {
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"default": BRIA_DEFAULT_BASE_URL,
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"tooltip": "Bria API base URL. Override for relays.",
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}),
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"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Text description of the desired image."}),
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},
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"optional": {
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"model": ("STRING", {"default": "bria-2.3", "tooltip": "Bria model id (e.g. bria-2.3)."}),
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"num_results": ("INT", {"default": 1, "min": 1, "max": 4, "tooltip": "Number of images to generate."}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 2**31 - 1, "control_after_generate": True}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "execute"
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CATEGORY = "api/byokey/image"
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DESCRIPTION = "Generate images from text with Bria."
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def execute(self, api_key, base_url, prompt, model="bria-2.3", num_results=1, seed=0):
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validate_prompt(prompt, min_length=1)
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base = base_url.rstrip("/")
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headers = {"api_token": api_key, "Content-Type": "application/json"}
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body = {"prompt": prompt, "num_results": num_results}
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if seed and seed > 0:
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body["seed"] = seed
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resp = run_async(http_request("POST", f"{base}/text-to-image/{model}", headers, body))
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results = resp.get("result") or []
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if not results:
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raise RuntimeError(f"Bria returned no results: {resp}")
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images = []
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for item in results:
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url = (item.get("urls") or [None])[0] or item.get("url")
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if url:
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images.append(bytes_to_tensor(run_async(download_bytes(url))))
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if not images:
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raise RuntimeError("Bria returned no image URLs")
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return (torch.cat(images, dim=0),)
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