255 lines
9.6 KiB
Python
255 lines
9.6 KiB
Python
import base64
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import io
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import json
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import random
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import requests
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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from PIL import Image
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import numpy as np
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import torch
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# Base ComfyUI defined nodes
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class BaseImageNode:
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BASE_INPUT = {
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"required": {
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"api_key": ("STRING", {"default": "", "multiline": False}),
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"model": ([], {}),
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"prompt": ("STRING", {"default": "", "multiline": True, "placeholder": "提示词"}),
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"image_size": (["1536x1024", "1024x1024", "1024x1536", "自定义"], {"default": "1024x1024"}),
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"custom_width": ("INT", {"default": 1024, "min": 512, "max": 4096, "step": 64}),
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"custom_height": ("INT", {"default": 1024, "min": 512, "max": 4096, "step": 64}),
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"retries": ("INT", {"default": 3, "min": 1, "max": 10}),
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"timeout": ("INT", {"default": 120, "min": 5, "max": 180}),
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},
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"optional": {
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"reference_image": ("IMAGE",),
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"extra": ("STRING",)
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}
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}
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EXTEND_INPUT = {} # replace it with your own input to extend
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SUPPORTED_MODELS = [] # replace it with your own supported moldes
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DELETED_INPUT = [] # replace it with your own deleted input
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CATEGORY = "OneThingAI/image generation"
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FUNCTION = "pre_input"
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UA = "OneThingAI ComfyUI/1.1"
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URL_GENERATIONS = "https://api-model.onethingai.com/v1/images/generations"
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URL_EDIT = "https://api-model.onethingai.com/v1/images/edits"
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RETURN_TYPES = ("IMAGE",)
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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@classmethod
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def INPUT_TYPES(cls):
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cls.BASE_INPUT["required"]["model"] = (cls.SUPPORTED_MODELS, {})
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required = {**cls.BASE_INPUT.get("required", {}), **cls.EXTEND_INPUT.get("required", {})}
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optional = {**cls.BASE_INPUT.get("optional", {}), **cls.EXTEND_INPUT.get("optional", {})}
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return {
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"required": {k: v for k, v in required.items() if k not in cls.DELETED_INPUT},
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"optional": {k: v for k, v in optional.items() if k not in cls.DELETED_INPUT}
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}
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@staticmethod
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def new_http_client(retries) -> requests.Session:
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retry_strategy = Retry(
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total=retries,
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backoff_factor=1,
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status_forcelist=[429, 500, 502, 503, 504],
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)
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session = requests.Session()
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adapter = HTTPAdapter(max_retries=retry_strategy)
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session.mount("https://", adapter)
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return session
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@staticmethod
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def read_reference_image(image):
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# Convert the first image if it's a batch
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if len(image.shape) == 4:
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image_to_encode = [img for img in image]
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else:
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image_to_encode = [image]
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buffered = []
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for img in image_to_encode:
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# Convert from torch tensor to PIL Image
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img = (img.cpu().numpy() * 255).astype(np.uint8)
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if len(img.shape) == 3:
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img = Image.fromarray(img)
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# write buffer
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buf = io.BytesIO()
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img.save(buf, format="PNG")
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buf.seek(0)
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buffered.append(buf)
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print("buffered:", buffered)
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return buffered
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@staticmethod
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def read_image_result(response: requests.Response):
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response.raise_for_status()
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# Parse response
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result = response.json()
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if "data" not in result or result["data"] is None:
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raise ValueError(f"Unexpected API response: {result}")
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# Process images
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if len(result["data"]) == 0:
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raise ValueError("no image result")
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if "b64_json" not in result["data"][0] or not result["data"][0]["b64_json"]:
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raise ValueError("no b64 json in result")
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image_bytes = base64.b64decode(result["data"][0]["b64_json"])
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image = Image.open(io.BytesIO(image_bytes))
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# Convert to RGB if necessary
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if image.mode != "RGB":
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image = image.convert("RGB")
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final_image = np.array(image)
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final_image = torch.from_numpy(final_image).float() / 255.0
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final_image = final_image.unsqueeze(0)
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return (final_image,)
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def pre_input(self, api_key, model, prompt, retries, timeout, reference_image=None, extra=None, **kwargs):
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client = self.new_http_client(retries)
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headers = {
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"Authorization": f"Bearer {api_key}",
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"User-Agent": self.UA,
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}
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payload = {
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"model": model,
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"prompt": prompt,
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}
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if "image_size" in kwargs:
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if kwargs["image_size"] == "自定义":
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payload["size"] = f"{kwargs['custom_width']}x{kwargs['custom_height']}"
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else:
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payload["size"] = kwargs["image_size"]
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if "seed" in kwargs:
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if kwargs["seed"] == -1:
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kwargs["seed"] = random.randint(1, 2147483647)
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# filter
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input_types = self.INPUT_TYPES()
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for key, settings in input_types["required"].items():
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if len(settings) > 1 and "enabled" in settings[1] and model not in settings[1]["enabled"]:
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if key in kwargs:
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del kwargs[key]
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# print(f"filtered input: {key} at models: {model}")
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for key, settings in input_types["optional"].items():
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if len(settings) > 1 and "enabled" in settings[1] and model not in settings[1]["enabled"]:
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if key == "reference_image":
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reference_image = None
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# print("reference_image off")
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continue
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if key in kwargs:
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del kwargs[key]
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# print(f"filtered input: {key} at models: {model}")
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extra_params = {}
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if extra:
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try:
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extra_params = json.loads(extra)
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except json.decoder.JSONDecodeError:
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raise ValueError("extra not a json string")
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payload, timeout, extra_params, reference_image, kwargs = self.post_input(payload, timeout, extra_params,
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reference_image, **kwargs)
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return self.generate(client, headers, payload, timeout, extra_params, reference_image, **kwargs)
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def post_input(self, payload: dict, timeout: int, extra_params: dict, reference_image=None, **kwargs) -> tuple[
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dict, int, dict, list, dict]:
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return payload, timeout, extra_params, reference_image, kwargs
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def generate(self, client: requests.Session, headers: dict[str, str], payload: dict, timeout: int,
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extra_params: dict, reference_image=None, **kwargs):
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# Implement it
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raise NotImplementedError()
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class OpenAICompatibleNode(BaseImageNode):
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def generate(self, client, headers, payload, timeout, extra_params, reference_image=None, **kwargs):
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payload.update(kwargs)
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payload.update(extra_params)
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payload["n"] = 1
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try:
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if reference_image is None:
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# generate mode, use json api
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headers["Content-Type"] = "application/json"
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response = client.post(
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self.URL_GENERATIONS,
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headers=headers,
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json=payload,
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timeout=timeout,
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verify=False
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)
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return self.read_image_result(response)
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else:
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# edit mode, use form api
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# headers["Content-Type"] = "multipart/form-data"
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img = self.read_reference_image(reference_image)
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if len(img) == 1:
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files = {"image": img[0]}
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else:
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files = [("image[]", i) for i in img]
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response = client.post(
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self.URL_EDIT,
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headers=headers,
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data=payload,
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files=files,
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timeout=timeout,
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verify=False
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)
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return self.read_image_result(response)
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except requests.exceptions.RequestException as e:
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raise RuntimeError(f"API request failed: {str(e)}")
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except json.JSONDecodeError:
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raise RuntimeError("Failed to parse API response")
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except Exception as e:
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raise RuntimeError(f"Error generating images: {str(e)}")
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finally:
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client.close()
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class VolcengineNode(BaseImageNode):
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def generate(self, client, headers, payload, timeout, extra_params, reference_image=None, **kwargs):
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payload.update(kwargs)
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payload.update(extra_params)
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headers["Content-Type"] = "application/json"
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if reference_image is not None:
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img = self.read_reference_image(reference_image)
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if len(img) == 1:
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payload["image"] = "data:image/png;base64," + base64.b64encode(img[0].getvalue()).decode()
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else:
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payload["image"] = ["data:image/png;base64,"+base64.b64encode(b64_img.getvalue()).decode() for b64_img in img]
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try:
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response = client.post(
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self.URL_GENERATIONS,
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headers=headers,
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json=payload,
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timeout=timeout,
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verify=False
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)
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return self.read_image_result(response)
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except requests.exceptions.RequestException as e:
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raise RuntimeError(f"API request failed: {str(e)}")
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except json.JSONDecodeError:
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raise RuntimeError("Failed to parse API response")
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except Exception as e:
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raise RuntimeError(f"Error generating images: {str(e)}")
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finally:
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client.close()
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