mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 06:37:06 +08:00
Move and fix BFL nodes to node_bfl.py (#49)
This commit is contained in:
Jedrzej Kosinski
Robin Huang
parent
d483297496
commit
f2b6bb1660
@ -1,29 +0,0 @@
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from __future__ import annotations
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from enum import Enum
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, confloat
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class BFLStatus(str, Enum):
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task_not_found = "Task not found"
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pending = "Pending"
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request_moderated = "Request Moderated"
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content_moderated = "Content Moderated"
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ready = "Ready"
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error = "Error"
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class BFLFluxProStatusResponse(BaseModel):
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id: str = Field(..., description="The unique identifier for the generation task.")
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status: BFLStatus = Field(..., description="The status of the task.")
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result: Optional[Dict[str, Any]] = Field(
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None, description="The result of the task (null if not completed)."
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)
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progress: confloat(ge=0.0, le=1.0) = Field(
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..., description="The progress of the task (0.0 to 1.0)."
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)
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details: Optional[Dict[str, Any]] = Field(
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None, description="Additional details about the task (null if not available)."
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)
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@ -14,12 +14,9 @@ from comfy_api_nodes.apis import (
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MinimaxTaskResultResponse,
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IdeogramGenerateRequest,
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IdeogramGenerateResponse,
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BFLFluxProGenerateRequest,
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BFLFluxProGenerateResponse,
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ImageRequest,
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Model
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)
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from comfy_api_nodes.apis.BFLPolling import BFLStatus
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from comfy_api_nodes.apis.client import (
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ApiClient,
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ApiEndpoint,
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@ -38,7 +35,6 @@ import torch
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import math
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import base64
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import logging
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import time
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import uuid
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import folder_paths
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from io import BytesIO
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@ -996,212 +992,6 @@ class IdeogramTextToImage(ComfyNodeABC):
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# def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
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# return ""
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class FluxProUltraImageNode(ComfyNodeABC):
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"""
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Generates images synchronously based on prompt and resolution.
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"""
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MINIMUM_RATIO = 1 / 4
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MAXIMUM_RATIO = 4 / 1
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MINIMUM_RATIO_STR = "1:4"
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MAXIMUM_RATIO_STR = "4:1"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": (
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IO.STRING,
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{
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation",
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},
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),
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"prompt_upsampling": (
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IO.BOOLEAN,
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{
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"default": False,
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"tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "The random seed used for creating the noise.",
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},
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),
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"aspect_ratio": (
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IO.STRING,
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{
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"default": "16:9",
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"tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
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},
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),
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"raw": (
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IO.BOOLEAN,
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{
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"default": False,
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"tooltip": "When True, generate less processed, more natural-looking images.",
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},
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),
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},
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"optional": {
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"image_prompt": (IO.IMAGE,),
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"image_prompt_strength": (
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IO.FLOAT,
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{
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"default": 0.1,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Blend between the prompt and the image prompt.",
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},
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),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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},
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}
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@classmethod
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def VALIDATE_INPUTS(cls, aspect_ratio: str):
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try:
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validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=cls.MINIMUM_RATIO,
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maximum_ratio=cls.MAXIMUM_RATIO,
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minimum_ratio_str=cls.MINIMUM_RATIO_STR,
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maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
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)
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except Exception as e:
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return str(e)
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return True
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RETURN_TYPES = (IO.IMAGE,)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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API_NODE = True
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CATEGORY = "api node/image/bfl"
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def api_call(
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self,
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prompt: str,
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aspect_ratio: str,
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prompt_upsampling=False,
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raw=False,
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seed=0,
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image_prompt=None,
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image_prompt_strength=0.1,
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auth_token=None,
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**kwargs,
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):
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/bfl/flux-pro-1.1-ultra/generate",
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method=HttpMethod.POST,
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request_model=BFLFluxProGenerateRequest,
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response_model=BFLFluxProGenerateResponse,
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),
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request=BFLFluxProGenerateRequest(
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prompt=prompt,
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prompt_upsampling=prompt_upsampling,
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seed=seed,
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aspect_ratio=validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=self.MINIMUM_RATIO,
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maximum_ratio=self.MAXIMUM_RATIO,
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minimum_ratio_str=self.MINIMUM_RATIO_STR,
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maximum_ratio_str=self.MAXIMUM_RATIO_STR,
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),
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raw=raw,
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image_prompt=(
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image_prompt
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if image_prompt is None
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else self._convert_image_to_base64(image_prompt)
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),
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image_prompt_strength=(
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None if image_prompt is None else round(image_prompt_strength, 2)
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),
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),
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auth_token=auth_token,
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)
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output_image = self._handle_bfl_synchronous_operation(operation)
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return (output_image,)
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def _handle_bfl_synchronous_operation(
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self, operation: SynchronousOperation, timeout_bfl_calls=360
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):
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response_api: BFLFluxProGenerateResponse = operation.execute()
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return self._poll_until_generated(
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response_api.polling_url, timeout=timeout_bfl_calls
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)
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def _poll_until_generated(self, polling_url: str, timeout=360):
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# used bfl-comfy-nodes to verify code implementation:
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# https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
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start_time = time.time()
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retries_404 = 0
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max_retries_404 = 5
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retry_404_seconds = 2
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retry_202_seconds = 2
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retry_pending_seconds = 1
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request = requests.Request(method=HttpMethod.GET, url=polling_url)
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# NOTE: should True loop be replaced with checking if workflow has been interrupted?
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while True:
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response = requests.Session().send(request.prepare())
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if response.status_code == 200:
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result = response.json()
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if result["status"] == BFLStatus.ready:
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img_url = result["result"]["sample"]
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img_response = requests.get(img_url)
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return process_image_response(img_response)
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elif result["status"] in [
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BFLStatus.request_moderated,
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BFLStatus.content_moderated,
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]:
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status = result["status"]
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raise Exception(
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f"BFL API did not return an image due to: {status}."
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)
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elif result["status"] == BFLStatus.error:
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raise Exception(f"BFL API encountered an error: {result}.")
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elif result["status"] == BFLStatus.pending:
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time.sleep(retry_pending_seconds)
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continue
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elif response.status_code == 404:
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if retries_404 < max_retries_404:
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retries_404 += 1
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time.sleep(retry_404_seconds)
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continue
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raise Exception(
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f"BFL API could not find task after {max_retries_404} tries."
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)
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elif response.status_code == 202:
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time.sleep(retry_202_seconds)
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elif time.time() - start_time > timeout:
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raise Exception(
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f"BFL API experienced a timeout; could not return request under {timeout} seconds."
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)
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else:
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raise Exception(f"BFL API encountered an error: {response.json()}")
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def _convert_image_to_base64(self, image: torch.Tensor):
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scaled_image = downscale_input(image, total_pixels=2048 * 2048)
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# remove batch dimension if present
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if len(scaled_image.shape) > 3:
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scaled_image = scaled_image[0]
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image_np = (scaled_image.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format="PNG")
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return base64.b64encode(img_byte_arr.getvalue()).decode()
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class MinimaxTextToVideoNode:
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"""
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Generates videos synchronously based on a prompt, and optional parameters using Minimax's API.
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@ -1348,7 +1138,6 @@ NODE_CLASS_MAPPINGS = {
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"OpenAIDalle3": OpenAIDalle3,
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"OpenAIGPTImage1": OpenAIGPTImage1,
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"IdeogramTextToImage": IdeogramTextToImage,
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"FluxProUltraImageNode": FluxProUltraImageNode,
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"MinimaxTextToVideoNode": MinimaxTextToVideoNode,
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}
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@ -1358,6 +1147,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"OpenAIDalle3": "OpenAI DALL·E 3",
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"OpenAIGPTImage1": "OpenAI GPT Image 1",
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"IdeogramTextToImage": "Ideogram Text to Image",
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"FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
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"MinimaxTextToVideoNode": "Minimax Text to Video",
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}
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