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Merge pull request #483 from ikohu-66/add_seedream_tools
Add seedream tools
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Calesthio
@@ -242,6 +242,8 @@ class ImageSelector(BaseTool):
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props = tool.input_schema.get("properties", {})
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if "query" in props and "query" not in adapted:
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adapted["query"] = adapted.get("prompt", "")
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if "n" in adapted and "num_images" in props and "num_images" not in adapted:
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adapted["num_images"] = adapted["n"]
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# Strip selector-only keys that downstream tools don't understand
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adapted.pop("preferred_provider", None)
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@@ -0,0 +1,275 @@
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"""Seedream V5 image generation via fal.ai API.
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deep-thinking prompt understanding, native text in 14 languages, and precise control over dense layouts and structured designs.
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"""
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from __future__ import annotations
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import os
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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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from tools.base_tool import (
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BaseTool,
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Determinism,
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ExecutionMode,
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ResourceProfile,
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RetryPolicy,
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ToolResult,
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ToolRuntime,
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ToolStability,
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ToolStatus,
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ToolTier,
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)
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class SeedreamImage(BaseTool):
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name = "seedream_image"
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version = "0.1.0"
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tier = ToolTier.GENERATE
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capability = "image_generation"
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provider = "bytedance"
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stability = ToolStability.EXPERIMENTAL
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execution_mode = ExecutionMode.ASYNC
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determinism = Determinism.STOCHASTIC
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runtime = ToolRuntime.API
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dependencies = ["env:FAL_KEY"]
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install_instructions = (
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"Set FAL_KEY to your fal.ai API key.\n"
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" Get one at https://fal.ai/dashboard/keys"
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)
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agent_skills = ["visual-style"]
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capabilities = [
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"generate_image",
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"text_to_image",
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"structured_designs",
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"dense_layouts",
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"multi_language_text",
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]
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supports = {
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"text_rendering": True,
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"color_palette": True,
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"custom_size": True,
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"structured_designs": True,
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"dense_layouts": True,
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"multi_language_text": True,
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}
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best_for = [
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"raster brand and campaign assets",
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"images with accurate text rendering",
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"structured designs and dense layouts",
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"multi-language text rendering (14 languages)",
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]
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input_schema = {
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"type": "object",
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"required": ["prompt"],
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"properties": {
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"prompt": {"type": "string"},
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"image_size": {
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"type": "string",
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"enum": [
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"square", "square_hd",
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"landscape_4_3", "landscape_16_9",
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"portrait_4_3", "portrait_16_9",
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"auto_1K","auto_2K"
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],
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"default": "auto_2K",
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},
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"num_images": {
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"type": "integer",
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"minimum": 1,
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"maximum": 4,
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"default": 1,
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},
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"output_format": {
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"type": "string",
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"enum": ["jpeg", "png"],
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"description": "Output image format. Use 'jpeg' for smaller file size with lossy compression (suitable for web/preview), or 'png' for lossless quality with transparency support (suitable for design assets and further editing).",
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},
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"enable_safety_checker": {
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"type": "boolean",
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"default": True,
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"description": "If set to true, the safety checker will be enabled.",
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},
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"output_path": {"type": "string"}
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},
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}
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resource_profile = ResourceProfile(
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cpu_cores=1, ram_mb=512, vram_mb=0, disk_mb=100, network_required=True
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)
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retry_policy = RetryPolicy(max_retries=2, retryable_errors=["rate_limit", "timeout"])
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idempotency_key_fields = [
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"prompt",
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"image_size",
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"output_format",
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"num_images",
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"enable_safety_checker",
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]
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side_effects = ["writes image file to output_path", "calls fal.ai queue API"]
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user_visible_verification = ["Inspect generated image for brand accuracy and text readability"]
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def _get_api_key(self) -> str | None:
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return os.environ.get("FAL_KEY") or os.environ.get("FAL_AI_API_KEY")
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def get_status(self) -> ToolStatus:
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if self._get_api_key():
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return ToolStatus.AVAILABLE
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return ToolStatus.UNAVAILABLE
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def estimate_cost(self, inputs: dict[str, Any]) -> float:
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image_size = inputs.get("image_size", "auto_2K")
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num_images = inputs.get("num_images", 1)
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size_price_map = {
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"square": 0.0675,
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"square_hd": 0.135,
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"landscape_4_3": 0.0675,
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"landscape_16_9": 0.135,
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"portrait_4_3": 0.0675,
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"portrait_16_9": 0.135,
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"auto_1K": 0.0675,
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"auto_2K": 0.135,
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}
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unit_price = size_price_map.get(image_size, 0.135)
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return round(unit_price * num_images, 4)
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@staticmethod
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def _output_paths(
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output_path: str | None, count: int, output_format: str
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) -> list[Path]:
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path = Path(output_path or f"seedream_image.{output_format}")
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if not path.suffix:
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path = path.with_suffix(f".{output_format}")
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if count == 1:
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return [path]
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return [
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path.with_name(f"{path.stem}_{index}{path.suffix}")
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for index in range(1, count + 1)
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]
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def execute(self, inputs: dict[str, Any]) -> ToolResult:
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import requests
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api_key = self._get_api_key()
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if not api_key:
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return ToolResult(
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success=False,
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error="FAL_KEY not set. " + self.install_instructions,
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)
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start = time.time()
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prompt = inputs["prompt"]
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num_images = inputs.get("num_images", 1)
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if isinstance(num_images, bool) or not isinstance(num_images, int):
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return ToolResult(
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success=False, error="num_images must be an integer from 1 to 4."
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)
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if not 1 <= num_images <= 4:
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return ToolResult(
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success=False, error="num_images must be between 1 and 4."
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)
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submit_url = "https://queue.fal.run/bytedance/seedream/v5/pro/text-to-image"
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payload: dict[str, Any] = {
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"prompt": prompt,
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"image_size": inputs.get("image_size", "auto_2K"),
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"output_format": inputs.get("output_format", "jpeg"),
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"num_images": num_images,
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"enable_safety_checker": inputs.get("enable_safety_checker", True),
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}
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try:
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headers = {
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"Authorization": f"Key {api_key}",
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"Content-Type": "application/json",
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}
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submit_resp = requests.post(
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submit_url,
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headers=headers,
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json=payload,
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timeout=(10, 60),
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)
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submit_resp.raise_for_status()
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submit_data = submit_resp.json()
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request_id = submit_data.get("request_id")
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if not request_id:
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raise RuntimeError(
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"Seedream submit succeeded but did not return request_id"
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)
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status_url = (
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f"https://queue.fal.run/bytedance/seedream/requests/"
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f"{request_id}/status"
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)
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elapsed = 0.0
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while elapsed < 300:
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status_resp = requests.get(
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status_url,
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headers=headers,
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timeout=30,
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)
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status_resp.raise_for_status()
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status_data = status_resp.json()
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status = status_data.get("status")
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if status == "COMPLETED":
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break
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elif status in ("FAILED", "CANCELLED"):
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error_msg = status_data.get("error", "Unknown error")
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raise RuntimeError(f"Seedream task {status}: {error_msg}")
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time.sleep(10)
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elapsed += 10
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if elapsed >= 300:
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raise RuntimeError(
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f"Seedream task timed out after {300}s"
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)
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result_resp = requests.get(
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f"https://queue.fal.run/bytedance/seedream/requests/"
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f"{request_id}",
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headers=headers,
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timeout=30,
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)
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result_resp.raise_for_status()
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result_data = result_resp.json()
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images = result_data.get("images", [])
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if not images:
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raise RuntimeError("Seedream completed but no images returned")
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ext = inputs.get("output_format", "jpeg")
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expected_paths = self._output_paths(
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inputs.get("output_path"), len(images), ext
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)
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output_paths = []
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for img, output_path in zip(images, expected_paths):
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image_url = img.get("url")
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if not image_url:
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continue
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image_resp = requests.get(image_url, timeout=60)
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image_resp.raise_for_status()
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_path.write_bytes(image_resp.content)
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output_paths.append(str(output_path))
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except Exception as e:
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return ToolResult(
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success=False,
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error=f"Seedream generation failed: {e}",
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)
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return ToolResult(
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success=True,
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data={
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"provider": "seedream",
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"model": "seedream_v5",
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"prompt": prompt,
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"request_id": request_id,
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"image_count": len(output_paths),
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"outputs": output_paths,
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},
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artifacts=output_paths,
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cost_usd=self.estimate_cost(inputs),
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duration_seconds=round(time.time() - start, 2),
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model="fal-ai/bytedance/seedream/v5",
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)
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