mirror of
https://github.com/NousResearch/hermes-agent.git
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5ffae9228b
Adds OpenAI's new GPT Image 2 model via FAL.ai, selectable through `hermes tools` → Image Generation. SOTA text rendering (including CJK) and world-aware photorealism. - FAL_MODELS entry with image_size_preset style - 4:3 presets on all aspect ratios — 16:9 (1024x576) falls below GPT-Image-2's 655,360 min-pixel floor and would be rejected - quality pinned to medium (same rule as gpt-image-1.5) for predictable Nous Portal billing - BYOK (openai_api_key) deliberately omitted from supports so all users stay on shared FAL billing - 6 new tests covering preset mapping, quality pinning, and supports-whitelist integrity - Docs table + aspect-ratio map updated Live-tested end-to-end: 39.9s cold request, clean 1024x768 PNG
498 lines
22 KiB
Python
498 lines
22 KiB
Python
"""Tests for tools/image_generation_tool.py — FAL multi-model support.
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Covers the pure logic of the new wrapper: catalog integrity, the three size
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families (image_size_preset / aspect_ratio / gpt_literal), the supports
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whitelist, default merging, GPT quality override, and model resolution
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fallback. Does NOT exercise fal_client submission — that's covered by
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tests/tools/test_managed_media_gateways.py.
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"""
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from __future__ import annotations
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from unittest.mock import patch
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import pytest
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# ---------------------------------------------------------------------------
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# Fixtures
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# ---------------------------------------------------------------------------
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@pytest.fixture
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def image_tool():
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"""Fresh import of tools.image_generation_tool per test."""
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import importlib
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import tools.image_generation_tool as mod
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return importlib.reload(mod)
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# ---------------------------------------------------------------------------
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# Catalog integrity
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# ---------------------------------------------------------------------------
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class TestFalCatalog:
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"""Every FAL_MODELS entry must have a consistent shape."""
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def test_default_model_is_klein(self, image_tool):
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assert image_tool.DEFAULT_MODEL == "fal-ai/flux-2/klein/9b"
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def test_default_model_in_catalog(self, image_tool):
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assert image_tool.DEFAULT_MODEL in image_tool.FAL_MODELS
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def test_all_entries_have_required_keys(self, image_tool):
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required = {
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"display", "speed", "strengths", "price",
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"size_style", "sizes", "defaults", "supports", "upscale",
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}
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for mid, meta in image_tool.FAL_MODELS.items():
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missing = required - set(meta.keys())
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assert not missing, f"{mid} missing required keys: {missing}"
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def test_size_style_is_valid(self, image_tool):
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valid = {"image_size_preset", "aspect_ratio", "gpt_literal"}
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for mid, meta in image_tool.FAL_MODELS.items():
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assert meta["size_style"] in valid, \
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f"{mid} has invalid size_style: {meta['size_style']}"
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def test_sizes_cover_all_aspect_ratios(self, image_tool):
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for mid, meta in image_tool.FAL_MODELS.items():
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assert set(meta["sizes"].keys()) >= {"landscape", "square", "portrait"}, \
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f"{mid} missing a required aspect_ratio key"
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def test_supports_is_a_set(self, image_tool):
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for mid, meta in image_tool.FAL_MODELS.items():
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assert isinstance(meta["supports"], set), \
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f"{mid}.supports must be a set, got {type(meta['supports'])}"
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def test_prompt_is_always_supported(self, image_tool):
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for mid, meta in image_tool.FAL_MODELS.items():
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assert "prompt" in meta["supports"], \
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f"{mid} must support 'prompt'"
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def test_only_flux2_pro_upscales_by_default(self, image_tool):
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"""Upscaling should default to False for all new models to preserve
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the <1s / fast-render value prop. Only flux-2-pro stays True for
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backward-compat with the previous default."""
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for mid, meta in image_tool.FAL_MODELS.items():
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if mid == "fal-ai/flux-2-pro":
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assert meta["upscale"] is True, \
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"flux-2-pro should keep upscale=True for backward-compat"
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else:
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assert meta["upscale"] is False, \
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f"{mid} should default to upscale=False"
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# ---------------------------------------------------------------------------
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# Payload building — three size families
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# ---------------------------------------------------------------------------
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class TestImageSizePresetFamily:
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"""Flux, z-image, qwen, recraft, ideogram all use preset enum sizes."""
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def test_klein_landscape_uses_preset(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hello", "landscape")
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assert p["image_size"] == "landscape_16_9"
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assert "aspect_ratio" not in p
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def test_klein_square_uses_preset(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hello", "square")
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assert p["image_size"] == "square_hd"
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def test_klein_portrait_uses_preset(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hello", "portrait")
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assert p["image_size"] == "portrait_16_9"
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class TestAspectRatioFamily:
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"""Nano-banana uses aspect_ratio enum, NOT image_size."""
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def test_nano_banana_landscape_uses_aspect_ratio(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/nano-banana-pro", "hello", "landscape")
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assert p["aspect_ratio"] == "16:9"
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assert "image_size" not in p
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def test_nano_banana_square_uses_aspect_ratio(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/nano-banana-pro", "hello", "square")
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assert p["aspect_ratio"] == "1:1"
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def test_nano_banana_portrait_uses_aspect_ratio(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/nano-banana-pro", "hello", "portrait")
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assert p["aspect_ratio"] == "9:16"
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class TestGptLiteralFamily:
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"""GPT-Image 1.5 uses literal size strings."""
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def test_gpt_landscape_is_literal(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hello", "landscape")
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assert p["image_size"] == "1536x1024"
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def test_gpt_square_is_literal(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hello", "square")
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assert p["image_size"] == "1024x1024"
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def test_gpt_portrait_is_literal(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hello", "portrait")
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assert p["image_size"] == "1024x1536"
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class TestGptImage2Presets:
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"""GPT Image 2 uses preset enum sizes (not literal strings like 1.5).
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Mapped to 4:3 variants so we stay above the 655,360 min-pixel floor
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(16:9 presets at 1024x576 = 589,824 would be rejected)."""
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def test_gpt2_landscape_uses_4_3_preset(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "landscape")
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assert p["image_size"] == "landscape_4_3"
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def test_gpt2_square_uses_square_hd(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "square")
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assert p["image_size"] == "square_hd"
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def test_gpt2_portrait_uses_4_3_preset(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hello", "portrait")
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assert p["image_size"] == "portrait_4_3"
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def test_gpt2_quality_pinned_to_medium(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hi", "square")
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assert p["quality"] == "medium"
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def test_gpt2_strips_byok_and_unsupported_overrides(self, image_tool):
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"""openai_api_key (BYOK) is deliberately not in supports — all users
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route through shared FAL billing. guidance_scale/num_inference_steps
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aren't in the model's API surface either."""
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p = image_tool._build_fal_payload(
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"fal-ai/gpt-image-2", "hi", "square",
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overrides={
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"openai_api_key": "sk-...",
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"guidance_scale": 7.5,
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"num_inference_steps": 50,
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},
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)
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assert "openai_api_key" not in p
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assert "guidance_scale" not in p
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assert "num_inference_steps" not in p
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def test_gpt2_strips_seed_even_if_passed(self, image_tool):
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# seed isn't in the GPT Image 2 API surface either.
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p = image_tool._build_fal_payload("fal-ai/gpt-image-2", "hi", "square", seed=42)
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assert "seed" not in p
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# ---------------------------------------------------------------------------
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# Supports whitelist — the main safety property
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# ---------------------------------------------------------------------------
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class TestSupportsFilter:
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"""No model should receive keys outside its `supports` set."""
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def test_payload_keys_are_subset_of_supports_for_all_models(self, image_tool):
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for mid, meta in image_tool.FAL_MODELS.items():
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payload = image_tool._build_fal_payload(mid, "test", "landscape", seed=42)
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unsupported = set(payload.keys()) - meta["supports"]
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assert not unsupported, \
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f"{mid} payload has unsupported keys: {unsupported}"
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def test_gpt_image_has_no_seed_even_if_passed(self, image_tool):
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# GPT-Image 1.5 does not support seed — the filter must strip it.
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hi", "square", seed=42)
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assert "seed" not in p
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def test_gpt_image_strips_unsupported_overrides(self, image_tool):
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p = image_tool._build_fal_payload(
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"fal-ai/gpt-image-1.5", "hi", "square",
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overrides={"guidance_scale": 7.5, "num_inference_steps": 50},
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)
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assert "guidance_scale" not in p
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assert "num_inference_steps" not in p
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def test_recraft_has_minimal_payload(self, image_tool):
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# Recraft V4 Pro supports prompt, image_size, enable_safety_checker,
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# colors, background_color (no seed, no style — V4 dropped V3's style enum).
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p = image_tool._build_fal_payload("fal-ai/recraft/v4/pro/text-to-image", "hi", "landscape")
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assert set(p.keys()) <= {
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"prompt", "image_size", "enable_safety_checker",
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"colors", "background_color",
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}
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def test_nano_banana_never_gets_image_size(self, image_tool):
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# Common bug: translator accidentally setting both image_size and aspect_ratio.
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p = image_tool._build_fal_payload("fal-ai/nano-banana-pro", "hi", "landscape", seed=1)
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assert "image_size" not in p
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assert p["aspect_ratio"] == "16:9"
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# ---------------------------------------------------------------------------
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# Default merging
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# ---------------------------------------------------------------------------
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class TestDefaults:
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"""Model-level defaults should carry through unless overridden."""
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def test_klein_default_steps_is_4(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hi", "square")
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assert p["num_inference_steps"] == 4
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def test_flux_2_pro_default_steps_is_50(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2-pro", "hi", "square")
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assert p["num_inference_steps"] == 50
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def test_override_replaces_default(self, image_tool):
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p = image_tool._build_fal_payload(
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"fal-ai/flux-2-pro", "hi", "square", overrides={"num_inference_steps": 25}
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)
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assert p["num_inference_steps"] == 25
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def test_none_override_does_not_replace_default(self, image_tool):
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"""None values from caller should be ignored (use default)."""
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p = image_tool._build_fal_payload(
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"fal-ai/flux-2-pro", "hi", "square",
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overrides={"num_inference_steps": None},
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)
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assert p["num_inference_steps"] == 50
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# ---------------------------------------------------------------------------
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# GPT-Image quality is pinned to medium (not user-configurable)
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# ---------------------------------------------------------------------------
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class TestGptQualityPinnedToMedium:
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"""GPT-Image quality is baked into the FAL_MODELS defaults at 'medium'
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and cannot be overridden via config. Pinning keeps Nous Portal billing
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predictable across all users."""
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def test_gpt_payload_always_has_medium_quality(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hi", "square")
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assert p["quality"] == "medium"
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def test_config_quality_setting_is_ignored(self, image_tool):
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"""Even if a user manually edits config.yaml and adds quality_setting,
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the payload must still use medium. No code path reads that field."""
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with patch("hermes_cli.config.load_config",
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return_value={"image_gen": {"quality_setting": "high"}}):
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p = image_tool._build_fal_payload("fal-ai/gpt-image-1.5", "hi", "square")
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assert p["quality"] == "medium"
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def test_non_gpt_model_never_gets_quality(self, image_tool):
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"""quality is only meaningful for GPT-Image models (1.5, 2) — other
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models should never have it in their payload."""
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gpt_models = {"fal-ai/gpt-image-1.5", "fal-ai/gpt-image-2"}
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for mid in image_tool.FAL_MODELS:
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if mid in gpt_models:
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continue
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p = image_tool._build_fal_payload(mid, "hi", "square")
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assert "quality" not in p, f"{mid} unexpectedly has 'quality' in payload"
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def test_honors_quality_setting_flag_is_removed(self, image_tool):
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"""The honors_quality_setting flag was the old override trigger.
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It must not be present on any model entry anymore."""
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for mid, meta in image_tool.FAL_MODELS.items():
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assert "honors_quality_setting" not in meta, (
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f"{mid} still has honors_quality_setting; "
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f"remove it — quality is pinned to medium"
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)
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def test_resolve_gpt_quality_function_is_gone(self, image_tool):
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"""The _resolve_gpt_quality() helper was removed — quality is now
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a static default, not a runtime lookup."""
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assert not hasattr(image_tool, "_resolve_gpt_quality"), (
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"_resolve_gpt_quality should not exist — quality is pinned"
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)
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# ---------------------------------------------------------------------------
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# Model resolution
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# ---------------------------------------------------------------------------
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class TestModelResolution:
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def test_no_config_falls_back_to_default(self, image_tool):
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with patch("hermes_cli.config.load_config", return_value={}):
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mid, meta = image_tool._resolve_fal_model()
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assert mid == "fal-ai/flux-2/klein/9b"
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def test_valid_config_model_is_used(self, image_tool):
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with patch("hermes_cli.config.load_config",
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return_value={"image_gen": {"model": "fal-ai/flux-2-pro"}}):
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mid, meta = image_tool._resolve_fal_model()
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assert mid == "fal-ai/flux-2-pro"
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assert meta["upscale"] is True # flux-2-pro keeps backward-compat upscaling
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def test_unknown_model_falls_back_to_default_with_warning(self, image_tool, caplog):
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with patch("hermes_cli.config.load_config",
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return_value={"image_gen": {"model": "fal-ai/nonexistent-9000"}}):
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mid, _ = image_tool._resolve_fal_model()
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assert mid == "fal-ai/flux-2/klein/9b"
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def test_env_var_fallback_when_no_config(self, image_tool, monkeypatch):
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monkeypatch.setenv("FAL_IMAGE_MODEL", "fal-ai/z-image/turbo")
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with patch("hermes_cli.config.load_config", return_value={}):
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mid, _ = image_tool._resolve_fal_model()
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assert mid == "fal-ai/z-image/turbo"
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def test_config_wins_over_env_var(self, image_tool, monkeypatch):
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monkeypatch.setenv("FAL_IMAGE_MODEL", "fal-ai/z-image/turbo")
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with patch("hermes_cli.config.load_config",
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return_value={"image_gen": {"model": "fal-ai/nano-banana-pro"}}):
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mid, _ = image_tool._resolve_fal_model()
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assert mid == "fal-ai/nano-banana-pro"
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# ---------------------------------------------------------------------------
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# Aspect ratio handling
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# ---------------------------------------------------------------------------
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class TestAspectRatioNormalization:
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def test_invalid_aspect_defaults_to_landscape(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hi", "cinemascope")
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assert p["image_size"] == "landscape_16_9"
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def test_uppercase_aspect_is_normalized(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hi", "PORTRAIT")
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assert p["image_size"] == "portrait_16_9"
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def test_empty_aspect_defaults_to_landscape(self, image_tool):
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p = image_tool._build_fal_payload("fal-ai/flux-2/klein/9b", "hi", "")
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assert p["image_size"] == "landscape_16_9"
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# ---------------------------------------------------------------------------
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# Schema + registry integrity
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# ---------------------------------------------------------------------------
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class TestRegistryIntegration:
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def test_schema_exposes_only_prompt_and_aspect_ratio_to_agent(self, image_tool):
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"""The agent-facing schema must stay tight — model selection is a
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user-level config choice, not an agent-level arg."""
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props = image_tool.IMAGE_GENERATE_SCHEMA["parameters"]["properties"]
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assert set(props.keys()) == {"prompt", "aspect_ratio"}
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def test_aspect_ratio_enum_is_three_values(self, image_tool):
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enum = image_tool.IMAGE_GENERATE_SCHEMA["parameters"]["properties"]["aspect_ratio"]["enum"]
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assert set(enum) == {"landscape", "square", "portrait"}
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# ---------------------------------------------------------------------------
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# Managed gateway 4xx translation
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# ---------------------------------------------------------------------------
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class _MockResponse:
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def __init__(self, status_code: int):
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self.status_code = status_code
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class _MockHttpxError(Exception):
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"""Simulates httpx.HTTPStatusError which exposes .response.status_code."""
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def __init__(self, status_code: int, message: str = "Bad Request"):
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super().__init__(message)
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self.response = _MockResponse(status_code)
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class TestExtractHttpStatus:
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"""Status-code extraction should work across exception shapes."""
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def test_extracts_from_response_attr(self, image_tool):
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exc = _MockHttpxError(403)
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assert image_tool._extract_http_status(exc) == 403
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def test_extracts_from_status_code_attr(self, image_tool):
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exc = Exception("fail")
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exc.status_code = 404 # type: ignore[attr-defined]
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assert image_tool._extract_http_status(exc) == 404
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def test_returns_none_for_non_http_exception(self, image_tool):
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assert image_tool._extract_http_status(ValueError("nope")) is None
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assert image_tool._extract_http_status(RuntimeError("nope")) is None
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def test_response_attr_without_status_code_returns_none(self, image_tool):
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class OddResponse:
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pass
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|
exc = Exception("weird")
|
|
exc.response = OddResponse() # type: ignore[attr-defined]
|
|
assert image_tool._extract_http_status(exc) is None
|
|
|
|
|
|
class TestManagedGatewayErrorTranslation:
|
|
"""4xx from the Nous managed gateway should be translated to a user-actionable message."""
|
|
|
|
def test_4xx_translates_to_value_error_with_remediation(self, image_tool, monkeypatch):
|
|
"""403 from managed gateway → ValueError mentioning FAL_KEY + hermes tools."""
|
|
from unittest.mock import MagicMock
|
|
|
|
# Simulate: managed mode active, managed submit raises 4xx.
|
|
managed_gateway = MagicMock()
|
|
managed_gateway.gateway_origin = "https://fal-queue-gateway.example.com"
|
|
managed_gateway.nous_user_token = "test-token"
|
|
monkeypatch.setattr(image_tool, "_resolve_managed_fal_gateway",
|
|
lambda: managed_gateway)
|
|
|
|
bad_request = _MockHttpxError(403, "Forbidden")
|
|
mock_managed_client = MagicMock()
|
|
mock_managed_client.submit.side_effect = bad_request
|
|
monkeypatch.setattr(image_tool, "_get_managed_fal_client",
|
|
lambda gw: mock_managed_client)
|
|
|
|
with pytest.raises(ValueError) as exc_info:
|
|
image_tool._submit_fal_request("fal-ai/nano-banana-pro", {"prompt": "x"})
|
|
|
|
msg = str(exc_info.value)
|
|
assert "fal-ai/nano-banana-pro" in msg
|
|
assert "403" in msg
|
|
assert "FAL_KEY" in msg
|
|
assert "hermes tools" in msg
|
|
# Original exception chained for debugging
|
|
assert exc_info.value.__cause__ is bad_request
|
|
|
|
def test_5xx_is_not_translated(self, image_tool, monkeypatch):
|
|
"""500s are real outages, not model-availability issues — don't rewrite them."""
|
|
from unittest.mock import MagicMock
|
|
|
|
managed_gateway = MagicMock()
|
|
monkeypatch.setattr(image_tool, "_resolve_managed_fal_gateway",
|
|
lambda: managed_gateway)
|
|
|
|
server_error = _MockHttpxError(502, "Bad Gateway")
|
|
mock_managed_client = MagicMock()
|
|
mock_managed_client.submit.side_effect = server_error
|
|
monkeypatch.setattr(image_tool, "_get_managed_fal_client",
|
|
lambda gw: mock_managed_client)
|
|
|
|
with pytest.raises(_MockHttpxError):
|
|
image_tool._submit_fal_request("fal-ai/flux-2-pro", {"prompt": "x"})
|
|
|
|
def test_direct_fal_errors_are_not_translated(self, image_tool, monkeypatch):
|
|
"""When user has direct FAL_KEY (managed gateway returns None), raw
|
|
errors from fal_client bubble up unchanged — fal_client already
|
|
provides reasonable error messages for direct usage."""
|
|
from unittest.mock import MagicMock
|
|
|
|
monkeypatch.setattr(image_tool, "_resolve_managed_fal_gateway",
|
|
lambda: None)
|
|
|
|
direct_error = _MockHttpxError(403, "Forbidden")
|
|
fake_fal_client = MagicMock()
|
|
fake_fal_client.submit.side_effect = direct_error
|
|
monkeypatch.setattr(image_tool, "fal_client", fake_fal_client)
|
|
|
|
with pytest.raises(_MockHttpxError):
|
|
image_tool._submit_fal_request("fal-ai/flux-2-pro", {"prompt": "x"})
|
|
|
|
def test_non_http_exception_from_managed_bubbles_up(self, image_tool, monkeypatch):
|
|
"""Connection errors, timeouts, etc. from managed mode aren't 4xx —
|
|
they should bubble up unchanged so callers can retry or diagnose."""
|
|
from unittest.mock import MagicMock
|
|
|
|
managed_gateway = MagicMock()
|
|
monkeypatch.setattr(image_tool, "_resolve_managed_fal_gateway",
|
|
lambda: managed_gateway)
|
|
|
|
conn_error = ConnectionError("network down")
|
|
mock_managed_client = MagicMock()
|
|
mock_managed_client.submit.side_effect = conn_error
|
|
monkeypatch.setattr(image_tool, "_get_managed_fal_client",
|
|
lambda gw: mock_managed_client)
|
|
|
|
with pytest.raises(ConnectionError):
|
|
image_tool._submit_fal_request("fal-ai/flux-2-pro", {"prompt": "x"})
|