mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-04-23 03:00:31 -04:00
* feat: initial external model support * feat: support reference images for external models * fix: sorting lint error * chore: hide Reidentify button for external models * review: enable auto-install/remove fro external models * feat: show external mode name during install * review: model descriptions * review: implemented review comments * review: added optional seed control for external models * chore: fix linter warning * review: save api keys to a seperate file * docs: updated external model docs * chore: fix linter errors * fix: sync configured external starter models on startup * feat(ui): add provider-specific external generation nodes * feat: expose external panel schemas in model configs * feat(ui): drive external panels from panel schema * docs: sync app config docstring order * feat: add gemini 3.1 flash image preview starter model * feat: update gemini image model limits * fix: resolve TypeScript errors and move external provider config to api_keys.yaml Add 'external', 'external_image_generator', and 'external_api' to Zod enum schemas (zBaseModelType, zModelType, zModelFormat) to match the generated OpenAPI types. Remove redundant union workarounds from component prop types and Record definitions. Fix type errors in ModelEdit (react-hook-form Control invariance), parsing.tsx (model identifier narrowing), buildExternalGraph (edge typing), and ModelSettings import/export buttons. Move external_gemini_base_url and external_openai_base_url into api_keys.yaml alongside the API keys so all external provider config lives in one dedicated file, separate from invokeai.yaml. * feat: add resolution presets and imageConfig support for Gemini 3 models Add combined resolution preset selector for external models that maps aspect ratio + image size to fixed dimensions. Gemini 3 Pro and 3.1 Flash now send imageConfig (aspectRatio + imageSize) via generationConfig instead of text-based aspect ratio hints used by Gemini 2.5 Flash. Backend: ExternalResolutionPreset model, resolution_presets capability field, image_size on ExternalGenerationRequest, and Gemini provider imageConfig logic. Frontend: ExternalSettingsAccordion with combo resolution select, dimension slider disabling for fixed-size models, and panel schema constraint wiring for Steps/Guidance/Seed controls. * Remove unused external model fields and add provider-specific parameters - Remove negative_prompt, steps, guidance, reference_image_weights, reference_image_modes from external model nodes (unused by any provider) - Remove supports_negative_prompt, supports_steps, supports_guidance from ExternalModelCapabilities - Add provider_options dict to ExternalGenerationRequest for provider-specific parameters - Add OpenAI-specific fields: quality, background, input_fidelity - Add Gemini-specific fields: temperature, thinking_level - Add new OpenAI starter models: GPT Image 1.5, GPT Image 1 Mini, DALL-E 3, DALL-E 2 - Fix OpenAI provider to use output_format (GPT Image) vs response_format (DALL-E) and send model ID in requests - Add fixed aspect ratio sizes for OpenAI models (bucketing) - Add ExternalProviderRateLimitError with retry logic for 429 responses - Add provider-specific UI components in ExternalSettingsAccordion - Simplify ParamSteps/ParamGuidance by removing dead external overrides - Update all backend and frontend tests * Chore Ruff check & format * Chore typegen * feat: full canvas workflow integration for external models - Add missing aspect ratios (4:5, 5:4, 8:1, 4:1, 1:4, 1:8) to type system for external model support - Sync canvas bbox when external model resolution preset is selected - Use params preset dimensions in buildExternalGraph to prevent "unsupported aspect ratio" errors - Lock all bbox controls (resize handles, aspect ratio select, width/height sliders, swap/optimal buttons) for external models with fixed dimension presets - Disable denoise strength slider for external models (not applicable) - Sync bbox aspect ratio changes back to paramsSlice for external models - Initialize bbox dimensions when switching to an external model * Chore typegen Linux seperator * feat: full canvas workflow integration for external models - Update buildExternalGraph test to include dimensions in mock params * Merge remote-tracking branch 'upstream/main' into external-models * Chore pnpm fix * add missing parameter * docs: add External Models guide with Gemini and OpenAI provider pages * fix(external-models): address PR review feedback - Gemini recall: write temperature, thinking_level, image_size to image metadata; wire external graph as metadata receiver; add recall handlers. - Canvas: gate regional guidance, inpaint mask, and control layer for external models. - Canvas: throw a clear error on outpainting for external models (was falling back to inpaint and hitting an API-side mask/image size mismatch). - Workflow editor: add ui_model_provider_id filter so OpenAI and Gemini nodes only list their own provider's models. - Workflow editor: silently drop seed when the selected model does not support it instead of raising a capability error. - Remove the legacy external_image_generation invocation and the graph-builder fallback; providers must register a dedicated node. - Regenerate schema.ts. - remove Gemini debug dumps to outputs/external_debug * fix(external-models): resolve TSC errors in metadata parsing and external graph - Export imageSizeChanged from paramsSlice (required by the new ImageSize recall handler). - Emit the external graph's metadata model entry via zModelIdentifierField since ExternalApiModelConfig is not part of the AnyModelConfig union. * chore: prettier format ModelIdentifierFieldInputComponent * fix: remove unsupported thinkingConfig from Gemini image models and restrict GPT Image models to txt2img * chore typegen * chore(docs): regenerate settings.json for external provider fields * fix(external): fix mask handling and mode support for external providers - Remove img2img and inpaint modes from Gemini models (Gemini has no bitmap mask or dedicated edit API; image editing works via reference images in the UI) - Fix DALL-E 2 inpainting: convert grayscale mask to RGBA with alpha channel transparency (OpenAI expects transparent=edit area) and convert init image to RGBA when mask is present * fix(external): update mode support and UI for external providers - Remove DALL-E 2 from starter models (deprecated, shutdown May 12 2026) - Enable img2img for GPT Image 1/1.5/1-mini (supports edits endpoint) - Set Gemini models to txt2img only (no mask/edit API; editing via ref images) - Hide mode/init_image/mask_image fields on Gemini node (not usable) - Hide mask_image field on OpenAI node (no model supports inpaint) * Chore typegen * fix(external): improve OpenAI node UX and disable cache by default - Hide OpenAI node's mode and init_image fields: OpenAI's API has no img2img/inpaint distinction (the edits endpoint is invoked automatically when reference images are provided). init_image is functionally identical to a reference image and was misleading users. - Default use_cache to False for external image generation nodes: external API calls are non-deterministic and incur usage costs. Cache hits returned stale image references that did not produce new gallery entries on repeat invokes. * fix(external): duplicate cached images on cache hit instead of skipping External image generation nodes use the standard invocation cache, but returning the cached output (with stale image_name references) on cache hits resulted in no new gallery entries — the Invoke button would spin indefinitely on repeat invokes with identical parameters. Override invoke_internal so that on cache hit, the cached images are loaded and re-saved as new gallery entries. The expensive API call is still skipped (cost saving), but the user sees a new image as expected. * Chore typegen + ruff * CHore ruff format * fix(external): restore OpenAI advanced settings on Remix recall Remix recall iterates through ImageMetadataHandlers but only Gemini's temperature handler was wired up — OpenAI's quality, background, and input_fidelity were stored in image metadata but never parsed back into the params slice. Add the three missing handlers so Remix restores these settings as expected. --------- Co-authored-by: Alexander Eichhorn <alex@eichhorn.dev> Co-authored-by: Alexander Eichhorn <alex@code-with.us> Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
335 lines
12 KiB
Python
335 lines
12 KiB
Python
from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import Any
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import pytest
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from pydantic import ValidationError
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from invokeai.app.invocations.baseinvocation import InvocationRegistry
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from invokeai.app.services.config.config_default import (
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DefaultInvokeAIAppConfig,
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InvokeAIAppConfig,
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get_config,
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load_and_migrate_config,
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)
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from invokeai.app.services.shared.graph import Graph
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from invokeai.frontend.cli.arg_parser import InvokeAIArgs
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v4_config = """
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schema_version: 4.0.0
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host: "192.168.1.1"
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port: 8080
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"""
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invalid_v5_config = """
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schema_version: 5.0.0
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host: "192.168.1.1"
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port: 8080
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"""
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v3_config = """
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InvokeAI:
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Web Server:
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host: 192.168.1.1
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port: 8080
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Features:
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esrgan: true
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internet_available: true
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log_tokenization: false
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patchmatch: true
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ignore_missing_core_models: false
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Paths:
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outdir: /some/outputs/dir
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conf_path: /custom/models.yaml
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Model Cache:
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max_cache_size: 100
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max_vram_cache_size: 50
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"""
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v3_config_with_bad_values = """
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InvokeAI:
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Web Server:
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port: "ice cream"
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"""
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invalid_config = """
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i like turtles
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"""
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@pytest.fixture
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def patch_rootdir(tmp_path: Path, monkeypatch: Any) -> None:
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"""This may be overkill since the current tests don't need the root dir to exist"""
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monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
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def test_path_resolution_root_not_set(patch_rootdir: None):
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"""Test path resolutions when the root is not explicitly set."""
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config = InvokeAIAppConfig()
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expected_root = InvokeAIAppConfig.find_root()
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assert config.root_path == expected_root
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def test_read_config_from_file(tmp_path: Path, patch_rootdir: None):
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"""Test reading configuration from a file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(v4_config)
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config = load_and_migrate_config(temp_config_file)
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assert config.host == "192.168.1.1"
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assert config.port == 8080
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def test_migrate_v3_config_from_file(tmp_path: Path, patch_rootdir: None):
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"""Test reading configuration from a file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(v3_config)
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config = load_and_migrate_config(temp_config_file)
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assert config.outputs_dir == Path("/some/outputs/dir")
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assert config.host == "192.168.1.1"
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assert config.port == 8080
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assert config.ram == 100
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assert config.vram == 50
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assert config.legacy_models_yaml_path == Path("/custom/models.yaml")
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# This should be stripped out
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assert not hasattr(config, "esrgan")
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@pytest.mark.parametrize(
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"legacy_conf_dir,expected_value,expected_is_set",
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[
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# not set, expected value is the default value
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("configs/stable-diffusion", Path("configs"), False),
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# not set, expected value is the default value
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("configs\\stable-diffusion", Path("configs"), False),
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# set, best-effort resolution of the path
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("partial_custom_path/stable-diffusion", Path("partial_custom_path"), True),
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# set, exact path
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("full/custom/path", Path("full/custom/path"), True),
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],
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)
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def test_migrate_v3_legacy_conf_dir_defaults(
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tmp_path: Path, patch_rootdir: None, legacy_conf_dir: str, expected_value: Path, expected_is_set: bool
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):
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"""Test reading configuration from a file."""
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config_content = f"InvokeAI:\n Paths:\n legacy_conf_dir: {legacy_conf_dir}"
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(config_content)
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config = load_and_migrate_config(temp_config_file)
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assert config.legacy_conf_dir == expected_value
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assert ("legacy_conf_dir" in config.model_fields_set) is expected_is_set
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def test_migrate_v3_backup(tmp_path: Path, patch_rootdir: None):
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"""Test the backup of the config file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(v3_config)
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load_and_migrate_config(temp_config_file)
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assert temp_config_file.with_suffix(".yaml.bak").exists()
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assert temp_config_file.with_suffix(".yaml.bak").read_text() == v3_config
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def test_failed_migrate_backup(tmp_path: Path, patch_rootdir: None):
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"""Test the failed migration of the config file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(v3_config_with_bad_values)
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with pytest.raises(RuntimeError):
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load_and_migrate_config(temp_config_file)
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assert temp_config_file.with_suffix(".yaml.bak").exists()
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assert temp_config_file.with_suffix(".yaml.bak").read_text() == v3_config_with_bad_values
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assert temp_config_file.exists()
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assert temp_config_file.read_text() == v3_config_with_bad_values
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def test_bails_on_invalid_config(tmp_path: Path, patch_rootdir: None):
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"""Test reading configuration from a file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(invalid_config)
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with pytest.raises(AssertionError):
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load_and_migrate_config(temp_config_file)
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def test_bails_on_config_with_unsupported_version(tmp_path: Path, patch_rootdir: None):
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"""Test reading configuration from a file."""
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temp_config_file = tmp_path / "temp_invokeai.yaml"
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temp_config_file.write_text(invalid_v5_config)
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with pytest.raises(RuntimeError, match="Invalid schema version"):
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load_and_migrate_config(temp_config_file)
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def test_write_config_to_file(patch_rootdir: None):
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"""Test writing configuration to a file, checking for correct output."""
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with TemporaryDirectory() as tmpdir:
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temp_config_path = Path(tmpdir) / "invokeai.yaml"
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config = InvokeAIAppConfig(host="192.168.1.1", port=8080)
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config.write_file(temp_config_path)
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# Load the file and check contents
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with open(temp_config_path, "r") as file:
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content = file.read()
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# This is a default value, so it should not be in the file
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assert "pil_compress_level" not in content
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assert "host: 192.168.1.1" in content
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assert "port: 8080" in content
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def test_update_config_with_dict(patch_rootdir: None):
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"""Test updating the config with a dictionary."""
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config = InvokeAIAppConfig()
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update_dict = {"host": "10.10.10.10", "port": 6060}
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config.update_config(update_dict)
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assert config.host == "10.10.10.10"
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assert config.port == 6060
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def test_update_config_with_object(patch_rootdir: None):
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"""Test updating the config with another config object."""
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config = InvokeAIAppConfig()
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new_config = InvokeAIAppConfig(host="10.10.10.10", port=6060)
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config.update_config(new_config)
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assert config.host == "10.10.10.10"
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assert config.port == 6060
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def test_set_and_resolve_paths(patch_rootdir: None):
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"""Test setting root and resolving paths based on it."""
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with TemporaryDirectory() as tmpdir:
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config = InvokeAIAppConfig()
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config._root = Path(tmpdir)
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assert config.models_path == Path(tmpdir).resolve() / "models"
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assert config.db_path == Path(tmpdir).resolve() / "databases" / "invokeai.db"
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def test_singleton_behavior(patch_rootdir: None):
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"""Test that get_config always returns the same instance."""
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get_config.cache_clear()
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config1 = get_config()
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config2 = get_config()
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assert config1 is config2
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get_config.cache_clear()
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def test_default_config(patch_rootdir: None):
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"""Test that the default config is as expected."""
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config = DefaultInvokeAIAppConfig()
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assert config.host == "127.0.0.1"
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def test_env_vars(patch_rootdir: None, monkeypatch: pytest.MonkeyPatch, tmp_path: Path):
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"""Test that environment variables are merged into the config"""
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monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
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monkeypatch.setenv("INVOKEAI_HOST", "1.2.3.4")
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monkeypatch.setenv("INVOKEAI_PORT", "1234")
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config = InvokeAIAppConfig()
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assert config.host == "1.2.3.4"
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assert config.port == 1234
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assert config.root_path == tmp_path
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def test_get_config_writing(patch_rootdir: None, monkeypatch: pytest.MonkeyPatch, tmp_path: Path):
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"""Test that get_config writes the appropriate files to disk"""
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# Trick the config into thinking it has already parsed args - this triggers the writing of the config file
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InvokeAIArgs.did_parse = True
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monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
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monkeypatch.setenv("INVOKEAI_HOST", "1.2.3.4")
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get_config.cache_clear()
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config = get_config()
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get_config.cache_clear()
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config_file_path = tmp_path / "invokeai.yaml"
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example_file_path = config_file_path.with_suffix(".example.yaml")
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assert config.config_file_path == config_file_path
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assert config_file_path.exists()
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assert example_file_path.exists()
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# The example file should have the default values
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example_file_content = example_file_path.read_text()
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assert "host: 127.0.0.1" in example_file_content
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assert "port: 9090" in example_file_content
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# It should also have the `remote_api_tokens` key
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assert "remote_api_tokens" in example_file_content
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# Neither env vars nor default values should be written to the config file
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config_file_content = config_file_path.read_text()
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assert "host" not in config_file_content
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# Undo our change to the singleton class
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InvokeAIArgs.did_parse = False
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def test_get_config_reads_external_api_keys_file(patch_rootdir: None, monkeypatch: pytest.MonkeyPatch, tmp_path: Path):
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"""Test that API keys are loaded from the dedicated api_keys.yaml file."""
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InvokeAIArgs.did_parse = True
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monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
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(tmp_path / "invokeai.yaml").write_text("schema_version: 4.0.2\n")
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(tmp_path / "api_keys.yaml").write_text("external_openai_api_key: openai-key\n")
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get_config.cache_clear()
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config = get_config()
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get_config.cache_clear()
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assert config.external_openai_api_key == "openai-key"
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InvokeAIArgs.did_parse = False
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def test_get_config_env_vars_override_external_api_keys_file(
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patch_rootdir: None, monkeypatch: pytest.MonkeyPatch, tmp_path: Path
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):
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"""Test that environment variables override values from api_keys.yaml."""
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InvokeAIArgs.did_parse = True
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monkeypatch.setenv("INVOKEAI_ROOT", str(tmp_path))
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monkeypatch.setenv("INVOKEAI_EXTERNAL_OPENAI_API_KEY", "env-openai-key")
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(tmp_path / "invokeai.yaml").write_text("schema_version: 4.0.2\n")
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(tmp_path / "api_keys.yaml").write_text("external_openai_api_key: file-openai-key\n")
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get_config.cache_clear()
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config = get_config()
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get_config.cache_clear()
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assert config.external_openai_api_key == "env-openai-key"
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InvokeAIArgs.did_parse = False
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def test_deny_nodes(patch_rootdir):
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# Allow integer, string and float, but explicitly deny float
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conf = get_config()
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conf.allow_nodes = ["integer", "string", "float"]
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conf.deny_nodes = ["float"]
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# We've changed the config, we need to invalidate the typeadapter cache so that the new config is used for
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# subsequent graph validations
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InvocationRegistry.invalidate_invocation_typeadapter()
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# confirm graph validation fails when using denied node
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Graph.model_validate({"nodes": {"1": {"id": "1", "type": "integer"}}})
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Graph.model_validate({"nodes": {"1": {"id": "1", "type": "string"}}})
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with pytest.raises(ValidationError):
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Graph.model_validate({"nodes": {"1": {"id": "1", "type": "float"}}})
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# confirm invocations union will not have denied nodes
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all_invocations = InvocationRegistry.get_invocation_classes()
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has_integer = len([i for i in all_invocations if i.get_type() == "integer"]) == 1
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has_string = len([i for i in all_invocations if i.get_type() == "string"]) == 1
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has_float = len([i for i in all_invocations if i.get_type() == "float"]) == 1
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assert has_integer
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assert has_string
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assert not has_float
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# Reset the config so that it doesn't affect other tests
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get_config.cache_clear()
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InvocationRegistry.invalidate_invocation_typeadapter()
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