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* 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>
86 lines
4.1 KiB
Python
86 lines
4.1 KiB
Python
# conftest.py is a special pytest file. Fixtures defined in this file will be accessible to all tests in this directory
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# without needing to explicitly import them. (https://docs.pytest.org/en/6.2.x/fixture.html)
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# We import the model_installer and torch_device fixtures here so that they can be used by all tests. Flake8 does not
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# play well with fixtures (F401 and F811), so this is cleaner than importing in all files that use these fixtures.
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import logging
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import shutil
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from pathlib import Path
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import pytest
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from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
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from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
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from invokeai.app.services.boards.boards_default import BoardService
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from invokeai.app.services.bulk_download.bulk_download_default import BulkDownloadService
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from invokeai.app.services.client_state_persistence.client_state_persistence_sqlite import ClientStatePersistenceSqlite
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.app.services.external_generation.external_generation_default import ExternalGenerationService
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from invokeai.app.services.image_records.image_records_sqlite import SqliteImageRecordStorage
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from invokeai.app.services.images.images_default import ImageService
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from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
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from invokeai.app.services.invocation_services import InvocationServices
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from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
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from invokeai.app.services.invoker import Invoker
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from invokeai.app.services.users.users_default import UserService
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from invokeai.backend.util.logging import InvokeAILogger
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from tests.backend.model_manager.model_manager_fixtures import * # noqa: F403
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from tests.fixtures.sqlite_database import create_mock_sqlite_database # noqa: F401
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from tests.test_nodes import TestEventService
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@pytest.fixture
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def mock_services() -> InvocationServices:
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configuration = InvokeAIAppConfig(use_memory_db=True, node_cache_size=0)
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logger = InvokeAILogger.get_logger()
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db = create_mock_sqlite_database(configuration, logger)
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# NOTE: none of these are actually called by the test invocations
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return InvocationServices(
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board_image_records=SqliteBoardImageRecordStorage(db=db),
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board_images=None, # type: ignore
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board_records=SqliteBoardRecordStorage(db=db),
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boards=BoardService(),
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bulk_download=BulkDownloadService(),
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configuration=configuration,
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events=TestEventService(),
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image_files=None, # type: ignore
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image_records=SqliteImageRecordStorage(db=db),
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images=ImageService(),
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invocation_cache=MemoryInvocationCache(max_cache_size=0),
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logger=logging, # type: ignore
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model_images=None, # type: ignore
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model_manager=None, # type: ignore
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download_queue=None, # type: ignore
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external_generation=ExternalGenerationService({}, logger),
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names=None, # type: ignore
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performance_statistics=InvocationStatsService(),
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session_processor=None, # type: ignore
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session_queue=None, # type: ignore
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urls=None, # type: ignore
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workflow_records=None, # type: ignore
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tensors=None, # type: ignore
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conditioning=None, # type: ignore
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style_preset_records=None, # type: ignore
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style_preset_image_files=None, # type: ignore
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workflow_thumbnails=None, # type: ignore
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model_relationship_records=None, # type: ignore
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model_relationships=None, # type: ignore
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client_state_persistence=ClientStatePersistenceSqlite(db=db),
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users=UserService(db),
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)
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@pytest.fixture()
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def mock_invoker(mock_services: InvocationServices) -> Invoker:
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return Invoker(services=mock_services)
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@pytest.fixture(scope="module")
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def invokeai_root_dir(tmp_path_factory) -> Path:
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root_template = Path(__file__).parent.resolve() / "backend/model_manager/data/invokeai_root"
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temp_dir: Path = tmp_path_factory.mktemp("data") / "invokeai_root"
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shutil.copytree(root_template, temp_dir)
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return temp_dir
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