[Modular] Save Modular Pipeline weights to Hub

.claude/CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Build, Lint, and Test Commands
+
+```bash
+# Install in development mode
+pip install -e ".[dev]"
+
+# Run full test suite (requires beefy machine)
+make test
+# Or directly:
+python -m pytest -n auto --dist=loadfile -s -v ./tests/
+
+# Run a single test file
+python -m pytest tests/<TEST_FILE>.py
+
+# Run slow tests (downloads many GBs of models)
+RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
+
+# Format code (ruff + doc-builder)
+make style
+
+# Check code quality without modifying
+make quality
+
+# Fast fixup for modified files only (recommended before commits)
+make fixup
+
+# Fix copied code snippets and dummy objects
+make fix-copies
+
+# Check repository consistency (dummies, inits, repo structure)
+make repo-consistency
+```
+
+## Code Architecture
+
+Diffusers is built on three core component types that work together:
+
+### Pipelines (`src/diffusers/pipelines/`)
+- End-to-end inference workflows combining models and schedulers
+- Base class: `DiffusionPipeline` (in `pipeline_utils.py`)
+- Follow **single-file policy**: each pipeline in its own directory
+- Loaded via `DiffusionPipeline.from_pretrained()` which reads `model_index.json`
+- Components registered via `register_modules()` become pipeline attributes
+- ~99 pipeline implementations (Stable Diffusion, SDXL, Flux, etc.)
+
+### Models (`src/diffusers/models/`)
+- Configurable neural network architectures extending PyTorch's Module
+- Base classes: `ModelMixin` + `ConfigMixin` (in `modeling_utils.py`)
+- **Do NOT follow single-file policy**: use shared building blocks (`attention.py`, `embeddings.py`, `resnet.py`)
+- Key subdirectories:
+  - `autoencoders/`: VAEs for latent space compression
+  - `unets/`: Diffusion model architectures (UNet2DConditionModel, etc.)
+  - `transformers/`: Transformer-based models (Flux, SD3, etc.)
+  - `controlnets/`: ControlNet variants
+
+### Schedulers (`src/diffusers/schedulers/`)
+- Guide denoising process during inference
+- Base class: `SchedulerMixin` + `ConfigMixin` (in `scheduling_utils.py`)
+- Follow **single-file policy**: one scheduler per file
+- Key methods: `set_num_inference_steps()`, `step()`, `timesteps` property
+- Easily swappable via `ConfigMixin.from_config()`
+- ~55 scheduler algorithms (DDPM, DDIM, Euler, DPM-Solver, etc.)
+
+### Supporting Systems
+
+- **Loaders** (`src/diffusers/loaders/`): Mixins for LoRA, IP-Adapter, textual inversion, single-file loading
+- **Quantizers** (`src/diffusers/quantizers/`): BitsAndBytes, GGUF, TorchAO, Quanto support
+- **Hooks** (`src/diffusers/hooks/`): Runtime optimizations (offloading, layer skipping, caching)
+- **Guiders** (`src/diffusers/guiders/`): Guidance algorithms (CFG, PAG, etc.)
+
+## Configuration System
+
+All components use `ConfigMixin` for serialization:
+- Constructor arguments stored via `register_to_config(**kwargs)`
+- Instantiate from config: `Component.from_config(config_dict)`
+- Save/load as JSON files
+
+## Key Design Principles
+
+1. **Usability over Performance**: Models load at float32/CPU by default
+2. **Simple over Easy**: Explicit > implicit; expose complexity rather than hide it
+3. **Single-file policy**: Pipelines and schedulers are self-contained; models share building blocks
+4. **Copy-paste over abstraction**: Prefer duplicated code over hasty abstractions for contributor-friendliness
+
+## Code Style
+
+- Uses `ruff` for linting and formatting (line length: 119)
+- Documentation follows [Google style](https://google.github.io/styleguide/pyguide.html)
+- Use `# Copied from` mechanism for sharing code between similar files
+- Avoid lambda functions and advanced PyTorch operators for readability
+
+## Testing
+
+- Tests use `pytest` with `pytest-xdist` for parallelization
+- Slow tests gated by `RUN_SLOW=yes` environment variable
+- Test dependencies: `pip install -e ".[test]"`

_modular_model_index.json

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+{
+    "_blocks_class_name": "SequentialPipelineBlocks",
+    "_class_name": "Flux2ModularPipeline",
+    "_diffusers_version": "0.36.0.dev0",
+    "scheduler": [
+        "diffusers",
+        "FlowMatchEulerDiscreteScheduler",
+        {
+            "repo": "hf-internal-testing/tiny-flux2",
+            "revision": null,
+            "subfolder": "scheduler",
+            "type_hint": [
+                "diffusers",
+                "FlowMatchEulerDiscreteScheduler"
+            ],
+            "variant": null
+        }
+    ],
+    "text_encoder": [
+        "transformers",
+        "Mistral3ForConditionalGeneration",
+        {
+            "repo": "hf-internal-testing/tiny-flux2",
+            "revision": null,
+            "subfolder": "text_encoder",
+            "type_hint": [
+                "transformers",
+                "Mistral3ForConditionalGeneration"
+            ],
+            "variant": null
+        }
+    ],
+    "tokenizer": [
+        "transformers",
+        "AutoProcessor",
+        {
+            "repo": "hf-internal-testing/Mistral-Small-3.1-24B-Instruct-2503-only-processor",
+            "revision": null,
+            "subfolder": "",
+            "type_hint": [
+                "transformers",
+                "AutoProcessor"
+            ],
+            "variant": null
+        }
+    ],
+    "transformer": [
+        "diffusers",
+        "Flux2Transformer2DModel",
+        {
+            "repo": "hf-internal-testing/tiny-flux2",
+            "revision": null,
+            "subfolder": "transformer",
+            "type_hint": [
+                "diffusers",
+                "Flux2Transformer2DModel"
+            ],
+            "variant": null
+        }
+    ],
+    "vae": [
+        "diffusers",
+        "AutoencoderKLFlux2",
+        {
+            "repo": "hf-internal-testing/tiny-flux2",
+            "revision": null,
+            "subfolder": "vae",
+            "type_hint": [
+                "diffusers",
+                "AutoencoderKLFlux2"
+            ],
+            "variant": null
+        }
+    ]
+}