Update Pix2Pix for Model Standards

.cursor/rules/mod-000a-reusable-layers-belong-in-nn.mdc

@@ -0,0 +1,58 @@
+---
+description: Reusable layers and building blocks should be placed in physicsnemo/nn, not physicsnemo/models. Examples include FullyConnected, attention layers, and UNetBlock.
+alwaysApply: false
+---
+
+When creating or refactoring reusable layer code, rule MOD-000a must be followed. Explicitly reference "Following rule MOD-000a, which states that reusable layers should go in physicsnemo/nn..." when explaining placement decisions.
+
+## MOD-000a: Reusable layers/blocks belong in physicsnemo.nn
+
+**Description:**
+
+Reusable layers that are the building blocks of more complex architectures
+should go into `physicsnemo/nn`. Those include for instance `FullyConnected`,
+various variants of attention layers, `UNetBlock` (a block of a U-Net), etc.
+
+All layers that are directly exposed to the user should be imported in
+`physicsnemo/nn/__init__.py`, such that they can be used as follows:
+
+```python
+from physicsnemo.nn import MyLayer
+```
+
+The only exception to this rule is for layers that are highly specific to a
+single example. In this case, it may be acceptable to place them in a module
+specific to the example code, such as `examples/<example_name>/utils/nn.py`.
+
+**Rationale:**
+
+Ensures consistency in the organization of reusable layers in the repository.
+Keeping all reusable components in a single location makes them easy to find
+and promotes code reuse across different models.
+
+**Example:**
+
+```python
+# Good: Reusable layer in physicsnemo/nn/attention.py
+class MultiHeadAttention(Module):
+    """A reusable attention layer that can be used in various architectures."""
+    pass
+
+# Good: Import in physicsnemo/nn/__init__.py
+from physicsnemo.nn.attention import MultiHeadAttention
+
+# Good: Example-specific layer in examples/weather/utils/nn.py
+class WeatherSpecificLayer(Module):
+    """Layer highly specific to the weather forecasting example."""
+    pass
+```
+
+**Anti-pattern:**
+
+```python
+# WRONG: Reusable layer placed in physicsnemo/models/
+# File: physicsnemo/models/attention.py
+class MultiHeadAttention(Module):
+    """Should be in physicsnemo/nn/ not physicsnemo/models/"""
+    pass
+```

.cursor/rules/mod-000b-complete-models-belong-in-models.mdc

@@ -0,0 +1,54 @@
+---
+description: Complete models composed of multiple layers should be placed in physicsnemo/models, not physicsnemo/nn. These are domain-specific or modality-specific models.
+alwaysApply: false
+---
+
+When creating or refactoring complete model code, rule MOD-000b must be followed. Explicitly reference "Following rule MOD-000b, which states that complete models should go in physicsnemo/models..." when explaining placement decisions.
+
+## MOD-000b: Complete models belong in physicsnemo.models
+
+**Description:**
+
+More complete models, composed of multiple layers and/or other sub-models,
+should go into `physicsnemo/models`. All models that are directly exposed to
+the user should be imported in `physicsnemo/models/__init__.py`, such that they
+can be used as follows:
+
+```python
+from physicsnemo.models import MyModel
+```
+
+The only exception to this rule is for models that are highly specific to a
+single example. In this case, it may be acceptable to place them in a module
+specific to the example code, such as `examples/<example_name>/utils/nn.py`.
+
+**Rationale:**
+
+Ensures consistency and clarity in the organization of models in the repository,
+in particular a clear separation between reusable layers and more complete
+models that are applicable to a specific domain or specific data modality.
+
+**Example:**
+
+```python
+# Good: Complete model in physicsnemo/models/transformer.py
+class TransformerModel(Module):
+    """A complete transformer model composed of attention and feedforward layers."""
+    def __init__(self):
+        super().__init__()
+        self.attention = MultiHeadAttention(...)
+        self.ffn = FeedForward(...)
+
+# Good: Import in physicsnemo/models/__init__.py
+from physicsnemo.models.transformer import TransformerModel
+```
+
+**Anti-pattern:**
+
+```python
+# WRONG: Complete model placed in physicsnemo/nn/
+# File: physicsnemo/nn/transformer.py
+class TransformerModel(Module):
+    """Should be in physicsnemo/models/ not physicsnemo/nn/"""
+    pass
+```

.cursor/rules/mod-001-use-physicsnemo-module-as-base-class.mdc

@@ -0,0 +1,47 @@
+---
+description: All model and layer classes must inherit from physicsnemo.Module (not torch.nn.Module directly) to ensure proper serialization, versioning, and registry functionality.
+alwaysApply: false
+---
+
+When creating or modifying model classes, rule MOD-001 must be strictly followed. Explicitly reference "Following rule MOD-001, which states that all model classes must inherit from physicsnemo.Module..." when explaining inheritance decisions.
+
+## MOD-001: Use physicsnemo.Module as model base classes
+
+**Description:**
+
+All model classes must inherit from `physicsnemo.Module`. Direct subclasses of
+`torch.nn.Module` are not allowed. Direct subclasses of `physicsnemo.Module`
+are allowed (note that `physicsnemo.Module` is a subclass of `torch.nn.Module`).
+Ensure proper initialization of parent classes using `super().__init__()`. Pass
+the `meta` argument to the `super().__init__()` call if appropriate, otherwise
+set it manually with `self.meta = meta`.
+
+**Rationale:**
+
+Ensures invariants and functionality of the `physicsnemo.Module` class for all
+models. In particular, instances of `physicsnemo.Module` benefit from features
+that are not available in `torch.nn.Module` instances. Those include serialization
+for checkpointing and loading modules and submodules, versioning system to
+handle backward compatibility, as well as ability to be registered in the
+`physicsnemo.registry` for easy instantiation and use in any codebase.
+
+**Example:**
+
+```python
+from physicsnemo import Module
+
+class MyModel(Module):
+    def __init__(self, input_dim: int, output_dim: int):
+        super().__init__(meta=MyModelMetaData())
+        self.linear = nn.Linear(input_dim, output_dim)
+```
+
+**Anti-pattern:**
+
+```python
+from torch import nn
+
+class MyModel(nn.Module):
+    def __init__(self, input_dim: int, output_dim: int):
+        self.linear = nn.Linear(input_dim, output_dim)
+```

.cursor/rules/mod-002a-experimental-models-belong-in-experimental.mdc

@@ -0,0 +1,65 @@
+---
+description: New model classes should start in physicsnemo/experimental/nn or physicsnemo/experimental/models during development, where backward compatibility is not guaranteed.
+alwaysApply: false
+---
+
+When creating new model or layer classes, rule MOD-002a must be followed. Explicitly reference "Following rule MOD-002a, which states that new models should start in physicsnemo/experimental/..." when explaining where to place new code.
+
+## MOD-002a: New models and layers belong in physicsnemo.experimental
+
+**Description:**
+
+For the vast majority of models, new classes are created either in
+`physicsnemo/experimental/nn` for reusable layers, or in
+`physicsnemo/experimental/models` for more complete models. The `experimental`
+folder is used to store models that are still under development (beta or alpha
+releases) during this stage, backward compatibility is not guaranteed.
+
+One exception is when the developer is highly confident that the model is
+sufficiently mature and applicable to many domains or use cases. In this case
+the model class can be created in the `physicsnemo/nn` or `physicsnemo/models`
+folders directly, and backward compatibility is guaranteed.
+
+Another exception is when the model class is highly specific to a single
+example. In this case, it may be acceptable to place it in a module specific to
+the example code, such as `examples/<example_name>/utils/nn.py`.
+
+After staying in experimental for a sufficient amount of time (typically at
+least 1 release cycle), the model class can be promoted to production. It is
+then moved to the `physicsnemo/nn` or `physicsnemo/models` folders, based on
+whether it's a reusable layer or complete model (see MOD-000a and MOD-000b).
+
+**Note:** Per MOD-008a, MOD-008b, and MOD-008c, it is forbidden to move a model
+out of the experimental stage/directory without the required CI tests.
+
+**Rationale:**
+
+The experimental stage allows rapid iteration without backward compatibility
+constraints, enabling developers to refine APIs based on user feedback. This
+protects users from unstable APIs while allowing innovation.
+
+**Example:**
+
+```python
+# Good: Stage 1 - New experimental model
+# File: physicsnemo/experimental/models/new_diffusion.py
+class DiffusionModel(Module):
+    """New diffusion model under active development. API may change."""
+    pass
+
+# Good: After 1+ release cycles, promoted to production
+# File: physicsnemo/models/diffusion.py (moved from experimental/)
+class DiffusionModel(Module):
+    """Stable diffusion model with backward compatibility guarantees."""
+    pass
+```
+
+**Anti-pattern:**
+
+```python
+# WRONG: New model directly in production folder
+# File: physicsnemo/models/brand_new_model.py (should be in experimental/ first)
+class BrandNewModel(Module):
+    """Skipped experimental stage - risky for stability"""
+    pass
+```

.cursor/rules/mod-002b-add-deprecation-warnings-to-model.mdc

@@ -0,0 +1,69 @@
+---
+description: Model classes being deprecated must include deprecation warnings in both docstring and runtime, explaining why and what users should use instead, for at least 1 release cycle.
+alwaysApply: false
+---
+
+When deprecating a model class, rule MOD-002b must be followed. Explicitly reference "Following rule MOD-002b, which requires adding deprecation warnings to both docstring and runtime..." when implementing deprecation.
+
+## MOD-002b: Add deprecation warnings to deprecating model class
+
+**Description:**
+
+For a model class in the pre-deprecation stage in `physicsnemo/nn` or
+`physicsnemo/models`, the developer should start planning its deprecation. This
+is done by adding a warning message to the model class, indicating that the
+model class is deprecated and will be removed in a future release.
+
+The warning message should be a clear and concise message that explains why the
+model class is being deprecated and what the user should do instead. The
+deprecation message should be added to both the docstring and should be raised
+at runtime. The developer is free to choose the mechanism to raise the
+deprecation warning.
+
+A model class cannot be deprecated without staying in the pre-deprecation stage
+for at least 1 release cycle before it can be deleted from the codebase.
+
+**Rationale:**
+
+Ensures users have sufficient time to migrate to newer alternatives, preventing
+breaking changes that could disrupt their workflows. This graduated approach
+balances innovation with stability, a critical requirement for a scientific
+computing framework.
+
+**Example:**
+
+```python
+# Good: Pre-deprecation with warning
+# File: physicsnemo/models/old_diffusion.py
+class DiffusionModel(Module):
+    """
+    Legacy diffusion model.
+
+    .. deprecated:: 0.5.0
+        ``OldDiffusionModel`` is deprecated and will be removed in version 0.7.0.
+        Use :class:`~physicsnemo.models.NewDiffusionModel` instead.
+    """
+    def __init__(self):
+        import warnings
+        warnings.warn(
+            "OldDiffusionModel is deprecated. Use NewDiffusionModel instead.",
+            DeprecationWarning,
+            stacklevel=2
+        )
+        super().__init__()
+```
+
+**Anti-pattern:**
+
+```python
+# WRONG: No deprecation warning in code
+# File: physicsnemo/models/old_model.py
+class OldModel(Module):
+    """Will be removed next release."""  # Docstring mentions it but no runtime warning
+    def __init__(self):
+        # Missing: warnings.warn(..., DeprecationWarning)
+        super().__init__()
+
+# WRONG: Deprecation without sufficient warning period
+# (Model deprecated and removed in same release)
+```

.cursor/rules/mod-002c-remove-deprecated-model-from-codebase.mdc

@@ -0,0 +1,50 @@
+---
+description: After at least 1 release cycle in pre-deprecation stage with warnings, deprecated model classes can be deleted from the codebase.
+alwaysApply: false
+---
+
+When removing deprecated models, rule MOD-002c must be followed. Explicitly reference "Following rule MOD-002c, which states that a model can only be deleted after at least 1 release cycle in pre-deprecation..." when removing code.
+
+## MOD-002c: Remove deprecated model from codebase
+
+**Description:**
+
+After staying in the pre-deprecation stage (Stage 3) for at least 1 release
+cycle, the model class is considered deprecated (Stage 4). It can then be
+deleted from the codebase.
+
+A model class cannot be deleted without first spending at least 1 release cycle
+in the pre-deprecation stage with proper deprecation warnings (see MOD-002b).
+
+**Rationale:**
+
+This ensures users have sufficient warning and time to migrate their code to
+newer alternatives. Premature deletion of models would break user code without
+adequate notice, violating the framework's commitment to stability.
+
+**Example:**
+
+```python
+# Good: Model spent 1 release cycle in pre-deprecation (v0.5.0 with warnings)
+# Now in v0.6.0, can be deleted
+# File: physicsnemo/models/old_diffusion.py - DELETED
+
+# Release timeline:
+# v0.5.0: Added deprecation warnings (Stage 3)
+# v0.6.0: Model can be safely removed (Stage 4)
+```
+
+**Anti-pattern:**
+
+```python
+# WRONG: Deleting model without deprecation period
+# v0.5.0: Model exists without warnings
+# v0.6.0: Model deleted - BREAKS USER CODE!
+
+# WRONG: Breaking changes in production without deprecation cycle
+# File: physicsnemo/models/diffusion.py
+class DiffusionModel(Module):
+    def __init__(self, new_required_param):  # Breaking change!
+        # Changed API without deprecation warning - breaks user code
+        pass
+```