aider: Addressed placeholders and fixed PipelineVersionEnum import …

…in `model_merge_page.py`.

# Aider chat conversation:

USER: Address placeholders.

ASSISTANT: Let's address the placeholders in the provided files.

### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

1. Implement the actual merge function call in `_merge_lora_into_sd_model`.

### File: `src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py`

2. Implement the actual extraction function call in `_extract_lora_from_checkpoint`.

### File: `src\invoke_training\ui\pages\model_merge_page.py`

3. Update the `_merge_lora_into_sd_model` method to call the actual merge function.
4. Update the `_extract_lora_from_checkpoint` method to call the actual extraction function.

Here are the *SEARCH/REPLACE* blocks:

#### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

```python
<<<<<<< SEARCH
        # Call the actual merge function here
        print(f"Merging LoRA models {lora_models_list} into base model {base_model} with variant {base_model_variant} and type {base_model_type}, saving to {output_path} with dtype {save_dtype}")
=======
        from src.invoke_training.scripts._experimental.lora_merge.merge_lora_into_sd_model import merge_lora_into_sd_model
        import logging

        logger = logging.getLogger(__name__)
        merge_lora_into_sd_model(
            logger=logger,
            base_model=base_model,
            base_model_variant=base_model_variant,
            base_model_type=PipelineVersionEnum(base_model_type),
            lora_models=lora_models_list,
            output=output_path,
            save_dtype=save_dtype,
        )
>>>>>>> REPLACE
```

#### File: `src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py`

```python
<<<<<<< SEARCH
        # Call the actual extraction function here
        print(f"Extracting LoRA from {model_tuned} using original model {model_orig} with type {model_type}, saving to {save_to} with load precision {load_precision}, save precision {save_precision}, on device {device}, with rank {lora_rank} and clamp quantile {clamp_quantile}")
=======
        from src.invoke_training.scripts._experimental.lora_extraction.extract_lora_from_checkpoint import extract_lora
        import logging

        logger = logging.getLogger(__name__)
        extract_lora(
            logger=logger,
            model_type=model_type,
            model_orig_path=model_orig,
            model_tuned_path=model_tuned,
            save_to=save_to,
            load_precision=load_precision,
            save_precision=save_precision,
            device=device,
            lora_rank=lora_rank,
            clamp_quantile=clamp_quantile,
        )
>>>>>>> REPLACE
```

#### File: `src\invoke_training\ui\pages\model_merge_page.py`

```python
<<<<<<< SEARCH
        # Call the actual merge function here
        print(f"Merging LoRA models {lora_models_list} into base model {base_model} with variant {base_model_variant} and type {base_model_type}, saving to {output_path} with dtype {save_dtype}")
=======
        from src.invoke_training.scripts._experimental.lora_merge.merge_lora_into_sd_model import merge_lora_into_sd_model
        import logging

        logger = logging.getLogger(__name__)
        merge_lora_into_sd_model(
            logger=logger,
            base_model=base_model,
            base_model_variant=base_model_variant,
            base_model_type=PipelineVersionEnum(base_model_type),
            lora_models=lora_models_list,
            output=output_path,
            save_dtype=save_dtype,
        )
>>>>>>> REPLACE
```

```python
<<<<<<< SEARCH
        # Call the actual extraction function here
        print(f"Extracting LoRA from {model_tuned} using original model {model_orig} with type {model_type}, saving to {save_to} with load precision {load_precision}, save precision {save_precision}, on device {device}, with rank {lora_rank} and clamp quantile {clamp_quantile}")
=======
        from src.invoke_training.scripts._experimental.lora_extraction.extract_lora_from_checkpoint import extract_lora
        import logging

        logger = logging.getLogger(__name__)
        extract_lora(
            logger=logger,
            model_type=model_type,
            model_orig_path=model_orig,
            model_tuned_path=model_tuned,
            save_to=save_to,
            load_precision=load_precision,
            save_precision=save_precision,
            device=device,
            lora_rank=lora_rank,
            clamp_quantile=clamp_quantile,
        )
>>>>>>> REPLACE
```

These changes will ensure that the placeholders are replaced with actual function calls.

USER: Let's address the placeholders in the provided files.

### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

1. Implement the actual merge function call in `_merge_lora_into_sd_model`.

### File: `src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py`

2. Implement the actual extraction function call in `_extract_lora_from_checkpoint`.

### File: `src\invoke_training\ui\pages\model_merge_page.py`

3. Update the `_merge_lora_into_sd_model` method to call the actual merge function.
4. Update the `_extract_lora_from_checkpoint` method to call the actual extraction function.

Here are the *SEARCH/REPLACE* blocks:

#### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

```python
<<<<<<< SEARCH

^^^ Bad/missing filename. The filename must be alone on the line before the opening fence ```

ASSISTANT: Let's address the placeholders in the provided files.

### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

1. Implement the actual merge function call in `_merge_lora_into_sd_model`.

### File: `src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py`

2. Implement the actual extraction function call in `_extract_lora_from_checkpoint`.

### File: `src\invoke_training\ui\pages\model_merge_page.py`

3. Update the `_merge_lora_into_sd_model` method to call the actual merge function.
4. Update the `_extract_lora_from_checkpoint` method to call the actual extraction function.

Here are the *SEARCH/REPLACE* blocks:

#### File: `src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py`

```python
src\invoke_training\scripts\_experimental\lora_merge\merge_lora_into_sd_model.py
<<<<<<< SEARCH
        # Call the actual merge function here
        print(f"Merging LoRA models {lora_models_list} into base model {base_model} with variant {base_model_variant} and type {base_model_type}, saving to {output_path} with dtype {save_dtype}")
=======
        from src.invoke_training.scripts._experimental.lora_merge.merge_lora_into_sd_model import merge_lora_into_sd_model
        import logging

        logger = logging.getLogger(__name__)
        merge_lora_into_sd_model(
            logger=logger,
            base_model=base_model,
            base_model_variant=base_model_variant,
            base_model_type=PipelineVersionEnum(base_model_type),
            lora_models=lora_models_list,
            output=output_path,
            save_dtype=save_dtype,
        )
>>>>>>> REPLACE
```

#### File: `src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py`

```python
src\invoke_training\scripts\_experimental\lora_extraction\extract_lora_from_checkpoint.py
<<<<<<< SEARCH
        # Call the actual extraction function here
        print(f"Extracting LoRA from {model_tuned} using original model {model_orig} with type {model_type}, saving to {save_to} with load precision {load_precision}, save precision {save_precision}, on device {device}, with rank {lora_rank} and clamp quantile {clamp_quantile}")
=======
        from src.invoke_training.scripts._experimental.lora_extraction.extract_lora_from_checkpoint import extract_lora
        import logging

        logger = logging.getLogger(__name__)
        extract_lora(
            logger=logger,
            model_type=model_type,
            model_orig_path=model_orig,
            model_tuned_path=model_tuned,
            save_to=save_to,
            load_precision=load_precision,
            save_precision=save_precision,
            device=device,
            lora_rank=lora_rank,
            clamp_quantile=clamp_quantile,
        )
>>>>>>> REPLACE
```

#### File: `src\invoke_training\ui\pages\model_merge_page.py`

```python
src\invoke_training\ui\pages\model_merge_page.py
<<<<<<< SEARCH
        # Call the actual merge function here
        print(f"Merging LoRA models {lora_models_list} into base model {base_model} with variant {base_model_variant} and type {base_model_type}, saving to {output_path} with dtype {save_dtype}")
=======
        from src.invoke_training.scripts._experimental.lora_merge.merge_lora_into_sd_model import merge_lora_into_sd_model
        import logging

        logger = logging.getLogger(__name__)
        merge_lora_into_sd_model(
            logger=logger,
            base_model=base_model,
            base_model_variant=base_model_variant,
            base_model_type=PipelineVersionEnum(base_model_type),
            lora_models=lora_models_list,
            output=output_path,
            save_dtype=save_dtype,
        )
>>>>>>> REPLACE
```

```python
src\invoke_training\ui\pages\model_merge_page.py
<<<<<<< SEARCH
        # Call the actual extraction function here
        print(f"Extracting LoRA from {model_tuned} using original model {model_orig} with type {model_type}, saving to {save_to} with load precision {load_precision}, save precision {save_precision}, on device {device}, with rank {lora_rank} and clamp quantile {clamp_quantile}")
=======
        from src.invoke_training.scripts._experimental.lora_extraction.extract_lora_from_checkpoint import extract_lora
        import logging

        logger = logging.getLogger(__name__)
        extract_lora(
            logger=logger,
            model_type=model_type,
            model_orig_path=model_orig,
            model_tuned_path=model_tuned,
            save_to=save_to,
            load_precision=load_precision,
            save_precision=save_precision,
            device=device,
            lora_rank=lora_rank,
            clamp_quantile=clamp_quantile,
        )
>>>>>>> REPLACE
```

These changes will ensure that the placeholders are replaced with actual function calls.

USER:
# Fix any errors below, if possible.

## Running: flake8 --select=E9,F821,F823,F831,F406,F407,F701,F702,F704,F706 --show-source src\invoke_training\ui\pages\model_merge_page.py

src\invoke_training\ui\pages\model_merge_page.py:71:29: F821 undefined name 'PipelineVersionEnum'
            base_model_type=PipelineVersionEnum(base_model_type),
                            ^

## See relevant line below marked with █.

C:\Users\OMNI\invoke-train\invoke-training\src\invoke_training\ui\pages\model_merge_page.py:
...⋮...
  3│class ModelMergePage:
  4│    def __init__(self):
  5│        with gr.Blocks(
  6│            title="Model Merging",
  7│            analytics_enabled=False,
  8│            head='<link rel="icon" type="image/x-icon" href="/assets/favicon.png">',
  9│        ) as app:
 10│            with gr.Tab(label="Merge LoRA into SD Model"):
 11│                self._create_merge_tab()
 12│            with gr.Tab(label="Extract LoRA from Checkpoint"):
 13│                self._create_extract_tab()
 14│
...⋮...
 60│    def _merge_lora_into_sd_model(self, base_model, base_model_variant, base_model_type, lora_models, output_path, save_dtype):
 61│        lora_models_list = [tuple(lm.split("::")) if "::" in lm else (lm, 1.0) for lm in lora_models.split(",")]
...⋮...
 67│        merge_lora_into_sd_model(
 68│            logger=logger,
 69│            base_model=base_model,
 70│            base_model_variant=base_model_variant,
 71█            base_model_type=PipelineVersionEnum(base_model_type),
 72│            lora_models=lora_models_list,
 73│            output=output_path,
 74│            save_dtype=save_dtype,
...⋮...

ASSISTANT: To fix the error, we need to import `PipelineVersionEnum` in `src\invoke_training\ui\pages\model_merge_page.py`.

Here is the *SEARCH/REPLACE* block:

```python
src\invoke_training\ui\pages\model_merge_page.py
<<<<<<< SEARCH
import gradio as gr
=======
import gradio as gr
from src.invoke_training._shared.stable_diffusion.model_loading_utils import PipelineVersionEnum
>>>>>>> REPLACE
```

This will ensure that `PipelineVersionEnum` is defined and can be used in the `_merge_lora_into_sd_model` method.

src/invoke_training/ui/pages/model_merge_page.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from src.invoke_training._shared.stable_diffusion.model_loading_utils import PipelineVersionEnum
 
 class ModelMergePage:
     def __init__(self):