feat: SD1.x/2.x と SDXL 向けの LECO 学習スクリプトを追加 (#2285)

* Add LECO training script and associated tests

- Implemented `sdxl_train_leco.py` for training with LECO prompts, including argument parsing, model setup, training loop, and weight saving functionality.
- Created unit tests for `load_prompt_settings` in `test_leco_train_util.py` to validate loading of prompt configurations in both original and slider formats.
- Added basic syntax tests for `train_leco.py` and `sdxl_train_leco.py` to ensure modules are importable.

* fix: use getattr for safe attribute access in argument verification

* feat: add CUDA device compatibility validation and corresponding tests

* Revert "feat: add CUDA device compatibility validation and corresponding tests"

This reverts commit 6d3e51431b.

* feat: update predict_noise_xl to use vector embedding from add_time_ids

* feat: implement checkpointing in predict_noise and predict_noise_xl functions

* feat: remove unused submodules and update .gitignore to exclude .codex-tmp

---------

Co-authored-by: Kohya S. <52813779+kohya-ss@users.noreply.github.com>

tests/library/test_leco_train_util.py

@@ -0,0 +1,114 @@
+from pathlib import Path
+
+import torch
+
+from library.leco_train_util import load_prompt_settings
+
+
+def test_load_prompt_settings_with_original_format(tmp_path: Path):
+    prompt_file = tmp_path / "prompts.yaml"
+    prompt_file.write_text(
+        """
+- target: "van gogh"
+  guidance_scale: 1.5
+  resolution: 512
+""".strip(),
+        encoding="utf-8",
+    )
+
+    prompts = load_prompt_settings(prompt_file)
+
+    assert len(prompts) == 1
+    assert prompts[0].target == "van gogh"
+    assert prompts[0].positive == "van gogh"
+    assert prompts[0].unconditional == ""
+    assert prompts[0].neutral == ""
+    assert prompts[0].action == "erase"
+    assert prompts[0].guidance_scale == 1.5
+
+
+def test_load_prompt_settings_with_slider_targets(tmp_path: Path):
+    prompt_file = tmp_path / "slider.yaml"
+    prompt_file.write_text(
+        """
+targets:
+  - target_class: ""
+    positive: "high detail"
+    negative: "low detail"
+    multiplier: 1.25
+    weight: 0.5
+guidance_scale: 2.0
+resolution: 768
+neutral: ""
+""".strip(),
+        encoding="utf-8",
+    )
+
+    prompts = load_prompt_settings(prompt_file)
+
+    assert len(prompts) == 4
+
+    first = prompts[0]
+    second = prompts[1]
+    third = prompts[2]
+    fourth = prompts[3]
+
+    assert first.target == ""
+    assert first.positive == "low detail"
+    assert first.unconditional == "high detail"
+    assert first.action == "erase"
+    assert first.multiplier == 1.25
+    assert first.weight == 0.5
+    assert first.get_resolution() == (768, 768)
+
+    assert second.positive == "high detail"
+    assert second.unconditional == "low detail"
+    assert second.action == "enhance"
+    assert second.multiplier == 1.25
+
+    assert third.action == "erase"
+    assert third.multiplier == -1.25
+
+    assert fourth.action == "enhance"
+    assert fourth.multiplier == -1.25
+
+
+def test_predict_noise_xl_uses_vector_embedding_from_add_time_ids():
+    from library import sdxl_train_util
+    from library.leco_train_util import PromptEmbedsXL, predict_noise_xl
+
+    class DummyScheduler:
+        def scale_model_input(self, latent_model_input, timestep):
+            return latent_model_input
+
+    class DummyUNet:
+        def __call__(self, x, timesteps, context, y):
+            self.x = x
+            self.timesteps = timesteps
+            self.context = context
+            self.y = y
+            return torch.zeros_like(x)
+
+    latents = torch.randn(1, 4, 8, 8)
+    prompt_embeds = PromptEmbedsXL(
+        text_embeds=torch.randn(2, 77, 2048),
+        pooled_embeds=torch.randn(2, 1280),
+    )
+    add_time_ids = torch.tensor(
+        [
+            [1024, 1024, 0, 0, 1024, 1024],
+            [1024, 1024, 0, 0, 1024, 1024],
+        ],
+        dtype=prompt_embeds.pooled_embeds.dtype,
+    )
+
+    unet = DummyUNet()
+    noise_pred = predict_noise_xl(unet, DummyScheduler(), torch.tensor(10), latents, prompt_embeds, add_time_ids)
+
+    expected_size_embeddings = sdxl_train_util.get_size_embeddings(
+        add_time_ids[:, :2], add_time_ids[:, 2:4], add_time_ids[:, 4:6], latents.device
+    ).to(prompt_embeds.pooled_embeds.dtype)
+
+    assert noise_pred.shape == latents.shape
+    assert unet.context is prompt_embeds.text_embeds
+    assert torch.equal(unet.y, torch.cat([prompt_embeds.pooled_embeds, expected_size_embeddings], dim=1))

tests/test_sdxl_train_leco.py

@@ -0,0 +1,16 @@
+import sdxl_train_leco
+from library import deepspeed_utils, sdxl_train_util, train_util
+
+
+def test_syntax():
+    assert sdxl_train_leco is not None
+
+
+def test_setup_parser_supports_shared_training_validation():
+    args = sdxl_train_leco.setup_parser().parse_args(["--prompts_file", "slider.yaml"])
+
+    train_util.verify_training_args(args)
+    sdxl_train_util.verify_sdxl_training_args(args, support_text_encoder_caching=False)
+
+    assert args.min_snr_gamma is None
+    assert deepspeed_utils.prepare_deepspeed_plugin(args) is None

tests/test_train_leco.py

@@ -0,0 +1,15 @@
+import train_leco
+from library import deepspeed_utils, train_util
+
+
+def test_syntax():
+    assert train_leco is not None
+
+
+def test_setup_parser_supports_shared_training_validation():
+    args = train_leco.setup_parser().parse_args(["--prompts_file", "slider.yaml"])
+
+    train_util.verify_training_args(args)
+
+    assert args.min_snr_gamma is None
+    assert deepspeed_utils.prepare_deepspeed_plugin(args) is None