add support for LCM LoRA weights loading

src/refiners/foundationals/latent_diffusion/lcm_lora.py

@@ -0,0 +1,81 @@
+import torch
+
+from refiners.fluxion.adapters.lora import Lora, auto_attach_loras
+
+from .lora import SDLoraManager
+from .stable_diffusion_xl import StableDiffusion_XL
+
+
+def _check_validity(debug_map: list[tuple[str, str]]):
+    # Check things are in the right block.
+    prefix_map = {
+        "down_blocks_0": ["DownBlocks.Chain_1", "DownBlocks.Chain_2", "DownBlocks.Chain_3", "DownBlocks.Chain_4"],
+        "down_blocks_1": ["DownBlocks.Chain_5", "DownBlocks.Chain_6", "DownBlocks.Chain_7"],
+        "down_blocks_2": ["DownBlocks.Chain_8", "DownBlocks.Chain_9"],
+        "mid_block": ["MiddleBlock"],
+        "up_blocks_0": ["UpBlocks.Chain_1", "UpBlocks.Chain_2", "UpBlocks.Chain_3"],
+        "up_blocks_1": ["UpBlocks.Chain_4", "UpBlocks.Chain_5", "UpBlocks.Chain_6"],
+        "up_blocks_2": ["UpBlocks.Chain_7", "UpBlocks.Chain_8", "UpBlocks.Chain_9"],
+    }
+    for key, path in debug_map:
+        for key_pfx, paths_pfxs in prefix_map.items():
+            if key.startswith(f"lora_unet_{key_pfx}"):
+                assert any(path.startswith(f"SDXLUNet.{x}") for x in paths_pfxs), f"bad mapping: {key} {path}"
+
+
+def add_lcm_lora(
+    manager: SDLoraManager,
+    name: str,
+    tensors: dict[str, torch.Tensor],
+    scale: float = 1.0 / 8.0,
+    check_validity: bool = True,
+) -> None:
+    # This is a complex LoRA so SDLoraManager.add_lora() is not enough.
+    # Instead, we add the LoRAs to the UNet in several iterations, using
+    # the filtering mechanism of `auto_attach_loras`.
+
+    assert isinstance(manager.target, StableDiffusion_XL)
+    unet = manager.target.unet
+
+    loras = Lora.from_dict(name, {k: v.to(unet.device, unet.dtype) for k, v in tensors.items()})
+    assert all(k.startswith("lora_unet_") for k in loras.keys())
+    loras = {k: loras[k] for k in sorted(loras.keys(), key=SDLoraManager.sort_keys)}
+
+    debug_map: list[tuple[str, str]] | None = [] if check_validity else None
+
+    # Projections are in `SDXLCrossAttention` but not in `CrossAttentionBlock`.
+    loras_projs = {k: v for k, v in loras.items() if k.endswith("proj_in") or k.endswith("proj_out")}
+    auto_attach_loras(
+        loras_projs,
+        unet,
+        exclude=["CrossAttentionBlock"],
+        include=["SDXLCrossAttention"],
+        debug_map=debug_map,
+    )
+
+    # Do *not* check for time because some keys include both `resnets` and `time_emb_proj`.
+    exclusions = {
+        "res": "ResidualBlock",
+        "downsample": "Downsample",
+        "upsample": "Upsample",
+    }
+    loras_excluded = {k: v for k, v in loras.items() if any(x in k for x in exclusions.keys())}
+    loras_remaining = {k: v for k, v in loras.items() if k not in loras_excluded and k not in loras_projs}
+
+    auto_attach_loras(
+        loras_remaining,
+        unet,
+        exclude=[*exclusions.values(), "TimestepEncoder"],
+        debug_map=debug_map,
+    )
+
+    # Process exclusions one by one to avoid mixing them up.
+    for exc, v in exclusions.items():
+        ls = {k: v for k, v in loras_excluded.items() if exc in k}
+        auto_attach_loras(ls, unet, include=[v], debug_map=debug_map)
+
+    if debug_map is not None:
+        _check_validity(debug_map)
+
+    # LoRAs are finally injected, set the scale with the manager.
+    manager.set_scale(name, scale)