Support loading non-diffusers Ideogram4 LoRA checkpoints

src/diffusers/loaders/lora_conversion_utils.py

@@ -2883,3 +2883,88 @@ def get_alpha_scales(down_weight, alpha_key):
 
     converted_state_dict = {f"transformer.{k}": v for k, v in converted_state_dict.items()}
     return converted_state_dict
+
+
+def _convert_non_diffusers_ideogram4_lora_to_diffusers(state_dict):
+    """
+    Convert non-diffusers Ideogram4 LoRA state dict to diffusers format.
+
+    Handles:
+    - `diffusion_model.` / `conditional_transformer.` prefix removal
+    - `lora_down`/`lora_up` (kohya) -> `lora_A`/`lora_B`, with `.alpha` folded into the weights
+    - fused `attention.qkv` -> split `to_q`/`to_k`/`to_v`; `attention.o` -> `to_out.0`
+    - `feed_forward.w1`/`w2`/`w3` and `adaln_modulation` map one-to-one
+    """
+    for prefix in ("diffusion_model.", "conditional_transformer."):
+        if any(k.startswith(prefix) for k in state_dict):
+            state_dict = {k.removeprefix(prefix): v for k, v in state_dict.items()}
+            break
+
+    is_kohya = any(".lora_down.weight" in k for k in state_dict)
+    down_suffix = ".lora_down.weight" if is_kohya else ".lora_A.weight"
+    up_suffix = ".lora_up.weight" if is_kohya else ".lora_B.weight"
+
+    def get_alpha_scales(down_weight, alpha_key):
+        rank = down_weight.shape[0]
+        alpha_tensor = state_dict.pop(alpha_key, None)
+        if alpha_tensor is None:
+            return 1.0, 1.0
+        # LoRA is scaled by `alpha / rank` in the forward pass; split the factor between down and up.
+        scale = alpha_tensor.item() / rank
+        scale_down, scale_up = scale, 1.0
+        while scale_down * 2 < scale_up:
+            scale_down *= 2
+            scale_up /= 2
+        return scale_down, scale_up
+
+    def pull(base):
+        """Pop the scaled (lora_A, lora_B) pair for a module path, or return None if absent."""
+        down_key = base + down_suffix
+        if down_key not in state_dict:
+            return None
+        down = state_dict.pop(down_key)
+        up = state_dict.pop(base + up_suffix)
+        scale_down, scale_up = get_alpha_scales(down, base + ".alpha")
+        return down * scale_down, up * scale_up
+
+    num_layers = 0
+    for k in state_dict:
+        match = re.match(r"layers\.(\d+)\.", k)
+        if match:
+            num_layers = max(num_layers, int(match.group(1)) + 1)
+
+    converted_state_dict = {}
+    for i in range(num_layers):
+        layer_prefix = f"layers.{i}"
+
+        # Fused qkv -> split to_q / to_k / to_v (shared down/lora_A, chunk up/lora_B in thirds).
+        qkv = pull(f"{layer_prefix}.attention.qkv")
+        if qkv is not None:
+            down, up = qkv
+            up_q, up_k, up_v = torch.chunk(up, 3, dim=0)
+            for proj, up_proj in (("to_q", up_q), ("to_k", up_k), ("to_v", up_v)):
+                converted_state_dict[f"{layer_prefix}.attention.{proj}.lora_A.weight"] = down.clone()
+                converted_state_dict[f"{layer_prefix}.attention.{proj}.lora_B.weight"] = up_proj.contiguous()
+
+        # attention.o -> attention.to_out.0
+        out = pull(f"{layer_prefix}.attention.o")
+        if out is not None:
+            down, up = out
+            converted_state_dict[f"{layer_prefix}.attention.to_out.0.lora_A.weight"] = down
+            converted_state_dict[f"{layer_prefix}.attention.to_out.0.lora_B.weight"] = up
+
+        # feed_forward.{w1,w2,w3} and adaln_modulation map one-to-one.
+        for module in ("feed_forward.w1", "feed_forward.w2", "feed_forward.w3", "adaln_modulation"):
+            pair = pull(f"{layer_prefix}.{module}")
+            if pair is not None:
+                down, up = pair
+                converted_state_dict[f"{layer_prefix}.{module}.lora_A.weight"] = down
+                converted_state_dict[f"{layer_prefix}.{module}.lora_B.weight"] = up
+
+    if len(state_dict) > 0:
+        raise ValueError(
+            f"`state_dict` should be empty at this point but has {sorted(state_dict.keys())}. "
+            "This may be an unsupported Ideogram4 LoRA layout."
+        )
+
+    return {f"transformer.{k}": v for k, v in converted_state_dict.items()}

src/diffusers/loaders/lora_pipeline.py

@@ -49,6 +49,7 @@
     _convert_non_diffusers_anima_lora_to_diffusers,
     _convert_non_diffusers_flux2_lora_to_diffusers,
     _convert_non_diffusers_hidream_lora_to_diffusers,
+    _convert_non_diffusers_ideogram4_lora_to_diffusers,
     _convert_non_diffusers_lora_to_diffusers,
     _convert_non_diffusers_ltx2_lora_to_diffusers,
     _convert_non_diffusers_ltxv_lora_to_diffusers,
@@ -6028,7 +6029,6 @@ class Ideogram4LoraLoaderMixin(LoraBaseMixin):
 
     @classmethod
     @validate_hf_hub_args
-    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.lora_state_dict
     def lora_state_dict(
         cls,
         pretrained_model_name_or_path_or_dict: str | dict[str, torch.Tensor],
@@ -6078,6 +6078,14 @@ def lora_state_dict(
             logger.warning(warn_msg)
             state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
 
+        # ai-toolkit (ostris) saves Ideogram4 LoRAs under a `diffusion_model.` prefix with a fused
+        # `attention.qkv` projection; convert those to the diffusers layout before loading.
+        is_non_diffusers_format = any(k.startswith("diffusion_model.") for k in state_dict) or any(
+            ".attention.qkv." in k for k in state_dict
+        )
+        if is_non_diffusers_format:
+            state_dict = _convert_non_diffusers_ideogram4_lora_to_diffusers(state_dict)
+
         out = (state_dict, metadata) if return_lora_metadata else state_dict
         return out