Native LongCat-Image implementation (#12597)

tests-unit/comfy_test/model_detection_test.py

@@ -0,0 +1,112 @@
+import torch
+
+from comfy.model_detection import detect_unet_config, model_config_from_unet_config
+import comfy.supported_models
+
+
+def _make_longcat_comfyui_sd():
+    """Minimal ComfyUI-format state dict for pre-converted LongCat-Image weights."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+    C_CTX = 3584
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, C_CTX)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["time_in.in_layer.weight"] = torch.empty(H, 256)
+    sd["time_in.in_layer.bias"] = torch.empty(H)
+    sd["time_in.out_layer.weight"] = torch.empty(H, H)
+    sd["time_in.out_layer.bias"] = torch.empty(H)
+
+    sd["final_layer.adaLN_modulation.1.weight"] = torch.empty(2 * H, H)
+    sd["final_layer.adaLN_modulation.1.bias"] = torch.empty(2 * H)
+    sd["final_layer.linear.weight"] = torch.empty(C_IN * 4, H)
+    sd["final_layer.linear.bias"] = torch.empty(C_IN * 4)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+        sd[f"double_blocks.{i}.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+        sd[f"double_blocks.{i}.img_mod.lin.weight"] = torch.empty(H, H)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+def _make_flux_schnell_comfyui_sd():
+    """Minimal ComfyUI-format state dict for standard Flux Schnell."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, 4096)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["double_blocks.0.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    sd["double_blocks.0.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+    sd["double_blocks.0.img_mod.lin.weight"] = torch.empty(H, H)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+class TestModelDetection:
+    """Verify that first-match model detection selects the correct model
+    based on list ordering and unet_config specificity."""
+
+    def test_longcat_before_schnell_in_models_list(self):
+        """LongCatImage must appear before FluxSchnell in the models list."""
+        models = comfy.supported_models.models
+        longcat_idx = next(i for i, m in enumerate(models) if m.__name__ == "LongCatImage")
+        schnell_idx = next(i for i, m in enumerate(models) if m.__name__ == "FluxSchnell")
+        assert longcat_idx < schnell_idx, (
+            f"LongCatImage (index {longcat_idx}) must come before "
+            f"FluxSchnell (index {schnell_idx}) in the models list"
+        )
+
+    def test_longcat_comfyui_detected_as_longcat(self):
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 3584
+        assert unet_config["vec_in_dim"] is None
+        assert unet_config["guidance_embed"] is False
+        assert unet_config["txt_ids_dims"] == [1, 2]
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "LongCatImage"
+
+    def test_longcat_comfyui_keys_pass_through_unchanged(self):
+        """Pre-converted weights should not be transformed by process_unet_state_dict."""
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        model_config = model_config_from_unet_config(unet_config, sd)
+
+        processed = model_config.process_unet_state_dict(dict(sd))
+        assert "img_in.weight" in processed
+        assert "txt_in.weight" in processed
+        assert "time_in.in_layer.weight" in processed
+        assert "final_layer.linear.weight" in processed
+
+    def test_flux_schnell_comfyui_detected_as_flux_schnell(self):
+        sd = _make_flux_schnell_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 4096
+        assert unet_config["txt_ids_dims"] == []
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "FluxSchnell"