add T2I-Adapter conversion script

scripts/conversion/convert_diffusers_t2i_adapter.py

@@ -0,0 +1,57 @@
+import argparse
+from pathlib import Path
+import torch
+from torch import nn
+from diffusers import T2IAdapter  # type: ignore
+from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, ConditionEncoderXL
+from refiners.fluxion.model_converter import ModelConverter
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert a pretrained diffusers T2I-Adapter model to refiners")
+    parser.add_argument(
+        "--from",
+        type=str,
+        dest="source_path",
+        required=True,
+        help="Path or repository name of the source model. (e.g.: 'ip-adapter_sd15.bin').",
+    )
+    parser.add_argument(
+        "--to",
+        type=str,
+        dest="output_path",
+        default=None,
+        help=(
+            "Path to save the converted model (extension will be .safetensors). If not specified, the output path will"
+            " be the source path with the extension changed to .safetensors."
+        ),
+    )
+    parser.add_argument(
+        "--half",
+        action="store_true",
+        dest="use_half",
+        default=False,
+        help="Use this flag to save the output file as half precision (default: full precision).",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        dest="verbose",
+        default=False,
+        help="Use this flag to print verbose output during conversion.",
+    )
+    args = parser.parse_args()
+    if args.output_path is None:
+        args.output_path = f"{Path(args.source_path).name}.safetensors"
+    assert args.output_path is not None
+
+    sdxl = "xl" in args.source_path
+    target = ConditionEncoderXL() if sdxl else ConditionEncoder()
+    source: nn.Module = T2IAdapter.from_pretrained(pretrained_model_name_or_path=args.source_path)  # type: ignore
+    assert isinstance(source, nn.Module), "Source model is not a nn.Module"
+
+    x = torch.randn(1, 3, 1024, 1024) if sdxl else torch.randn(1, 3, 512, 512)
+    converter = ModelConverter(source_model=source, target_model=target, verbose=args.verbose)
+    if not converter.run(source_args=(x,)):
+        raise RuntimeError("Model conversion failed")
+
+    converter.save_to_safetensors(path=args.output_path, half=args.use_half)