add conversion script for BDDMPipeline

scripts/conversion_bddm.py

+
+import argparse
+import torch
+
+from diffusers.pipelines.bddm import DiffWave, BDDMPipeline
+from diffusers import DDPMScheduler
+
+
+def convert_bddm_orginal(checkpoint_path, noise_scheduler_checkpoint_path, output_path):
+    sd = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
+    noise_scheduler_sd = torch.load(noise_scheduler_checkpoint_path, map_location="cpu")
+
+    model = DiffWave()
+    model.load_state_dict(sd, strict=False)
+
+    ts, _, betas, _ = noise_scheduler_sd
+    ts, betas = list(ts.numpy().tolist()), list(betas.numpy().tolist())
+
+    noise_scheduler = DDPMScheduler(
+        timesteps=12,
+        trained_betas=betas,
+        timestep_values=ts,
+        clip_sample=False,
+        tensor_format="np",
+    )
+
+    pipeline = BDDMPipeline(model, noise_scheduler)
+    pipeline.save_pretrained(output_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint_path", type=str, required=True)
+    parser.add_argument("--noise_scheduler_checkpoint_path", type=str, required=True)
+    parser.add_argument("--output_path", type=str, required=True)
+    args = parser.parse_args()
+
+    convert_bddm_orginal(args.checkpoint_path, args.noise_scheduler_checkpoint_path, args.output_path)
+
+