add conversion script for LatentDiffusionUncondPipeline

scripts/conversion_ldm_uncond.py

+import argparse
+
+import OmegaConf
+import torch
+
+from diffusers import UNetLDMModel, VQModel, LatentDiffusionUncondPipeline, DDIMScheduler
+
+def convert_ldm_original(checkpoint_path, config_path, output_path):
+    config = OmegaConf.load(config_path)
+    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
+    keys = list(state_dict.keys())
+
+    # extract state_dict for VQVAE
+    first_stage_dict = {}
+    first_stage_key = "first_stage_model."
+    for key in keys:
+        if key.startswith(first_stage_key):
+            first_stage_dict[key.replace(first_stage_key, "")] = state_dict[key]
+    
+    # extract state_dict for UNetLDM
+    unet_state_dict = {}
+    unet_key = "model.diffusion_model."
+    for key in keys:
+        if key.startswith(unet_key):
+            unet_state_dict[key.replace(unet_key, "")] = state_dict[key]
+    
+    vqvae_init_args = config.model.params.first_stage_config.params
+    unet_init_args = config.model.params.unet_config.params
+
+    vqvae = VQModel(**vqvae_init_args).eval()
+    vqvae.load_state_dict(first_stage_dict)
+
+    unet = UNetLDMModel(**unet_init_args).eval()
+    unet.load_state_dict(unet_state_dict)
+
+    noise_scheduler = DDIMScheduler(
+        timesteps=config.model.params.timesteps,
+        beta_schedule="scaled_linear",
+        beta_start=config.model.params.linear_start,
+        beta_end=config.model.params.linear_end,
+        clip_sample=False,
+    )
+
+    pipeline = LatentDiffusionUncondPipeline(vqvae, unet, 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("--config_path", type=str, required=True)
+    parser.add_argument("--output_path", type=str, required=True)
+    args = parser.parse_args()
+
+    convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
+

src/diffusers/pipelines/latent_diffusion_uncond/convert_ldm_to_diffusers.py

-import argparse
-
-import torch
-
-from diffusers import UNetLDMModel, VQModel
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--checkpoint_path", type=str, required=True)
-    parser.add_argument("--config_path", type=str, required=True)
-    parser.add_argument("--output_path", type=str, required=True)
-    args = parser.parse_args()
-