[Conversion] Small fixes (#3848)

* [Conversion] Small fixes

* Update src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

scripts/convert_vae_pt_to_diffusers.py

@@ -129,11 +129,19 @@ def vae_pt_to_vae_diffuser(
     original_config = OmegaConf.load(io_obj)
     image_size = 512
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    checkpoint = torch.load(checkpoint_path, map_location=device)
+    if checkpoint_path.endswith("safetensors"):
+        from safetensors import safe_open
+
+        checkpoint = {}
+        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                checkpoint[key] = f.get_tensor(key)
+    else:
+        checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"]
 
     # Convert the VAE model.
     vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
-    converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint["state_dict"], vae_config)
+    converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
     vae = AutoencoderKL(**vae_config)
     vae.load_state_dict(converted_vae_checkpoint)

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -286,10 +286,11 @@ def create_unet_diffusers_config(original_config, image_size: int, controlnet=Fa
         "use_linear_projection": use_linear_projection,
         "class_embed_type": class_embed_type,
         "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
-        "conditioning_channels": unet_params.hint_channels,
     }
 
-    if not controlnet:
+    if controlnet:
+        config["conditioning_channels"] = unet_params.hint_channels
+    else:
         config["out_channels"] = unet_params.out_channels
         config["up_block_types"] = tuple(up_block_types)