quench safety checker warnings from diffusers

ldm/invoke/ckpt_to_diffuser.py

@@ -45,11 +45,11 @@ from diffusers import (
     PNDMScheduler,
     StableDiffusionPipeline,
     UNet2DConditionModel,
+    logging as dlogging,
 )
 from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
 from diffusers.utils import is_safetensors_available
-from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline

ldm/invoke/merge_diffusers.py

@@ -8,12 +8,13 @@ import argparse
 import os
 import sys
 import traceback
+import warnings
 from argparse import Namespace
 from pathlib import Path
 from typing import List, Union
 
 import npyscreen
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, logging as dlogging
 from omegaconf import OmegaConf
 
 from ldm.invoke.globals import (
@@ -46,18 +47,24 @@ def merge_diffusion_models(
     **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
          cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
     """
-    pipe = DiffusionPipeline.from_pretrained(
-        model_ids_or_paths[0],
-        cache_dir=kwargs.get("cache_dir", global_cache_dir()),
-        custom_pipeline="checkpoint_merger",
-    )
-    merged_pipe = pipe.merge(
-        pretrained_model_name_or_path_list=model_ids_or_paths,
-        alpha=alpha,
-        interp=interp,
-        force=force,
-        **kwargs,
-    )
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore')
+        verbosity = dlogging.get_verbosity()
+        dlogging.set_verbosity_error()
+        
+        pipe = DiffusionPipeline.from_pretrained(
+            model_ids_or_paths[0],
+            cache_dir=kwargs.get("cache_dir", global_cache_dir()),
+            custom_pipeline="checkpoint_merger",
+        )
+        merged_pipe = pipe.merge(
+            pretrained_model_name_or_path_list=model_ids_or_paths,
+            alpha=alpha,
+            interp=interp,
+            force=force,
+            **kwargs,
+        )
+        dlogging.set_verbosity(verbosity)
     return merged_pipe