[Chore] switch to `logger.warning` (#7289)

switch to logger.warning

examples/wuerstchen/text_to_image/train_text_to_image_prior.py

@@ -182,7 +182,7 @@ def log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dty
                 }
             )
         else:
-            logger.warn(f"image logging not implemented for {tracker.name}")
+            logger.warning(f"image logging not implemented for {tracker.name}")
 
     del pipeline
     torch.cuda.empty_cache()

src/diffusers/loaders/lora.py

@@ -430,7 +430,7 @@ class LoraLoaderMixin:
             # contain the module names of the `unet` as its keys WITHOUT any prefix.
             if not USE_PEFT_BACKEND:
                 warn_message = "You have saved the LoRA weights using the old format. To convert the old LoRA weights to the new format, you can first load them in a dictionary and then create a new dictionary like the following: `new_state_dict = {f'unet.{module_name}': params for module_name, params in old_state_dict.items()}`."
-                logger.warn(warn_message)
+                logger.warning(warn_message)
 
         if len(state_dict.keys()) > 0:
             if adapter_name in getattr(unet, "peft_config", {}):
@@ -882,7 +882,7 @@ class LoraLoaderMixin:
         if fuse_unet or fuse_text_encoder:
             self.num_fused_loras += 1
             if self.num_fused_loras > 1:
-                logger.warn(
+                logger.warning(
                     "The current API is supported for operating with a single LoRA file. You are trying to load and fuse more than one LoRA which is not well-supported.",
                 )
 

src/diffusers/loaders/single_file_utils.py

@@ -884,7 +884,7 @@ def create_diffusers_controlnet_model_from_ldm(
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:
-            logger.warn(
+            logger.warning(
                 f"Some weights of the model checkpoint were not used when initializing {controlnet.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
     else:
@@ -1060,7 +1060,7 @@ def create_text_encoder_from_ldm_clip_checkpoint(config_name, checkpoint, local_
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:
-            logger.warn(
+            logger.warning(
                 f"Some weights of the model checkpoint were not used when initializing {text_model.__class__.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
     else:
@@ -1155,7 +1155,7 @@ def create_text_encoder_from_open_clip_checkpoint(
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:
-            logger.warn(
+            logger.warning(
                 f"Some weights of the model checkpoint were not used when initializing {text_model.__class__.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
 
@@ -1221,7 +1221,7 @@ def create_diffusers_unet_model_from_ldm(
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:
-            logger.warn(
+            logger.warning(
                 f"Some weights of the model checkpoint were not used when initializing {unet.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
     else:
@@ -1283,7 +1283,7 @@ def create_diffusers_vae_model_from_ldm(
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:
-            logger.warn(
+            logger.warning(
                 f"Some weights of the model checkpoint were not used when initializing {vae.__name__}: \n {[', '.join(unexpected_keys)]}"
             )
     else:

src/diffusers/loaders/unet.py

@@ -345,7 +345,7 @@ class UNet2DConditionLoadersMixin:
         is_model_cpu_offload = False
         is_sequential_cpu_offload = False
 
-        # For PEFT backend the Unet is already offloaded at this stage as it is handled inside `lora_lora_weights_into_unet`
+        # For PEFT backend the Unet is already offloaded at this stage as it is handled inside `load_lora_weights_into_unet`
         if not USE_PEFT_BACKEND:
             if _pipeline is not None:
                 for _, component in _pipeline.components.items():
@@ -384,7 +384,7 @@ class UNet2DConditionLoadersMixin:
             is_text_encoder_present = any(key.startswith(self.text_encoder_name) for key in state_dict.keys())
             if is_text_encoder_present:
                 warn_message = "The state_dict contains LoRA params corresponding to the text encoder which are not being used here. To use both UNet and text encoder related LoRA params, use [`pipe.load_lora_weights()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.load_lora_weights)."
-                logger.warn(warn_message)
+                logger.warning(warn_message)
             unet_keys = [k for k in state_dict.keys() if k.startswith(self.unet_name)]
             state_dict = {k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys}
 

src/diffusers/models/modeling_utils.py

@@ -677,7 +677,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                             unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
                     if len(unexpected_keys) > 0:
-                        logger.warn(
+                        logger.warning(
                             f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
                         )
 
@@ -705,7 +705,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                         # the weights so we don't have to do this again.
 
                         if "'Attention' object has no attribute" in str(e):
-                            logger.warn(
+                            logger.warning(
                                 f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
                                 " was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
                                 " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"

src/diffusers/models/unets/unet_2d_blocks.py

@@ -69,7 +69,7 @@ def get_down_block(
 ):
     # If attn head dim is not defined, we default it to the number of heads
     if attention_head_dim is None:
-        logger.warn(
+        logger.warning(
             f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
         )
         attention_head_dim = num_attention_heads
@@ -354,7 +354,7 @@ def get_up_block(
 ) -> nn.Module:
     # If attn head dim is not defined, we default it to the number of heads
     if attention_head_dim is None:
-        logger.warn(
+        logger.warning(
             f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
         )
         attention_head_dim = num_attention_heads
@@ -673,7 +673,7 @@ class UNetMidBlock2D(nn.Module):
         attentions = []
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
             )
             attention_head_dim = in_channels
@@ -1035,7 +1035,7 @@ class AttnDownBlock2D(nn.Module):
         self.downsample_type = downsample_type
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
             )
             attention_head_dim = out_channels
@@ -1480,7 +1480,7 @@ class AttnDownEncoderBlock2D(nn.Module):
         attentions = []
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
             )
             attention_head_dim = out_channels
@@ -1579,7 +1579,7 @@ class AttnSkipDownBlock2D(nn.Module):
         self.resnets = nn.ModuleList([])
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
             )
             attention_head_dim = out_channels
@@ -2244,7 +2244,7 @@ class AttnUpBlock2D(nn.Module):
         self.upsample_type = upsample_type
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
             )
             attention_head_dim = out_channels
@@ -2719,7 +2719,7 @@ class AttnUpDecoderBlock2D(nn.Module):
         attentions = []
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}."
             )
             attention_head_dim = out_channels
@@ -2841,7 +2841,7 @@ class AttnSkipUpBlock2D(nn.Module):
             )
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}."
             )
             attention_head_dim = out_channels

src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py

@@ -156,7 +156,7 @@ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
         self.dtype = dtype
 
         if safety_checker is None:
-            logger.warn(
+            logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"

src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -416,13 +416,13 @@ class IFPipeline(DiffusionPipeline, LoraLoaderMixin):
 
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -460,13 +460,13 @@ class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -175,7 +175,7 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
             )
 
         if unet.config.in_channels != 6:
-            logger.warn(
+            logger.warning(
                 "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
             )
 
@@ -209,13 +209,13 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -500,13 +500,13 @@ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -177,7 +177,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
             )
 
         if unet.config.in_channels != 6:
-            logger.warn(
+            logger.warning(
                 "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
             )
 
@@ -211,13 +211,13 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -133,7 +133,7 @@ class IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
             )
 
         if unet.config.in_channels != 6:
-            logger.warn(
+            logger.warning(
                 "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
             )
 
@@ -167,13 +167,13 @@ class IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -2158,7 +2158,7 @@ class UNetMidBlockFlat(nn.Module):
         attentions = []
 
         if attention_head_dim is None:
-            logger.warn(
+            logger.warning(
                 f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
             )
             attention_head_dim = in_channels

src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py

@@ -481,7 +481,7 @@ class KandinskyInpaintPipeline(DiffusionPipeline):
         if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
             "0.23.0.dev0"
         ):
-            logger.warn(
+            logger.warning(
                 "Please note that the expected format of `mask_image` has recently been changed. "
                 "Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. "
                 "As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. "

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py

@@ -372,7 +372,7 @@ class KandinskyV22InpaintPipeline(DiffusionPipeline):
         if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
             "0.23.0.dev0"
         ):
-            logger.warn(
+            logger.warning(
                 "Please note that the expected format of `mask_image` has recently been changed. "
                 "Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. "
                 "As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. "

src/diffusers/pipelines/pipeline_utils.py

@@ -256,7 +256,9 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     break
 
             if save_method_name is None:
-                logger.warn(f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.")
+                logger.warning(
+                    f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved."
+                )
                 # make sure that unsaveable components are not tried to be loaded afterward
                 self.register_to_config(**{pipeline_component_name: (None, None)})
                 continue
@@ -1202,7 +1204,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             try:
                 info = model_info(pretrained_model_name, token=token, revision=revision)
             except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
-                logger.warn(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
+                logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
                 local_files_only = True
                 model_info_call_error = e  # save error to reraise it if model is not cached locally
 
@@ -1353,7 +1355,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     len(safetensors_variant_filenames) > 0
                     and safetensors_model_filenames != safetensors_variant_filenames
                 ):
-                    logger.warn(
+                    logger.warning(
                         f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(safetensors_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
                     )
             else:
@@ -1366,7 +1368,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")}
                 bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")}
                 if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:
-                    logger.warn(
+                    logger.warning(
                         f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
                     )
 

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py

@@ -514,13 +514,13 @@ class PixArtAlphaPipeline(DiffusionPipeline):
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
     def _text_preprocessing(self, text, clean_caption=False):
         if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
             clean_caption = False
 
         if not isinstance(text, (tuple, list)):

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py

@@ -147,7 +147,7 @@ class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
         self.dtype = dtype
 
         if safety_checker is None:
-            logger.warn(
+            logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py

@@ -82,7 +82,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMi
         super().__init__()
 
         if safety_checker is None and requires_safety_checker:
-            logger.warn(
+            logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                 " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                 " results in services or applications open to the public. Both the diffusers team and Hugging Face"