Don't override torch_dtype and don't use when quantization_config is set

src/diffusers/loaders/single_file.py

@@ -360,12 +360,12 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
         cache_dir = kwargs.pop("cache_dir", None)
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
+        torch_dtype = kwargs.pop("torch_dtype", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
         is_legacy_loading = False
 
-        if not isinstance(torch_dtype, torch.dtype):
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
                 f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."

src/diffusers/loaders/single_file_model.py

@@ -255,12 +255,12 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         subfolder = kwargs.pop("subfolder", None)
         revision = kwargs.pop("revision", None)
         config_revision = kwargs.pop("config_revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
+        torch_dtype = kwargs.pop("torch_dtype", None)
         quantization_config = kwargs.pop("quantization_config", None)
         device = kwargs.pop("device", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
-        if not isinstance(torch_dtype, torch.dtype):
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
                 f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."

src/diffusers/models/modeling_utils.py

@@ -870,7 +870,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         local_files_only = kwargs.pop("local_files_only", None)
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
+        torch_dtype = kwargs.pop("torch_dtype", None)
         subfolder = kwargs.pop("subfolder", None)
         device_map = kwargs.pop("device_map", None)
         max_memory = kwargs.pop("max_memory", None)
@@ -883,7 +883,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
-        if not isinstance(torch_dtype, torch.dtype):
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
                 f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."

src/diffusers/pipelines/kolors/text_encoder.py

@@ -104,13 +104,6 @@ def forward(self, hidden_states: torch.Tensor):
         return (self.weight * hidden_states).to(input_dtype)
 
 
-def _config_to_kwargs(args):
-    common_kwargs = {
-        "dtype": args.torch_dtype,
-    }
-    return common_kwargs
-
-
 class CoreAttention(torch.nn.Module):
     def __init__(self, config: ChatGLMConfig, layer_number):
         super(CoreAttention, self).__init__()
@@ -314,7 +307,6 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
             self.qkv_hidden_size,
             bias=config.add_bias_linear or config.add_qkv_bias,
             device=device,
-            **_config_to_kwargs(config),
         )
 
         self.core_attention = CoreAttention(config, self.layer_number)
@@ -325,7 +317,6 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
             config.hidden_size,
             bias=config.add_bias_linear,
             device=device,
-            **_config_to_kwargs(config),
         )
 
     def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
@@ -449,7 +440,6 @@ def __init__(self, config: ChatGLMConfig, device=None):
             config.ffn_hidden_size * 2,
             bias=self.add_bias,
             device=device,
-            **_config_to_kwargs(config),
         )
 
         def swiglu(x):
@@ -459,9 +449,7 @@ def swiglu(x):
         self.activation_func = swiglu
 
         # Project back to h.
-        self.dense_4h_to_h = nn.Linear(
-            config.ffn_hidden_size, config.hidden_size, bias=self.add_bias, device=device, **_config_to_kwargs(config)
-        )
+        self.dense_4h_to_h = nn.Linear(config.ffn_hidden_size, config.hidden_size, bias=self.add_bias, device=device)
 
     def forward(self, hidden_states):
         # [s, b, 4hp]
@@ -488,18 +476,14 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
 
         LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
         # Layernorm on the input data.
-        self.input_layernorm = LayerNormFunc(
-            config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype
-        )
+        self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device)
 
         # Self attention.
         self.self_attention = SelfAttention(config, layer_number, device=device)
         self.hidden_dropout = config.hidden_dropout
 
         # Layernorm on the attention output
-        self.post_attention_layernorm = LayerNormFunc(
-            config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype
-        )
+        self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device)
 
         # MLP
         self.mlp = MLP(config, device=device)
@@ -569,9 +553,7 @@ def build_layer(layer_number):
         if self.post_layer_norm:
             LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
             # Final layer norm before output.
-            self.final_layernorm = LayerNormFunc(
-                config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype
-            )
+            self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device)
 
         self.gradient_checkpointing = False
 
@@ -679,9 +661,7 @@ def __init__(self, config: ChatGLMConfig, device=None):
 
         self.hidden_size = config.hidden_size
         # Word embeddings (parallel).
-        self.word_embeddings = nn.Embedding(
-            config.padded_vocab_size, self.hidden_size, dtype=config.torch_dtype, device=device
-        )
+        self.word_embeddings = nn.Embedding(config.padded_vocab_size, self.hidden_size, device=device)
         self.fp32_residual_connection = config.fp32_residual_connection
 
     def forward(self, input_ids):
@@ -784,16 +764,13 @@ def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
             config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
         )
 
-        self.rotary_pos_emb = RotaryEmbedding(
-            rotary_dim // 2, original_impl=config.original_rope, device=device, dtype=config.torch_dtype
-        )
+        self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device)
         self.encoder = init_method(GLMTransformer, config, **init_kwargs)
         self.output_layer = init_method(
             nn.Linear,
             config.hidden_size,
             config.padded_vocab_size,
             bias=False,
-            dtype=config.torch_dtype,
             **init_kwargs,
         )
         self.pre_seq_len = config.pre_seq_len

src/diffusers/pipelines/pipeline_utils.py

@@ -686,7 +686,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
         from_flax = kwargs.pop("from_flax", False)
-        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
+        torch_dtype = kwargs.pop("torch_dtype", None)
         custom_pipeline = kwargs.pop("custom_pipeline", None)
         custom_revision = kwargs.pop("custom_revision", None)
         provider = kwargs.pop("provider", None)
@@ -703,7 +703,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
-        if not isinstance(torch_dtype, torch.dtype):
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
                 f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
@@ -1456,8 +1456,8 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
 
             if load_components_from_hub and not trust_remote_code:
                 raise ValueError(
-                    f"The repository for {pretrained_model_name} contains custom code in {'.py, '.join([os.path.join(k, v) for k,v in custom_components.items()])} which must be executed to correctly "
-                    f"load the model. You can inspect the repository content at {', '.join([f'https://hf.co/{pretrained_model_name}/{k}/{v}.py' for k,v in custom_components.items()])}.\n"
+                    f"The repository for {pretrained_model_name} contains custom code in {'.py, '.join([os.path.join(k, v) for k, v in custom_components.items()])} which must be executed to correctly "
+                    f"load the model. You can inspect the repository content at {', '.join([f'https://hf.co/{pretrained_model_name}/{k}/{v}.py' for k, v in custom_components.items()])}.\n"
                     f"Please pass the argument `trust_remote_code=True` to allow custom code to be run."
                 )
 

tests/pipelines/kolors/test_kolors.py

@@ -90,7 +90,7 @@ def get_dummy_components(self, time_cond_proj_dim=None):
         )
         torch.manual_seed(0)
         text_encoder = ChatGLMModel.from_pretrained(
-            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.float32
         )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
 

tests/pipelines/kolors/test_kolors_img2img.py

@@ -94,7 +94,7 @@ def get_dummy_components(self, time_cond_proj_dim=None):
         )
         torch.manual_seed(0)
         text_encoder = ChatGLMModel.from_pretrained(
-            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.float32
         )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
 

tests/pipelines/pag/test_pag_kolors.py

@@ -99,7 +99,7 @@ def get_dummy_components(self, time_cond_proj_dim=None):
         )
         torch.manual_seed(0)
         text_encoder = ChatGLMModel.from_pretrained(
-            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.float32
         )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")