model can run in other precisions without autocast

Author: NouamaneTazi

src/diffusers/models/resnet.py

@@ -333,7 +333,7 @@ def forward(self, x, temb):
 
         # make sure hidden states is in float32
         # when running in half-precision
-        hidden_states = self.norm1(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.norm1.float()(hidden_states.float()).type(hidden_states.dtype)
         hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
@@ -351,7 +351,7 @@ def forward(self, x, temb):
 
         # make sure hidden states is in float32
         # when running in half-precision
-        hidden_states = self.norm2(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.norm2.float()(hidden_states.float()).type(hidden_states.dtype)
         hidden_states = self.nonlinearity(hidden_states)
 
         hidden_states = self.dropout(hidden_states)

src/diffusers/models/unet_2d_condition.py

@@ -177,7 +177,7 @@ def forward(
         timesteps = timesteps.expand(sample.shape[0])
 
         t_emb = self.time_proj(timesteps)
-        emb = self.time_embedding(t_emb)
+        emb = self.time_embedding(t_emb.to(self.dtype))
 
         # 2. pre-process
         sample = self.conv_in(sample)
@@ -215,7 +215,7 @@ def forward(
         # 6. post-process
         # make sure hidden states is in float32
         # when running in half-precision
-        sample = self.conv_norm_out(sample.float()).type(sample.dtype)
+        sample = self.conv_norm_out.float()(sample.float()).type(sample.dtype)
         sample = self.conv_act(sample)
         sample = self.conv_out(sample)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -204,6 +204,7 @@ def __call__(
                 latents_shape,
                 generator=generator,
                 device=self.device,
+                dtype=text_embeddings.dtype,
             )
         else:
             if latents.shape != latents_shape:
@@ -263,7 +264,7 @@ def __call__(
 
         # run safety checker
         safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
-        image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
+        image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values.to(text_embeddings.dtype))
 
         if output_type == "pil":
             image = self.numpy_to_pil(image)