Revert "[Flux] reduce explicit device transfers and typecasting in flux."

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -371,7 +371,7 @@ def encode_prompt(
                 unscale_lora_layers(self.text_encoder_2, lora_scale)
 
         dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(prompt_embeds.shape[1], 3, dtype=dtype, device=device)
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
@@ -427,7 +427,7 @@ def check_inputs(
 
     @staticmethod
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -437,7 +437,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     def _pack_latents(latents, batch_size, num_channels_latents, height, width):

src/diffusers/pipelines/flux/pipeline_flux_controlnet.py

@@ -452,7 +452,7 @@ def check_inputs(
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -462,7 +462,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents

src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py

@@ -407,7 +407,7 @@ def encode_prompt(
                 unscale_lora_layers(self.text_encoder_2, lora_scale)
 
         dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(prompt_embeds.shape[1], 3, dtype=dtype, device=device)
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
@@ -495,7 +495,7 @@ def check_inputs(
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -505,7 +505,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents

src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

@@ -417,7 +417,7 @@ def encode_prompt(
                 unscale_lora_layers(self.text_encoder_2, lora_scale)
 
         dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(prompt_embeds.shape[1], 3, dtype=dtype, device=device)
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
@@ -522,7 +522,7 @@ def check_inputs(
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -532,7 +532,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents

src/diffusers/pipelines/flux/pipeline_flux_img2img.py

@@ -391,7 +391,7 @@ def encode_prompt(
                 unscale_lora_layers(self.text_encoder_2, lora_scale)
 
         dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(prompt_embeds.shape[1], 3, dtype=dtype, device=device)
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
@@ -479,7 +479,7 @@ def check_inputs(
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -489,7 +489,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents

src/diffusers/pipelines/flux/pipeline_flux_inpaint.py

@@ -395,7 +395,7 @@ def encode_prompt(
                 unscale_lora_layers(self.text_encoder_2, lora_scale)
 
         dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(prompt_embeds.shape[1], 3, dtype=dtype, device=device)
+        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
@@ -500,7 +500,7 @@ def check_inputs(
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3, device=device, dtype=dtype)
+        latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
         latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
 
@@ -510,7 +510,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
             latent_image_id_height * latent_image_id_width, latent_image_id_channels
         )
 
-        return latent_image_ids
+        return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
     # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents