Fix depth pipeline for latest Diffusers

generator_process/actions/depth_to_image.py

@@ -194,7 +194,7 @@ def __call__(
             width = width or self.unet.config.sample_size * self.vae_scale_factor
 
             # 1. Check inputs
-            self.check_inputs(prompt, height, width, strength, callback_steps)
+            self.check_inputs(prompt=prompt, image=image, mask_image=depth_image, height=height, width=width, strength=strength, callback_steps=callback_steps, output_type=output_type)
 
             # 2. Define call parameters
             batch_size = 1 if isinstance(prompt, str) else len(prompt)
@@ -366,11 +366,10 @@ def __call__(
 
     # Inference
     with torch.inference_mode() if device not in ('mps', "dml") else nullcontext():
-        def callback(pipe, step, timestep, callback_kwargs):
+        def callback(step, _, latents):
             if future.check_cancelled():
                 raise InterruptedError()
-            future.add_response(step_latents(pipe, step_preview_mode, callback_kwargs["latents"], generator, step, steps))
-            return callback_kwargs
+            future.add_response(step_latents(pipe, step_preview_mode, latents, generator, step, steps))
         try:
             result = pipe(
                 prompt=prompt,
@@ -383,7 +382,7 @@ def callback(pipe, step, timestep, callback_kwargs):
                 num_inference_steps=steps,
                 guidance_scale=cfg_scale,
                 generator=generator,
-                callback_on_step_end=callback,
+                callback=callback,
                 callback_steps=1,
                 output_type="np"
             )