lpw_stable_diffusion: Add is_cancelled_callback (#1053)

* [Community Pipelines] lpw_stable_diffusion: Add is_cancelled_callback

* [Community pipelines] lpw_stable_diffusion_onnx: Add is_cancelled_callback

examples/community/lpw_stable_diffusion.py

@@ -498,6 +498,7 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: Optional[int] = 1,
         **kwargs,
     ):
@@ -560,11 +561,15 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
             callback (`Callable`, *optional*):
                 A function that will be called every `callback_steps` steps during inference. The function will be
                 called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
 
         Returns:
+            `None` if cancelled by `is_cancelled_callback`,
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
             When returning a tuple, the first element is a list with the generated images, and the second element is a
@@ -757,8 +762,11 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
                 latents = (init_latents_proper * mask) + (latents * (1 - mask))
 
             # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
+            if i % callback_steps == 0:
+                if callback is not None:
                     callback(i, t, latents)
+                if is_cancelled_callback is not None and is_cancelled_callback():
+                    return None
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/lpw_stable_diffusion_onnx.py

@@ -435,6 +435,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
         callback_steps: Optional[int] = 1,
         **kwargs,
     ):
@@ -496,11 +497,15 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
             callback (`Callable`, *optional*):
                 A function that will be called every `callback_steps` steps during inference. The function will be
                 called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
 
         Returns:
+            `None` if cancelled by `is_cancelled_callback`,
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
             When returning a tuple, the first element is a list with the generated images, and the second element is a
@@ -668,8 +673,11 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
                 latents = (init_latents_proper * mask) + (latents * (1 - mask))
 
             # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
+            if i % callback_steps == 0:
+                if callback is not None:
                     callback(i, t, latents)
+                if is_cancelled_callback is not None and is_cancelled_callback():
+                    return None
 
         latents = 1 / 0.18215 * latents
         # image = self.vae_decoder(latent_sample=latents)[0]