Interruptable Pipelines (huggingface#5867)

* add interruptable pipelines

* add tests

* updatemsmq

* add interrupt property

* make fix copies

* Revert "make fix copies"

This reverts commit 914b353.

* add docs

* add tutorial

* Update docs/source/en/tutorials/interrupting_diffusion_process.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/tutorials/interrupting_diffusion_process.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* update

* fix quality issues

* fix

* update

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: 4 people

docs/source/en/using-diffusers/callback.md

@@ -63,3 +63,42 @@ With callbacks, you can implement features such as dynamic CFG without having to
 🤗 Diffusers currently only supports `callback_on_step_end`, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require a callback function with a different execution point!
 
 </Tip>
+
+
+## Using Callbacks to interrupt the Diffusion Process
+
+The following Pipelines support interrupting the diffusion process via callback
+
+- [StableDiffusionPipeline](../api/pipelines/stable_diffusion/overview.md)
+- [StableDiffusionImg2ImgPipeline](..api/pipelines/stable_diffusion/img2img.md)
+- [StableDiffusionInpaintPipeline](..api/pipelines/stable_diffusion/inpaint.md)
+- [StableDiffusionXLPipeline](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
+- [StableDiffusionXLImg2ImgPipeline](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
+- [StableDiffusionXLInpaintPipeline](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
+
+Interrupting the diffusion process is particularly useful when building UIs that work with Diffusers because it allows users to stop the generation process if they're unhappy with the intermediate results. You can incorporate this into your pipeline with a callback.
+
+This callback function should take the following arguments: `pipe`, `i`, `t`, and `callback_kwargs` (this must be returned). Set the pipeline's `_interrupt` attribute to `True` to stop the diffusion process after a certain number of steps. You are also free to implement your own custom stopping logic inside the callback.
+
+In this example, the diffusion process is stopped after 10 steps even though `num_inference_steps` is set to 50.
+
+```python
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe.enable_model_cpu_offload()
+num_inference_steps = 50
+
+def interrupt_callback(pipe, i, t, callback_kwargs):
+    stop_idx = 10
+    if i == stop_idx:
+        pipe._interrupt = True
+
+    return callback_kwargs
+
+pipe(
+    "A photo of a cat",
+    num_inference_steps=num_inference_steps,
+    callback_on_step_end=interrupt_callback,
+)
+```

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -768,6 +768,10 @@ def cross_attention_kwargs(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -909,6 +913,7 @@ def __call__(
         self._guidance_rescale = guidance_rescale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -986,6 +991,9 @@ def __call__(
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -832,6 +832,10 @@ def cross_attention_kwargs(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -963,6 +967,7 @@ def __call__(
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1041,6 +1046,9 @@ def __call__(
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -958,6 +958,10 @@ def cross_attention_kwargs(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     def __call__(
         self,
@@ -1144,6 +1148,7 @@ def __call__(
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1288,6 +1293,9 @@ def __call__(
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -849,6 +849,10 @@ def denoising_end(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -1067,6 +1071,7 @@ def __call__(
         self._clip_skip = clip_skip
         self._cross_attention_kwargs = cross_attention_kwargs
         self._denoising_end = denoising_end
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1196,6 +1201,9 @@ def __call__(
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -990,6 +990,10 @@ def denoising_start(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -1221,6 +1225,7 @@ def __call__(
         self._cross_attention_kwargs = cross_attention_kwargs
         self._denoising_end = denoising_end
         self._denoising_start = denoising_start
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1376,6 +1381,9 @@ def denoising_value_valid(dnv):
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1210,6 +1210,10 @@ def denoising_start(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    @property
+    def interrupt(self):
+        return self._interrupt
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -1462,6 +1466,7 @@ def __call__(
         self._cross_attention_kwargs = cross_attention_kwargs
         self._denoising_end = denoising_end
         self._denoising_start = denoising_start
+        self._interrupt = False
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1684,6 +1689,8 @@ def denoising_value_valid(dnv):
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -692,6 +692,58 @@ def test_fused_qkv_projections(self):
             original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
         ), "Original outputs should match when fused QKV projections are disabled."
 
+    def test_pipeline_interrupt(self):
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionPipeline(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "hey"
+        num_inference_steps = 3
+
+        # store intermediate latents from the generation process
+        class PipelineState:
+            def __init__(self):
+                self.state = []
+
+            def apply(self, pipe, i, t, callback_kwargs):
+                self.state.append(callback_kwargs["latents"])
+                return callback_kwargs
+
+        pipe_state = PipelineState()
+        sd_pipe(
+            prompt,
+            num_inference_steps=num_inference_steps,
+            output_type="np",
+            generator=torch.Generator("cpu").manual_seed(0),
+            callback_on_step_end=pipe_state.apply,
+        ).images
+
+        # interrupt generation at step index
+        interrupt_step_idx = 1
+
+        def callback_on_step_end(pipe, i, t, callback_kwargs):
+            if i == interrupt_step_idx:
+                pipe._interrupt = True
+
+            return callback_kwargs
+
+        output_interrupted = sd_pipe(
+            prompt,
+            num_inference_steps=num_inference_steps,
+            output_type="latent",
+            generator=torch.Generator("cpu").manual_seed(0),
+            callback_on_step_end=callback_on_step_end,
+        ).images
+
+        # fetch intermediate latents at the interrupted step
+        # from the completed generation process
+        intermediate_latent = pipe_state.state[interrupt_step_idx]
+
+        # compare the intermediate latent to the output of the interrupted process
+        # they should be the same
+        assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -320,6 +320,62 @@ def test_inference_batch_single_identical(self):
     def test_float16_inference(self):
         super().test_float16_inference(expected_max_diff=5e-1)
 
+    def test_pipeline_interrupt(self):
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        prompt = "hey"
+        num_inference_steps = 3
+
+        # store intermediate latents from the generation process
+        class PipelineState:
+            def __init__(self):
+                self.state = []
+
+            def apply(self, pipe, i, t, callback_kwargs):
+                self.state.append(callback_kwargs["latents"])
+                return callback_kwargs
+
+        pipe_state = PipelineState()
+        sd_pipe(
+            prompt,
+            image=inputs["image"],
+            num_inference_steps=num_inference_steps,
+            output_type="np",
+            generator=torch.Generator("cpu").manual_seed(0),
+            callback_on_step_end=pipe_state.apply,
+        ).images
+
+        # interrupt generation at step index
+        interrupt_step_idx = 1
+
+        def callback_on_step_end(pipe, i, t, callback_kwargs):
+            if i == interrupt_step_idx:
+                pipe._interrupt = True
+
+            return callback_kwargs
+
+        output_interrupted = sd_pipe(
+            prompt,
+            image=inputs["image"],
+            num_inference_steps=num_inference_steps,
+            output_type="latent",
+            generator=torch.Generator("cpu").manual_seed(0),
+            callback_on_step_end=callback_on_step_end,
+        ).images
+
+        # fetch intermediate latents at the interrupted step
+        # from the completed generation process
+        intermediate_latent = pipe_state.state[interrupt_step_idx]
+
+        # compare the intermediate latent to the output of the interrupted process
+        # they should be the same
+        assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
+
 
 @slow
 @require_torch_gpu