add callbacks to denoising step (huggingface#5427)

* draft1 * update * style * move to the end of loop * update * update callbak_on_step_end_inputs * Revert "update" This reverts commit 5f9b153. * Revert "update callbak_on_step_end_inputs" This reverts commit 44889f4. * update * update test required_optional_params * remove self.lora_scale * img2img * inpaint * Apply suggestions from code review Co-authored-by: Patrick von Platen <[email protected]> * fix * apply feedbacks on img2img + inpaint: keep only important pipeline attributes * depth * pix2pix * make _callback_tensor_inputs an class variable so that we can use it for testing * add a basic tst for callback * add a read-only tensor input timesteps + fix tests * add second test for callback cfg * sdxl * sdxl img2img * sdxl inpaint * kandinsky prior * kandinsky decoder * kandinsky img2img + combined * kandinsky inpaint * fix copies * fix * consistent default inputs * fix copies * wuerstchen_prior prior * test_wuerstchen_decoder + fix test for prior * wuerstchen_combined pipeline + skip tests * skip test for kandinsky combined * lcm * remove timesteps etc * add doc string * copies * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py Co-authored-by: Sayak Paul <[email protected]> * Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py Co-authored-by: Sayak Paul <[email protected]> * make style and improve tests * up * up * fix more * fix cfg test * tests for callbacks * fix for real * update * lcm img2img * add doc * add doc page to index --------- Co-authored-by: yiyixuxu <yixu310@gmail,com> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Sayak Paul <[email protected]> Co-authored-by: Dhruv Nair <[email protected]>
dingsihan · Nov 5, 2023 · 2b23ec8 · 2b23ec8
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diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
@@ -76,6 +76,8 @@
       title: Kandinsky
     - local: using-diffusers/controlnet
       title: ControlNet
+    - local: using-diffusers/callback
+      title: Callback
     - local: using-diffusers/shap-e
       title: Shap-E
     - local: using-diffusers/diffedit

diff --git a/docs/source/en/using-diffusers/callback.md b/docs/source/en/using-diffusers/callback.md
@@ -0,0 +1,60 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Using callback 
+
+[[open-in-colab]]
+
+Most 🤗 Diffusers pipeline now accept a `callback_on_step_end` argument that allows you to change the default behavior of denoising loop with custom defined functions. Here is an example of a callback function we can write to disable classifier free guidance after 40% of inference steps to save compute with minimum tradeoff in performance.
+
+```python
+def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):    
+        # adjust the batch_size of prompt_embeds according to guidance_scale
+        if step_index == int(pipe.num_timestep * 0.4):
+                prompt_embeds = callback_kwargs["prompt_embeds"]
+                prompt_embeds =prompt_embeds.chunk(2)[-1]
+
+        # update guidance_scale and prompt_embeds
+        pipe._guidance_scale = 0.0
+        callback_kwargs["prompt_embeds"] = prompt_embeds
+        return callback_kwargs
+```
+
+Your callback function has below arguments:
+* `pipe` is the pipeline instance, which provides access to useful properties such as `num_timestep` and `guidance_scale`. You can modify these properties by updating the underlying attributes. In this example, we disable CFG by setting `pipe._guidance_scale` to be `0`.
+* `step_index` and `timestep` tell you where you are in the denoising loop. In our example, we use `step_index` to decide when to turn off CFG.
+* `callback_kwargs` is a dict that contains tensor variables you can modify during the denoising loop. It only includes variables specified in the `callback_on_step_end_tensor_inputs` argument passed to the pipeline's `__call__` method. Different pipelines may use different sets of variables so please check the pipeline class's `_callback_tensor_inputs` attribute for the list of variables that you can modify. Common variables include `latents` and `prompt_embeds`. In our example, we need to adjust the batch size of `prompt_embeds` after setting `guidance_scale` to be `0` in order for it to work properly.
+
+You can pass the callback function as `callback_on_step_end` argument to the pipeline along with `callback_on_step_end_tensor_inputs`. 
+
+```
+import torch
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+generator = torch.Generator(device="cuda").manual_seed(1)
+out= pipe(prompt, generator=generator, callback_on_step_end = callback_custom_cfg, callback_on_step_end_tensor_inputs=['prompt_embeds'])
+
+out.images[0].save("out_custom_cfg.png")
+```
+
+Your callback function will be executed at the end of each denoising step and modify pipeline attributes and tensor variables for the next denoising step. We successfully added the "dynamic CFG" feature to the stable diffusion pipeline without having to modify the code at all.
+
+<Tip>
+
+Currently we only support `callback_on_step_end`. If you have a solid use case and require a callback function with a different execution point, please open an [feature request](https://github.com/huggingface/diffusers/issues/new/choose) so we can add it!
+
+</Tip>
diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py
@@ -110,6 +110,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
     def __init__(
         self,
@@ -500,17 +501,23 @@ def check_inputs(
         negative_prompt=None,
         prompt_embeds=None,
         negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
     ):
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
+        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 f" {type(callback_steps)}."
             )
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found"
+                f" {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
 
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
@@ -581,6 +588,33 @@ def disable_freeu(self):
         """Disables the FreeU mechanism if enabled."""
         self.unet.disable_freeu()
 
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -599,11 +633,12 @@ def __call__(
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         guidance_rescale: float = 0.0,
         clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -647,12 +682,6 @@ def __call__(
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
                 plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
             cross_attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                 [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
@@ -663,6 +692,15 @@ def __call__(
             clip_skip (`int`, *optional*):
                 Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                 the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeine class.
 
         Examples:
 
@@ -673,16 +711,47 @@ def __call__(
                 second element is a list of `bool`s indicating whether the corresponding generated image contains
                 "not-safe-for-work" (nsfw) content.
         """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using"
+                " `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using"
+                " `callback_on_step_end`",
+            )
+
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor
         # to deal with lora scaling and other possible forward hooks
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
-            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+            prompt,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
         )
 
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1
@@ -692,29 +761,27 @@ def __call__(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
 
         # 3. Encode input prompt
-        lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
 
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
             device,
             num_images_per_prompt,
-            do_classifier_free_guidance,
+            self.do_classifier_free_guidance,
             negative_prompt,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             lora_scale=lora_scale,
-            clip_skip=clip_skip,
+            clip_skip=self.clip_skip,
         )
         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
-        if do_classifier_free_guidance:
+        if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
         # 4. Prepare timesteps
@@ -739,33 +806,44 @@ def __call__(
 
         # 7. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                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)
 
                 # predict the noise residual
                 noise_pred = self.unet(
                     latent_model_input,
                     t,
                     encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
                     return_dict=False,
                 )[0]
 
                 # perform guidance
-                if do_classifier_free_guidance:
+                if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                     # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()