[SD3] CFG Cutoff fix and official callback (#11890)

fix and official callback
Co-authored-by: YiYi Xu <yixu310@gmail.com>

src/diffusers/callbacks.py

@@ -207,3 +207,38 @@ class IPAdapterScaleCutoffCallback(PipelineCallback):
         if step_index == cutoff_step:
             pipeline.set_ip_adapter_scale(0.0)
         return callback_kwargs
+
+
+class SD3CFGCutoffCallback(PipelineCallback):
+    """
+    Callback function for Stable Diffusion 3 Pipelines. After certain number of steps (set by `cutoff_step_ratio` or
+    `cutoff_step_index`), this callback will disable the CFG.
+
+    Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
+    """
+
+    tensor_inputs = ["prompt_embeds", "pooled_prompt_embeds"]
+
+    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+        cutoff_step_ratio = self.config.cutoff_step_ratio
+        cutoff_step_index = self.config.cutoff_step_index
+
+        # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
+        cutoff_step = (
+            cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
+        )
+
+        if step_index == cutoff_step:
+            prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
+            prompt_embeds = prompt_embeds[-1:]  # "-1" denotes the embeddings for conditional text tokens.
+
+            pooled_prompt_embeds = callback_kwargs[self.tensor_inputs[1]]
+            pooled_prompt_embeds = pooled_prompt_embeds[
+                -1:
+            ]  # "-1" denotes the embeddings for conditional pooled text tokens.
+
+            pipeline._guidance_scale = 0.0
+
+            callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
+            callback_kwargs[self.tensor_inputs[1]] = pooled_prompt_embeds
+        return callback_kwargs

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -25,6 +25,7 @@ from transformers import (
     T5TokenizerFast,
 )
 
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
@@ -184,7 +185,7 @@ class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingle
 
     model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
     _optional_components = ["image_encoder", "feature_extractor"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "pooled_prompt_embeds"]
 
     def __init__(
         self,
@@ -923,6 +924,9 @@ class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingle
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt,
@@ -1109,10 +1113,7 @@ class StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingle
 
                     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)
-                    negative_pooled_prompt_embeds = callback_outputs.pop(
-                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
-                    )
+                    pooled_prompt_embeds = callback_outputs.pop("pooled_prompt_embeds", pooled_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):