Add dynamic_shifting to SD3

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -68,6 +68,20 @@
 """
 
 
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
 def retrieve_timesteps(
     scheduler,
@@ -702,6 +716,7 @@ def __call__(
         skip_layer_guidance_scale: int = 2.8,
         skip_layer_guidance_stop: int = 0.2,
         skip_layer_guidance_start: int = 0.01,
+        mu: Optional[float] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -802,6 +817,7 @@ def __call__(
                 `skip_guidance_layers` will start. The guidance will be applied to the layers specified in
                 `skip_guidance_layers` from the fraction specified in `skip_layer_guidance_start`. Recommended value by
                 StabiltyAI for Stable Diffusion 3.5 Medium is 0.01.
+            mu (`float`, *optional*): `mu` value used for `dynamic_shifting`.
 
         Examples:
 
@@ -882,12 +898,7 @@ def __call__(
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
 
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 5. Prepare latent variables
+        # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
@@ -900,6 +911,33 @@ def __call__(
             latents,
         )
 
+        # 5. Prepare timesteps
+        scheduler_kwargs = {}
+        if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
+            _, _, height, width = latents.shape
+            image_seq_len = (height // self.transformer.config.patch_size) * (
+                width // self.transformer.config.patch_size
+            )
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            scheduler_kwargs["mu"] = mu
+        elif mu is not None:
+            scheduler_kwargs["mu"] = mu
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            **scheduler_kwargs,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
         # 6. Denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py

@@ -75,6 +75,20 @@
 """
 
 
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
 def retrieve_latents(
     encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
@@ -748,6 +762,7 @@ def __call__(
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 256,
+        mu: Optional[float] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -832,6 +847,7 @@ def __call__(
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                 `._callback_tensor_inputs` attribute of your pipeline class.
             max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+            mu (`float`, *optional*): `mu` value used for `dynamic_shifting`.
 
         Examples:
 
@@ -913,7 +929,24 @@ def __call__(
         image = self.image_processor.preprocess(image, height=height, width=width)
 
         # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
+        scheduler_kwargs = {}
+        if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
+            image_seq_len = (int(height) // self.vae_scale_factor // self.transformer.config.patch_size) * (
+                int(width) // self.vae_scale_factor // self.transformer.config.patch_size
+            )
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            scheduler_kwargs["mu"] = mu
+        elif mu is not None:
+            scheduler_kwargs["mu"] = mu
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs
+        )
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -74,6 +74,20 @@
 """
 
 
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
 def retrieve_latents(
     encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
@@ -838,6 +852,7 @@ def __call__(
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 256,
+        mu: Optional[float] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -947,6 +962,7 @@ def __call__(
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                 `._callback_tensor_inputs` attribute of your pipeline class.
             max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+            mu (`float`, *optional*): `mu` value used for `dynamic_shifting`.
 
         Examples:
 
@@ -1023,7 +1039,24 @@ def __call__(
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
 
         # 3. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
+        scheduler_kwargs = {}
+        if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None:
+            image_seq_len = (int(height) // self.vae_scale_factor // self.transformer.config.patch_size) * (
+                int(width) // self.vae_scale_factor // self.transformer.config.patch_size
+            )
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            scheduler_kwargs["mu"] = mu
+        elif mu is not None:
+            scheduler_kwargs["mu"] = mu
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs
+        )
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         # check that number of inference steps is not < 1 - as this doesn't make sense
         if num_inference_steps < 1: