update hunyuan_controlnet, kolors, lumina and latte to 0.31.0

mindone/diffusers/pipelines/auto_pipeline.py

@@ -48,12 +48,16 @@
 )
 from .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline
 from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
+from .lumina import LuminaText2ImgPipeline
 from .pag import (
     HunyuanDiTPAGPipeline,
     PixArtSigmaPAGPipeline,
     StableDiffusion3PAGPipeline,
+    StableDiffusionControlNetPAGInpaintPipeline,
     StableDiffusionControlNetPAGPipeline,
+    StableDiffusionPAGImg2ImgPipeline,
     StableDiffusionPAGPipeline,
+    StableDiffusionXLControlNetPAGImg2ImgPipeline,
     StableDiffusionXLControlNetPAGPipeline,
     StableDiffusionXLPAGImg2ImgPipeline,
     StableDiffusionXLPAGInpaintPipeline,
@@ -99,6 +103,7 @@
         ("stable-diffusion-xl-controlnet-pag", StableDiffusionXLControlNetPAGPipeline),
         ("pixart-sigma-pag", PixArtSigmaPAGPipeline),
         ("flux", FluxPipeline),
+        ("lumina", LuminaText2ImgPipeline),
     ]
 )
 
@@ -112,8 +117,10 @@
         ("kandinsky22", KandinskyV22Img2ImgCombinedPipeline),
         ("kandinsky3", Kandinsky3Img2ImgPipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
+        ("stable-diffusion-pag", StableDiffusionPAGImg2ImgPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetImg2ImgPipeline),
         ("stable-diffusion-xl-pag", StableDiffusionXLPAGImg2ImgPipeline),
+        ("stable-diffusion-xl-controlnet-pag", StableDiffusionXLControlNetPAGImg2ImgPipeline),
         ("lcm", LatentConsistencyModelImg2ImgPipeline),
     ]
 )
@@ -127,6 +134,7 @@
         ("kandinsky", KandinskyInpaintCombinedPipeline),
         ("kandinsky22", KandinskyV22InpaintCombinedPipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetInpaintPipeline),
+        ("stable-diffusion-controlnet-pag", StableDiffusionControlNetPAGInpaintPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetInpaintPipeline),
         ("stable-diffusion-xl-pag", StableDiffusionXLPAGInpaintPipeline),
     ]

mindone/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py

@@ -135,9 +135,19 @@ def get_resize_crop_region_for_grid(src, tgt_size):
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    r"""
+    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
+    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+    Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+    Args:
+        noise_cfg (`ms.Tensor`):
+            The predicted noise tensor for the guided diffusion process.
+        noise_pred_text (`ms.Tensor`):
+            The predicted noise tensor for the text-guided diffusion process.
+        guidance_rescale (`float`, *optional*, defaults to 0.0):
+            A rescale factor applied to the noise predictions.
+    Returns:
+        noise_cfg (`ms.Tensor`): The rescaled noise prediction tensor.
     """
     std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
     std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
@@ -219,6 +229,8 @@ def __init__(
         requires_safety_checker: bool = True,
     ):
         super().__init__()
+        if isinstance(controlnet, (list, tuple)):
+            controlnet = HunyuanDiT2DMultiControlNetModel(controlnet)
 
         self.register_modules(
             vae=vae,

mindone/diffusers/pipelines/kolors/pipeline_kolors.py

@@ -66,7 +66,7 @@ def retrieve_timesteps(
     sigmas: Optional[List[float]] = None,
     **kwargs,
 ):
-    """
+    r"""
     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
 

mindone/diffusers/pipelines/kolors/pipeline_kolors_img2img.py

@@ -87,7 +87,7 @@ def retrieve_timesteps(
     sigmas: Optional[List[float]] = None,
     **kwargs,
 ):
-    """
+    r"""
     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
 
@@ -560,22 +560,24 @@ def get_timesteps(self, num_inference_steps, strength, denoising_start=None):
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+            return timesteps, num_inference_steps - t_start
+
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -586,11 +588,12 @@ def get_timesteps(self, num_inference_steps, strength, denoising_start=None):
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents
     def prepare_latents(
         self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None, add_noise=True

mindone/diffusers/pipelines/kolors/tokenizer.py

@@ -277,6 +277,7 @@ def _pad(
         padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
         pad_to_multiple_of: Optional[int] = None,
         return_attention_mask: Optional[bool] = None,
+        padding_side: Optional[bool] = None,
     ) -> dict:
         """
         Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
@@ -298,6 +299,9 @@ def _pad(
             pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                 This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                 `>= 7.5` (Volta).
+            padding_side (`str`, *optional*):
+                The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+                Default value is picked from the class attribute of the same name.
             return_attention_mask:
                 (optional) Set to False to avoid returning attention mask (default: set to model specifics)
         """

mindone/diffusers/pipelines/latte/pipeline_latte.py

@@ -70,7 +70,7 @@ def retrieve_timesteps(
     sigmas: Optional[List[float]] = None,
     **kwargs,
 ):
-    """
+    r"""
     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
 

mindone/diffusers/pipelines/lumina/pipeline_lumina.py

@@ -68,7 +68,7 @@ def retrieve_timesteps(
     sigmas: Optional[List[float]] = None,
     **kwargs,
 ):
-    """
+    r"""
     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.