remove unneeded code for now

painebenjamin · Jan 17, 2024 · 00da07b · 00da07b
1 parent d57e3c7
commit 00da07b
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Showing 3 changed files with 2 additions and 54 deletions.
diff --git a/src/python/enfugue/diffusion/invocation/layers.py b/src/python/enfugue/diffusion/invocation/layers.py
@@ -127,7 +127,6 @@ class LayeredInvocation:
     max_guidance_scale: float=3.0
     # dragnuwa
     motion_vectors: Optional[List[List[MotionVectorPointDict]]]=None
-    optical_flow: Optional[Dict[OPTICAL_FLOW_METHOD_LITERAL, Union[List[Image],List[List[Image]]]]]=None
     motion_vector_repeat_window: bool=False
     gaussian_sigma: int=20
     # img2img
@@ -1766,32 +1765,10 @@ def execute_stable_video(
             "motion_bucket_id": self.motion_bucket_id
         }
 
-        if self.motion_vectors or self.optical_flow:
+        if self.motion_vectors:
             svd_kwargs["motion_vectors"] = self.motion_vectors
             svd_kwargs["motion_vector_repeat_window"] = self.motion_vector_repeat_window
             svd_kwargs["gaussian_sigma"] = self.gaussian_sigma
-            if self.optical_flow:
-                optical_flow = {}
-                for method in self.optical_flow:
-                    frame_lists = self.optical_flow[method]
-                    if not frame_lists:
-                        continue
-                    if not isinstance(frame_lists[0], list):
-                        frame_lists = [frame_lists]
-                    optical_flow[method] = [
-                        [
-                            self.prepare_image(
-                                width=self.width,
-                                height=self.height,
-                                image=frame,
-                                animation_frames=self.animation_frames,
-                                return_mask=False
-                            )
-                            for frame in frame_list
-                        ]
-                        for frame_list in frame_lists
-                    ]
-            svd_kwargs["optical_flow"] = optical_flow
             logger.debug(f"Calling DragNUWA pipeline with arguments {redact_for_log(svd_kwargs)}")
             frames = pipeline.dragnuwa_img2vid(
                 task_callback=task_callback,

diff --git a/src/python/enfugue/diffusion/manager.py b/src/python/enfugue/diffusion/manager.py
@@ -4823,7 +4823,6 @@ def dragnuwa_img2vid(
         self,
         image: Union[str, PIL.Image.Image],
         motion_vectors: List[List[MotionVectorPointDict]],
-        optical_flow: Dict[OPTICAL_FLOW_METHOD_LITERAL, List[List[PIL.Image.Image]]]={},
         motion_vector_repeat_window: bool=False,
         decode_chunk_size: Optional[int]=1,
         num_frames: int=14,
@@ -4929,7 +4928,6 @@ def decode_svd_latents(latents, iteration):
                 num_inference_steps=num_inference_steps,
                 min_guidance_scale=min_guidance_scale,
                 max_guidance_scale=max_guidance_scale,
-                optical_flow=optical_flow,
                 progress_callback=progress_callback,
                 latent_callback=latent_callback,
                 latent_callback_steps=image_callback_steps

diff --git a/src/python/enfugue/diffusion/support/drag/helper.py b/src/python/enfugue/diffusion/support/drag/helper.py
@@ -46,7 +46,6 @@ def __call__(
         num_frames: int=14,
         frame_window_size: int=14,
         num_inference_steps: int=25,
-        optical_flow: Optional[Dict[OPTICAL_FLOW_METHOD_LITERAL, List[List[Image]]]] = None,
         motion_vectors: List[List[MotionVectorPointDict]] = [],
         motion_vector_repeat_window: bool = False,
         progress_callback: Optional[Callable[[int, int, float], None]]=None,
@@ -57,15 +56,10 @@ def __call__(
         Executes the network
         """
         import torch
-        import numpy as np
         from torchvision import transforms, utils
         from PIL import Image
         from einops import repeat, rearrange
-        from enfugue.diffusion.util import (
-            Video,
-            motion_vector_conditioning_tensor,
-            optical_flow_conditioning_tensor,
-        )
+        from enfugue.diffusion.util import motion_vector_conditioning_tensor
 
         # Set args
         self.network.args.width = width
@@ -94,25 +88,6 @@ def __call__(
             # Repeat condition
             condition = condition.repeat(num_iterations, 1, 1, 1)[:num_frames-num_zero_frames, :, :, :]
 
-        # Calculate optical flow and add to condition
-        if optical_flow:
-            for optical_flow_method in optical_flow:
-                for image_sequence in optical_flow[optical_flow_method]:
-                    image_sequence = image_sequence[:condition_frames]
-                    sequence_gaussian_sigma: Optional[int] = None
-                    if optical_flow_method == "lucas-kanade":
-                        flow = Video(image_sequence).sparse_flow()
-                        sequence_gaussian_sigma = gaussian_sigma
-                    else:
-                        flow = Video(image_sequence).dense_flow(optical_flow_method)
-                    sequence_condition = optical_flow_conditioning_tensor(
-                        np.array([flow_frame for flow_frame in flow]),
-                        gaussian_sigma=sequence_gaussian_sigma,
-                        device=self.device,
-                        dtype=self.dtype
-                    )
-                    condition[0:sequence_condition.shape[0]] += sequence_condition[:condition.shape[0]]
-
         # Insert zero-frames
         for i in range(num_zero_frames):
             index = (i + 1) * frame_window_size
@@ -267,7 +242,6 @@ def __call__(
         num_inference_steps: int=25,
         min_guidance_scale: float=1.0,
         max_guidance_scale: float=3.0,
-        optical_flow: Optional[Dict[OPTICAL_FLOW_METHOD_LITERAL, List[List[Image]]]] = None,
         progress_callback: Optional[Callable[[int, int, float], None]]=None,
         motion_vector_repeat_window: bool=False,
         latent_callback: Optional[Callable[[Tensor, int], None]]=None,
@@ -288,7 +262,6 @@ def __call__(
             num_inference_steps=num_inference_steps,
             min_guidance_scale=min_guidance_scale,
             max_guidance_scale=max_guidance_scale,
-            optical_flow=optical_flow,
             motion_vectors=motion_vectors,
             motion_bucket_id=motion_bucket_id,
             batch_size=batch_size,