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refactor 2

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This commit is contained in:
Aryan
2025-08-14 01:26:39 +02:00
parent 47bf390bb4
commit 84c17be0f0
1 changed files with 20 additions and 30 deletions
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src/diffusers/pipelines/ltx/pipeline_ltx_condition_infinite.py
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@@ -979,6 +979,11 @@ class LTXConditionInfinitePipeline(DiffusionPipeline, FromSingleFileMixin, LTXVi
last_latent_tile_num_frames = last_latent_chunk.shape[2]
latent_chunk = torch.cat([last_latent_chunk, latent_chunk], dim=2)
total_latent_num_frames = last_latent_tile_num_frames + latent_tile_num_frames
conditioning_mask = torch.zeros(
(batch_size, total_latent_num_frames), dtype=torch.float32, device=device,
)
conditioning_mask[:, :last_latent_tile_num_frames] = 1.0
else:
total_latent_num_frames = latent_tile_num_frames
@@ -998,6 +1003,21 @@ class LTXConditionInfinitePipeline(DiffusionPipeline, FromSingleFileMixin, LTXVi
device=device,
)
if start_index > 0:
conditioning_mask = conditioning_mask.gather(1, video_ids[:, 0])
video_ids = self._scale_video_ids(
video_ids,
scale_factor=self.vae_spatial_compression_ratio,
scale_factor_t=self.vae_temporal_compression_ratio,
frame_index=0,
device=device
)
video_ids = video_ids.float()
video_ids[:, 0] = video_ids[:, 0] * (1.0 / frame_rate)
if self.do_classifier_free_guidance:
video_ids = torch.cat([video_ids, video_ids], dim=0)
# Set timesteps
inner_timesteps, inner_num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
sigmas = self.scheduler.sigmas
@@ -1005,18 +1025,6 @@ class LTXConditionInfinitePipeline(DiffusionPipeline, FromSingleFileMixin, LTXVi
self._num_timesteps = len(inner_timesteps)
if start_index == 0:
video_ids = self._scale_video_ids(
video_ids,
scale_factor=self.vae_spatial_compression_ratio,
scale_factor_t=self.vae_temporal_compression_ratio,
frame_index=0,
device=device
)
video_ids = video_ids.float()
video_ids[:, 0] = video_ids[:, 0] * (1.0 / frame_rate)
if self.do_classifier_free_guidance:
video_ids = torch.cat([video_ids, video_ids], dim=0)
with self.progress_bar(total=inner_num_inference_steps) as progress_bar:
for i, t in enumerate(inner_timesteps):
if self.interrupt:
@@ -1079,24 +1087,6 @@ class LTXConditionInfinitePipeline(DiffusionPipeline, FromSingleFileMixin, LTXVi
)
first_tile_out_latents = tile_out_latents.clone()
else:
conditioning_mask = torch.zeros(
(batch_size, total_latent_num_frames), dtype=torch.float32, device=device,
)
conditioning_mask[:, :last_latent_tile_num_frames] = 1.0
conditioning_mask = conditioning_mask.gather(1, video_ids[:, 0])
video_ids = self._scale_video_ids(
video_ids,
scale_factor=self.vae_spatial_compression_ratio,
scale_factor_t=self.vae_temporal_compression_ratio,
frame_index=0,
device=device
)
video_ids = video_ids.float()
video_ids[:, 0] = video_ids[:, 0] * (1.0 / frame_rate)
if self.do_classifier_free_guidance:
video_ids = torch.cat([video_ids, video_ids], dim=0)
with self.progress_bar(total=inner_num_inference_steps) as progress_bar:
for i, t in enumerate(inner_timesteps):
if self.interrupt: