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update

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This commit is contained in:
Dhruv Nair
2025-04-02 20:33:17 +02:00
parent 5ac65c4513
commit ca5cfbd37e
3 changed files with 21 additions and 0 deletions
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7
src/diffusers/pipelines/ltx/pipeline_ltx.py
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@@ -489,6 +489,10 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
def num_timesteps(self):
return self._num_timesteps
@property
def current_timestep(self):
return self._current_timestep
@property
def attention_kwargs(self):
return self._attention_kwargs
@@ -622,6 +626,7 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
self._current_timestep = None
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
@@ -706,6 +711,8 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
if self.interrupt:
continue
self._current_timestep = t
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = latent_model_input.to(prompt_embeds.dtype)

7
src/diffusers/pipelines/ltx/pipeline_ltx_condition.py
View File

@@ -774,6 +774,10 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
def num_timesteps(self):
return self._num_timesteps
@property
def current_timestep(self):
return self._current_timestep
@property
def attention_kwargs(self):
return self._attention_kwargs
@@ -933,6 +937,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
self._current_timestep = None
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
@@ -1066,6 +1071,8 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
if self.interrupt:
continue
self._current_timestep = t
if image_cond_noise_scale > 0:
# Add timestep-dependent noise to the hard-conditioning latents
# This helps with motion continuity, especially when conditioned on a single frame

7
src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py
View File

@@ -550,6 +550,10 @@ class LTXImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLo
def num_timesteps(self):
return self._num_timesteps
@property
def current_timestep(self):
return self._current_timestep
@property
def attention_kwargs(self):
return self._attention_kwargs
@@ -686,6 +690,7 @@ class LTXImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLo
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
self._current_timestep = None
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
@@ -778,6 +783,8 @@ class LTXImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLo
if self.interrupt:
continue
self._current_timestep = t
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = latent_model_input.to(prompt_embeds.dtype)