nodes/kijai-ComfyUI-HunyuanVideoWrapper
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Add Mobius looping option

Browse Source 
https://github.com/YisuiTT/Mobius/
This commit is contained in:
kijai
2025-03-30 19:07:04 +03:00
parent 1b4164f5b3
commit 75190b756a
2 changed files with 47 additions and 3 deletions
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hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
+24 -2
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@@ -454,6 +454,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
image_cond_latents: Optional[torch.Tensor] = None,
riflex_freq_index: Optional[int] = None,
i2v_stability=True,
loop_args: Optional[Dict] = None,
**kwargs,
):
r"""
@@ -689,6 +690,15 @@ class HunyuanVideoPipeline(DiffusionPipeline):
callback = prepare_callback(self.comfy_model, num_inference_steps)
#print(self.scheduler.sigmas)
latent_shift_loop = False
if loop_args is not None:
latent_shift_loop = True
is_looped = True
latent_skip = loop_args["shift_skip"]
latent_shift_start_percent = loop_args["start_percent"]
latent_shift_end_percent = loop_args["end_percent"]
shift_idx = 0
logger.info(f"Sampling {video_length} frames in {latents.shape[2]} latents at {width}x{height} with {len(timesteps)} inference steps")
@@ -698,9 +708,11 @@ class HunyuanVideoPipeline(DiffusionPipeline):
if self.interrupt:
continue
current_step_percentage = i / len(timesteps)
if image_cond_latents is not None and i2v_condition_type == "token_replace":
latents = torch.concat([original_image_latents, latents[:, :, 1:, :, :]], dim=2)
latent_model_input = latents
input_prompt_embeds = prompt_embeds
#input_prompt_mask = prompt_mask
@@ -708,7 +720,12 @@ class HunyuanVideoPipeline(DiffusionPipeline):
cfg_enabled = False
stg_enabled = False
current_step_percentage = i / len(timesteps)
### latent shift
if latent_shift_loop:
if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent:
latent_model_input = torch.cat([latent_model_input[:, :, shift_idx:]] + [latent_model_input[:, :, :shift_idx]], dim=2)
if self.do_spatio_temporal_guidance:
if stg_start_percent <= current_step_percentage <= stg_end_percent:
stg_enabled = True
@@ -904,6 +921,11 @@ class HunyuanVideoPipeline(DiffusionPipeline):
noise_pred = noise_pred_text + self._stg_scale * (
noise_pred_text - noise_pred_perturb
)
if latent_shift_loop:
#reverse latent shift
if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent:
noise_pred = torch.cat([noise_pred[:, :, latent_video_length - shift_idx:]] + [noise_pred[:, :, :latent_video_length - shift_idx]], dim=2)
shift_idx = (shift_idx + latent_skip) % latent_video_length
# compute the previous noisy sample x_t -> x_t-1
if image_cond_latents is not None and i2v_condition_type == "token_replace":