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stuff and more stuff

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This commit is contained in:
DeepBeepMeep
2025-07-02 14:08:59 +02:00
parent e0666a3e6d
commit 28fc48db2d
125 changed files with 11593 additions and 578 deletions
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10
hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
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@@ -949,11 +949,15 @@ class HunyuanVideoPipeline(DiffusionPipeline):
# width = width or self.transformer.config.sample_size * self.vae_scale_factor
# to deal with lora scaling and other possible forward hooks
trans = self.transformer
if trans.enable_cache:
teacache_multiplier = trans.teacache_multiplier
if trans.enable_cache == "tea":
teacache_multiplier = trans.cache_multiplier
trans.accumulated_rel_l1_distance = 0
trans.rel_l1_thresh = 0.1 if teacache_multiplier < 2 else 0.15
# trans.cache_start_step = int(tea_cache_start_step_perc*num_inference_steps/100)
elif trans.enable_cache == "mag":
trans.compute_magcache_threshold(trans.cache_start_step, num_inference_steps, trans.cache_multiplier)
trans.accumulated_err, trans.accumulated_steps, trans.accumulated_ratio = 0, 0, 1.0
else:
trans.enable_cache == None
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,

17
hyvideo/diffusion/pipelines/pipeline_hunyuan_video_audio.py
View File

@@ -934,10 +934,15 @@ class HunyuanVideoAudioPipeline(DiffusionPipeline):
transformer = self.transformer
if transformer.enable_cache:
teacache_multiplier = transformer.teacache_multiplier
if transformer.enable_cache == "tea":
teacache_multiplier = transformer.cache_multiplier
transformer.accumulated_rel_l1_distance = 0
transformer.rel_l1_thresh = 0.1 if teacache_multiplier < 2 else 0.15
elif transformer.enable_cache == "mag":
transformer.compute_magcache_threshold(transformer.cache_start_step, num_inference_steps, transformer.cache_multiplier)
transformer.accumulated_err, transformer.accumulated_steps, transformer.accumulated_ratio = 0, 0, 1.0
else:
transformer.enable_cache == None
# 1. Check inputs. Raise error if not correct
self.check_inputs(
@@ -1136,7 +1141,7 @@ class HunyuanVideoAudioPipeline(DiffusionPipeline):
if self._interrupt:
return [None]
if transformer.enable_cache:
if transformer.enable_cache == "tea":
cache_size = round( infer_length / frames_per_batch )
transformer.previous_residual = [None] * latent_items
cache_all_previous_residual = [None] * latent_items
@@ -1144,6 +1149,8 @@ class HunyuanVideoAudioPipeline(DiffusionPipeline):
cache_should_calc = [True] * cache_size
cache_accumulated_rel_l1_distance = [0.] * cache_size
cache_teacache_skipped_steps = [0] * cache_size
elif transformer.enable_cache == "mag":
transformer.previous_residual = [None] * latent_items
with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -1180,7 +1187,7 @@ class HunyuanVideoAudioPipeline(DiffusionPipeline):
img_ref_len = (latent_model_input.shape[-1] // 2) * (latent_model_input.shape[-2] // 2) * ( 1)
img_all_len = (latents_all.shape[-1] // 2) * (latents_all.shape[-2] // 2) * latents_all.shape[-3]
if transformer.enable_cache and cache_size > 1:
if transformer.enable_cache == "tea" and cache_size > 1:
for l in range(latent_items):
if cache_all_previous_residual[l] != None:
bsz = cache_all_previous_residual[l].shape[0]
@@ -1297,7 +1304,7 @@ class HunyuanVideoAudioPipeline(DiffusionPipeline):
pred_latents[:, :, p] += latents[:, :, iii]
counter[:, :, p] += 1
if transformer.enable_cache and cache_size > 1:
if transformer.enable_cache == "tea" and cache_size > 1:
for l in range(latent_items):
if transformer.previous_residual[l] != None:
bsz = transformer.previous_residual[l].shape[0]