nodes/kijai-ComfyUI-HunyuanVideoWrapper
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Update pipeline_hunyuan_video.py

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kijai
2025-03-06 23:09:37 +02:00
parent 16b2c11d2b
commit e4b52667d6
1 changed files with 26 additions and 6 deletions
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hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
+26 -6
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@@ -235,7 +235,6 @@ class HunyuanVideoPipeline(DiffusionPipeline):
freenoise=False,
context_size=None,
context_overlap=None,
leapfusion_img2vid=False,
official_i2v=False,
image_cond_latents=None,
):
@@ -647,7 +646,6 @@ class HunyuanVideoPipeline(DiffusionPipeline):
freenoise=freenoise,
context_size=context_frames,
context_overlap=context_overlap,
leapfusion_img2vid=leapfusion_img2vid,
official_i2v=official_i2v,
image_cond_latents=image_cond_latents,
)
@@ -725,7 +723,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
if leapfusion_img2vid:
latent_model_input[:, :, [0,], :, :] = original_latents[:, :, [0,], :, :].to(latent_model_input)
if image_cond_latents is not None:
if image_cond_latents is not None and not use_context_schedule:
latent_image_input = (
torch.cat([image_cond_latents] * 2) if cfg_enabled else image_cond_latents
)
@@ -749,13 +747,33 @@ class HunyuanVideoPipeline(DiffusionPipeline):
guidance_expand = None
if use_context_schedule:
counter = torch.zeros_like(latent_model_input)
noise_pred = torch.zeros_like(latent_model_input)
counter = torch.zeros_like(latent_model_input)[:, :16]
noise_pred = torch.zeros_like(latent_model_input)[:, :16]
print("noise_pred", noise_pred.shape)
print("counter", counter.shape)
context_queue = list(context(
i, num_inference_steps, latents.shape[2], context_frames, context_stride, context_overlap,
))
if image_cond_latents is not None:
latent_image_input = (
torch.cat([image_cond_latents] * 2) if cfg_enabled else image_cond_latents
)
if i2v_mask is not None:
i2v_mask = torch.cat([i2v_mask] * 2) if cfg_enabled else i2v_mask
for c in context_queue:
partial_latent_model_input = latent_model_input[:, :, c, :, :]
partial_latent_model_input = latent_model_input[:, :, c]
if i2v_mask is not None:
#doesn't work properly
new_mask = torch.zeros_like(i2v_mask)
new_mask[..., 0, :, :] = 1.0
new_image_input = torch.zeros_like(latent_image_input)
new_image_input[..., 0, :, :] = latent_image_input[..., 0, :, :]
partial_latent_image_input = torch.cat([new_image_input[..., c, :, :], new_mask[..., c, :, :]], dim=1)
partial_latent_model_input = torch.cat([partial_latent_model_input, partial_latent_image_input], dim=1)
#print("partial_latent_model_input", partial_latent_model_input.shape)
with torch.autocast(
device_type="cuda", dtype=self.base_dtype, enabled=True):
@@ -773,6 +791,8 @@ class HunyuanVideoPipeline(DiffusionPipeline):
return_dict=True,
)["x"]
window_mask = torch.ones_like(noise_pred_context)
# Apply left-side blending for all except first chunk
if min(c) > 0:
ramp_up = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device)