nodes/kijai-ComfyUI-WanVideoWrapper
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Support HuMo 1.7B

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kijai
2025-09-16 17:41:07 +03:00
parent 6d05cc5cf9
commit e9672e8d56
1 changed files with 48 additions and 15 deletions
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nodes.py
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@@ -2124,7 +2124,16 @@ class WanVideoSampler:
humo_audio_scale = image_embeds.get("humo_audio_scale", 1.0)
humo_image_cond = image_embeds.get("humo_image_cond", None)
humo_image_cond_neg = image_embeds.get("humo_image_cond_neg", None)
pos_latent = neg_latent = None
if transformer.dim == 1536 and humo_image_cond is not None: #small humo model
#noise = torch.cat([noise[:, :-humo_reference_count], humo_image_cond[4:, -humo_reference_count:]], dim=1)
pos_latent = humo_image_cond[4:, -humo_reference_count:].to(device, dtype)
neg_latent = torch.zeros_like(pos_latent)
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
humo_image_cond = humo_image_cond_neg = None
humo_audio_cfg_scale = image_embeds.get("humo_audio_cfg_scale", 1.0)
humo_start_percent = image_embeds.get("humo_start_percent", 0.0)
humo_end_percent = image_embeds.get("humo_end_percent", 1.0)
@@ -2871,6 +2880,8 @@ class WanVideoSampler:
try:
if not batched_cfg:
#conditional (positive) pass
if pos_latent is not None: # for humo
base_params['x'] = [torch.cat([z[:, :-humo_reference_count], pos_latent], dim=1)]
noise_pred_cond, cache_state_cond = transformer(
context=positive_embeds,
pred_id=cache_state[0] if cache_state else None,
@@ -2888,28 +2899,50 @@ class WanVideoSampler:
base_params['clip_fea'] = clip_fea_neg if clip_fea_neg is not None else clip_fea
if humo_audio_input_neg is not None:
base_params['humo_audio'] = humo_audio_input_neg
if neg_latent is not None:
base_params['x'] = [torch.cat([z[:, :-humo_reference_count], neg_latent], dim=1)]
noise_pred_uncond, cache_state_uncond = transformer(
context=negative_embeds if humo_audio_input_neg is None else positive_embeds, #ti
context=negative_embeds if humo_audio_input_neg is None else positive_embeds, #ti #t
pred_id=cache_state[1] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond_neg,
**base_params)
noise_pred_uncond = noise_pred_uncond[0]
# HuMo
if humo_audio_input_neg is not None and not math.isclose(humo_audio_cfg_scale[idx], 1.0):
if not math.isclose(humo_audio_cfg_scale[idx], 1.0):
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
if t > 980 and humo_image_cond_neg_input is not None: # use image cond for first timesteps
base_params['y'] = [humo_image_cond_neg_input]
noise_pred_humo_audio_uncond, cache_state_humo = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
if humo_image_cond is not None and humo_audio_input_neg is not None:
if t > 980 and humo_image_cond_neg_input is not None: # use image cond for first timesteps
base_params['y'] = [humo_image_cond_neg_input]
noise_pred_humo_audio_uncond, cache_state_humo = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (noise_pred_uncond + humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio_uncond[0])
+ (cfg_scale - 2.0) * (noise_pred_humo_audio_uncond[0] - noise_pred_uncond))
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_humo]
elif humo_audio_input is not None:
if cache_state is not None and len(cache_state) != 4:
cache_state.append(None)
# audio
noise_pred_humo_null, cache_state_humo = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
# negative
if humo_audio_input is not None:
base_params['humo_audio'] = humo_audio_input
noise_pred_humo_audio, cache_state_humo2 = transformer(
context=positive_embeds, pred_id=cache_state[3] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio[0])
+ cfg_scale * (noise_pred_humo_audio[0] - noise_pred_uncond)
+ cfg_scale * (noise_pred_uncond - noise_pred_humo_null[0])
+ noise_pred_humo_null[0])
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_humo, cache_state_humo2]
noise_pred = (noise_pred_uncond + humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio_uncond[0])
+ (cfg_scale - 2.0) * (noise_pred_humo_audio_uncond[0] - noise_pred_uncond))
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_humo]
#phantom
if use_phantom and not math.isclose(phantom_cfg_scale[idx], 1.0):
noise_pred_phantom, cache_state_phantom = transformer(
@@ -4131,7 +4164,7 @@ class WanVideoSampler:
callback_latent = (latent_model_input[:, :orig_noise_len].to(device) - noise_pred[:, :orig_noise_len].to(device) * t.to(device) / 1000).detach()
#elif phantom_latents is not None:
# callback_latent = (latent_model_input[:,:-phantom_latents.shape[1]].to(device) - noise_pred[:,:-phantom_latents.shape[1]].to(device) * t.to(device) / 1000).detach()
elif humo_image_cond is not None and humo_reference_count > 0:
elif humo_reference_count > 0:
callback_latent = (latent_model_input[:,:-humo_reference_count].to(device) - noise_pred[:,:-humo_reference_count].to(device) * t.to(device) / 1000).detach()
else:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach()
@@ -4153,7 +4186,7 @@ class WanVideoSampler:
if phantom_latents is not None:
latent = latent[:,:-phantom_latents.shape[1]]
if humo_image_cond is not None and humo_reference_count > 0:
if humo_reference_count > 0:
latent = latent[:,:-humo_reference_count]
cache_states = None