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Merged
pull merged 2 commits into from
Dec 9, 2025
Merged

[pull] master from comfyanonymous:master #467

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e2a800e
Fix for HunyuanVideo1.5 meanflow distil (#11212)
kijai Dec 9, 2025
791e30f
Fix nan issue when quantizing fp16 tensor. (#11213)
comfyanonymous Dec 9, 2025
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3 changes: 2 additions & 1 deletion comfy/ldm/hunyuan_video/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ class HunyuanVideoParams:
meanflow: bool
use_cond_type_embedding: bool
vision_in_dim: int
meanflow_sum: bool


class SelfAttentionRef(nn.Module):
Expand Down Expand Up @@ -317,7 +318,7 @@ def forward_orig(
timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
vec = (vec + vec_r) / 2
vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2

if ref_latent is not None:
ref_latent_ids = self.img_ids(ref_latent)
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2 changes: 2 additions & 0 deletions comfy/model_detection.py
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Expand Up @@ -180,8 +180,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["use_cond_type_embedding"] = False
if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:
dit_config["vision_in_dim"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]
dit_config["meanflow_sum"] = True
else:
dit_config["vision_in_dim"] = None
dit_config["meanflow_sum"] = False
return dit_config

if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
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5 changes: 4 additions & 1 deletion comfy/quant_ops.py
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Expand Up @@ -399,7 +399,10 @@ def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_roun
orig_dtype = tensor.dtype

if isinstance(scale, str) and scale == "recalculate":
scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(dtype).max
if tensor.dtype not in [torch.float32, torch.bfloat16]: # Prevent scale from being too small
tensor_info = torch.finfo(tensor.dtype)
scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max))

if scale is not None:
if not isinstance(scale, torch.Tensor):
Expand Down
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