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Support hunyuan image distilled model. (#9807)

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comfyanonymous 2025-09-10 20:17:34 -07:00 committed by GitHub
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2 changed files with 24 additions and 2 deletions
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14
comfy/ldm/hunyuan_video/model.py
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@ -41,6 +41,7 @@ class HunyuanVideoParams:
qkv_bias: bool qkv_bias: bool
guidance_embed: bool guidance_embed: bool
byt5: bool byt5: bool
meanflow: bool
class SelfAttentionRef(nn.Module): class SelfAttentionRef(nn.Module):
@ -256,6 +257,11 @@ class HunyuanVideo(nn.Module):
else: else:
self.byt5_in = None self.byt5_in = None
if params.meanflow:
self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
else:
self.time_r_in = None
if final_layer: if final_layer:
self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
@ -282,6 +288,14 @@ class HunyuanVideo(nn.Module):
img = self.img_in(img) img = self.img_in(img)
vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
if self.time_r_in is not None:
w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved
if len(w) > 0:
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
if ref_latent is not None: if ref_latent is not None:
ref_latent_ids = self.img_ids(ref_latent) ref_latent_ids = self.img_ids(ref_latent)
ref_latent = self.img_in(ref_latent) ref_latent = self.img_in(ref_latent)

12
comfy/model_detection.py
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@ -142,12 +142,20 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels
dit_config["patch_size"] = list(in_w.shape[2:]) dit_config["patch_size"] = list(in_w.shape[2:])
dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"]) dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"])
if '{}vector_in.in_layer.weight'.format(key_prefix) in state_dict: if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):
dit_config["vec_in_dim"] = 768 dit_config["vec_in_dim"] = 768
dit_config["axes_dim"] = [16, 56, 56]
else: else:
dit_config["vec_in_dim"] = None dit_config["vec_in_dim"] = None
if len(dit_config["patch_size"]) == 2:
dit_config["axes_dim"] = [64, 64] dit_config["axes_dim"] = [64, 64]
else:
dit_config["axes_dim"] = [16, 56, 56]
if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):
dit_config["meanflow"] = True
else:
dit_config["meanflow"] = False
dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1] dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]
dit_config["hidden_size"] = in_w.shape[0] dit_config["hidden_size"] = in_w.shape[0]