File size: 4,980 Bytes
bc8c4af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | def WanVideoDiTFromDiffusers(state_dict):
rename_dict = {
"blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight",
"blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight",
"blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias",
"blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight",
"blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias",
"blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight",
"blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias",
"blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight",
"blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias",
"blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight",
"blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight",
"blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight",
"blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias",
"blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight",
"blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias",
"blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight",
"blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias",
"blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight",
"blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias",
"blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight",
"blocks.0.attn2.add_k_proj.bias":"blocks.0.cross_attn.k_img.bias",
"blocks.0.attn2.add_k_proj.weight":"blocks.0.cross_attn.k_img.weight",
"blocks.0.attn2.add_v_proj.bias":"blocks.0.cross_attn.v_img.bias",
"blocks.0.attn2.add_v_proj.weight":"blocks.0.cross_attn.v_img.weight",
"blocks.0.attn2.norm_added_k.weight":"blocks.0.cross_attn.norm_k_img.weight",
"blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias",
"blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight",
"blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias",
"blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight",
"blocks.0.norm2.bias": "blocks.0.norm3.bias",
"blocks.0.norm2.weight": "blocks.0.norm3.weight",
"blocks.0.scale_shift_table": "blocks.0.modulation",
"condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias",
"condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight",
"condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias",
"condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight",
"condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias",
"condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight",
"condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias",
"condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight",
"condition_embedder.time_proj.bias": "time_projection.1.bias",
"condition_embedder.time_proj.weight": "time_projection.1.weight",
"condition_embedder.image_embedder.ff.net.0.proj.bias":"img_emb.proj.1.bias",
"condition_embedder.image_embedder.ff.net.0.proj.weight":"img_emb.proj.1.weight",
"condition_embedder.image_embedder.ff.net.2.bias":"img_emb.proj.3.bias",
"condition_embedder.image_embedder.ff.net.2.weight":"img_emb.proj.3.weight",
"condition_embedder.image_embedder.norm1.bias":"img_emb.proj.0.bias",
"condition_embedder.image_embedder.norm1.weight":"img_emb.proj.0.weight",
"condition_embedder.image_embedder.norm2.bias":"img_emb.proj.4.bias",
"condition_embedder.image_embedder.norm2.weight":"img_emb.proj.4.weight",
"patch_embedding.bias": "patch_embedding.bias",
"patch_embedding.weight": "patch_embedding.weight",
"scale_shift_table": "head.modulation",
"proj_out.bias": "head.head.bias",
"proj_out.weight": "head.head.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
state_dict_[rename_dict[name]] = state_dict[name]
else:
name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:])
if name_ in rename_dict:
name_ = rename_dict[name_]
name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:])
state_dict_[name_] = state_dict[name]
return state_dict_
def WanVideoDiTStateDictConverter(state_dict):
state_dict_ = {}
for name in state_dict:
if name.startswith("vace"):
continue
if name.split(".")[0] in ["pose_patch_embedding", "face_adapter", "face_encoder", "motion_encoder"]:
continue
name_ = name
if name_.startswith("model."):
name_ = name_[len("model."):]
state_dict_[name_] = state_dict[name]
return state_dict_
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