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Support diffusers-format LoRAs for Anima #14182

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Support diffusers-format LoRAs for Anima #14182

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11 changes: 11 additions & 0 deletions comfy/lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -357,6 +357,17 @@ def model_lora_keys_unet(model, key_map={}):
key_lora = k[len("diffusion_model."):-len(".weight")]
key_map["transformer.{}".format(key_lora)] = k

if isinstance(model, comfy.model_base.Anima):
diffusers_keys = comfy.utils.anima_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
for k in diffusers_keys:
if k.endswith(".weight"):
to = diffusers_keys[k]
key_lora = k[:-len(".weight")]
key_map["diffusion_model.{}".format(key_lora)] = to
key_map["transformer.{}".format(key_lora)] = to
key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
key_map[key_lora] = to

return key_map


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24 changes: 24 additions & 0 deletions comfy/utils.py
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Expand Up @@ -818,6 +818,30 @@ def add_block_keys(prefix_from, prefix_to, has_adaln=True):

return key_map

def anima_to_diffusers(mmdit_config, output_prefix=""):
num_blocks = mmdit_config.get("num_blocks", 0)
key_map = {}

for i in range(num_blocks):
prefix_from = "transformer_blocks.{}".format(i)
prefix_to = "{}blocks.{}".format(output_prefix, i)

# attn1 is self attention, attn2 is cross attention
for attn_from, attn_to in (("attn1", "self_attn"), ("attn2", "cross_attn")):
block_map = {
"{}.to_q.weight".format(attn_from): "{}.q_proj.weight".format(attn_to),
"{}.to_k.weight".format(attn_from): "{}.k_proj.weight".format(attn_to),
"{}.to_v.weight".format(attn_from): "{}.v_proj.weight".format(attn_to),
"{}.to_out.0.weight".format(attn_from): "{}.output_proj.weight".format(attn_to),
}
for k, v in block_map.items():
key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v)

key_map["{}.ff.net.0.proj.weight".format(prefix_from)] = "{}.mlp.layer1.weight".format(prefix_to)
key_map["{}.ff.net.2.weight".format(prefix_from)] = "{}.mlp.layer2.weight".format(prefix_to)

return key_map

def repeat_to_batch_size(tensor, batch_size, dim=0):
if tensor.shape[dim] > batch_size:
return tensor.narrow(dim, 0, batch_size)
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