|
|
|
|
|
|
|
import torch |
|
|
|
|
|
|
|
|
|
|
|
|
|
unet_conversion_map = [ |
|
|
|
("time_embed.0.weight", "time_embedding.linear_1.weight"), |
|
("time_embed.0.bias", "time_embedding.linear_1.bias"), |
|
("time_embed.2.weight", "time_embedding.linear_2.weight"), |
|
("time_embed.2.bias", "time_embedding.linear_2.bias"), |
|
("label_emb.0.0.weight", "add_embedding.linear_1.weight"), |
|
("label_emb.0.0.bias", "add_embedding.linear_1.bias"), |
|
("label_emb.0.2.weight", "add_embedding.linear_2.weight"), |
|
("label_emb.0.2.bias", "add_embedding.linear_2.bias"), |
|
("input_blocks.0.0.weight", "conv_in.weight"), |
|
("input_blocks.0.0.bias", "conv_in.bias"), |
|
("middle_block_out.0.weight", "controlnet_mid_block.weight"), |
|
("middle_block_out.0.bias", "controlnet_mid_block.bias"), |
|
] |
|
|
|
unet_conversion_map_resnet = [ |
|
|
|
("in_layers.0", "norm1"), |
|
("in_layers.2", "conv1"), |
|
("out_layers.0", "norm2"), |
|
("out_layers.3", "conv2"), |
|
("emb_layers.1", "time_emb_proj"), |
|
("skip_connection", "conv_shortcut"), |
|
] |
|
|
|
unet_conversion_map_layer = [] |
|
|
|
|
|
for i in range(4): |
|
|
|
|
|
for j in range(10): |
|
|
|
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." |
|
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." |
|
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) |
|
|
|
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." |
|
sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." |
|
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) |
|
|
|
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." |
|
sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." |
|
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) |
|
|
|
|
|
hf_mid_atn_prefix = "mid_block.attentions.0." |
|
sd_mid_atn_prefix = "middle_block.1." |
|
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) |
|
|
|
for j in range(2): |
|
hf_mid_res_prefix = f"mid_block.resnets.{j}." |
|
sd_mid_res_prefix = f"middle_block.{2*j}." |
|
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) |
|
|
|
|
|
|
|
controlnet_cond_embedding_names = ['conv_in'] + [f'blocks.{i}' for i in range(6)] + ['conv_out'] |
|
for i, hf_prefix in enumerate(controlnet_cond_embedding_names): |
|
hf_prefix = f"controlnet_cond_embedding.{hf_prefix}." |
|
sd_prefix = f"input_hint_block.{i*2}." |
|
unet_conversion_map_layer.append((sd_prefix, hf_prefix)) |
|
|
|
for i in range(12): |
|
hf_prefix = f"controlnet_down_blocks.{i}." |
|
sd_prefix = f"zero_convs.{i}.0." |
|
unet_conversion_map_layer.append((sd_prefix, hf_prefix)) |
|
|
|
|
|
def convert_from_diffuser_state_dict(unet_state_dict): |
|
mapping = {k: k for k in unet_state_dict.keys()} |
|
for sd_name, hf_name in unet_conversion_map: |
|
mapping[hf_name] = sd_name |
|
for k, v in mapping.items(): |
|
if "resnets" in k: |
|
for sd_part, hf_part in unet_conversion_map_resnet: |
|
v = v.replace(hf_part, sd_part) |
|
mapping[k] = v |
|
for k, v in mapping.items(): |
|
for sd_part, hf_part in unet_conversion_map_layer: |
|
v = v.replace(hf_part, sd_part) |
|
mapping[k] = v |
|
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items() if k in unet_state_dict} |
|
return new_state_dict |
|
|