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Update Ruff to latest Version (#10919)

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* update

* update

* update

* update
This commit is contained in:
Dhruv Nair
2025-04-09 13:21:34 +02:00
committed by GitHub
parent 552cd32058
commit edc154da09
200 changed files with 4753 additions and 4694 deletions
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2
scripts/convert_amused.py
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@@ -468,7 +468,7 @@ def make_vqvae(old_vae):
# assert (old_output == new_output).all()
print("skipping full vae equivalence check")
print(f"vae full diff { (old_output - new_output).float().abs().sum()}")
print(f"vae full diff {(old_output - new_output).float().abs().sum()}")
return new_vae

4
scripts/convert_consistency_to_diffusers.py
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@@ -239,7 +239,7 @@ def con_pt_to_diffuser(checkpoint_path: str, unet_config):
if i != len(up_block_types) - 1:
new_prefix = f"up_blocks.{i}.upsamplers.0"
old_prefix = f"output_blocks.{current_layer-1}.1"
old_prefix = f"output_blocks.{current_layer - 1}.1"
new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1):
@@ -255,7 +255,7 @@ def con_pt_to_diffuser(checkpoint_path: str, unet_config):
if i != len(up_block_types) - 1:
new_prefix = f"up_blocks.{i}.upsamplers.0"
old_prefix = f"output_blocks.{current_layer-1}.2"
old_prefix = f"output_blocks.{current_layer - 1}.2"
new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"]

12
scripts/convert_dance_diffusion_to_diffusers.py
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@@ -261,9 +261,9 @@ def main(args):
model_name = args.model_path.split("/")[-1].split(".")[0]
if not os.path.isfile(args.model_path):
assert (
model_name == args.model_path
), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
assert model_name == args.model_path, (
f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
)
args.model_path = download(model_name)
sample_rate = MODELS_MAP[model_name]["sample_rate"]
@@ -290,9 +290,9 @@ def main(args):
assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
assert diffusers_state_dict[key].squeeze().shape == value.squeeze().shape, (
f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
)
if key == "time_proj.weight":
value = value.squeeze()

18
scripts/convert_diffusers_to_original_sdxl.py
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@@ -52,18 +52,18 @@ for i in range(3):
for j in range(2):
# loop over resnets/attentions for downblocks
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
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))
if i > 0:
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
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))
for j in range(4):
# loop over resnets/attentions for upblocks
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i < 2:
@@ -75,12 +75,12 @@ for i in range(3):
if i < 3:
# no downsample in down_blocks.3
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv."))
@@ -89,7 +89,7 @@ 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}."
sd_mid_res_prefix = f"middle_block.{2 * j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
@@ -137,20 +137,20 @@ for i in range(4):
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"up.{3-i}.upsample."
sd_upsample_prefix = f"up.{3 - i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{i}."
sd_mid_res_prefix = f"mid.block_{i+1}."
sd_mid_res_prefix = f"mid.block_{i + 1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))

20
scripts/convert_diffusers_to_original_stable_diffusion.py
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@@ -47,36 +47,36 @@ for i in range(4):
for j in range(2):
# loop over resnets/attentions for downblocks
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
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))
if i < 3:
# no attention layers in down_blocks.3
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
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))
for j in range(3):
# loop over resnets/attentions for upblocks
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
hf_mid_atn_prefix = "mid_block.attentions.0."
@@ -85,7 +85,7 @@ 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}."
sd_mid_res_prefix = f"middle_block.{2 * j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
@@ -133,20 +133,20 @@ for i in range(4):
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"up.{3-i}.upsample."
sd_upsample_prefix = f"up.{3 - i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{i}."
sd_mid_res_prefix = f"mid.block_{i+1}."
sd_mid_res_prefix = f"mid.block_{i + 1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))

6
scripts/convert_hunyuandit_controlnet_to_diffusers.py
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@@ -21,9 +21,9 @@ def main(args):
model_config = HunyuanDiT2DControlNetModel.load_config(
"Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", subfolder="transformer"
)
model_config[
"use_style_cond_and_image_meta_size"
] = args.use_style_cond_and_image_meta_size ### version <= v1.1: True; version >= v1.2: False
model_config["use_style_cond_and_image_meta_size"] = (
args.use_style_cond_and_image_meta_size
) ### version <= v1.1: True; version >= v1.2: False
print(model_config)
for key in state_dict:

9
scripts/convert_hunyuandit_to_diffusers.py
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@@ -13,15 +13,14 @@ def main(args):
state_dict = state_dict[args.load_key]
except KeyError:
raise KeyError(
f"{args.load_key} not found in the checkpoint."
f"Please load from the following keys:{state_dict.keys()}"
f"{args.load_key} not found in the checkpoint.Please load from the following keys:{state_dict.keys()}"
)
device = "cuda"
model_config = HunyuanDiT2DModel.load_config("Tencent-Hunyuan/HunyuanDiT-Diffusers", subfolder="transformer")
model_config[
"use_style_cond_and_image_meta_size"
] = args.use_style_cond_and_image_meta_size ### version <= v1.1: True; version >= v1.2: False
model_config["use_style_cond_and_image_meta_size"] = (
args.use_style_cond_and_image_meta_size
) ### version <= v1.1: True; version >= v1.2: False
# input_size -> sample_size, text_dim -> cross_attention_dim
for key in state_dict:

10
scripts/convert_k_upscaler_to_diffusers.py
View File

@@ -142,14 +142,14 @@ def block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type):
diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
idx = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
self_attention_prefix = f"{block_prefix}.{idx}"
cross_attention_prefix = f"{block_prefix}.{idx }"
cross_attention_prefix = f"{block_prefix}.{idx}"
cross_attention_index = 1 if not attention.add_self_attention else 2
idx = (
n * attention_idx + cross_attention_index
if block_type == "up"
else n * attention_idx + cross_attention_index + 1
)
cross_attention_prefix = f"{block_prefix}.{idx }"
cross_attention_prefix = f"{block_prefix}.{idx}"
diffusers_checkpoint.update(
cross_attn_to_diffusers_checkpoint(
@@ -220,9 +220,9 @@ def unet_model_from_original_config(original_config):
block_out_channels = original_config["channels"]
assert (
len(set(original_config["depths"])) == 1
), "UNet2DConditionModel currently do not support blocks with different number of layers"
assert len(set(original_config["depths"])) == 1, (
"UNet2DConditionModel currently do not support blocks with different number of layers"
)
layers_per_block = original_config["depths"][0]
class_labels_dim = original_config["mapping_cond_dim"]

118
scripts/convert_mochi_to_diffusers.py
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@@ -168,28 +168,28 @@ def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_pa
# Convert block_in (MochiMidBlock3D)
for i in range(3): # layers_per_block[-1] = 3
new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.0.weight"
f"blocks.0.{i + 1}.stack.0.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.0.bias"
f"blocks.0.{i + 1}.stack.0.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.2.weight"
f"blocks.0.{i + 1}.stack.2.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.2.bias"
f"blocks.0.{i + 1}.stack.2.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.3.weight"
f"blocks.0.{i + 1}.stack.3.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.3.bias"
f"blocks.0.{i + 1}.stack.3.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.5.weight"
f"blocks.0.{i + 1}.stack.5.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
f"blocks.0.{i+1}.stack.5.bias"
f"blocks.0.{i + 1}.stack.5.bias"
)
# Convert up_blocks (MochiUpBlock3D)
@@ -197,33 +197,35 @@ def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_pa
for block in range(3):
for i in range(down_block_layers[block]):
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.0.weight"
f"blocks.{block + 1}.blocks.{i}.stack.0.weight"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.0.bias"
f"blocks.{block + 1}.blocks.{i}.stack.0.bias"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.2.weight"
f"blocks.{block + 1}.blocks.{i}.stack.2.weight"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.2.bias"
f"blocks.{block + 1}.blocks.{i}.stack.2.bias"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.3.weight"
f"blocks.{block + 1}.blocks.{i}.stack.3.weight"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.3.bias"
f"blocks.{block + 1}.blocks.{i}.stack.3.bias"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.5.weight"
f"blocks.{block + 1}.blocks.{i}.stack.5.weight"
)
new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
f"blocks.{block+1}.blocks.{i}.stack.5.bias"
f"blocks.{block + 1}.blocks.{i}.stack.5.bias"
)
new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = decoder_state_dict.pop(
f"blocks.{block+1}.proj.weight"
f"blocks.{block + 1}.proj.weight"
)
new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop(
f"blocks.{block + 1}.proj.bias"
)
new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop(f"blocks.{block+1}.proj.bias")
# Convert block_out (MochiMidBlock3D)
for i in range(3): # layers_per_block[0] = 3
@@ -267,133 +269,133 @@ def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_pa
# Convert block_in (MochiMidBlock3D)
for i in range(3): # layers_per_block[0] = 3
new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.0.weight"
f"layers.{i + 1}.stack.0.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.0.bias"
f"layers.{i + 1}.stack.0.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.2.weight"
f"layers.{i + 1}.stack.2.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.2.bias"
f"layers.{i + 1}.stack.2.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.3.weight"
f"layers.{i + 1}.stack.3.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.3.bias"
f"layers.{i + 1}.stack.3.bias"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.5.weight"
f"layers.{i + 1}.stack.5.weight"
)
new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
f"layers.{i+1}.stack.5.bias"
f"layers.{i + 1}.stack.5.bias"
)
# Convert down_blocks (MochiDownBlock3D)
down_block_layers = [3, 4, 6] # layers_per_block[1], layers_per_block[2], layers_per_block[3]
for block in range(3):
new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.weight"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.0.weight"
f"layers.{block + 4}.layers.0.weight"
)
new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.0.bias"
f"layers.{block + 4}.layers.0.bias"
)
for i in range(down_block_layers[block]):
# Convert resnets
new_state_dict[
f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"
] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.0.weight")
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = (
encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.0.weight")
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.0.bias"
f"layers.{block + 4}.layers.{i + 1}.stack.0.bias"
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.2.weight"
f"layers.{block + 4}.layers.{i + 1}.stack.2.weight"
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.2.bias"
f"layers.{block + 4}.layers.{i + 1}.stack.2.bias"
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = (
encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.3.weight")
)
new_state_dict[
f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"
] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.3.weight")
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.3.bias"
f"layers.{block + 4}.layers.{i + 1}.stack.3.bias"
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.5.weight"
f"layers.{block + 4}.layers.{i + 1}.stack.5.weight"
)
new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.stack.5.bias"
f"layers.{block + 4}.layers.{i + 1}.stack.5.bias"
)
# Convert attentions
qkv_weight = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight")
qkv_weight = encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.qkv.weight")
q, k, v = qkv_weight.chunk(3, dim=0)
new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"] = q
new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"] = k
new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"] = v
new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"
f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.weight"
)
new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"
f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.bias"
)
new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"
f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.weight"
)
new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"
f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.bias"
)
# Convert block_out (MochiMidBlock3D)
for i in range(3): # layers_per_block[-1] = 3
# Convert resnets
new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.0.weight"
f"layers.{i + 7}.stack.0.weight"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.0.bias"
f"layers.{i + 7}.stack.0.bias"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.2.weight"
f"layers.{i + 7}.stack.2.weight"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.2.bias"
f"layers.{i + 7}.stack.2.bias"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.3.weight"
f"layers.{i + 7}.stack.3.weight"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.3.bias"
f"layers.{i + 7}.stack.3.bias"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.5.weight"
f"layers.{i + 7}.stack.5.weight"
)
new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.stack.5.bias"
f"layers.{i + 7}.stack.5.bias"
)
# Convert attentions
qkv_weight = encoder_state_dict.pop(f"layers.{i+7}.attn_block.attn.qkv.weight")
qkv_weight = encoder_state_dict.pop(f"layers.{i + 7}.attn_block.attn.qkv.weight")
q, k, v = qkv_weight.chunk(3, dim=0)
new_state_dict[f"{prefix}block_out.attentions.{i}.to_q.weight"] = q
new_state_dict[f"{prefix}block_out.attentions.{i}.to_k.weight"] = k
new_state_dict[f"{prefix}block_out.attentions.{i}.to_v.weight"] = v
new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.attn_block.attn.out.weight"
f"layers.{i + 7}.attn_block.attn.out.weight"
)
new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.attn_block.attn.out.bias"
f"layers.{i + 7}.attn_block.attn.out.bias"
)
new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
f"layers.{i+7}.attn_block.norm.weight"
f"layers.{i + 7}.attn_block.norm.weight"
)
new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
f"layers.{i+7}.attn_block.norm.bias"
f"layers.{i + 7}.attn_block.norm.bias"
)
# Convert output layers

2
scripts/convert_original_audioldm2_to_diffusers.py
View File

@@ -662,7 +662,7 @@ def convert_open_clap_checkpoint(checkpoint):
# replace sequential layers with list
sequential_layer = re.match(sequential_layers_pattern, key).group(1)
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer)//3}.linear.")
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.")
elif re.match(text_projection_pattern, key):
projecton_layer = int(re.match(text_projection_pattern, key).group(1))

2
scripts/convert_original_audioldm_to_diffusers.py
View File

@@ -636,7 +636,7 @@ def convert_open_clap_checkpoint(checkpoint):
# replace sequential layers with list
sequential_layer = re.match(sequential_layers_pattern, key).group(1)
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer)//3}.linear.")
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.")
elif re.match(text_projection_pattern, key):
projecton_layer = int(re.match(text_projection_pattern, key).group(1))

2
scripts/convert_original_musicldm_to_diffusers.py
View File

@@ -642,7 +642,7 @@ def convert_open_clap_checkpoint(checkpoint):
# replace sequential layers with list
sequential_layer = re.match(sequential_layers_pattern, key).group(1)
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer)//3}.linear.")
key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.")
elif re.match(text_projection_pattern, key):
projecton_layer = int(re.match(text_projection_pattern, key).group(1))

18
scripts/convert_stable_audio.py
View File

@@ -95,18 +95,18 @@ def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_lay
# get idx of the layer
idx = int(new_key.split("coder.layers.")[1].split(".")[0])
new_key = new_key.replace(f"coder.layers.{idx}", f"coder.block.{idx-1}")
new_key = new_key.replace(f"coder.layers.{idx}", f"coder.block.{idx - 1}")
if "encoder" in new_key:
for i in range(3):
new_key = new_key.replace(f"block.{idx-1}.layers.{i}", f"block.{idx-1}.res_unit{i+1}")
new_key = new_key.replace(f"block.{idx-1}.layers.3", f"block.{idx-1}.snake1")
new_key = new_key.replace(f"block.{idx-1}.layers.4", f"block.{idx-1}.conv1")
new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i + 1}")
new_key = new_key.replace(f"block.{idx - 1}.layers.3", f"block.{idx - 1}.snake1")
new_key = new_key.replace(f"block.{idx - 1}.layers.4", f"block.{idx - 1}.conv1")
else:
for i in range(2, 5):
new_key = new_key.replace(f"block.{idx-1}.layers.{i}", f"block.{idx-1}.res_unit{i-1}")
new_key = new_key.replace(f"block.{idx-1}.layers.0", f"block.{idx-1}.snake1")
new_key = new_key.replace(f"block.{idx-1}.layers.1", f"block.{idx-1}.conv_t1")
new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i - 1}")
new_key = new_key.replace(f"block.{idx - 1}.layers.0", f"block.{idx - 1}.snake1")
new_key = new_key.replace(f"block.{idx - 1}.layers.1", f"block.{idx - 1}.conv_t1")
new_key = new_key.replace("layers.0.beta", "snake1.beta")
new_key = new_key.replace("layers.0.alpha", "snake1.alpha")
@@ -118,9 +118,9 @@ def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_lay
new_key = new_key.replace("layers.3.weight_", "conv2.weight_")
if idx == num_autoencoder_layers + 1:
new_key = new_key.replace(f"block.{idx-1}", "snake1")
new_key = new_key.replace(f"block.{idx - 1}", "snake1")
elif idx == num_autoencoder_layers + 2:
new_key = new_key.replace(f"block.{idx-1}", "conv2")
new_key = new_key.replace(f"block.{idx - 1}", "conv2")
else:
new_key = new_key

12
scripts/convert_svd_to_diffusers.py
View File

@@ -381,9 +381,9 @@ def convert_ldm_unet_checkpoint(
# TODO resnet time_mixer.mix_factor
if f"input_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
new_checkpoint[
f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
] = unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"]
new_checkpoint[f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = (
unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"]
)
if len(attentions):
paths = renew_attention_paths(attentions)
@@ -478,9 +478,9 @@ def convert_ldm_unet_checkpoint(
)
if f"output_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
new_checkpoint[
f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
] = unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"]
new_checkpoint[f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = (
unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"]
)
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
if ["conv.bias", "conv.weight"] in output_block_list.values():

24
scripts/convert_vq_diffusion_to_diffusers.py
View File

@@ -51,9 +51,9 @@ PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchV
def vqvae_model_from_original_config(original_config):
assert (
original_config["target"] in PORTED_VQVAES
), f"{original_config['target']} has not yet been ported to diffusers."
assert original_config["target"] in PORTED_VQVAES, (
f"{original_config['target']} has not yet been ported to diffusers."
)
original_config = original_config["params"]
@@ -464,15 +464,15 @@ PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_ima
def transformer_model_from_original_config(
original_diffusion_config, original_transformer_config, original_content_embedding_config
):
assert (
original_diffusion_config["target"] in PORTED_DIFFUSIONS
), f"{original_diffusion_config['target']} has not yet been ported to diffusers."
assert (
original_transformer_config["target"] in PORTED_TRANSFORMERS
), f"{original_transformer_config['target']} has not yet been ported to diffusers."
assert (
original_content_embedding_config["target"] in PORTED_CONTENT_EMBEDDINGS
), f"{original_content_embedding_config['target']} has not yet been ported to diffusers."
assert original_diffusion_config["target"] in PORTED_DIFFUSIONS, (
f"{original_diffusion_config['target']} has not yet been ported to diffusers."
)
assert original_transformer_config["target"] in PORTED_TRANSFORMERS, (
f"{original_transformer_config['target']} has not yet been ported to diffusers."
)
assert original_content_embedding_config["target"] in PORTED_CONTENT_EMBEDDINGS, (
f"{original_content_embedding_config['target']} has not yet been ported to diffusers."
)
original_diffusion_config = original_diffusion_config["params"]
original_transformer_config = original_transformer_config["params"]