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# For using the diffusers format weights
# Based on the original ComfyUI function +
# https://github.com/PixArt-alpha/PixArt-alpha/blob/master/tools/convert_pixart_alpha_to_diffusers.py
import torch
conversion_map_ms = [ # for multi_scale_train (MS)
# Resolution
("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
# Aspect ratio
("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
]
def get_depth(state_dict):
return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
def get_lora_depth(state_dict):
return sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys())
def get_conversion_map(state_dict):
conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers)
# Patch embeddings
("x_embedder.proj.weight", "pos_embed.proj.weight"),
("x_embedder.proj.bias", "pos_embed.proj.bias"),
# Caption projection
("y_embedder.y_embedding", "caption_projection.y_embedding"),
("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
# AdaLN-single LN
("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
# Shared norm
("t_block.1.weight", "adaln_single.linear.weight"),
("t_block.1.bias", "adaln_single.linear.bias"),
# Final block
("final_layer.linear.weight", "proj_out.weight"),
("final_layer.linear.bias", "proj_out.bias"),
("final_layer.scale_shift_table", "scale_shift_table"),
]
# Add actual transformer blocks
for depth in range(get_depth(state_dict)):
# Transformer blocks
conversion_map += [
(f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
# Projection
(f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
(f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
# Feed-forward
(f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
(f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
(f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
(f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"),
# Cross-attention (proj)
(f"blocks.{depth}.cross_attn.proj.weight", f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
(f"blocks.{depth}.cross_attn.proj.bias", f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
]
return conversion_map
def find_prefix(state_dict, target_key):
prefix = ""
for k in state_dict.keys():
if k.endswith(target_key):
prefix = k.split(target_key)[0]
break
return prefix
def convert_state_dict(state_dict):
if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
cmap = get_conversion_map(state_dict) + conversion_map_ms
else:
cmap = get_conversion_map(state_dict)
missing = [k for k, v in cmap if v not in state_dict]
new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing}
matched = list(v for k, v in cmap if v in state_dict.keys())
for depth in range(get_depth(state_dict)):
for wb in ["weight", "bias"]:
# Self Attention
key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
), dim=0)
matched += [key('q'), key('k'), key('v')]
# Cross-attention (linear)
key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
state_dict[key('k')], state_dict[key('v')]
), dim=0)
matched += [key('q'), key('k'), key('v')]
if len(matched) < len(state_dict):
print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(list(set(state_dict.keys()) - set(matched)))
if len(missing) > 0:
print(f"PixArt: UNET conversion has missing keys!")
print(missing)
return new_state_dict
# Same as above but for LoRA weights:
def convert_lora_state_dict(state_dict, peft=True):
# koyha
rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
rep_bk = lambda x: x.replace(".weight", ".lora_up.weight")
rep_pk = lambda x: x.replace(".weight", ".alpha")
if peft: # peft
rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
rep_pp = lambda x: x.replace(".weight", ".alpha")
prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
state_dict = {k[len(prefix):]: v for k, v in state_dict.items()}
else: # OneTrainer
rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight"
rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight"
rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
prefix = "lora_transformer_"
t5_marker = "lora_te_encoder"
t5_keys = []
for key in list(state_dict.keys()):
if key.startswith(prefix):
state_dict[key[len(prefix):]] = state_dict.pop(key)
elif t5_marker in key:
t5_keys.append(state_dict.pop(key))
if len(t5_keys) > 0:
print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
cmap = []
cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model
for k, v in cmap_unet:
if v.endswith(".weight"):
cmap.append((rep_ak(k), rep_ap(v)))
cmap.append((rep_bk(k), rep_bp(v)))
if not peft:
cmap.append((rep_pk(k), rep_pp(v)))
missing = [k for k, v in cmap if v not in state_dict]
new_state_dict = {k: state_dict[v] for k, v in cmap if k not in missing}
matched = list(v for k, v in cmap if v in state_dict.keys())
lora_depth = get_lora_depth(state_dict)
for fp, fk in ((rep_ap, rep_ak), (rep_bp, rep_bk)):
for depth in range(lora_depth):
# Self Attention
key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat((
state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
), dim=0)
matched += [key('q'), key('k'), key('v')]
if not peft:
akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")]
matched += [akey('q'), akey('k'), akey('v')]
# Self Attention projection?
key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')]
matched += [key('out.0')]
# Cross-attention (linear)
key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")] = state_dict[key('q')]
new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat((
state_dict[key('k')], state_dict[key('v')]
), dim=0)
matched += [key('q'), key('k'), key('v')]
if not peft:
akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))] = state_dict[akey("q")]
new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")]
matched += [akey('q'), akey('k'), akey('v')]
# Cross Attention projection?
key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')]
matched += [key('out.0')]
key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight")
new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")] = state_dict[key]
matched += [key]
key = fp(f"transformer_blocks.{depth}.ff.net.2.weight")
new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")] = state_dict[key]
matched += [key]
if len(matched) < len(state_dict):
print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(list(set(state_dict.keys()) - set(matched)))
if len(missing) > 0:
print(f"PixArt: LoRA conversion has missing keys! (probably)")
print(missing)
return new_state_dict |