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Configuration error
Configuration error
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import os
import copy
import json
import torch
import comfy.lora
import comfy.model_management
from comfy.model_patcher import ModelPatcher
from .diffusers_convert import convert_lora_state_dict
class EXM_PixArt_ModelPatcher(ModelPatcher):
def calculate_weight(self, patches, weight, key):
"""
This is almost the same as the comfy function, but stripped down to just the LoRA patch code.
The problem with the original code is the q/k/v keys being combined into one for the attention.
In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv).
This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict.
The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead.
This is the best thing I could think of that would fix that, but it's very fragile.
- Check key shape to determine if it needs the fallback logic
- Cut the input into parts based on the shape (undoing the torch.cat)
- Do the matrix multiplication logic
- Recombine them to match the expected shape
"""
for p in patches:
alpha = p[0]
v = p[1]
strength_model = p[2]
if strength_model != 1.0:
weight *= strength_model
if isinstance(v, list):
v = (self.calculate_weight(v[1:], v[0].clone(), key), )
if len(v) == 2:
patch_type = v[0]
v = v[1]
if patch_type == "lora":
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
if v[2] is not None:
alpha *= v[2] / mat2.shape[0]
try:
mat1 = mat1.flatten(start_dim=1)
mat2 = mat2.flatten(start_dim=1)
ch1 = mat1.shape[0] // mat2.shape[1]
ch2 = mat2.shape[0] // mat1.shape[1]
### Fallback logic for shape mismatch ###
if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0]/mat2.shape[1])%1 == 0:
mat1 = mat1.chunk(ch1, dim=0)
mat2 = mat2.chunk(ch1, dim=0)
weight += torch.cat(
[alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)],
dim=0,
).reshape(weight.shape).type(weight.dtype)
else:
weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
return weight
def clone(self):
n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
n.patches = {}
for k in self.patches:
n.patches[k] = self.patches[k][:]
n.object_patches = self.object_patches.copy()
n.model_options = copy.deepcopy(self.model_options)
n.model_keys = self.model_keys
return n
def replace_model_patcher(model):
n = EXM_PixArt_ModelPatcher(
model = model.model,
size = model.size,
load_device = model.load_device,
offload_device = model.offload_device,
current_device = model.current_device,
weight_inplace_update = model.weight_inplace_update,
)
n.patches = {}
for k in model.patches:
n.patches[k] = model.patches[k][:]
n.object_patches = model.object_patches.copy()
n.model_options = copy.deepcopy(model.model_options)
return n
def find_peft_alpha(path):
def load_json(json_path):
with open(json_path) as f:
data = json.load(f)
alpha = data.get("lora_alpha")
alpha = alpha or data.get("alpha")
if not alpha:
print(" Found config but `lora_alpha` is missing!")
else:
print(f" Found config at {json_path} [alpha:{alpha}]")
return alpha
# For some weird reason peft doesn't include the alpha in the actual model
print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...")
files = [
f"{os.path.splitext(path)[0]}.json",
f"{os.path.splitext(path)[0]}.config.json",
os.path.join(os.path.dirname(path),"adapter_config.json"),
]
for file in files:
if os.path.isfile(file):
return load_json(file)
print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.")
return 8.0
def load_pixart_lora(model, lora, lora_path, strength):
k_back = lambda x: x.replace(".lora_up.weight", "")
# need to convert the actual weights for this to work.
if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
lora = convert_lora_state_dict(lora, peft=True)
alpha = find_peft_alpha(lora_path)
lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
else: # OneTrainer
lora = convert_lora_state_dict(lora, peft=False)
key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake
loaded = comfy.lora.load_lora(lora, key_map)
if model is not None:
# switch to custom model patcher when using LoRAs
if isinstance(model, EXM_PixArt_ModelPatcher):
new_modelpatcher = model.clone()
else:
new_modelpatcher = replace_model_patcher(model)
k = new_modelpatcher.add_patches(loaded, strength)
else:
k = ()
new_modelpatcher = None
k = set(k)
for x in loaded:
if (x not in k):
print("NOT LOADED", x)
return new_modelpatcher
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