mirror of
https://github.com/ostris/ai-toolkit.git
synced 2026-02-04 04:38:57 +00:00
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
import os
|
|
|
|
import torch
|
|
from transformers import T5EncoderModel, T5Tokenizer
|
|
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
|
|
from safetensors.torch import load_file, save_file
|
|
from collections import OrderedDict
|
|
import json
|
|
|
|
model_path = "/mnt/Models/stable-diffusion/models/stable-diffusion/Ostris/objective_reality_v2.safetensors"
|
|
te_path = "google/flan-t5-xl"
|
|
te_aug_path = "/mnt/Train/out/ip_adapter/t5xx_sd15_v1/t5xx_sd15_v1_000032000.safetensors"
|
|
output_path = "/home/jaret/Dev/models/hf/t5xl_sd15_v1"
|
|
|
|
print("Loading te adapter")
|
|
te_aug_sd = load_file(te_aug_path)
|
|
|
|
print("Loading model")
|
|
sd = StableDiffusionPipeline.from_single_file(model_path, torch_dtype=torch.float16)
|
|
|
|
print("Loading Text Encoder")
|
|
# Load the text encoder
|
|
te = T5EncoderModel.from_pretrained(te_path, torch_dtype=torch.float16)
|
|
|
|
# patch it
|
|
sd.text_encoder = te
|
|
sd.tokenizer = T5Tokenizer.from_pretrained(te_path)
|
|
|
|
unet_sd = sd.unet.state_dict()
|
|
|
|
weight_idx = 1
|
|
|
|
new_cross_attn_dim = None
|
|
|
|
print("Patching UNet")
|
|
for name in sd.unet.attn_processors.keys():
|
|
cross_attention_dim = None if name.endswith("attn1.processor") else sd.unet.config['cross_attention_dim']
|
|
if name.startswith("mid_block"):
|
|
hidden_size = sd.unet.config['block_out_channels'][-1]
|
|
elif name.startswith("up_blocks"):
|
|
block_id = int(name[len("up_blocks.")])
|
|
hidden_size = list(reversed(sd.unet.config['block_out_channels']))[block_id]
|
|
elif name.startswith("down_blocks"):
|
|
block_id = int(name[len("down_blocks.")])
|
|
hidden_size = sd.unet.config['block_out_channels'][block_id]
|
|
else:
|
|
# they didnt have this, but would lead to undefined below
|
|
raise ValueError(f"unknown attn processor name: {name}")
|
|
if cross_attention_dim is None:
|
|
pass
|
|
else:
|
|
layer_name = name.split(".processor")[0]
|
|
to_k_adapter = unet_sd[layer_name + ".to_k.weight"]
|
|
to_v_adapter = unet_sd[layer_name + ".to_v.weight"]
|
|
|
|
te_aug_name = None
|
|
while True:
|
|
te_aug_name = f"te_adapter.adapter_modules.{weight_idx}.to_k_adapter"
|
|
if f"{te_aug_name}.weight" in te_aug_sd:
|
|
# increment so we dont redo it next time
|
|
weight_idx += 1
|
|
break
|
|
else:
|
|
weight_idx += 1
|
|
|
|
if weight_idx > 1000:
|
|
raise ValueError("Could not find the next weight")
|
|
|
|
unet_sd[layer_name + ".to_k.weight"] = te_aug_sd[te_aug_name + ".weight"]
|
|
unet_sd[layer_name + ".to_v.weight"] = te_aug_sd[te_aug_name.replace('to_k', 'to_v') + ".weight"]
|
|
|
|
if new_cross_attn_dim is None:
|
|
new_cross_attn_dim = unet_sd[layer_name + ".to_k.weight"].shape[1]
|
|
|
|
|
|
print("Saving unmodified model")
|
|
sd.save_pretrained(
|
|
output_path,
|
|
safe_serialization=True,
|
|
)
|
|
|
|
# overwrite the unet
|
|
unet_folder = os.path.join(output_path, "unet")
|
|
|
|
# move state_dict to cpu
|
|
unet_sd = {k: v.clone().cpu().to(torch.float16) for k, v in unet_sd.items()}
|
|
|
|
meta = OrderedDict()
|
|
meta["format"] = "pt"
|
|
|
|
print("Patching new unet")
|
|
|
|
save_file(unet_sd, os.path.join(unet_folder, "diffusion_pytorch_model.safetensors"), meta)
|
|
|
|
# load the json file
|
|
with open(os.path.join(unet_folder, "config.json"), 'r') as f:
|
|
config = json.load(f)
|
|
|
|
config['cross_attention_dim'] = new_cross_attn_dim
|
|
|
|
# save it
|
|
with open(os.path.join(unet_folder, "config.json"), 'w') as f:
|
|
json.dump(config, f, indent=2)
|
|
|
|
print("Done")
|