lint LoRA code and extras, replace public paths with context ones
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@ -1,10 +1,5 @@
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{
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"diffusion": [
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{
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"name": "diffusion-ugly-sonic",
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"source": "runwayml/stable-diffusion-v1-5",
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"inversion": "sd-concepts-library/ugly-sonic"
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},
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{
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"name": "diffusion-knollingcase",
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"source": "Aybeeceedee/knollingcase"
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@ -42,6 +37,10 @@
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{
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"name": "minecraft",
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"source": "sd-concepts-library/minecraft-concept-art"
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},
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{
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"name": "ugly-sonic",
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"source": "sd-concepts-library/ugly-sonic"
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}
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]
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}
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@ -1,17 +1,24 @@
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from logging import getLogger
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from os import path
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from sys import argv
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from typing import List, Tuple
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import onnx.checker
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import torch
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from numpy import ndarray
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from onnx import ModelProto, TensorProto, helper, load, numpy_helper, save_model
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from sys import argv
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from safetensors import safe_open
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import torch
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import onnx.checker
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from ..utils import ConversionContext
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logger = getLogger(__name__)
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###
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# everything in this file is still super experimental and may not produce valid ONNX models
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###
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def load_lora(filename: str):
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model = load(filename)
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@ -51,13 +58,13 @@ def blend_loras(
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return results
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def convert_diffusion_lora(part: str):
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def convert_diffusion_lora(context: ConversionContext, component: str):
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lora_weights = [
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f"diffusion-lora-jack/{part}/model.onnx",
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f"diffusion-lora-taters/{part}/model.onnx",
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f"diffusion-lora-jack/{component}/model.onnx",
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f"diffusion-lora-taters/{component}/model.onnx",
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]
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base = load_lora(f"stable-diffusion-onnx-v1-5/{part}/model.onnx")
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base = load_lora(f"stable-diffusion-onnx-v1-5/{component}/model.onnx")
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weights = [load_lora(f) for f in lora_weights]
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alphas = [1 / len(weights)] * len(weights)
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logger.info("blending LoRAs with alphas: %s, %s", weights, alphas)
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@ -91,17 +98,19 @@ def convert_diffusion_lora(part: str):
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opset = model.opset_import.add()
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opset.version = 14
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onnx_path = path.join(context.cache_path, f"lora-{component}.onnx")
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tensor_path = path.join(context.cache_path, f"lora-{component}.tensors")
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save_model(
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model,
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f"/tmp/lora-{part}.onnx",
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onnx_path,
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save_as_external_data=True,
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all_tensors_to_one_file=True,
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location=f"/tmp/lora-{part}.tensors",
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location=tensor_path,
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)
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logger.info(
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"saved model to %s and tensors to %s",
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f"/tmp/lora-{part}.onnx",
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f"/tmp/lora-{part}.tensors",
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onnx_path,
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tensor_path,
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)
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@ -124,40 +133,59 @@ def merge_lora():
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for key in lora_model.keys():
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if "lora_down" in key:
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lora_key = key[:key.index("lora_down")].replace("lora_unet_", "")
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lora_key = key[: key.index("lora_down")].replace("lora_unet_", "")
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if lora_key.startswith(base_key):
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print("down for key:", base_key, lora_key)
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up_key = key.replace("lora_down", "lora_up")
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alpha_key = key[:key.index("lora_down")] + 'alpha'
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alpha_key = key[: key.index("lora_down")] + "alpha"
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down_weight = lora_model.get_tensor(key).to(dtype=torch.float32)
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up_weight = lora_model.get_tensor(up_key).to(dtype=torch.float32)
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dim = down_weight.size()[0]
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alpha = lora_model.get(alpha_key).numpy() or dim
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scale = alpha / dim
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np_vals = numpy_helper.to_array(base_node)
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print(np_vals.shape, up_weight.shape, down_weight.shape)
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squoze = (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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squoze = (
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(
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up_weight.squeeze(3).squeeze(2)
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@ down_weight.squeeze(3).squeeze(2)
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)
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.unsqueeze(2)
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.unsqueeze(3)
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)
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print(squoze.shape)
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np_vals = np_vals + (alpha * squoze.numpy())
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try:
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if len(up_weight.size()) == 2:
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squoze = (up_weight @ down_weight)
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squoze = up_weight @ down_weight
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print(squoze.shape)
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np_vals = np_vals + (squoze.numpy() * (alpha / dim))
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else:
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squoze = (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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squoze = (
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(
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up_weight.squeeze(3).squeeze(2)
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@ down_weight.squeeze(3).squeeze(2)
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)
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.unsqueeze(2)
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.unsqueeze(3)
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)
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print(squoze.shape)
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np_vals = np_vals + (alpha * squoze.numpy())
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# retensor = numpy_helper.from_array(np_vals, base_node.name)
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retensor = helper.make_tensor(base_node.name, base_node.data_type, base_node.dim, np_vals, raw=True)
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retensor = helper.make_tensor(
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base_node.name,
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base_node.data_type,
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base_node.dim,
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np_vals,
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raw=True,
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)
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print(retensor)
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# TypeError: does not support assignment
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@ -167,7 +195,6 @@ def merge_lora():
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except Exception as e:
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print(e)
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if retensor is None:
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print("no lora found for key", base_key)
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lora_nodes.append(base_node)
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@ -179,7 +206,6 @@ def merge_lora():
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onnx.checker.check_model(base_model)
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if __name__ == "__main__":
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convert_diffusion_lora("unet")
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convert_diffusion_lora("text_encoder")
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