add none option to inversion menu
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@ -22,6 +22,28 @@
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"name": "diffusion-unstable-ink-dream-v6",
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"source": "civitai://5796",
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"format": "safetensors"
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},
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{
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"name": "stable-diffusion-onnx-v1-5",
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"source": "runwayml/stable-diffusion-v1-5",
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"inversions": [
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{
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"name": "line-art",
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"source": "sd-concepts-library/line-art"
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},
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{
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"name": "cubex",
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"source": "sd-concepts-library/cubex"
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},
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{
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"name": "birb",
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"source": "sd-concepts-library/birb-style"
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},
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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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}
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],
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"correction": [],
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@ -10,8 +10,8 @@ from jsonschema import ValidationError, validate
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from yaml import safe_load
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from .correction_gfpgan import convert_correction_gfpgan
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from .diffusion.original import convert_diffusion_original
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from .diffusion.diffusers import convert_diffusion_diffusers
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from .diffusion.original import convert_diffusion_original
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from .diffusion.textual_inversion import convert_diffusion_textual_inversion
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from .upscale_resrgan import convert_upscale_resrgan
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from .utils import (
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@ -233,8 +233,12 @@ def convert_models(ctx: ConversionContext, args, models: Models):
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for inversion in model.get("inversions", []):
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inversion_name = inversion["name"]
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inversion_source = inversion["source"]
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inversion_source = fetch_model(ctx, f"{name}-inversion-{inversion_name}", inversion_source)
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convert_diffusion_textual_inversion(ctx, inversion_name, model["source"], inversion_source)
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inversion_source = fetch_model(
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ctx, f"{name}-inversion-{inversion_name}", inversion_source
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)
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convert_diffusion_textual_inversion(
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ctx, inversion_name, model["source"], inversion_source
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)
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except Exception as e:
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logger.error("error converting diffusion model %s: %s", name, e)
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@ -1,94 +1,105 @@
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from numpy import ndarray
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from onnx import TensorProto, helper, load, numpy_helper, ModelProto, save_model
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from typing import Dict, List, Tuple
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from logging import getLogger
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from typing import List, Tuple
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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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logger = getLogger(__name__)
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def load_lora(filename: str):
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model = load(filename)
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model = load(filename)
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for weight in model.graph.initializer:
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# print(weight.name, numpy_helper.to_array(weight).shape)
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pass
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for weight in model.graph.initializer:
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# print(weight.name, numpy_helper.to_array(weight).shape)
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pass
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return model
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return model
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def blend_loras(base: ModelProto, weights: List[ModelProto], alphas: List[float]) -> List[Tuple[TensorProto, ndarray]]:
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total = 1 + sum(alphas)
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def blend_loras(
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base: ModelProto, weights: List[ModelProto], alphas: List[float]
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) -> List[Tuple[TensorProto, ndarray]]:
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total = 1 + sum(alphas)
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results = []
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results = []
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for base_node in base.graph.initializer:
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logger.info("blending initializer node %s", base_node.name)
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base_weights = numpy_helper.to_array(base_node).copy()
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for base_node in base.graph.initializer:
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logger.info("blending initializer node %s", base_node.name)
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base_weights = numpy_helper.to_array(base_node).copy()
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for weight, alpha in zip(weights, alphas):
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weight_node = next(iter([f for f in weight.graph.initializer if f.name == base_node.name]), None)
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for weight, alpha in zip(weights, alphas):
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weight_node = next(
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iter([f for f in weight.graph.initializer if f.name == base_node.name]),
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None,
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)
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if weight_node is not None:
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base_weights += numpy_helper.to_array(weight_node) * alpha
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else:
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logger.warning("missing weights: %s in %s", base_node.name, weight.doc_string)
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if weight_node is not None:
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base_weights += numpy_helper.to_array(weight_node) * alpha
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else:
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logger.warning(
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"missing weights: %s in %s", base_node.name, weight.doc_string
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)
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results.append((base_node, base_weights / total))
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results.append((base_node, base_weights / total))
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return results
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return results
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def convert_diffusion_lora(part: 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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]
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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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]
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base = load_lora(f"stable-diffusion-onnx-v1-5/{part}/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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base = load_lora(f"stable-diffusion-onnx-v1-5/{part}/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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result = blend_loras(base, weights, alphas)
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logger.info("blended result keys: %s", len(result))
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result = blend_loras(base, weights, alphas)
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logger.info("blended result keys: %s", len(result))
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del weights
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del alphas
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del weights
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del alphas
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tensors = []
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for node, tensor in result:
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logger.info("remaking tensor for %s", node.name)
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tensors.append(helper.make_tensor(node.name, node.data_type, node.dims, tensor))
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tensors = []
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for node, tensor in result:
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logger.info("remaking tensor for %s", node.name)
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tensors.append(helper.make_tensor(node.name, node.data_type, node.dims, tensor))
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del result
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del result
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graph = helper.make_graph(
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base.graph.node,
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base.graph.name,
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base.graph.input,
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base.graph.output,
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tensors,
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base.graph.doc_string,
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base.graph.value_info,
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base.graph.sparse_initializer,
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)
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model = helper.make_model(graph)
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graph = helper.make_graph(
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base.graph.node,
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base.graph.name,
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base.graph.input,
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base.graph.output,
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tensors,
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base.graph.doc_string,
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base.graph.value_info,
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base.graph.sparse_initializer,
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)
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model = helper.make_model(graph)
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del model.opset_import[:]
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opset = model.opset_import.add()
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opset.version = 14
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del model.opset_import[:]
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opset = model.opset_import.add()
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opset.version = 14
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save_model(
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model,
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f"/tmp/lora-{part}.onnx",
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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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)
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logger.info("saved model to %s and tensors to %s", f"/tmp/lora-{part}.onnx", f"/tmp/lora-{part}.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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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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)
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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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)
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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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convert_diffusion_lora("unet")
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convert_diffusion_lora("text_encoder")
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@ -53,13 +53,13 @@ from transformers import (
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CLIPVisionConfig,
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)
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from .diffusers import convert_diffusion_diffusers
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from ..utils import ConversionContext, ModelDict, load_tensor, load_yaml, sanitize_name
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from .diffusers import convert_diffusion_diffusers
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logger = getLogger(__name__)
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class TrainingConfig():
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class TrainingConfig:
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"""
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From https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/dreambooth/db_config.py
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"""
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@ -184,7 +184,9 @@ class TrainingConfig():
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backup_dir = os.path.join(models_path, "backups")
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if not os.path.exists(backup_dir):
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os.makedirs(backup_dir)
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config_file = os.path.join(models_path, "backups", f"db_config_{self.revision}.json")
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config_file = os.path.join(
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models_path, "backups", f"db_config_{self.revision}.json"
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)
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with open(config_file, "w") as outfile:
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json.dump(self.__dict__, outfile, indent=4)
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@ -238,7 +240,9 @@ def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
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new_item = new_item.replace("emb_layers.1", "time_emb_proj")
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new_item = new_item.replace("skip_connection", "conv_shortcut")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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new_item = shave_segments(
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new_item, n_shave_prefix_segments=n_shave_prefix_segments
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)
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mapping.append({"old": old_item, "new": new_item})
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@ -253,7 +257,9 @@ def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
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for old_item in old_list:
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new_item = old_item
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new_item = new_item.replace("nin_shortcut", "conv_shortcut")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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new_item = shave_segments(
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new_item, n_shave_prefix_segments=n_shave_prefix_segments
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)
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mapping.append({"old": old_item, "new": new_item})
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@ -295,7 +301,9 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
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new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
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new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
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new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
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new_item = shave_segments(
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new_item, n_shave_prefix_segments=n_shave_prefix_segments
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)
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mapping.append({"old": old_item, "new": new_item})
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@ -303,7 +311,12 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
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def assign_to_checkpoint(
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paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
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paths,
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checkpoint,
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old_checkpoint,
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attention_paths_to_split=None,
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additional_replacements=None,
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config=None,
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):
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"""
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This does the final conversion step: take locally converted weights and apply a global renaming
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@ -312,7 +325,9 @@ def assign_to_checkpoint(
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Assigns the weights to the new checkpoint.
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"""
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assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
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assert isinstance(
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paths, list
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), "Paths should be a list of dicts containing 'old' and 'new' keys."
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# Splits the attention layers into three variables.
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if attention_paths_to_split is not None:
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@ -324,7 +339,9 @@ def assign_to_checkpoint(
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num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
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old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
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old_tensor = old_tensor.reshape(
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(num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]
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)
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query, key, value = old_tensor.split(channels // num_heads, dim=1)
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checkpoint[path_map["query"]] = query.reshape(target_shape)
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@ -335,7 +352,10 @@ def assign_to_checkpoint(
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new_path = path["new"]
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# These have already been assigned
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if attention_paths_to_split is not None and new_path in attention_paths_to_split:
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if (
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attention_paths_to_split is not None
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and new_path in attention_paths_to_split
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):
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continue
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# Global renaming happens here
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@ -373,19 +393,29 @@ def create_unet_diffusers_config(original_config, image_size: int):
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unet_params = original_config.model.params.unet_config.params
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vae_params = original_config.model.params.first_stage_config.params.ddconfig
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block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
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block_out_channels = [
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unet_params.model_channels * mult for mult in unet_params.channel_mult
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]
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down_block_types = []
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resolution = 1
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
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block_type = (
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"CrossAttnDownBlock2D"
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if resolution in unet_params.attention_resolutions
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else "DownBlock2D"
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)
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down_block_types.append(block_type)
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if i != len(block_out_channels) - 1:
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resolution *= 2
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up_block_types = []
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for i in range(len(block_out_channels)):
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block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
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block_type = (
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"CrossAttnUpBlock2D"
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if resolution in unet_params.attention_resolutions
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else "UpBlock2D"
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)
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up_block_types.append(block_type)
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resolution //= 2
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@ -393,7 +423,9 @@ def create_unet_diffusers_config(original_config, image_size: int):
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head_dim = unet_params.num_heads if "num_heads" in unet_params else None
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use_linear_projection = (
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unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
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unet_params.use_linear_in_transformer
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if "use_linear_in_transformer" in unet_params
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else False
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)
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if use_linear_projection:
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# stable diffusion 2-base-512 and 2-768
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@ -482,7 +514,9 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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for key in keys:
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if key.startswith("model.diffusion_model"):
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flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
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flat_ema_key
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)
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else:
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print(
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"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
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@ -493,33 +527,53 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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if key.startswith(unet_key):
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
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new_checkpoint = {"time_embedding.linear_1.weight": unet_state_dict["time_embed.0.weight"],
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"time_embedding.linear_1.bias": unet_state_dict["time_embed.0.bias"],
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"time_embedding.linear_2.weight": unet_state_dict["time_embed.2.weight"],
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"time_embedding.linear_2.bias": unet_state_dict["time_embed.2.bias"],
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"conv_in.weight": unet_state_dict["input_blocks.0.0.weight"],
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"conv_in.bias": unet_state_dict["input_blocks.0.0.bias"],
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"conv_norm_out.weight": unet_state_dict["out.0.weight"],
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"conv_norm_out.bias": unet_state_dict["out.0.bias"],
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"conv_out.weight": unet_state_dict["out.2.weight"],
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"conv_out.bias": unet_state_dict["out.2.bias"]}
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new_checkpoint = {
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"time_embedding.linear_1.weight": unet_state_dict["time_embed.0.weight"],
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"time_embedding.linear_1.bias": unet_state_dict["time_embed.0.bias"],
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"time_embedding.linear_2.weight": unet_state_dict["time_embed.2.weight"],
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"time_embedding.linear_2.bias": unet_state_dict["time_embed.2.bias"],
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"conv_in.weight": unet_state_dict["input_blocks.0.0.weight"],
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"conv_in.bias": unet_state_dict["input_blocks.0.0.bias"],
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"conv_norm_out.weight": unet_state_dict["out.0.weight"],
|
||||
"conv_norm_out.bias": unet_state_dict["out.0.bias"],
|
||||
"conv_out.weight": unet_state_dict["out.2.weight"],
|
||||
"conv_out.bias": unet_state_dict["out.2.bias"],
|
||||
}
|
||||
|
||||
# Retrieves the keys for the input blocks only
|
||||
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
|
||||
num_input_blocks = len(
|
||||
{
|
||||
".".join(layer.split(".")[:2])
|
||||
for layer in unet_state_dict
|
||||
if "input_blocks" in layer
|
||||
}
|
||||
)
|
||||
input_blocks = {
|
||||
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
|
||||
for layer_id in range(num_input_blocks)
|
||||
}
|
||||
|
||||
# Retrieves the keys for the middle blocks only
|
||||
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
|
||||
num_middle_blocks = len(
|
||||
{
|
||||
".".join(layer.split(".")[:2])
|
||||
for layer in unet_state_dict
|
||||
if "middle_block" in layer
|
||||
}
|
||||
)
|
||||
middle_blocks = {
|
||||
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
|
||||
for layer_id in range(num_middle_blocks)
|
||||
}
|
||||
|
||||
# Retrieves the keys for the output blocks only
|
||||
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
|
||||
num_output_blocks = len(
|
||||
{
|
||||
".".join(layer.split(".")[:2])
|
||||
for layer in unet_state_dict
|
||||
if "output_blocks" in layer
|
||||
}
|
||||
)
|
||||
output_blocks = {
|
||||
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
|
||||
for layer_id in range(num_output_blocks)
|
||||
|
|
@ -530,29 +584,45 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
|
|||
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
|
||||
|
||||
resnets = [
|
||||
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
|
||||
key
|
||||
for key in input_blocks[i]
|
||||
if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
|
||||
]
|
||||
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
|
||||
|
||||
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
|
||||
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
|
||||
f"input_blocks.{i}.0.op.weight"
|
||||
)
|
||||
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
|
||||
f"input_blocks.{i}.0.op.bias"
|
||||
)
|
||||
new_checkpoint[
|
||||
f"down_blocks.{block_id}.downsamplers.0.conv.weight"
|
||||
] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
|
||||
new_checkpoint[
|
||||
f"down_blocks.{block_id}.downsamplers.0.conv.bias"
|
||||
] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
|
||||
|
||||
paths = renew_resnet_paths(resnets)
|
||||
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
||||
meta_path = {
|
||||
"old": f"input_blocks.{i}.0",
|
||||
"new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}",
|
||||
}
|
||||
assign_to_checkpoint(
|
||||
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
||||
paths,
|
||||
new_checkpoint,
|
||||
unet_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
if len(attentions):
|
||||
paths = renew_attention_paths(attentions)
|
||||
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
|
||||
meta_path = {
|
||||
"old": f"input_blocks.{i}.1",
|
||||
"new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
|
||||
}
|
||||
assign_to_checkpoint(
|
||||
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
||||
paths,
|
||||
new_checkpoint,
|
||||
unet_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
resnet_0 = middle_blocks[0]
|
||||
|
|
@ -568,7 +638,11 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
|
|||
attentions_paths = renew_attention_paths(attentions)
|
||||
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
|
||||
assign_to_checkpoint(
|
||||
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
||||
attentions_paths,
|
||||
new_checkpoint,
|
||||
unet_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
for i in range(num_output_blocks):
|
||||
|
|
@ -586,25 +660,36 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
|
|||
|
||||
if len(output_block_list) > 1:
|
||||
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
|
||||
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
|
||||
attentions = [
|
||||
key for key in output_blocks[i] if f"output_blocks.{i}.1" in key
|
||||
]
|
||||
|
||||
resnet_0_paths = renew_resnet_paths(resnets)
|
||||
paths = renew_resnet_paths(resnets)
|
||||
|
||||
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
||||
meta_path = {
|
||||
"old": f"output_blocks.{i}.0",
|
||||
"new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}",
|
||||
}
|
||||
assign_to_checkpoint(
|
||||
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
||||
paths,
|
||||
new_checkpoint,
|
||||
unet_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
|
||||
if ["conv.bias", "conv.weight"] in output_block_list.values():
|
||||
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
|
||||
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
||||
f"output_blocks.{i}.{index}.conv.weight"
|
||||
]
|
||||
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
||||
f"output_blocks.{i}.{index}.conv.bias"
|
||||
]
|
||||
index = list(output_block_list.values()).index(
|
||||
["conv.bias", "conv.weight"]
|
||||
)
|
||||
new_checkpoint[
|
||||
f"up_blocks.{block_id}.upsamplers.0.conv.weight"
|
||||
] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
|
||||
new_checkpoint[
|
||||
f"up_blocks.{block_id}.upsamplers.0.conv.bias"
|
||||
] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]
|
||||
|
||||
# Clear attentions as they have been attributed above.
|
||||
if len(attentions) == 2:
|
||||
|
|
@ -617,13 +702,27 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
|
|||
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
|
||||
}
|
||||
assign_to_checkpoint(
|
||||
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
||||
paths,
|
||||
new_checkpoint,
|
||||
unet_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
else:
|
||||
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
|
||||
resnet_0_paths = renew_resnet_paths(
|
||||
output_block_layers, n_shave_prefix_segments=1
|
||||
)
|
||||
for path in resnet_0_paths:
|
||||
old_path = ".".join(["output_blocks", str(i), path["old"]])
|
||||
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
|
||||
new_path = ".".join(
|
||||
[
|
||||
"up_blocks",
|
||||
str(block_id),
|
||||
"resnets",
|
||||
str(layer_in_block_id),
|
||||
path["new"],
|
||||
]
|
||||
)
|
||||
|
||||
new_checkpoint[new_path] = unet_state_dict[old_path]
|
||||
|
||||
|
|
@ -645,49 +744,75 @@ def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
|
|||
else:
|
||||
vae_state_dict[key] = checkpoint.get(key)
|
||||
|
||||
new_checkpoint = {"encoder.conv_in.weight": vae_state_dict["encoder.conv_in.weight"],
|
||||
"encoder.conv_in.bias": vae_state_dict["encoder.conv_in.bias"],
|
||||
"encoder.conv_out.weight": vae_state_dict["encoder.conv_out.weight"],
|
||||
"encoder.conv_out.bias": vae_state_dict["encoder.conv_out.bias"],
|
||||
"encoder.conv_norm_out.weight": vae_state_dict["encoder.norm_out.weight"],
|
||||
"encoder.conv_norm_out.bias": vae_state_dict["encoder.norm_out.bias"],
|
||||
"decoder.conv_in.weight": vae_state_dict["decoder.conv_in.weight"],
|
||||
"decoder.conv_in.bias": vae_state_dict["decoder.conv_in.bias"],
|
||||
"decoder.conv_out.weight": vae_state_dict["decoder.conv_out.weight"],
|
||||
"decoder.conv_out.bias": vae_state_dict["decoder.conv_out.bias"],
|
||||
"decoder.conv_norm_out.weight": vae_state_dict["decoder.norm_out.weight"],
|
||||
"decoder.conv_norm_out.bias": vae_state_dict["decoder.norm_out.bias"],
|
||||
"quant_conv.weight": vae_state_dict["quant_conv.weight"],
|
||||
"quant_conv.bias": vae_state_dict["quant_conv.bias"],
|
||||
"post_quant_conv.weight": vae_state_dict["post_quant_conv.weight"],
|
||||
"post_quant_conv.bias": vae_state_dict["post_quant_conv.bias"]}
|
||||
new_checkpoint = {
|
||||
"encoder.conv_in.weight": vae_state_dict["encoder.conv_in.weight"],
|
||||
"encoder.conv_in.bias": vae_state_dict["encoder.conv_in.bias"],
|
||||
"encoder.conv_out.weight": vae_state_dict["encoder.conv_out.weight"],
|
||||
"encoder.conv_out.bias": vae_state_dict["encoder.conv_out.bias"],
|
||||
"encoder.conv_norm_out.weight": vae_state_dict["encoder.norm_out.weight"],
|
||||
"encoder.conv_norm_out.bias": vae_state_dict["encoder.norm_out.bias"],
|
||||
"decoder.conv_in.weight": vae_state_dict["decoder.conv_in.weight"],
|
||||
"decoder.conv_in.bias": vae_state_dict["decoder.conv_in.bias"],
|
||||
"decoder.conv_out.weight": vae_state_dict["decoder.conv_out.weight"],
|
||||
"decoder.conv_out.bias": vae_state_dict["decoder.conv_out.bias"],
|
||||
"decoder.conv_norm_out.weight": vae_state_dict["decoder.norm_out.weight"],
|
||||
"decoder.conv_norm_out.bias": vae_state_dict["decoder.norm_out.bias"],
|
||||
"quant_conv.weight": vae_state_dict["quant_conv.weight"],
|
||||
"quant_conv.bias": vae_state_dict["quant_conv.bias"],
|
||||
"post_quant_conv.weight": vae_state_dict["post_quant_conv.weight"],
|
||||
"post_quant_conv.bias": vae_state_dict["post_quant_conv.bias"],
|
||||
}
|
||||
|
||||
# Retrieves the keys for the encoder down blocks only
|
||||
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
|
||||
num_down_blocks = len(
|
||||
{
|
||||
".".join(layer.split(".")[:3])
|
||||
for layer in vae_state_dict
|
||||
if "encoder.down" in layer
|
||||
}
|
||||
)
|
||||
down_blocks = {
|
||||
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
|
||||
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
|
||||
for layer_id in range(num_down_blocks)
|
||||
}
|
||||
|
||||
# Retrieves the keys for the decoder up blocks only
|
||||
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
|
||||
num_up_blocks = len(
|
||||
{
|
||||
".".join(layer.split(".")[:3])
|
||||
for layer in vae_state_dict
|
||||
if "decoder.up" in layer
|
||||
}
|
||||
)
|
||||
up_blocks = {
|
||||
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
|
||||
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
|
||||
for layer_id in range(num_up_blocks)
|
||||
}
|
||||
|
||||
for i in range(num_down_blocks):
|
||||
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
|
||||
resnets = [
|
||||
key
|
||||
for key in down_blocks[i]
|
||||
if f"down.{i}" in key and f"down.{i}.downsample" not in key
|
||||
]
|
||||
|
||||
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
|
||||
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
|
||||
f"encoder.down.{i}.downsample.conv.weight"
|
||||
)
|
||||
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
|
||||
f"encoder.down.{i}.downsample.conv.bias"
|
||||
)
|
||||
new_checkpoint[
|
||||
f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
|
||||
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
|
||||
new_checkpoint[
|
||||
f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
|
||||
] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
|
||||
|
||||
paths = renew_vae_resnet_paths(resnets)
|
||||
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
|
||||
num_mid_res_blocks = 2
|
||||
|
|
@ -696,31 +821,51 @@ def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
|
|||
|
||||
paths = renew_vae_resnet_paths(resnets)
|
||||
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
|
||||
paths = renew_vae_attention_paths(mid_attentions)
|
||||
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
conv_attn_to_linear(new_checkpoint)
|
||||
|
||||
for i in range(num_up_blocks):
|
||||
block_id = num_up_blocks - 1 - i
|
||||
resnets = [
|
||||
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
|
||||
key
|
||||
for key in up_blocks[block_id]
|
||||
if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
|
||||
]
|
||||
|
||||
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
|
||||
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
|
||||
f"decoder.up.{block_id}.upsample.conv.weight"
|
||||
]
|
||||
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
|
||||
f"decoder.up.{block_id}.upsample.conv.bias"
|
||||
]
|
||||
new_checkpoint[
|
||||
f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
|
||||
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
|
||||
new_checkpoint[
|
||||
f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
|
||||
] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
|
||||
|
||||
paths = renew_vae_resnet_paths(resnets)
|
||||
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
|
||||
num_mid_res_blocks = 2
|
||||
|
|
@ -729,12 +874,24 @@ def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
|
|||
|
||||
paths = renew_vae_resnet_paths(resnets)
|
||||
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
|
||||
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
|
||||
paths = renew_vae_attention_paths(mid_attentions)
|
||||
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
||||
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
||||
assign_to_checkpoint(
|
||||
paths,
|
||||
new_checkpoint,
|
||||
vae_state_dict,
|
||||
additional_replacements=[meta_path],
|
||||
config=config,
|
||||
)
|
||||
conv_attn_to_linear(new_checkpoint)
|
||||
return new_checkpoint
|
||||
|
||||
|
|
@ -769,14 +926,16 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
|
|||
for i, hf_layer in enumerate(hf_layers):
|
||||
if i != 0:
|
||||
i += i
|
||||
pt_layer = pt_layers[i: i + 2]
|
||||
pt_layer = pt_layers[i : i + 2]
|
||||
_copy_layer(hf_layer, pt_layer)
|
||||
|
||||
hf_model = LDMBertModel(config).eval()
|
||||
|
||||
# copy embeds
|
||||
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
|
||||
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
|
||||
hf_model.model.embed_positions.weight.data = (
|
||||
checkpoint.transformer.pos_emb.emb.weight
|
||||
)
|
||||
|
||||
# copy layer norm
|
||||
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
|
||||
|
|
@ -799,9 +958,13 @@ def convert_ldm_clip_checkpoint(checkpoint):
|
|||
for key in keys:
|
||||
if key.startswith("cond_stage_model.transformer"):
|
||||
if key.find("text_model") == -1:
|
||||
text_model_dict["text_model." + key[len("cond_stage_model.transformer."):]] = checkpoint[key]
|
||||
text_model_dict[
|
||||
"text_model." + key[len("cond_stage_model.transformer.") :]
|
||||
] = checkpoint[key]
|
||||
else:
|
||||
text_model_dict[key[len("cond_stage_model.transformer."):]] = checkpoint[key]
|
||||
text_model_dict[
|
||||
key[len("cond_stage_model.transformer.") :]
|
||||
] = checkpoint[key]
|
||||
|
||||
text_model.load_state_dict(text_model_dict)
|
||||
|
||||
|
|
@ -809,12 +972,16 @@ def convert_ldm_clip_checkpoint(checkpoint):
|
|||
|
||||
|
||||
textenc_conversion_lst = [
|
||||
('cond_stage_model.model.positional_embedding',
|
||||
"text_model.embeddings.position_embedding.weight"),
|
||||
('cond_stage_model.model.token_embedding.weight',
|
||||
"text_model.embeddings.token_embedding.weight"),
|
||||
('cond_stage_model.model.ln_final.weight', 'text_model.final_layer_norm.weight'),
|
||||
('cond_stage_model.model.ln_final.bias', 'text_model.final_layer_norm.bias')
|
||||
(
|
||||
"cond_stage_model.model.positional_embedding",
|
||||
"text_model.embeddings.position_embedding.weight",
|
||||
),
|
||||
(
|
||||
"cond_stage_model.model.token_embedding.weight",
|
||||
"text_model.embeddings.token_embedding.weight",
|
||||
),
|
||||
("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
|
||||
("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
|
||||
]
|
||||
textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
|
||||
|
||||
|
|
@ -827,8 +994,14 @@ textenc_transformer_conversion_lst = [
|
|||
(".c_proj.", ".fc2."),
|
||||
(".attn", ".self_attn"),
|
||||
("ln_final.", "transformer.text_model.final_layer_norm."),
|
||||
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
|
||||
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
|
||||
(
|
||||
"token_embedding.weight",
|
||||
"transformer.text_model.embeddings.token_embedding.weight",
|
||||
),
|
||||
(
|
||||
"positional_embedding",
|
||||
"transformer.text_model.embeddings.position_embedding.weight",
|
||||
),
|
||||
]
|
||||
protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
|
||||
textenc_pattern = re.compile("|".join(protected.keys()))
|
||||
|
|
@ -844,7 +1017,9 @@ def convert_paint_by_example_checkpoint(checkpoint):
|
|||
|
||||
for key in keys:
|
||||
if key.startswith("cond_stage_model.transformer"):
|
||||
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
|
||||
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[
|
||||
key
|
||||
]
|
||||
|
||||
# load clip vision
|
||||
model.model.load_state_dict(text_model_dict)
|
||||
|
|
@ -902,19 +1077,25 @@ def convert_paint_by_example_checkpoint(checkpoint):
|
|||
|
||||
|
||||
def convert_open_clip_checkpoint(checkpoint):
|
||||
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
|
||||
text_model = CLIPTextModel.from_pretrained(
|
||||
"stabilityai/stable-diffusion-2", subfolder="text_encoder"
|
||||
)
|
||||
|
||||
keys = list(checkpoint.keys())
|
||||
text_model_dict = {}
|
||||
if 'cond_stage_model.model.text_projection' in checkpoint:
|
||||
d_model = int(checkpoint['cond_stage_model.model.text_projection'].shape[0])
|
||||
if "cond_stage_model.model.text_projection" in checkpoint:
|
||||
d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])
|
||||
else:
|
||||
logger.debug("no projection shape found, setting to 1024")
|
||||
d_model = 1024
|
||||
text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
|
||||
text_model_dict[
|
||||
"text_model.embeddings.position_ids"
|
||||
] = text_model.text_model.embeddings.get_buffer("position_ids")
|
||||
|
||||
for key in keys:
|
||||
if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer
|
||||
if (
|
||||
"resblocks.23" in key
|
||||
): # Diffusers drops the final layer and only uses the penultimate layer
|
||||
continue
|
||||
if key in textenc_conversion_map:
|
||||
text_model_dict[textenc_conversion_map[key]] = checkpoint[key]
|
||||
|
|
@ -922,18 +1103,34 @@ def convert_open_clip_checkpoint(checkpoint):
|
|||
new_key = key[len("cond_stage_model.model.transformer.") :]
|
||||
if new_key.endswith(".in_proj_weight"):
|
||||
new_key = new_key[: -len(".in_proj_weight")]
|
||||
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
|
||||
text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
|
||||
text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
|
||||
text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
|
||||
new_key = textenc_pattern.sub(
|
||||
lambda m: protected[re.escape(m.group(0))], new_key
|
||||
)
|
||||
text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][
|
||||
:d_model, :
|
||||
]
|
||||
text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][
|
||||
d_model : d_model * 2, :
|
||||
]
|
||||
text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][
|
||||
d_model * 2 :, :
|
||||
]
|
||||
elif new_key.endswith(".in_proj_bias"):
|
||||
new_key = new_key[: -len(".in_proj_bias")]
|
||||
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
|
||||
new_key = textenc_pattern.sub(
|
||||
lambda m: protected[re.escape(m.group(0))], new_key
|
||||
)
|
||||
text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
|
||||
text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
|
||||
text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
|
||||
text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][
|
||||
d_model : d_model * 2
|
||||
]
|
||||
text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][
|
||||
d_model * 2 :
|
||||
]
|
||||
else:
|
||||
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
|
||||
new_key = textenc_pattern.sub(
|
||||
lambda m: protected[re.escape(m.group(0))], new_key
|
||||
)
|
||||
|
||||
text_model_dict[new_key] = checkpoint[key]
|
||||
|
||||
|
|
@ -992,7 +1189,15 @@ def download_model(db_config: TrainingConfig, token):
|
|||
|
||||
siblings = repo_info.siblings
|
||||
|
||||
diffusion_dirs = ["text_encoder", "unet", "vae", "tokenizer", "scheduler", "feature_extractor", "safety_checker"]
|
||||
diffusion_dirs = [
|
||||
"text_encoder",
|
||||
"unet",
|
||||
"vae",
|
||||
"tokenizer",
|
||||
"scheduler",
|
||||
"feature_extractor",
|
||||
"safety_checker",
|
||||
]
|
||||
config_file = None
|
||||
model_index = None
|
||||
model_files = []
|
||||
|
|
@ -1031,9 +1236,9 @@ def download_model(db_config: TrainingConfig, token):
|
|||
(x for x in model_files if "nonema" in x),
|
||||
next(
|
||||
(x for x in model_files if ".safetensors" in x),
|
||||
model_files[0] if model_files else None
|
||||
)
|
||||
)
|
||||
model_files[0] if model_files else None,
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
files_to_fetch = None
|
||||
|
|
@ -1061,7 +1266,7 @@ def download_model(db_config: TrainingConfig, token):
|
|||
filename=repo_file,
|
||||
repo_type="model",
|
||||
revision=repo_info.sha,
|
||||
token=token
|
||||
token=token,
|
||||
)
|
||||
replace_symlinks(out, db_config.model_dir)
|
||||
dest = None
|
||||
|
|
@ -1074,7 +1279,9 @@ def download_model(db_config: TrainingConfig, token):
|
|||
for diffusion_dir in diffusion_dirs:
|
||||
if diffusion_dir in out:
|
||||
out_model = db_config.pretrained_model_name_or_path
|
||||
dest = os.path.join(db_config.pretrained_model_name_or_path, diffusion_dir)
|
||||
dest = os.path.join(
|
||||
db_config.pretrained_model_name_or_path, diffusion_dir
|
||||
)
|
||||
if not dest:
|
||||
if ".ckpt" in out or ".safetensors" in out:
|
||||
dest = os.path.join(db_config.model_dir, "src")
|
||||
|
|
@ -1095,9 +1302,11 @@ def get_config_path(
|
|||
model_version: str = "v1",
|
||||
train_type: str = "default",
|
||||
config_base_name: str = "training",
|
||||
prediction_type: str = "epsilon"
|
||||
prediction_type: str = "epsilon",
|
||||
):
|
||||
train_type = f"{train_type}" if not prediction_type == "v_prediction" else f"{train_type}-v"
|
||||
train_type = (
|
||||
f"{train_type}" if not prediction_type == "v_prediction" else f"{train_type}-v"
|
||||
)
|
||||
|
||||
parts = os.path.join(
|
||||
os.path.dirname(os.path.realpath(__file__)),
|
||||
|
|
@ -1106,21 +1315,20 @@ def get_config_path(
|
|||
"..",
|
||||
"models",
|
||||
"configs",
|
||||
f"{model_version}-{config_base_name}-{train_type}.yaml"
|
||||
f"{model_version}-{config_base_name}-{train_type}.yaml",
|
||||
)
|
||||
return os.path.abspath(parts)
|
||||
|
||||
|
||||
def get_config_file(train_unfrozen=False, v2=False, prediction_type="epsilon", config_file=None):
|
||||
def get_config_file(
|
||||
train_unfrozen=False, v2=False, prediction_type="epsilon", config_file=None
|
||||
):
|
||||
if config_file is not None:
|
||||
return config_file
|
||||
|
||||
config_base_name = "training"
|
||||
|
||||
model_versions = {
|
||||
"v1": "v1",
|
||||
"v2": "v2"
|
||||
}
|
||||
model_versions = {"v1": "v1", "v2": "v2"}
|
||||
train_types = {
|
||||
"default": "default",
|
||||
"unfrozen": "unfrozen",
|
||||
|
|
@ -1134,7 +1342,9 @@ def get_config_file(train_unfrozen=False, v2=False, prediction_type="epsilon", c
|
|||
else:
|
||||
model_train_type = train_types["default"]
|
||||
|
||||
return get_config_path(model_version_name, model_train_type, config_base_name, prediction_type)
|
||||
return get_config_path(
|
||||
model_version_name, model_train_type, config_base_name, prediction_type
|
||||
)
|
||||
|
||||
|
||||
def extract_checkpoint(
|
||||
|
|
@ -1182,8 +1392,9 @@ def extract_checkpoint(
|
|||
msg = None
|
||||
|
||||
# Create empty config
|
||||
db_config = TrainingConfig(ctx, model_name=new_model_name, scheduler=scheduler_type,
|
||||
src=checkpoint_file)
|
||||
db_config = TrainingConfig(
|
||||
ctx, model_name=new_model_name, scheduler=scheduler_type, src=checkpoint_file
|
||||
)
|
||||
|
||||
original_config_file = None
|
||||
|
||||
|
|
@ -1221,9 +1432,13 @@ def extract_checkpoint(
|
|||
else:
|
||||
prediction_type = "epsilon"
|
||||
|
||||
original_config_file = get_config_file(train_unfrozen, v2, prediction_type, config_file=config_file)
|
||||
original_config_file = get_config_file(
|
||||
train_unfrozen, v2, prediction_type, config_file=config_file
|
||||
)
|
||||
|
||||
logger.info(f"Pred and size are {prediction_type} and {image_size}, using config: {original_config_file}")
|
||||
logger.info(
|
||||
f"Pred and size are {prediction_type} and {image_size}, using config: {original_config_file}"
|
||||
)
|
||||
db_config.resolution = image_size
|
||||
db_config.lifetime_revision = revision
|
||||
db_config.epoch = epoch
|
||||
|
|
@ -1233,12 +1448,18 @@ def extract_checkpoint(
|
|||
|
||||
# Use existing YAML if present
|
||||
if checkpoint_file is not None:
|
||||
config_check = checkpoint_file.replace(".ckpt", ".yaml") if ".ckpt" in checkpoint_file else checkpoint_file.replace(".safetensors", ".yaml")
|
||||
config_check = (
|
||||
checkpoint_file.replace(".ckpt", ".yaml")
|
||||
if ".ckpt" in checkpoint_file
|
||||
else checkpoint_file.replace(".safetensors", ".yaml")
|
||||
)
|
||||
if os.path.exists(config_check):
|
||||
original_config_file = config_check
|
||||
|
||||
if original_config_file is None or not os.path.exists(original_config_file):
|
||||
logger.warning("unable to select a config file: %s" % (original_config_file))
|
||||
logger.warning(
|
||||
"unable to select a config file: %s" % (original_config_file)
|
||||
)
|
||||
return
|
||||
|
||||
logger.debug("trying to load: %s", original_config_file)
|
||||
|
|
@ -1281,7 +1502,9 @@ def extract_checkpoint(
|
|||
|
||||
# Convert the UNet2DConditionModel model.
|
||||
logger.info("converting UNet")
|
||||
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
|
||||
unet_config = create_unet_diffusers_config(
|
||||
original_config, image_size=image_size
|
||||
)
|
||||
unet_config["upcast_attention"] = upcast_attention
|
||||
unet = UNet2DConditionModel(**unet_config)
|
||||
|
||||
|
|
@ -1297,22 +1520,30 @@ def extract_checkpoint(
|
|||
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
|
||||
|
||||
if vae_file is None:
|
||||
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
|
||||
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
|
||||
checkpoint, vae_config
|
||||
)
|
||||
else:
|
||||
vae_file = os.path.join(ctx.model_path, vae_file)
|
||||
logger.debug("loading custom VAE: %s", vae_file)
|
||||
vae_checkpoint = load_tensor(vae_file, map_location=map_location)
|
||||
converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_checkpoint, vae_config, first_stage=False)
|
||||
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
|
||||
vae_checkpoint, vae_config, first_stage=False
|
||||
)
|
||||
|
||||
vae = AutoencoderKL(**vae_config)
|
||||
vae.load_state_dict(converted_vae_checkpoint)
|
||||
|
||||
# Convert the text model.
|
||||
logger.info("converting text encoder")
|
||||
text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
|
||||
text_model_type = original_config.model.params.cond_stage_config.target.split(
|
||||
"."
|
||||
)[-1]
|
||||
if text_model_type == "FrozenOpenCLIPEmbedder":
|
||||
text_model = convert_open_clip_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
|
||||
tokenizer = CLIPTokenizer.from_pretrained(
|
||||
"stabilityai/stable-diffusion-2", subfolder="tokenizer"
|
||||
)
|
||||
pipe = StableDiffusionPipeline(
|
||||
vae=vae,
|
||||
text_encoder=text_model,
|
||||
|
|
@ -1326,7 +1557,9 @@ def extract_checkpoint(
|
|||
elif text_model_type == "PaintByExample":
|
||||
vision_model = convert_paint_by_example_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||
"CompVis/stable-diffusion-safety-checker"
|
||||
)
|
||||
pipe = PaintByExamplePipeline(
|
||||
vae=vae,
|
||||
image_encoder=vision_model,
|
||||
|
|
@ -1338,8 +1571,12 @@ def extract_checkpoint(
|
|||
elif text_model_type == "FrozenCLIPEmbedder":
|
||||
text_model = convert_ldm_clip_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
||||
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
|
||||
"CompVis/stable-diffusion-safety-checker"
|
||||
)
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||
"CompVis/stable-diffusion-safety-checker"
|
||||
)
|
||||
pipe = StableDiffusionPipeline(
|
||||
vae=vae,
|
||||
text_encoder=text_model,
|
||||
|
|
@ -1347,16 +1584,24 @@ def extract_checkpoint(
|
|||
unet=unet,
|
||||
scheduler=scheduler,
|
||||
safety_checker=safety_checker,
|
||||
feature_extractor=feature_extractor
|
||||
feature_extractor=feature_extractor,
|
||||
)
|
||||
else:
|
||||
text_config = create_ldm_bert_config(original_config)
|
||||
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
|
||||
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
|
||||
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet,
|
||||
scheduler=scheduler)
|
||||
pipe = LDMTextToImagePipeline(
|
||||
vqvae=vae,
|
||||
bert=text_model,
|
||||
tokenizer=tokenizer,
|
||||
unet=unet,
|
||||
scheduler=scheduler,
|
||||
)
|
||||
except Exception:
|
||||
logger.error("exception setting up output: %s", traceback.format_exception(*sys.exc_info()))
|
||||
logger.error(
|
||||
"exception setting up output: %s",
|
||||
traceback.format_exception(*sys.exc_info()),
|
||||
)
|
||||
pipe = None
|
||||
|
||||
if pipe is None or db_config is None:
|
||||
|
|
@ -1371,12 +1616,18 @@ def extract_checkpoint(
|
|||
scheduler = db_config.scheduler
|
||||
required_dirs = ["unet", "vae", "text_encoder", "scheduler", "tokenizer"]
|
||||
if original_config_file is not None and os.path.exists(original_config_file):
|
||||
logger.debug("copying original config: %s -> %s", original_config_file, db_config.model_dir)
|
||||
logger.debug(
|
||||
"copying original config: %s -> %s",
|
||||
original_config_file,
|
||||
db_config.model_dir,
|
||||
)
|
||||
shutil.copy(original_config_file, db_config.model_dir)
|
||||
basename = os.path.basename(original_config_file)
|
||||
new_ex_path = os.path.join(db_config.model_dir, basename)
|
||||
new_name = os.path.join(db_config.model_dir, f"{db_config.model_name}.yaml")
|
||||
logger.debug("copying model config to new name: %s -> %s", new_ex_path, new_name)
|
||||
logger.debug(
|
||||
"copying model config to new name: %s -> %s", new_ex_path, new_name
|
||||
)
|
||||
if os.path.exists(new_name):
|
||||
os.remove(new_name)
|
||||
os.rename(new_ex_path, new_name)
|
||||
|
|
@ -1407,7 +1658,9 @@ def convert_diffusion_original(
|
|||
source = source or model["source"]
|
||||
|
||||
dest = os.path.join(ctx.model_path, name)
|
||||
logger.info("converting original Diffusers checkpoint %s: %s -> %s", name, source, dest)
|
||||
logger.info(
|
||||
"converting original Diffusers checkpoint %s: %s -> %s", name, source, dest
|
||||
)
|
||||
|
||||
if os.path.exists(dest):
|
||||
logger.info("ONNX pipeline already exists, skipping")
|
||||
|
|
@ -1420,8 +1673,18 @@ def convert_diffusion_original(
|
|||
if os.path.exists(torch_path):
|
||||
logger.info("torch pipeline already exists, reusing: %s", torch_path)
|
||||
else:
|
||||
logger.info("converting original Diffusers check to Torch model: %s -> %s", source, torch_path)
|
||||
extract_checkpoint(ctx, torch_name, source, config_file=model.get("config"), vae_file=model.get("vae"))
|
||||
logger.info(
|
||||
"converting original Diffusers check to Torch model: %s -> %s",
|
||||
source,
|
||||
torch_path,
|
||||
)
|
||||
extract_checkpoint(
|
||||
ctx,
|
||||
torch_name,
|
||||
source,
|
||||
config_file=model.get("config"),
|
||||
vae_file=model.get("vae"),
|
||||
)
|
||||
logger.info("converted original Diffusers checkpoint to Torch model")
|
||||
|
||||
# VAE has already been converted and will confuse HF repo lookup
|
||||
|
|
|
|||
|
|
@ -1,24 +1,29 @@
|
|||
from os import makedirs, path
|
||||
from huggingface_hub.file_download import hf_hub_download
|
||||
from transformers import CLIPTokenizer, CLIPTextModel
|
||||
from torch.onnx import export
|
||||
from logging import getLogger
|
||||
|
||||
from ..utils import ConversionContext
|
||||
from os import makedirs, path
|
||||
|
||||
import torch
|
||||
from huggingface_hub.file_download import hf_hub_download
|
||||
from torch.onnx import export
|
||||
from transformers import CLIPTextModel, CLIPTokenizer
|
||||
|
||||
from ..utils import ConversionContext
|
||||
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def convert_diffusion_textual_inversion(context: ConversionContext, name: str, base_model: str, inversion: str):
|
||||
def convert_diffusion_textual_inversion(
|
||||
context: ConversionContext, name: str, base_model: str, inversion: str
|
||||
):
|
||||
dest_path = path.join(context.model_path, f"inversion-{name}")
|
||||
logger.info("converting Textual Inversion: %s + %s -> %s", base_model, inversion, dest_path)
|
||||
logger.info(
|
||||
"converting Textual Inversion: %s + %s -> %s", base_model, inversion, dest_path
|
||||
)
|
||||
|
||||
if path.exists(dest_path):
|
||||
logger.info("ONNX model already exists, skipping.")
|
||||
return
|
||||
|
||||
makedirs(path.join(dest_path, "text_encoder"))
|
||||
makedirs(path.join(dest_path, "text_encoder"), exist_ok=True)
|
||||
|
||||
embeds_file = hf_hub_download(repo_id=inversion, filename="learned_embeds.bin")
|
||||
token_file = hf_hub_download(repo_id=inversion, filename="token_identifier.txt")
|
||||
|
|
@ -71,9 +76,7 @@ def convert_diffusion_textual_inversion(context: ConversionContext, name: str, b
|
|||
export(
|
||||
text_encoder,
|
||||
# casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
|
||||
(
|
||||
text_input.input_ids.to(dtype=torch.int32)
|
||||
),
|
||||
(text_input.input_ids.to(dtype=torch.int32)),
|
||||
f=path.join(dest_path, "text_encoder", "model.onnx"),
|
||||
input_names=["input_ids"],
|
||||
output_names=["last_hidden_state", "pooler_output"],
|
||||
|
|
|
|||
|
|
@ -17,9 +17,9 @@ from diffusers import (
|
|||
KDPM2AncestralDiscreteScheduler,
|
||||
KDPM2DiscreteScheduler,
|
||||
LMSDiscreteScheduler,
|
||||
OnnxRuntimeModel,
|
||||
PNDMScheduler,
|
||||
StableDiffusionPipeline,
|
||||
OnnxRuntimeModel,
|
||||
)
|
||||
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ import { useStore } from 'zustand';
|
|||
|
||||
import { STALE_TIME } from '../../config.js';
|
||||
import { ClientContext, StateContext } from '../../state.js';
|
||||
import { MODEL_LABELS, PLATFORM_LABELS } from '../../strings.js';
|
||||
import { INVERSION_LABELS, MODEL_LABELS, PLATFORM_LABELS } from '../../strings.js';
|
||||
import { QueryList } from '../input/QueryList.js';
|
||||
|
||||
export function ModelControl() {
|
||||
|
|
@ -56,12 +56,13 @@ export function ModelControl() {
|
|||
/>
|
||||
<QueryList
|
||||
id='inversion'
|
||||
labels={MODEL_LABELS}
|
||||
labels={INVERSION_LABELS}
|
||||
name='Textual Inversion'
|
||||
query={{
|
||||
result: models,
|
||||
selector: (result) => result.inversion,
|
||||
}}
|
||||
showEmpty={true}
|
||||
value={params.inversion}
|
||||
onChange={(inversion) => {
|
||||
setModel({
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ export interface QueryListProps<T> {
|
|||
value: string;
|
||||
|
||||
query: QueryListComplete | QueryListFilter<T>;
|
||||
showEmpty?: boolean;
|
||||
|
||||
onChange?: (value: string) => void;
|
||||
}
|
||||
|
|
@ -28,17 +29,25 @@ export function hasFilter<T>(query: QueryListComplete | QueryListFilter<T>): que
|
|||
return Reflect.has(query, 'selector');
|
||||
}
|
||||
|
||||
export function filterQuery<T>(query: QueryListComplete | QueryListFilter<T>): Array<string> {
|
||||
export function filterQuery<T>(query: QueryListComplete | QueryListFilter<T>, showEmpty: boolean): Array<string> {
|
||||
if (hasFilter(query)) {
|
||||
const data = mustExist(query.result.data);
|
||||
return (query as QueryListFilter<unknown>).selector(data);
|
||||
const selected = (query as QueryListFilter<unknown>).selector(data);
|
||||
if (showEmpty) {
|
||||
return ['', ...selected];
|
||||
}
|
||||
return selected;
|
||||
} else {
|
||||
return mustExist(query.result.data);
|
||||
const data = Array.from(mustExist(query.result.data));
|
||||
if (showEmpty) {
|
||||
return ['', ...data];
|
||||
}
|
||||
return data;
|
||||
}
|
||||
}
|
||||
|
||||
export function QueryList<T>(props: QueryListProps<T>) {
|
||||
const { labels, query, value } = props;
|
||||
const { labels, query, showEmpty = false, value } = props;
|
||||
const { result } = query;
|
||||
|
||||
function firstValidValue(): string {
|
||||
|
|
@ -52,7 +61,7 @@ export function QueryList<T>(props: QueryListProps<T>) {
|
|||
// update state when previous selection was invalid: https://github.com/ssube/onnx-web/issues/120
|
||||
useEffect(() => {
|
||||
if (result.status === 'success' && doesExist(result.data) && doesExist(props.onChange)) {
|
||||
const data = filterQuery(query);
|
||||
const data = filterQuery(query, showEmpty);
|
||||
if (data.includes(value) === false) {
|
||||
props.onChange(data[0]);
|
||||
}
|
||||
|
|
@ -77,7 +86,7 @@ export function QueryList<T>(props: QueryListProps<T>) {
|
|||
|
||||
// else: success
|
||||
const labelID = `query-list-${props.id}-labels`;
|
||||
const data = filterQuery(query);
|
||||
const data = filterQuery(query, showEmpty);
|
||||
|
||||
return <FormControl>
|
||||
<InputLabel id={labelID}>{props.name}</InputLabel>
|
||||
|
|
|
|||
|
|
@ -32,6 +32,14 @@ export const MODEL_LABELS: Record<string, string> = {
|
|||
'diffusion-unstable-ink-dream-v6': 'Unstable Ink Dream v6',
|
||||
};
|
||||
|
||||
export const INVERSION_LABELS: Record<string, string> = {
|
||||
'': 'None',
|
||||
'inversion-cubex': 'Cubex',
|
||||
'inversion-birb': 'Birb Style',
|
||||
'inversion-line-art': 'Line Art',
|
||||
'inversion-minecraft': 'Minecraft Concept',
|
||||
};
|
||||
|
||||
export const PLATFORM_LABELS: Record<string, string> = {
|
||||
amd: 'AMD GPU',
|
||||
// eslint-disable-next-line id-blacklist
|
||||
|
|
|
|||
Loading…
Reference in New Issue