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137 lines
4.4 KiB
Python
137 lines
4.4 KiB
Python
"""Functions for managing model weights information"""
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import safetensors
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from imaginairy.utils.downloads import get_cached_url_path
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from imaginairy.utils.model_manager import (
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open_weights,
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resolve_model_weights_config,
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)
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from imaginairy.weight_management import utils
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from imaginairy.weight_management.pattern_collapse import find_state_dict_key_patterns
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from imaginairy.weight_management.utils import save_model_info
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def save_compvis_patterns():
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model_weights_config = resolve_model_weights_config(
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model_weights="openjourney-v1",
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)
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weights_path = get_cached_url_path(
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model_weights_config.weights_location, category="weights"
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)
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with safetensors.safe_open(weights_path, "pytorch") as f:
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weights_keys = f.keys()
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text_encoder_prefix = "cond_stage_model.transformer.text_model"
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text_encoder_keys = [k for k in weights_keys if k.startswith(text_encoder_prefix)]
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.TEXT_ENCODER,
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format_name=utils.FORMAT_NAMES.COMPVIS,
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weights_keys=text_encoder_keys,
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)
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vae_prefix = "first_stage_model"
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vae_keys = [k for k in weights_keys if k.startswith(vae_prefix)]
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.VAE,
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format_name=utils.FORMAT_NAMES.COMPVIS,
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weights_keys=vae_keys,
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)
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unet_prefix = "model.diffusion_model"
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unet_keys = [k for k in weights_keys if k.startswith(unet_prefix)]
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.UNET,
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format_name=utils.FORMAT_NAMES.COMPVIS,
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weights_keys=unet_keys,
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)
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def save_diffusers_patterns():
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.VAE,
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format_name=utils.FORMAT_NAMES.DIFFUSERS,
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weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors",
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)
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.UNET,
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format_name=utils.FORMAT_NAMES.DIFFUSERS,
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weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors",
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)
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.TEXT_ENCODER,
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format_name=utils.FORMAT_NAMES.DIFFUSERS,
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weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/text_encoder/model.fp16.safetensors",
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)
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def save_lora_patterns():
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filepath = "/Users/bryce/projects/sandbox-img-gen/refiners/weights/pytorch_lora_weights-refiners.safetensors"
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state_dict = open_weights(filepath, device="cpu")
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.LORA,
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format_name=utils.FORMAT_NAMES.REFINERS,
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weights_keys=list(state_dict.keys()),
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)
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save_weight_info(
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model_name=utils.MODEL_NAMES.SD15,
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component_name=utils.COMPONENT_NAMES.LORA,
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format_name=utils.FORMAT_NAMES.DIFFUSERS,
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weights_url="https://huggingface.co/pcuenq/pokemon-lora/resolve/main/pytorch_lora_weights.bin",
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)
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def save_weight_info(
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model_name, component_name, format_name, weights_url=None, weights_keys=None
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):
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if weights_keys is None and weights_url is None:
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msg = "Either weights_keys or weights_location must be provided"
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raise ValueError(msg)
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if weights_keys is None:
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weights_path = get_cached_url_path(weights_url, category="weights")
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state_dict = open_weights(weights_path, device="cpu")
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weights_keys = list(state_dict.keys())
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# prefixes = utils.prefixes_only(weights_keys)
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save_model_info(
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model_name=model_name,
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component_name=component_name,
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format_name=format_name,
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info_type="weights_keys",
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data=weights_keys,
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)
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patterns = find_state_dict_key_patterns(weights_keys)
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save_model_info(
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model_name=model_name,
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component_name=component_name,
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format_name=format_name,
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info_type="patterns",
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data=patterns,
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)
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def save_patterns():
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save_lora_patterns()
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# save_compvis_patterns()
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# save_diffusers_patterns()
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if __name__ == "__main__":
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save_patterns()
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