diff --git a/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-inference.yaml b/configs/stable-diffusion/v1-inference.yaml similarity index 100% rename from configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-inference.yaml rename to configs/stable-diffusion/v1-inference.yaml diff --git a/scripts/prune-ckpt.py b/scripts/prune-ckpt.py index e79c137..f7a57c3 100644 --- a/scripts/prune-ckpt.py +++ b/scripts/prune-ckpt.py @@ -3,7 +3,7 @@ import torch import fire -def prune_it(p): +def prune_it(p, keep_only_ema=False): print(f"prunin' in path: {p}") size_initial = os.path.getsize(p) nsd = dict() @@ -16,12 +16,30 @@ def prune_it(p): print(f"removing optimizer states for path {p}") if "global_step" in sd: print(f"This is global step {sd['global_step']}.") - fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" + if keep_only_ema: + sd = nsd["state_dict"].copy() + # infer ema keys + ema_keys = {k: "model_ema." + k[6:].replace(".", "") for k in sd.keys() if k.startswith("model.")} + new_sd = dict() + + for k in sd: + if k in ema_keys: + new_sd[k] = sd[ema_keys[k]] + elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]: + new_sd[k] = sd[k] + + assert len(new_sd) == len(sd) - len(ema_keys) + nsd["state_dict"] = new_sd + + fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" if not keep_only_ema else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt" print(f"saving pruned checkpoint at: {fn}") torch.save(nsd, fn) newsize = os.path.getsize(fn) - print(f"New ckpt size: {newsize*1e-9:.2f} GB. " - f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states") + MSG = f"New ckpt size: {newsize*1e-9:.2f} GB. " + \ + f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states" + if keep_only_ema: + MSG += " and non-EMA weights" + print(MSG) if __name__ == "__main__":