mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
chore: remove unused files
This commit is contained in:
psychedelicious
@ -1,14 +0,0 @@
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#!/usr/bin/env python
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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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import warnings
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from invokeai.app.cli_app import invoke_cli
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warnings.warn(
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"dream.py is being deprecated, please run invoke.py for the " "new UI/API or legacy_api.py for the old API",
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DeprecationWarning,
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)
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invoke_cli()
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@ -1,4 +0,0 @@
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from invokeai.backend.install.migrate_to_3 import main
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if __name__=='__main__':
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main()
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@ -1,41 +0,0 @@
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#!/bin/bash
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wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip
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wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip
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wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
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wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip
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wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
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wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip
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wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
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wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
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wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip
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cd models/first_stage_models/kl-f4
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unzip -o model.zip
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cd ../kl-f8
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unzip -o model.zip
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cd ../kl-f16
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unzip -o model.zip
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cd ../kl-f32
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unzip -o model.zip
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cd ../vq-f4
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unzip -o model.zip
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cd ../vq-f4-noattn
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unzip -o model.zip
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cd ../vq-f8
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unzip -o model.zip
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cd ../vq-f8-n256
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unzip -o model.zip
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cd ../vq-f16
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unzip -o model.zip
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cd ../..
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@ -1,49 +0,0 @@
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#!/bin/bash
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wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip
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wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip
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wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip
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wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip
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wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip
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wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip
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wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip
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wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
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wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip
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wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
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wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip
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cd models/ldm/celeba256
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unzip -o celeba-256.zip
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cd ../ffhq256
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unzip -o ffhq-256.zip
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cd ../lsun_churches256
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unzip -o lsun_churches-256.zip
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cd ../lsun_beds256
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unzip -o lsun_beds-256.zip
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cd ../text2img256
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unzip -o model.zip
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cd ../cin256
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unzip -o model.zip
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cd ../semantic_synthesis512
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unzip -o model.zip
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cd ../semantic_synthesis256
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unzip -o model.zip
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cd ../bsr_sr
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unzip -o model.zip
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cd ../layout2img-openimages256
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unzip -o model.zip
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cd ../inpainting_big
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unzip -o model.zip
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cd ../..
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@ -1,285 +0,0 @@
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"""make variations of input image"""
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import argparse
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import os
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import PIL
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import torch
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import numpy as np
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from omegaconf import OmegaConf
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from PIL import Image
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from tqdm import tqdm, trange
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from itertools import islice
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from einops import rearrange, repeat
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from torchvision.utils import make_grid
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from torch import autocast
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from contextlib import nullcontext
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from pytorch_lightning import seed_everything
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.invoke.devices import choose_torch_device
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def chunk(it, size):
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it = iter(it)
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return iter(lambda: tuple(islice(it, size)), ())
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.to(choose_torch_device())
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model.eval()
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return model
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def load_img(path):
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image = Image.open(path).convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h}) from {path}")
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--prompt",
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type=str,
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nargs="?",
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default="a painting of a virus monster playing guitar",
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help="the prompt to render",
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)
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parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")
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parser.add_argument(
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"--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/img2img-samples"
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)
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parser.add_argument(
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"--skip_grid",
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action="store_true",
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help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
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)
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parser.add_argument(
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"--skip_save",
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action="store_true",
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help="do not save indiviual samples. For speed measurements.",
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)
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parser.add_argument(
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"--ddim_steps",
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type=int,
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default=50,
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help="number of ddim sampling steps",
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)
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parser.add_argument(
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"--plms",
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action="store_true",
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help="use plms sampling",
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)
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parser.add_argument(
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"--fixed_code",
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action="store_true",
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help="if enabled, uses the same starting code across all samples ",
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)
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parser.add_argument(
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"--ddim_eta",
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type=float,
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default=0.0,
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help="ddim eta (eta=0.0 corresponds to deterministic sampling",
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)
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parser.add_argument(
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"--n_iter",
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type=int,
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default=1,
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help="sample this often",
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)
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parser.add_argument(
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"--C",
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type=int,
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default=4,
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help="latent channels",
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)
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parser.add_argument(
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"--f",
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type=int,
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default=8,
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help="downsampling factor, most often 8 or 16",
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)
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parser.add_argument(
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"--n_samples",
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type=int,
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default=2,
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help="how many samples to produce for each given prompt. A.k.a batch size",
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)
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parser.add_argument(
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"--n_rows",
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type=int,
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default=0,
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help="rows in the grid (default: n_samples)",
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)
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parser.add_argument(
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"--scale",
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type=float,
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default=5.0,
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help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
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)
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parser.add_argument(
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"--strength",
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type=float,
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default=0.75,
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help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
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)
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parser.add_argument(
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"--from-file",
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type=str,
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help="if specified, load prompts from this file",
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)
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parser.add_argument(
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"--config",
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type=str,
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default="configs/stable-diffusion/v1-inference.yaml",
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help="path to config which constructs model",
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)
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parser.add_argument(
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"--ckpt",
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type=str,
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default="models/ldm/stable-diffusion-v1/model.ckpt",
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help="path to checkpoint of model",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=42,
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help="the seed (for reproducible sampling)",
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)
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parser.add_argument(
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"--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
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)
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opt = parser.parse_args()
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seed_everything(opt.seed)
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config = OmegaConf.load(f"{opt.config}")
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model = load_model_from_config(config, f"{opt.ckpt}")
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device = torch.device(choose_torch_device())
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model = model.to(device)
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if opt.plms:
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raise NotImplementedError("PLMS sampler not (yet) supported")
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sampler = PLMSSampler(model)
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else:
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sampler = DDIMSampler(model)
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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batch_size = opt.n_samples
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n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
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if not opt.from_file:
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prompt = opt.prompt
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assert prompt is not None
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data = [batch_size * [prompt]]
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else:
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print(f"reading prompts from {opt.from_file}")
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with open(opt.from_file, "r") as f:
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data = f.read().splitlines()
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data = list(chunk(data, batch_size))
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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assert os.path.isfile(opt.init_img)
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init_image = load_img(opt.init_img).to(device)
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init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
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sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
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assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
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t_enc = int(opt.strength * opt.ddim_steps)
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print(f"target t_enc is {t_enc} steps")
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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if device.type in ["mps", "cpu"]:
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precision_scope = nullcontext # have to use f32 on mps
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with torch.no_grad():
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with precision_scope(device.type):
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with model.ema_scope():
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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uc = None
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if opt.scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
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# decode it
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samples = sampler.decode(
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z_enc,
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c,
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t_enc,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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)
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x_samples = model.decode_first_stage(samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save:
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for x_sample in x_samples:
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x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png")
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)
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base_count += 1
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all_samples.append(x_samples)
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|
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if not opt.skip_grid:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, "n b c h w -> (n b) c h w")
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grid = make_grid(grid, nrow=n_rows)
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# to image
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grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
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grid_count += 1
|
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|
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
|
||||
|
||||
|
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if __name__ == "__main__":
|
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main()
|
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@ -1,94 +0,0 @@
|
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import argparse
|
||||
import glob
|
||||
import os
|
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from omegaconf import OmegaConf
|
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from PIL import Image
|
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from tqdm import tqdm
|
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import numpy as np
|
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import torch
|
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from main import instantiate_from_config
|
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from ldm.models.diffusion.ddim import DDIMSampler
|
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from ldm.invoke.devices import choose_torch_device
|
||||
|
||||
|
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def make_batch(image, mask, device):
|
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image = np.array(Image.open(image).convert("RGB"))
|
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image = image.astype(np.float32) / 255.0
|
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image = image[None].transpose(0, 3, 1, 2)
|
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image = torch.from_numpy(image)
|
||||
|
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mask = np.array(Image.open(mask).convert("L"))
|
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mask = mask.astype(np.float32) / 255.0
|
||||
mask = mask[None, None]
|
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mask[mask < 0.5] = 0
|
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mask[mask >= 0.5] = 1
|
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mask = torch.from_numpy(mask)
|
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|
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masked_image = (1 - mask) * image
|
||||
|
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batch = {"image": image, "mask": mask, "masked_image": masked_image}
|
||||
for k in batch:
|
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batch[k] = batch[k].to(device=device)
|
||||
batch[k] = batch[k] * 2.0 - 1.0
|
||||
return batch
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--indir",
|
||||
type=str,
|
||||
nargs="?",
|
||||
help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--outdir",
|
||||
type=str,
|
||||
nargs="?",
|
||||
help="dir to write results to",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--steps",
|
||||
type=int,
|
||||
default=50,
|
||||
help="number of ddim sampling steps",
|
||||
)
|
||||
opt = parser.parse_args()
|
||||
|
||||
masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png")))
|
||||
images = [x.replace("_mask.png", ".png") for x in masks]
|
||||
print(f"Found {len(masks)} inputs.")
|
||||
|
||||
config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
|
||||
model = instantiate_from_config(config.model)
|
||||
model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False)
|
||||
|
||||
device = choose_torch_device()
|
||||
model = model.to(device)
|
||||
sampler = DDIMSampler(model)
|
||||
|
||||
os.makedirs(opt.outdir, exist_ok=True)
|
||||
with torch.no_grad():
|
||||
with model.ema_scope():
|
||||
for image, mask in tqdm(zip(images, masks)):
|
||||
outpath = os.path.join(opt.outdir, os.path.split(image)[1])
|
||||
batch = make_batch(image, mask, device=device)
|
||||
|
||||
# encode masked image and concat downsampled mask
|
||||
c = model.cond_stage_model.encode(batch["masked_image"])
|
||||
cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:])
|
||||
c = torch.cat((c, cc), dim=1)
|
||||
|
||||
shape = (c.shape[1] - 1,) + c.shape[2:]
|
||||
samples_ddim, _ = sampler.sample(
|
||||
S=opt.steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False
|
||||
)
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
|
||||
image = torch.clamp((batch["image"] + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
mask = torch.clamp((batch["mask"] + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
inpainted = (1 - mask) * image + mask * predicted_image
|
||||
inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
|
||||
Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
|
||||
@ -1,397 +0,0 @@
|
||||
import argparse
|
||||
import glob
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
from tqdm import tqdm, trange
|
||||
from itertools import islice
|
||||
from einops import rearrange
|
||||
from torchvision.utils import make_grid
|
||||
import scann
|
||||
import time
|
||||
from multiprocessing import cpu_count
|
||||
|
||||
from ldm.util import instantiate_from_config, parallel_data_prefetch
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
|
||||
|
||||
DATABASES = [
|
||||
"openimages",
|
||||
"artbench-art_nouveau",
|
||||
"artbench-baroque",
|
||||
"artbench-expressionism",
|
||||
"artbench-impressionism",
|
||||
"artbench-post_impressionism",
|
||||
"artbench-realism",
|
||||
"artbench-romanticism",
|
||||
"artbench-renaissance",
|
||||
"artbench-surrealism",
|
||||
"artbench-ukiyo_e",
|
||||
]
|
||||
|
||||
|
||||
def chunk(it, size):
|
||||
it = iter(it)
|
||||
return iter(lambda: tuple(islice(it, size)), ())
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
model.cuda()
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
class Searcher(object):
|
||||
def __init__(self, database, retriever_version="ViT-L/14"):
|
||||
assert database in DATABASES
|
||||
# self.database = self.load_database(database)
|
||||
self.database_name = database
|
||||
self.searcher_savedir = f"data/rdm/searchers/{self.database_name}"
|
||||
self.database_path = f"data/rdm/retrieval_databases/{self.database_name}"
|
||||
self.retriever = self.load_retriever(version=retriever_version)
|
||||
self.database = {"embedding": [], "img_id": [], "patch_coords": []}
|
||||
self.load_database()
|
||||
self.load_searcher()
|
||||
|
||||
def train_searcher(self, k, metric="dot_product", searcher_savedir=None):
|
||||
print("Start training searcher")
|
||||
searcher = scann.scann_ops_pybind.builder(
|
||||
self.database["embedding"] / np.linalg.norm(self.database["embedding"], axis=1)[:, np.newaxis], k, metric
|
||||
)
|
||||
self.searcher = searcher.score_brute_force().build()
|
||||
print("Finish training searcher")
|
||||
|
||||
if searcher_savedir is not None:
|
||||
print(f'Save trained searcher under "{searcher_savedir}"')
|
||||
os.makedirs(searcher_savedir, exist_ok=True)
|
||||
self.searcher.serialize(searcher_savedir)
|
||||
|
||||
def load_single_file(self, saved_embeddings):
|
||||
compressed = np.load(saved_embeddings)
|
||||
self.database = {key: compressed[key] for key in compressed.files}
|
||||
print("Finished loading of clip embeddings.")
|
||||
|
||||
def load_multi_files(self, data_archive):
|
||||
out_data = {key: [] for key in self.database}
|
||||
for d in tqdm(data_archive, desc=f"Loading datapool from {len(data_archive)} individual files."):
|
||||
for key in d.files:
|
||||
out_data[key].append(d[key])
|
||||
|
||||
return out_data
|
||||
|
||||
def load_database(self):
|
||||
print(f'Load saved patch embedding from "{self.database_path}"')
|
||||
file_content = glob.glob(os.path.join(self.database_path, "*.npz"))
|
||||
|
||||
if len(file_content) == 1:
|
||||
self.load_single_file(file_content[0])
|
||||
elif len(file_content) > 1:
|
||||
data = [np.load(f) for f in file_content]
|
||||
prefetched_data = parallel_data_prefetch(
|
||||
self.load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type="dict"
|
||||
)
|
||||
|
||||
self.database = {
|
||||
key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in self.database
|
||||
}
|
||||
else:
|
||||
raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
|
||||
|
||||
print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
|
||||
|
||||
def load_retriever(
|
||||
self,
|
||||
version="ViT-L/14",
|
||||
):
|
||||
model = FrozenClipImageEmbedder(model=version)
|
||||
if torch.cuda.is_available():
|
||||
model.cuda()
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
def load_searcher(self):
|
||||
print(f"load searcher for database {self.database_name} from {self.searcher_savedir}")
|
||||
self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
|
||||
print("Finished loading searcher.")
|
||||
|
||||
def search(self, x, k):
|
||||
if self.searcher is None and self.database["embedding"].shape[0] < 2e4:
|
||||
self.train_searcher(k) # quickly fit searcher on the fly for small databases
|
||||
assert self.searcher is not None, "Cannot search with uninitialized searcher"
|
||||
if isinstance(x, torch.Tensor):
|
||||
x = x.detach().cpu().numpy()
|
||||
if len(x.shape) == 3:
|
||||
x = x[:, 0]
|
||||
query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
|
||||
|
||||
start = time.time()
|
||||
nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
|
||||
end = time.time()
|
||||
|
||||
out_embeddings = self.database["embedding"][nns]
|
||||
out_img_ids = self.database["img_id"][nns]
|
||||
out_pc = self.database["patch_coords"][nns]
|
||||
|
||||
out = {
|
||||
"nn_embeddings": out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
|
||||
"img_ids": out_img_ids,
|
||||
"patch_coords": out_pc,
|
||||
"queries": x,
|
||||
"exec_time": end - start,
|
||||
"nns": nns,
|
||||
"q_embeddings": query_embeddings,
|
||||
}
|
||||
|
||||
return out
|
||||
|
||||
def __call__(self, x, n):
|
||||
return self.search(x, n)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
# TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
|
||||
# TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
|
||||
parser.add_argument(
|
||||
"--prompt",
|
||||
type=str,
|
||||
nargs="?",
|
||||
default="a painting of a virus monster playing guitar",
|
||||
help="the prompt to render",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--skip_grid",
|
||||
action="store_true",
|
||||
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ddim_steps",
|
||||
type=int,
|
||||
default=50,
|
||||
help="number of ddim sampling steps",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--n_repeat",
|
||||
type=int,
|
||||
default=1,
|
||||
help="number of repeats in CLIP latent space",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--plms",
|
||||
action="store_true",
|
||||
help="use plms sampling",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ddim_eta",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n_iter",
|
||||
type=int,
|
||||
default=1,
|
||||
help="sample this often",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--H",
|
||||
type=int,
|
||||
default=768,
|
||||
help="image height, in pixel space",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--W",
|
||||
type=int,
|
||||
default=768,
|
||||
help="image width, in pixel space",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--n_samples",
|
||||
type=int,
|
||||
default=3,
|
||||
help="how many samples to produce for each given prompt. A.k.a batch size",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--n_rows",
|
||||
type=int,
|
||||
default=0,
|
||||
help="rows in the grid (default: n_samples)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--scale",
|
||||
type=float,
|
||||
default=5.0,
|
||||
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--from-file",
|
||||
type=str,
|
||||
help="if specified, load prompts from this file",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
type=str,
|
||||
default="configs/retrieval-augmented-diffusion/768x768.yaml",
|
||||
help="path to config which constructs model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ckpt",
|
||||
type=str,
|
||||
default="models/rdm/rdm768x768/model.ckpt",
|
||||
help="path to checkpoint of model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--clip_type",
|
||||
type=str,
|
||||
default="ViT-L/14",
|
||||
help="which CLIP model to use for retrieval and NN encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--database",
|
||||
type=str,
|
||||
default="artbench-surrealism",
|
||||
choices=DATABASES,
|
||||
help="The database used for the search, only applied when --use_neighbors=True",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_neighbors",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="Include neighbors in addition to text prompt for conditioning",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--knn",
|
||||
default=10,
|
||||
type=int,
|
||||
help="The number of included neighbors, only applied when --use_neighbors=True",
|
||||
)
|
||||
|
||||
opt = parser.parse_args()
|
||||
|
||||
config = OmegaConf.load(f"{opt.config}")
|
||||
model = load_model_from_config(config, f"{opt.ckpt}")
|
||||
|
||||
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
||||
model = model.to(device)
|
||||
|
||||
clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
|
||||
|
||||
if opt.plms:
|
||||
sampler = PLMSSampler(model)
|
||||
else:
|
||||
sampler = DDIMSampler(model)
|
||||
|
||||
os.makedirs(opt.outdir, exist_ok=True)
|
||||
outpath = opt.outdir
|
||||
|
||||
batch_size = opt.n_samples
|
||||
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
|
||||
if not opt.from_file:
|
||||
prompt = opt.prompt
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
|
||||
else:
|
||||
print(f"reading prompts from {opt.from_file}")
|
||||
with open(opt.from_file, "r") as f:
|
||||
data = f.read().splitlines()
|
||||
data = list(chunk(data, batch_size))
|
||||
|
||||
sample_path = os.path.join(outpath, "samples")
|
||||
os.makedirs(sample_path, exist_ok=True)
|
||||
base_count = len(os.listdir(sample_path))
|
||||
grid_count = len(os.listdir(outpath)) - 1
|
||||
|
||||
print(f"sampling scale for cfg is {opt.scale:.2f}")
|
||||
|
||||
searcher = None
|
||||
if opt.use_neighbors:
|
||||
searcher = Searcher(opt.database)
|
||||
|
||||
with torch.no_grad():
|
||||
with model.ema_scope():
|
||||
for n in trange(opt.n_iter, desc="Sampling"):
|
||||
all_samples = list()
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
print("sampling prompts:", prompts)
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
c = clip_text_encoder.encode(prompts)
|
||||
uc = None
|
||||
if searcher is not None:
|
||||
nn_dict = searcher(c, opt.knn)
|
||||
c = torch.cat([c, torch.from_numpy(nn_dict["nn_embeddings"]).cuda()], dim=1)
|
||||
if opt.scale != 1.0:
|
||||
uc = torch.zeros_like(c)
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
shape = [16, opt.H // 16, opt.W // 16] # note: currently hardcoded for f16 model
|
||||
samples_ddim, _ = sampler.sample(
|
||||
S=opt.ddim_steps,
|
||||
conditioning=c,
|
||||
batch_size=c.shape[0],
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt.scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt.ddim_eta,
|
||||
)
|
||||
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
for x_sample in x_samples_ddim:
|
||||
x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
|
||||
Image.fromarray(x_sample.astype(np.uint8)).save(
|
||||
os.path.join(sample_path, f"{base_count:05}.png")
|
||||
)
|
||||
base_count += 1
|
||||
all_samples.append(x_samples_ddim)
|
||||
|
||||
if not opt.skip_grid:
|
||||
# additionally, save as grid
|
||||
grid = torch.stack(all_samples, 0)
|
||||
grid = rearrange(grid, "n b c h w -> (n b) c h w")
|
||||
grid = make_grid(grid, nrow=n_rows)
|
||||
|
||||
# to image
|
||||
grid_np = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
|
||||
Image.fromarray(grid_np.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
|
||||
grid_count += 1
|
||||
|
||||
print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
|
||||
File diff suppressed because one or more lines are too long
@ -1,898 +0,0 @@
|
||||
import argparse
|
||||
import datetime
|
||||
import glob
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import time
|
||||
import torch
|
||||
import torchvision
|
||||
import pytorch_lightning as pl
|
||||
|
||||
from packaging import version
|
||||
from omegaconf import OmegaConf
|
||||
from torch.utils.data import DataLoader, Dataset
|
||||
from functools import partial
|
||||
from PIL import Image
|
||||
|
||||
from pytorch_lightning import seed_everything
|
||||
from pytorch_lightning.trainer import Trainer
|
||||
from pytorch_lightning.callbacks import Callback
|
||||
from pytorch_lightning.utilities.distributed import rank_zero_only
|
||||
from pytorch_lightning.utilities import rank_zero_info
|
||||
|
||||
from ldm.data.base import Txt2ImgIterableBaseDataset
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
def fix_func(orig):
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
|
||||
def new_func(*args, **kw):
|
||||
device = kw.get("device", "mps")
|
||||
kw["device"] = "cpu"
|
||||
return orig(*args, **kw).to(device)
|
||||
|
||||
return new_func
|
||||
return orig
|
||||
|
||||
|
||||
torch.rand = fix_func(torch.rand)
|
||||
torch.rand_like = fix_func(torch.rand_like)
|
||||
torch.randn = fix_func(torch.randn)
|
||||
torch.randn_like = fix_func(torch.randn_like)
|
||||
torch.randint = fix_func(torch.randint)
|
||||
torch.randint_like = fix_func(torch.randint_like)
|
||||
torch.bernoulli = fix_func(torch.bernoulli)
|
||||
torch.multinomial = fix_func(torch.multinomial)
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
sd = pl_sd["state_dict"]
|
||||
config.model.params.ckpt_path = ckpt
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
model.cuda()
|
||||
return model
|
||||
|
||||
|
||||
def get_parser(**parser_kwargs):
|
||||
def str2bool(v):
|
||||
if isinstance(v, bool):
|
||||
return v
|
||||
if v.lower() in ("yes", "true", "t", "y", "1"):
|
||||
return True
|
||||
elif v.lower() in ("no", "false", "f", "n", "0"):
|
||||
return False
|
||||
else:
|
||||
raise argparse.ArgumentTypeError("Boolean value expected.")
|
||||
|
||||
parser = argparse.ArgumentParser(**parser_kwargs)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--name",
|
||||
type=str,
|
||||
const=True,
|
||||
default="",
|
||||
nargs="?",
|
||||
help="postfix for logdir",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-r",
|
||||
"--resume",
|
||||
type=str,
|
||||
const=True,
|
||||
default="",
|
||||
nargs="?",
|
||||
help="resume from logdir or checkpoint in logdir",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-b",
|
||||
"--base",
|
||||
nargs="*",
|
||||
metavar="base_config.yaml",
|
||||
help="paths to base configs. Loaded from left-to-right. "
|
||||
"Parameters can be overwritten or added with command-line options of the form `--key value`.",
|
||||
default=list(),
|
||||
)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--train",
|
||||
type=str2bool,
|
||||
const=True,
|
||||
default=False,
|
||||
nargs="?",
|
||||
help="train",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-test",
|
||||
type=str2bool,
|
||||
const=True,
|
||||
default=False,
|
||||
nargs="?",
|
||||
help="disable test",
|
||||
)
|
||||
parser.add_argument("-p", "--project", help="name of new or path to existing project")
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--debug",
|
||||
type=str2bool,
|
||||
nargs="?",
|
||||
const=True,
|
||||
default=False,
|
||||
help="enable post-mortem debugging",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--seed",
|
||||
type=int,
|
||||
default=23,
|
||||
help="seed for seed_everything",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-f",
|
||||
"--postfix",
|
||||
type=str,
|
||||
default="",
|
||||
help="post-postfix for default name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-l",
|
||||
"--logdir",
|
||||
type=str,
|
||||
default="logs",
|
||||
help="directory for logging dat shit",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scale_lr",
|
||||
type=str2bool,
|
||||
nargs="?",
|
||||
const=True,
|
||||
default=True,
|
||||
help="scale base-lr by ngpu * batch_size * n_accumulate",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--datadir_in_name",
|
||||
type=str2bool,
|
||||
nargs="?",
|
||||
const=True,
|
||||
default=True,
|
||||
help="Prepend the final directory in the data_root to the output directory name",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--actual_resume",
|
||||
type=str,
|
||||
default="",
|
||||
help="Path to model to actually resume from",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--data_root",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to directory with training images",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--embedding_manager_ckpt",
|
||||
type=str,
|
||||
default="",
|
||||
help="Initialize embedding manager from a checkpoint",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--init_word",
|
||||
type=str,
|
||||
help="Word to use as source for initial token embedding.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def nondefault_trainer_args(opt):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser = Trainer.add_argparse_args(parser)
|
||||
args = parser.parse_args([])
|
||||
return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
|
||||
|
||||
|
||||
class WrappedDataset(Dataset):
|
||||
"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
|
||||
|
||||
def __init__(self, dataset):
|
||||
self.data = dataset
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.data[idx]
|
||||
|
||||
|
||||
def worker_init_fn(_):
|
||||
worker_info = torch.utils.data.get_worker_info()
|
||||
|
||||
dataset = worker_info.dataset
|
||||
worker_id = worker_info.id
|
||||
|
||||
if isinstance(dataset, Txt2ImgIterableBaseDataset):
|
||||
split_size = dataset.num_records // worker_info.num_workers
|
||||
# reset num_records to the true number to retain reliable length information
|
||||
dataset.sample_ids = dataset.valid_ids[worker_id * split_size : (worker_id + 1) * split_size]
|
||||
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
|
||||
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
|
||||
else:
|
||||
return np.random.seed(np.random.get_state()[1][0] + worker_id)
|
||||
|
||||
|
||||
class DataModuleFromConfig(pl.LightningDataModule):
|
||||
def __init__(
|
||||
self,
|
||||
batch_size,
|
||||
train=None,
|
||||
validation=None,
|
||||
test=None,
|
||||
predict=None,
|
||||
wrap=False,
|
||||
num_workers=None,
|
||||
shuffle_test_loader=False,
|
||||
use_worker_init_fn=False,
|
||||
shuffle_val_dataloader=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.batch_size = batch_size
|
||||
self.dataset_configs = dict()
|
||||
self.num_workers = num_workers if num_workers is not None else batch_size * 2
|
||||
self.use_worker_init_fn = use_worker_init_fn
|
||||
if train is not None:
|
||||
self.dataset_configs["train"] = train
|
||||
self.train_dataloader = self._train_dataloader
|
||||
if validation is not None:
|
||||
self.dataset_configs["validation"] = validation
|
||||
self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
|
||||
if test is not None:
|
||||
self.dataset_configs["test"] = test
|
||||
self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
|
||||
if predict is not None:
|
||||
self.dataset_configs["predict"] = predict
|
||||
self.predict_dataloader = self._predict_dataloader
|
||||
self.wrap = wrap
|
||||
|
||||
def prepare_data(self):
|
||||
for data_cfg in self.dataset_configs.values():
|
||||
instantiate_from_config(data_cfg)
|
||||
|
||||
def setup(self, stage=None):
|
||||
self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
|
||||
if self.wrap:
|
||||
for k in self.datasets:
|
||||
self.datasets[k] = WrappedDataset(self.datasets[k])
|
||||
|
||||
def _train_dataloader(self):
|
||||
is_iterable_dataset = isinstance(self.datasets["train"], Txt2ImgIterableBaseDataset)
|
||||
if is_iterable_dataset or self.use_worker_init_fn:
|
||||
init_fn = worker_init_fn
|
||||
else:
|
||||
init_fn = None
|
||||
return DataLoader(
|
||||
self.datasets["train"],
|
||||
batch_size=self.batch_size,
|
||||
num_workers=self.num_workers,
|
||||
shuffle=False if is_iterable_dataset else True,
|
||||
worker_init_fn=init_fn,
|
||||
)
|
||||
|
||||
def _val_dataloader(self, shuffle=False):
|
||||
if isinstance(self.datasets["validation"], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
|
||||
init_fn = worker_init_fn
|
||||
else:
|
||||
init_fn = None
|
||||
return DataLoader(
|
||||
self.datasets["validation"],
|
||||
batch_size=self.batch_size,
|
||||
num_workers=self.num_workers,
|
||||
worker_init_fn=init_fn,
|
||||
shuffle=shuffle,
|
||||
)
|
||||
|
||||
def _test_dataloader(self, shuffle=False):
|
||||
is_iterable_dataset = isinstance(self.datasets["train"], Txt2ImgIterableBaseDataset)
|
||||
if is_iterable_dataset or self.use_worker_init_fn:
|
||||
init_fn = worker_init_fn
|
||||
else:
|
||||
init_fn = None
|
||||
|
||||
# do not shuffle dataloader for iterable dataset
|
||||
shuffle = shuffle and (not is_iterable_dataset)
|
||||
|
||||
return DataLoader(
|
||||
self.datasets["test"],
|
||||
batch_size=self.batch_size,
|
||||
num_workers=self.num_workers,
|
||||
worker_init_fn=init_fn,
|
||||
shuffle=shuffle,
|
||||
)
|
||||
|
||||
def _predict_dataloader(self, shuffle=False):
|
||||
if isinstance(self.datasets["predict"], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
|
||||
init_fn = worker_init_fn
|
||||
else:
|
||||
init_fn = None
|
||||
return DataLoader(
|
||||
self.datasets["predict"],
|
||||
batch_size=self.batch_size,
|
||||
num_workers=self.num_workers,
|
||||
worker_init_fn=init_fn,
|
||||
)
|
||||
|
||||
|
||||
class SetupCallback(Callback):
|
||||
def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
|
||||
super().__init__()
|
||||
self.resume = resume
|
||||
self.now = now
|
||||
self.logdir = logdir
|
||||
self.ckptdir = ckptdir
|
||||
self.cfgdir = cfgdir
|
||||
self.config = config
|
||||
self.lightning_config = lightning_config
|
||||
|
||||
def on_keyboard_interrupt(self, trainer, pl_module):
|
||||
if trainer.global_rank == 0:
|
||||
print("Summoning checkpoint.")
|
||||
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
|
||||
trainer.save_checkpoint(ckpt_path)
|
||||
|
||||
def on_pretrain_routine_start(self, trainer, pl_module):
|
||||
if trainer.global_rank == 0:
|
||||
# Create logdirs and save configs
|
||||
os.makedirs(self.logdir, exist_ok=True)
|
||||
os.makedirs(self.ckptdir, exist_ok=True)
|
||||
os.makedirs(self.cfgdir, exist_ok=True)
|
||||
|
||||
if "callbacks" in self.lightning_config:
|
||||
if "metrics_over_trainsteps_checkpoint" in self.lightning_config["callbacks"]:
|
||||
os.makedirs(
|
||||
os.path.join(self.ckptdir, "trainstep_checkpoints"),
|
||||
exist_ok=True,
|
||||
)
|
||||
print("Project config")
|
||||
print(OmegaConf.to_yaml(self.config))
|
||||
OmegaConf.save(
|
||||
self.config,
|
||||
os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)),
|
||||
)
|
||||
|
||||
print("Lightning config")
|
||||
print(OmegaConf.to_yaml(self.lightning_config))
|
||||
OmegaConf.save(
|
||||
OmegaConf.create({"lightning": self.lightning_config}),
|
||||
os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)),
|
||||
)
|
||||
|
||||
else:
|
||||
# ModelCheckpoint callback created log directory --- remove it
|
||||
if not self.resume and os.path.exists(self.logdir):
|
||||
dst, name = os.path.split(self.logdir)
|
||||
dst = os.path.join(dst, "child_runs", name)
|
||||
os.makedirs(os.path.split(dst)[0], exist_ok=True)
|
||||
try:
|
||||
os.rename(self.logdir, dst)
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
|
||||
|
||||
class ImageLogger(Callback):
|
||||
def __init__(
|
||||
self,
|
||||
batch_frequency,
|
||||
max_images,
|
||||
clamp=True,
|
||||
increase_log_steps=True,
|
||||
rescale=True,
|
||||
disabled=False,
|
||||
log_on_batch_idx=False,
|
||||
log_first_step=False,
|
||||
log_images_kwargs=None,
|
||||
):
|
||||
super().__init__()
|
||||
self.rescale = rescale
|
||||
self.batch_freq = batch_frequency
|
||||
self.max_images = max_images
|
||||
self.logger_log_images = {}
|
||||
self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
|
||||
if not increase_log_steps:
|
||||
self.log_steps = [self.batch_freq]
|
||||
self.clamp = clamp
|
||||
self.disabled = disabled
|
||||
self.log_on_batch_idx = log_on_batch_idx
|
||||
self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
|
||||
self.log_first_step = log_first_step
|
||||
|
||||
@rank_zero_only
|
||||
def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
|
||||
root = os.path.join(save_dir, "images", split)
|
||||
for k in images:
|
||||
grid = torchvision.utils.make_grid(images[k], nrow=4)
|
||||
if self.rescale:
|
||||
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
|
||||
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
|
||||
grid = grid.numpy()
|
||||
grid = (grid * 255).astype(np.uint8)
|
||||
filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
|
||||
path = os.path.join(root, filename)
|
||||
os.makedirs(os.path.split(path)[0], exist_ok=True)
|
||||
Image.fromarray(grid).save(path)
|
||||
|
||||
def log_img(self, pl_module, batch, batch_idx, split="train"):
|
||||
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
|
||||
if (
|
||||
self.check_frequency(check_idx)
|
||||
and hasattr(pl_module, "log_images") # batch_idx % self.batch_freq == 0
|
||||
and callable(pl_module.log_images)
|
||||
and self.max_images > 0
|
||||
):
|
||||
logger = type(pl_module.logger)
|
||||
|
||||
is_train = pl_module.training
|
||||
if is_train:
|
||||
pl_module.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
|
||||
|
||||
for k in images:
|
||||
N = min(images[k].shape[0], self.max_images)
|
||||
images[k] = images[k][:N]
|
||||
if isinstance(images[k], torch.Tensor):
|
||||
images[k] = images[k].detach().cpu()
|
||||
if self.clamp:
|
||||
images[k] = torch.clamp(images[k], -1.0, 1.0)
|
||||
|
||||
self.log_local(
|
||||
pl_module.logger.save_dir,
|
||||
split,
|
||||
images,
|
||||
pl_module.global_step,
|
||||
pl_module.current_epoch,
|
||||
batch_idx,
|
||||
)
|
||||
|
||||
logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
|
||||
logger_log_images(pl_module, images, pl_module.global_step, split)
|
||||
|
||||
if is_train:
|
||||
pl_module.train()
|
||||
|
||||
def check_frequency(self, check_idx):
|
||||
if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
|
||||
check_idx > 0 or self.log_first_step
|
||||
):
|
||||
try:
|
||||
self.log_steps.pop(0)
|
||||
except IndexError as e:
|
||||
print(e)
|
||||
pass
|
||||
return True
|
||||
return False
|
||||
|
||||
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None):
|
||||
if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
|
||||
self.log_img(pl_module, batch, batch_idx, split="train")
|
||||
|
||||
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None):
|
||||
if not self.disabled and pl_module.global_step > 0:
|
||||
self.log_img(pl_module, batch, batch_idx, split="val")
|
||||
if hasattr(pl_module, "calibrate_grad_norm"):
|
||||
if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
|
||||
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
|
||||
|
||||
|
||||
class CUDACallback(Callback):
|
||||
# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
|
||||
def on_train_epoch_start(self, trainer, pl_module):
|
||||
# Reset the memory use counter
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
|
||||
torch.cuda.synchronize(trainer.root_gpu)
|
||||
self.start_time = time.time()
|
||||
|
||||
def on_train_epoch_end(self, trainer, pl_module, outputs=None):
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.synchronize(trainer.root_gpu)
|
||||
epoch_time = time.time() - self.start_time
|
||||
|
||||
try:
|
||||
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
|
||||
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
|
||||
max_memory = trainer.training_type_plugin.reduce(max_memory)
|
||||
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
|
||||
class ModeSwapCallback(Callback):
|
||||
def __init__(self, swap_step=2000):
|
||||
super().__init__()
|
||||
self.is_frozen = False
|
||||
self.swap_step = swap_step
|
||||
|
||||
def on_train_epoch_start(self, trainer, pl_module):
|
||||
if trainer.global_step < self.swap_step and not self.is_frozen:
|
||||
self.is_frozen = True
|
||||
trainer.optimizers = [pl_module.configure_opt_embedding()]
|
||||
|
||||
if trainer.global_step > self.swap_step and self.is_frozen:
|
||||
self.is_frozen = False
|
||||
trainer.optimizers = [pl_module.configure_opt_model()]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# custom parser to specify config files, train, test and debug mode,
|
||||
# postfix, resume.
|
||||
# `--key value` arguments are interpreted as arguments to the trainer.
|
||||
# `nested.key=value` arguments are interpreted as config parameters.
|
||||
# configs are merged from left-to-right followed by command line parameters.
|
||||
|
||||
# model:
|
||||
# base_learning_rate: float
|
||||
# target: path to lightning module
|
||||
# params:
|
||||
# key: value
|
||||
# data:
|
||||
# target: main.DataModuleFromConfig
|
||||
# params:
|
||||
# batch_size: int
|
||||
# wrap: bool
|
||||
# train:
|
||||
# target: path to train dataset
|
||||
# params:
|
||||
# key: value
|
||||
# validation:
|
||||
# target: path to validation dataset
|
||||
# params:
|
||||
# key: value
|
||||
# test:
|
||||
# target: path to test dataset
|
||||
# params:
|
||||
# key: value
|
||||
# lightning: (optional, has sane defaults and can be specified on cmdline)
|
||||
# trainer:
|
||||
# additional arguments to trainer
|
||||
# logger:
|
||||
# logger to instantiate
|
||||
# modelcheckpoint:
|
||||
# modelcheckpoint to instantiate
|
||||
# callbacks:
|
||||
# callback1:
|
||||
# target: importpath
|
||||
# params:
|
||||
# key: value
|
||||
|
||||
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
||||
|
||||
# add cwd for convenience and to make classes in this file available when
|
||||
# running as `python main.py`
|
||||
# (in particular `main.DataModuleFromConfig`)
|
||||
sys.path.append(os.getcwd())
|
||||
|
||||
parser = get_parser()
|
||||
parser = Trainer.add_argparse_args(parser)
|
||||
|
||||
opt, unknown = parser.parse_known_args()
|
||||
if opt.name and opt.resume:
|
||||
raise ValueError(
|
||||
"-n/--name and -r/--resume cannot be specified both."
|
||||
"If you want to resume training in a new log folder, "
|
||||
"use -n/--name in combination with --resume_from_checkpoint"
|
||||
)
|
||||
if opt.resume:
|
||||
if not os.path.exists(opt.resume):
|
||||
raise ValueError("Cannot find {}".format(opt.resume))
|
||||
if os.path.isfile(opt.resume):
|
||||
paths = opt.resume.split("/")
|
||||
# idx = len(paths)-paths[::-1].index("logs")+1
|
||||
# logdir = "/".join(paths[:idx])
|
||||
logdir = "/".join(paths[:-2])
|
||||
ckpt = opt.resume
|
||||
else:
|
||||
assert os.path.isdir(opt.resume), opt.resume
|
||||
logdir = opt.resume.rstrip("/")
|
||||
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
|
||||
|
||||
opt.resume_from_checkpoint = ckpt
|
||||
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
|
||||
opt.base = base_configs + opt.base
|
||||
_tmp = logdir.split("/")
|
||||
nowname = _tmp[-1]
|
||||
else:
|
||||
if opt.name:
|
||||
name = "_" + opt.name
|
||||
elif opt.base:
|
||||
cfg_fname = os.path.split(opt.base[0])[-1]
|
||||
cfg_name = os.path.splitext(cfg_fname)[0]
|
||||
name = "_" + cfg_name
|
||||
else:
|
||||
name = ""
|
||||
|
||||
if opt.datadir_in_name:
|
||||
now = os.path.basename(os.path.normpath(opt.data_root)) + now
|
||||
|
||||
nowname = now + name + opt.postfix
|
||||
logdir = os.path.join(opt.logdir, nowname)
|
||||
|
||||
ckptdir = os.path.join(logdir, "checkpoints")
|
||||
cfgdir = os.path.join(logdir, "configs")
|
||||
seed_everything(opt.seed)
|
||||
|
||||
try:
|
||||
# init and save configs
|
||||
configs = [OmegaConf.load(cfg) for cfg in opt.base]
|
||||
cli = OmegaConf.from_dotlist(unknown)
|
||||
config = OmegaConf.merge(*configs, cli)
|
||||
lightning_config = config.pop("lightning", OmegaConf.create())
|
||||
# merge trainer cli with config
|
||||
trainer_config = lightning_config.get("trainer", OmegaConf.create())
|
||||
# default to ddp
|
||||
trainer_config["accelerator"] = "auto"
|
||||
for k in nondefault_trainer_args(opt):
|
||||
trainer_config[k] = getattr(opt, k)
|
||||
if "gpus" not in trainer_config:
|
||||
del trainer_config["accelerator"]
|
||||
cpu = True
|
||||
else:
|
||||
gpuinfo = trainer_config["gpus"]
|
||||
print(f"Running on GPUs {gpuinfo}")
|
||||
cpu = False
|
||||
trainer_opt = argparse.Namespace(**trainer_config)
|
||||
lightning_config.trainer = trainer_config
|
||||
|
||||
# model
|
||||
|
||||
# config.model.params.personalization_config.params.init_word = opt.init_word
|
||||
config.model.params.personalization_config.params.embedding_manager_ckpt = opt.embedding_manager_ckpt
|
||||
|
||||
if opt.init_word:
|
||||
config.model.params.personalization_config.params.initializer_words = [opt.init_word]
|
||||
|
||||
if opt.actual_resume:
|
||||
model = load_model_from_config(config, opt.actual_resume)
|
||||
else:
|
||||
model = instantiate_from_config(config.model)
|
||||
|
||||
# trainer and callbacks
|
||||
trainer_kwargs = dict()
|
||||
|
||||
# default logger configs
|
||||
def_logger = "csv"
|
||||
def_logger_target = "CSVLogger"
|
||||
default_logger_cfgs = {
|
||||
"wandb": {
|
||||
"target": "pytorch_lightning.loggers.WandbLogger",
|
||||
"params": {
|
||||
"name": nowname,
|
||||
"save_dir": logdir,
|
||||
"offline": opt.debug,
|
||||
"id": nowname,
|
||||
},
|
||||
},
|
||||
def_logger: {
|
||||
"target": "pytorch_lightning.loggers." + def_logger_target,
|
||||
"params": {
|
||||
"name": def_logger,
|
||||
"save_dir": logdir,
|
||||
},
|
||||
},
|
||||
}
|
||||
default_logger_cfg = default_logger_cfgs[def_logger]
|
||||
if "logger" in lightning_config:
|
||||
logger_cfg = lightning_config.logger
|
||||
else:
|
||||
logger_cfg = OmegaConf.create()
|
||||
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
|
||||
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
|
||||
|
||||
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
|
||||
# specify which metric is used to determine best models
|
||||
default_modelckpt_cfg = {
|
||||
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
|
||||
"params": {
|
||||
"dirpath": ckptdir,
|
||||
"filename": "{epoch:06}",
|
||||
"verbose": True,
|
||||
"save_last": True,
|
||||
},
|
||||
}
|
||||
if hasattr(model, "monitor"):
|
||||
print(f"Monitoring {model.monitor} as checkpoint metric.")
|
||||
default_modelckpt_cfg["params"]["monitor"] = model.monitor
|
||||
default_modelckpt_cfg["params"]["save_top_k"] = 1
|
||||
|
||||
if "modelcheckpoint" in lightning_config:
|
||||
modelckpt_cfg = lightning_config.modelcheckpoint
|
||||
else:
|
||||
modelckpt_cfg = OmegaConf.create()
|
||||
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
|
||||
print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
|
||||
if version.parse(pl.__version__) < version.parse("1.4.0"):
|
||||
trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
|
||||
|
||||
# add callback which sets up log directory
|
||||
default_callbacks_cfg = {
|
||||
"setup_callback": {
|
||||
"target": "main.SetupCallback",
|
||||
"params": {
|
||||
"resume": opt.resume,
|
||||
"now": now,
|
||||
"logdir": logdir,
|
||||
"ckptdir": ckptdir,
|
||||
"cfgdir": cfgdir,
|
||||
"config": config,
|
||||
"lightning_config": lightning_config,
|
||||
},
|
||||
},
|
||||
"image_logger": {
|
||||
"target": "main.ImageLogger",
|
||||
"params": {
|
||||
"batch_frequency": 750,
|
||||
"max_images": 4,
|
||||
"clamp": True,
|
||||
},
|
||||
},
|
||||
"learning_rate_logger": {
|
||||
"target": "main.LearningRateMonitor",
|
||||
"params": {
|
||||
"logging_interval": "step",
|
||||
# "log_momentum": True
|
||||
},
|
||||
},
|
||||
"cuda_callback": {"target": "main.CUDACallback"},
|
||||
}
|
||||
if version.parse(pl.__version__) >= version.parse("1.4.0"):
|
||||
default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
|
||||
|
||||
if "callbacks" in lightning_config:
|
||||
callbacks_cfg = lightning_config.callbacks
|
||||
else:
|
||||
callbacks_cfg = OmegaConf.create()
|
||||
|
||||
if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
|
||||
print(
|
||||
"Caution: Saving checkpoints every n train steps without deleting. This might require some free space."
|
||||
)
|
||||
default_metrics_over_trainsteps_ckpt_dict = {
|
||||
"metrics_over_trainsteps_checkpoint": {
|
||||
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
|
||||
"params": {
|
||||
"dirpath": os.path.join(ckptdir, "trainstep_checkpoints"),
|
||||
"filename": "{epoch:06}-{step:09}",
|
||||
"verbose": True,
|
||||
"save_top_k": -1,
|
||||
"every_n_train_steps": 10000,
|
||||
"save_weights_only": True,
|
||||
},
|
||||
}
|
||||
}
|
||||
default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
|
||||
|
||||
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
|
||||
if "ignore_keys_callback" in callbacks_cfg and hasattr(trainer_opt, "resume_from_checkpoint"):
|
||||
callbacks_cfg.ignore_keys_callback.params["ckpt_path"] = trainer_opt.resume_from_checkpoint
|
||||
elif "ignore_keys_callback" in callbacks_cfg:
|
||||
del callbacks_cfg["ignore_keys_callback"]
|
||||
|
||||
trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
|
||||
trainer_kwargs["max_steps"] = trainer_opt.max_steps
|
||||
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
trainer_opt.accelerator = "mps"
|
||||
trainer_opt.detect_anomaly = False
|
||||
|
||||
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
|
||||
trainer.logdir = logdir
|
||||
|
||||
# data
|
||||
config.data.params.train.params.data_root = opt.data_root
|
||||
config.data.params.validation.params.data_root = opt.data_root
|
||||
data = instantiate_from_config(config.data)
|
||||
|
||||
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
|
||||
# calling these ourselves should not be necessary but it is.
|
||||
# lightning still takes care of proper multiprocessing though
|
||||
data.prepare_data()
|
||||
data.setup()
|
||||
print("#### Data #####")
|
||||
for k in data.datasets:
|
||||
print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
|
||||
|
||||
# configure learning rate
|
||||
bs, base_lr = (
|
||||
config.data.params.batch_size,
|
||||
config.model.base_learning_rate,
|
||||
)
|
||||
if not cpu:
|
||||
gpus = str(lightning_config.trainer.gpus).strip(", ").split(",")
|
||||
ngpu = len(gpus)
|
||||
else:
|
||||
ngpu = 1
|
||||
if "accumulate_grad_batches" in lightning_config.trainer:
|
||||
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
|
||||
else:
|
||||
accumulate_grad_batches = 1
|
||||
print(f"accumulate_grad_batches = {accumulate_grad_batches}")
|
||||
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
|
||||
if opt.scale_lr:
|
||||
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
|
||||
print(
|
||||
"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
|
||||
model.learning_rate,
|
||||
accumulate_grad_batches,
|
||||
ngpu,
|
||||
bs,
|
||||
base_lr,
|
||||
)
|
||||
)
|
||||
else:
|
||||
model.learning_rate = base_lr
|
||||
print("++++ NOT USING LR SCALING ++++")
|
||||
print(f"Setting learning rate to {model.learning_rate:.2e}")
|
||||
|
||||
# allow checkpointing via USR1
|
||||
def melk(*args, **kwargs):
|
||||
# run all checkpoint hooks
|
||||
if trainer.global_rank == 0:
|
||||
print("Summoning checkpoint.")
|
||||
ckpt_path = os.path.join(ckptdir, "last.ckpt")
|
||||
trainer.save_checkpoint(ckpt_path)
|
||||
|
||||
def divein(*args, **kwargs):
|
||||
if trainer.global_rank == 0:
|
||||
import pudb
|
||||
|
||||
pudb.set_trace()
|
||||
|
||||
import signal
|
||||
|
||||
signal.signal(signal.SIGTERM, melk)
|
||||
signal.signal(signal.SIGTERM, divein)
|
||||
|
||||
# run
|
||||
if opt.train:
|
||||
try:
|
||||
trainer.fit(model, data)
|
||||
except Exception:
|
||||
melk()
|
||||
raise
|
||||
if not opt.no_test and not trainer.interrupted:
|
||||
trainer.test(model, data)
|
||||
except Exception:
|
||||
if opt.debug and trainer.global_rank == 0:
|
||||
try:
|
||||
import pudb as debugger
|
||||
except ImportError:
|
||||
import pdb as debugger
|
||||
debugger.post_mortem()
|
||||
raise
|
||||
finally:
|
||||
# move newly created debug project to debug_runs
|
||||
if opt.debug and not opt.resume and trainer.global_rank == 0:
|
||||
dst, name = os.path.split(logdir)
|
||||
dst = os.path.join(dst, "debug_runs", name)
|
||||
os.makedirs(os.path.split(dst)[0], exist_ok=True)
|
||||
os.rename(logdir, dst)
|
||||
# if trainer.global_rank == 0:
|
||||
# print(trainer.profiler.summary())
|
||||
@ -1,130 +0,0 @@
|
||||
from ldm.modules.encoders.modules import FrozenCLIPEmbedder, BERTEmbedder
|
||||
from ldm.modules.embedding_manager import EmbeddingManager
|
||||
from ldm.invoke.globals import Globals
|
||||
|
||||
import argparse
|
||||
from functools import partial
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def get_placeholder_loop(placeholder_string, embedder, use_bert):
|
||||
new_placeholder = None
|
||||
|
||||
while True:
|
||||
if new_placeholder is None:
|
||||
new_placeholder = input(
|
||||
f"Placeholder string {placeholder_string} was already used. Please enter a replacement string: "
|
||||
)
|
||||
else:
|
||||
new_placeholder = input(
|
||||
f"Placeholder string '{new_placeholder}' maps to more than a single token. Please enter another string: "
|
||||
)
|
||||
|
||||
token = (
|
||||
get_bert_token_for_string(embedder.tknz_fn, new_placeholder)
|
||||
if use_bert
|
||||
else get_clip_token_for_string(embedder.tokenizer, new_placeholder)
|
||||
)
|
||||
|
||||
if token is not None:
|
||||
return new_placeholder, token
|
||||
|
||||
|
||||
def get_clip_token_for_string(tokenizer, string):
|
||||
batch_encoding = tokenizer(
|
||||
string,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_length=True,
|
||||
return_overflowing_tokens=False,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
tokens = batch_encoding["input_ids"]
|
||||
|
||||
if torch.count_nonzero(tokens - 49407) == 2:
|
||||
return tokens[0, 1]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def get_bert_token_for_string(tokenizer, string):
|
||||
token = tokenizer(string)
|
||||
if torch.count_nonzero(token) == 3:
|
||||
return token[0, 1]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"--root_dir",
|
||||
type=str,
|
||||
default=".",
|
||||
help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--manager_ckpts", type=str, nargs="+", required=True, help="Paths to a set of embedding managers to be merged."
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--output_path",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Output path for the merged manager",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"-sd",
|
||||
"--use_bert",
|
||||
action="store_true",
|
||||
help="Flag to denote that we are not merging stable diffusion embeddings",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
Globals.root = args.root_dir
|
||||
|
||||
if args.use_bert:
|
||||
embedder = BERTEmbedder(n_embed=1280, n_layer=32).cuda()
|
||||
else:
|
||||
embedder = FrozenCLIPEmbedder().cuda()
|
||||
|
||||
EmbeddingManager = partial(EmbeddingManager, embedder, ["*"])
|
||||
|
||||
string_to_token_dict = {}
|
||||
string_to_param_dict = torch.nn.ParameterDict()
|
||||
|
||||
placeholder_to_src = {}
|
||||
|
||||
for manager_ckpt in args.manager_ckpts:
|
||||
print(f"Parsing {manager_ckpt}...")
|
||||
|
||||
manager = EmbeddingManager()
|
||||
manager.load(manager_ckpt)
|
||||
|
||||
for placeholder_string in manager.string_to_token_dict:
|
||||
if placeholder_string not in string_to_token_dict:
|
||||
string_to_token_dict[placeholder_string] = manager.string_to_token_dict[placeholder_string]
|
||||
string_to_param_dict[placeholder_string] = manager.string_to_param_dict[placeholder_string]
|
||||
|
||||
placeholder_to_src[placeholder_string] = manager_ckpt
|
||||
else:
|
||||
new_placeholder, new_token = get_placeholder_loop(placeholder_string, embedder, use_bert=args.use_bert)
|
||||
string_to_token_dict[new_placeholder] = new_token
|
||||
string_to_param_dict[new_placeholder] = manager.string_to_param_dict[placeholder_string]
|
||||
|
||||
placeholder_to_src[new_placeholder] = manager_ckpt
|
||||
|
||||
print("Saving combined manager...")
|
||||
merged_manager = EmbeddingManager()
|
||||
merged_manager.string_to_param_dict = string_to_param_dict
|
||||
merged_manager.string_to_token_dict = string_to_token_dict
|
||||
merged_manager.save(args.output_path)
|
||||
|
||||
print("Managers merged. Final list of placeholders: ")
|
||||
print(placeholder_to_src)
|
||||
@ -1,305 +0,0 @@
|
||||
import argparse
|
||||
import datetime
|
||||
import glob
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import yaml
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from tqdm import trange
|
||||
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
def rescale(x: float) -> float:
|
||||
return (x + 1.0) / 2.0
|
||||
|
||||
|
||||
def custom_to_pil(x):
|
||||
x = x.detach().cpu()
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
x = (x + 1.0) / 2.0
|
||||
x = x.permute(1, 2, 0).numpy()
|
||||
x = (255 * x).astype(np.uint8)
|
||||
x = Image.fromarray(x)
|
||||
if not x.mode == "RGB":
|
||||
x = x.convert("RGB")
|
||||
return x
|
||||
|
||||
|
||||
def custom_to_np(x):
|
||||
# saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py
|
||||
sample = x.detach().cpu()
|
||||
sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)
|
||||
sample = sample.permute(0, 2, 3, 1)
|
||||
sample = sample.contiguous()
|
||||
return sample
|
||||
|
||||
|
||||
def logs2pil(logs, keys=["sample"]):
|
||||
imgs = dict()
|
||||
for k in logs:
|
||||
try:
|
||||
if len(logs[k].shape) == 4:
|
||||
img = custom_to_pil(logs[k][0, ...])
|
||||
elif len(logs[k].shape) == 3:
|
||||
img = custom_to_pil(logs[k])
|
||||
else:
|
||||
print(f"Unknown format for key {k}. ")
|
||||
img = None
|
||||
except Exception:
|
||||
img = None
|
||||
imgs[k] = img
|
||||
return imgs
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convsample(model, shape, return_intermediates=True, verbose=True, make_prog_row=False):
|
||||
if not make_prog_row:
|
||||
return model.p_sample_loop(None, shape, return_intermediates=return_intermediates, verbose=verbose)
|
||||
else:
|
||||
return model.progressive_denoising(None, shape, verbose=True)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convsample_ddim(model, steps, shape, eta=1.0):
|
||||
ddim = DDIMSampler(model)
|
||||
bs = shape[0]
|
||||
shape = shape[1:]
|
||||
samples, intermediates = ddim.sample(
|
||||
steps,
|
||||
batch_size=bs,
|
||||
shape=shape,
|
||||
eta=eta,
|
||||
verbose=False,
|
||||
)
|
||||
return samples, intermediates
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def make_convolutional_sample(
|
||||
model,
|
||||
batch_size,
|
||||
vanilla=False,
|
||||
custom_steps=None,
|
||||
eta=1.0,
|
||||
):
|
||||
log = dict()
|
||||
|
||||
shape = [
|
||||
batch_size,
|
||||
model.model.diffusion_model.in_channels,
|
||||
model.model.diffusion_model.image_size,
|
||||
model.model.diffusion_model.image_size,
|
||||
]
|
||||
|
||||
with model.ema_scope("Plotting"):
|
||||
t0 = time.time()
|
||||
if vanilla:
|
||||
sample, progrow = convsample(model, shape, make_prog_row=True)
|
||||
else:
|
||||
sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape, eta=eta)
|
||||
|
||||
t1 = time.time()
|
||||
|
||||
x_sample = model.decode_first_stage(sample)
|
||||
|
||||
log["sample"] = x_sample
|
||||
log["time"] = t1 - t0
|
||||
log["throughput"] = sample.shape[0] / (t1 - t0)
|
||||
print(f'Throughput for this batch: {log["throughput"]}')
|
||||
return log
|
||||
|
||||
|
||||
def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
|
||||
if vanilla:
|
||||
print(f"Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.")
|
||||
else:
|
||||
print(f"Using DDIM sampling with {custom_steps} sampling steps and eta={eta}")
|
||||
|
||||
tstart = time.time()
|
||||
n_saved = len(glob.glob(os.path.join(logdir, "*.png"))) - 1
|
||||
# path = logdir
|
||||
if model.cond_stage_model is None:
|
||||
all_images = []
|
||||
|
||||
print(f"Running unconditional sampling for {n_samples} samples")
|
||||
for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
|
||||
logs = make_convolutional_sample(
|
||||
model, batch_size=batch_size, vanilla=vanilla, custom_steps=custom_steps, eta=eta
|
||||
)
|
||||
n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
|
||||
all_images.extend([custom_to_np(logs["sample"])])
|
||||
if n_saved >= n_samples:
|
||||
print(f"Finish after generating {n_saved} samples")
|
||||
break
|
||||
all_img = np.concatenate(all_images, axis=0)
|
||||
all_img = all_img[:n_samples]
|
||||
shape_str = "x".join([str(x) for x in all_img.shape])
|
||||
nppath = os.path.join(nplog, f"{shape_str}-samples.npz")
|
||||
np.savez(nppath, all_img)
|
||||
|
||||
else:
|
||||
raise NotImplementedError("Currently only sampling for unconditional models supported.")
|
||||
|
||||
print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")
|
||||
|
||||
|
||||
def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
|
||||
for k in logs:
|
||||
if k == key:
|
||||
batch = logs[key]
|
||||
if np_path is None:
|
||||
for x in batch:
|
||||
img = custom_to_pil(x)
|
||||
imgpath = os.path.join(path, f"{key}_{n_saved:06}.png")
|
||||
img.save(imgpath)
|
||||
n_saved += 1
|
||||
else:
|
||||
npbatch = custom_to_np(batch)
|
||||
shape_str = "x".join([str(x) for x in npbatch.shape])
|
||||
nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")
|
||||
np.savez(nppath, npbatch)
|
||||
n_saved += npbatch.shape[0]
|
||||
return n_saved
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-r",
|
||||
"--resume",
|
||||
type=str,
|
||||
nargs="?",
|
||||
help="load from logdir or checkpoint in logdir",
|
||||
)
|
||||
parser.add_argument("-n", "--n_samples", type=int, nargs="?", help="number of samples to draw", default=50000)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--eta",
|
||||
type=float,
|
||||
nargs="?",
|
||||
help="eta for ddim sampling (0.0 yields deterministic sampling)",
|
||||
default=1.0,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-v",
|
||||
"--vanilla_sample",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="vanilla sampling (default option is DDIM sampling)?",
|
||||
)
|
||||
parser.add_argument("-l", "--logdir", type=str, nargs="?", help="extra logdir", default="none")
|
||||
parser.add_argument(
|
||||
"-c", "--custom_steps", type=int, nargs="?", help="number of steps for ddim and fastdpm sampling", default=50
|
||||
)
|
||||
parser.add_argument("--batch_size", type=int, nargs="?", help="the bs", default=10)
|
||||
return parser
|
||||
|
||||
|
||||
def load_model_from_config(config, sd):
|
||||
model = instantiate_from_config(config)
|
||||
model.load_state_dict(sd, strict=False)
|
||||
model.cuda()
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def load_model(config, ckpt, gpu, eval_mode):
|
||||
if ckpt:
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
global_step = pl_sd["global_step"]
|
||||
else:
|
||||
pl_sd = {"state_dict": None}
|
||||
global_step = None
|
||||
model = load_model_from_config(config.model, pl_sd["state_dict"])
|
||||
|
||||
return model, global_step
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
|
||||
sys.path.append(os.getcwd())
|
||||
command = " ".join(sys.argv)
|
||||
|
||||
parser = get_parser()
|
||||
opt, unknown = parser.parse_known_args()
|
||||
ckpt = None
|
||||
|
||||
if not os.path.exists(opt.resume):
|
||||
raise ValueError("Cannot find {}".format(opt.resume))
|
||||
if os.path.isfile(opt.resume):
|
||||
# paths = opt.resume.split("/")
|
||||
try:
|
||||
logdir = "/".join(opt.resume.split("/")[:-1])
|
||||
# idx = len(paths)-paths[::-1].index("logs")+1
|
||||
print(f"Logdir is {logdir}")
|
||||
except ValueError:
|
||||
paths = opt.resume.split("/")
|
||||
idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
|
||||
logdir = "/".join(paths[:idx])
|
||||
ckpt = opt.resume
|
||||
else:
|
||||
assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"
|
||||
logdir = opt.resume.rstrip("/")
|
||||
ckpt = os.path.join(logdir, "model.ckpt")
|
||||
|
||||
base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))
|
||||
opt.base = base_configs
|
||||
|
||||
configs = [OmegaConf.load(cfg) for cfg in opt.base]
|
||||
cli = OmegaConf.from_dotlist(unknown)
|
||||
config = OmegaConf.merge(*configs, cli)
|
||||
|
||||
gpu = True
|
||||
eval_mode = True
|
||||
|
||||
if opt.logdir != "none":
|
||||
locallog = logdir.split(os.sep)[-1]
|
||||
if locallog == "":
|
||||
locallog = logdir.split(os.sep)[-2]
|
||||
print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
|
||||
logdir = os.path.join(opt.logdir, locallog)
|
||||
|
||||
print(config)
|
||||
|
||||
model, global_step = load_model(config, ckpt, gpu, eval_mode)
|
||||
print(f"global step: {global_step}")
|
||||
print(75 * "=")
|
||||
print("logging to:")
|
||||
logdir = os.path.join(logdir, "samples", f"{global_step:08}", now)
|
||||
imglogdir = os.path.join(logdir, "img")
|
||||
numpylogdir = os.path.join(logdir, "numpy")
|
||||
|
||||
os.makedirs(imglogdir)
|
||||
os.makedirs(numpylogdir)
|
||||
print(logdir)
|
||||
print(75 * "=")
|
||||
|
||||
# write config out
|
||||
sampling_file = os.path.join(logdir, "sampling_config.yaml")
|
||||
sampling_conf = vars(opt)
|
||||
|
||||
with open(sampling_file, "w") as f:
|
||||
yaml.dump(sampling_conf, f, default_flow_style=False)
|
||||
print(sampling_conf)
|
||||
|
||||
run(
|
||||
model,
|
||||
imglogdir,
|
||||
eta=opt.eta,
|
||||
vanilla=opt.vanilla_sample,
|
||||
n_samples=opt.n_samples,
|
||||
custom_steps=opt.custom_steps,
|
||||
batch_size=opt.batch_size,
|
||||
nplog=numpylogdir,
|
||||
)
|
||||
|
||||
print("done.")
|
||||
@ -1,169 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
import scann
|
||||
import argparse
|
||||
import glob
|
||||
from multiprocessing import cpu_count
|
||||
from tqdm import tqdm
|
||||
|
||||
from ldm.util import parallel_data_prefetch
|
||||
|
||||
|
||||
def search_bruteforce(searcher):
|
||||
return searcher.score_brute_force().build()
|
||||
|
||||
|
||||
def search_partioned_ah(
|
||||
searcher, dims_per_block, aiq_threshold, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search
|
||||
):
|
||||
return (
|
||||
searcher.tree(
|
||||
num_leaves=num_leaves, num_leaves_to_search=num_leaves_to_search, training_sample_size=partioning_trainsize
|
||||
)
|
||||
.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold)
|
||||
.reorder(reorder_k)
|
||||
.build()
|
||||
)
|
||||
|
||||
|
||||
def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
|
||||
return (
|
||||
searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
|
||||
)
|
||||
|
||||
|
||||
def load_datapool(dpath):
|
||||
def load_single_file(saved_embeddings):
|
||||
compressed = np.load(saved_embeddings)
|
||||
database = {key: compressed[key] for key in compressed.files}
|
||||
return database
|
||||
|
||||
def load_multi_files(data_archive):
|
||||
database = {key: [] for key in data_archive[0].files}
|
||||
for d in tqdm(data_archive, desc=f"Loading datapool from {len(data_archive)} individual files."):
|
||||
for key in d.files:
|
||||
database[key].append(d[key])
|
||||
|
||||
return database
|
||||
|
||||
print(f'Load saved patch embedding from "{dpath}"')
|
||||
file_content = glob.glob(os.path.join(dpath, "*.npz"))
|
||||
|
||||
if len(file_content) == 1:
|
||||
data_pool = load_single_file(file_content[0])
|
||||
elif len(file_content) > 1:
|
||||
data = [np.load(f) for f in file_content]
|
||||
prefetched_data = parallel_data_prefetch(
|
||||
load_multi_files, data, n_proc=min(len(data), cpu_count()), target_data_type="dict"
|
||||
)
|
||||
|
||||
data_pool = {
|
||||
key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()
|
||||
}
|
||||
else:
|
||||
raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
|
||||
|
||||
print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
|
||||
return data_pool
|
||||
|
||||
|
||||
def train_searcher(
|
||||
opt,
|
||||
metric="dot_product",
|
||||
partioning_trainsize=None,
|
||||
reorder_k=None,
|
||||
# todo tune
|
||||
aiq_thld=0.2,
|
||||
dims_per_block=2,
|
||||
num_leaves=None,
|
||||
num_leaves_to_search=None,
|
||||
):
|
||||
data_pool = load_datapool(opt.database)
|
||||
k = opt.knn
|
||||
|
||||
if not reorder_k:
|
||||
reorder_k = 2 * k
|
||||
|
||||
# normalize
|
||||
# embeddings =
|
||||
searcher = scann.scann_ops_pybind.builder(
|
||||
data_pool["embedding"] / np.linalg.norm(data_pool["embedding"], axis=1)[:, np.newaxis], k, metric
|
||||
)
|
||||
pool_size = data_pool["embedding"].shape[0]
|
||||
|
||||
print(*(["#"] * 100))
|
||||
print("Initializing scaNN searcher with the following values:")
|
||||
print(f"k: {k}")
|
||||
print(f"metric: {metric}")
|
||||
print(f"reorder_k: {reorder_k}")
|
||||
print(f"anisotropic_quantization_threshold: {aiq_thld}")
|
||||
print(f"dims_per_block: {dims_per_block}")
|
||||
print(*(["#"] * 100))
|
||||
print("Start training searcher....")
|
||||
print(f"N samples in pool is {pool_size}")
|
||||
|
||||
# this reflects the recommended design choices proposed at
|
||||
# https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
|
||||
if pool_size < 2e4:
|
||||
print("Using brute force search.")
|
||||
searcher = search_bruteforce(searcher)
|
||||
elif 2e4 <= pool_size and pool_size < 1e5:
|
||||
print("Using asymmetric hashing search and reordering.")
|
||||
searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
|
||||
else:
|
||||
print("Using using partioning, asymmetric hashing search and reordering.")
|
||||
|
||||
if not partioning_trainsize:
|
||||
partioning_trainsize = data_pool["embedding"].shape[0] // 10
|
||||
if not num_leaves:
|
||||
num_leaves = int(np.sqrt(pool_size))
|
||||
|
||||
if not num_leaves_to_search:
|
||||
num_leaves_to_search = max(num_leaves // 20, 1)
|
||||
|
||||
print("Partitioning params:")
|
||||
print(f"num_leaves: {num_leaves}")
|
||||
print(f"num_leaves_to_search: {num_leaves_to_search}")
|
||||
# self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
|
||||
searcher = search_partioned_ah(
|
||||
searcher, dims_per_block, aiq_thld, reorder_k, partioning_trainsize, num_leaves, num_leaves_to_search
|
||||
)
|
||||
|
||||
print("Finish training searcher")
|
||||
searcher_savedir = opt.target_path
|
||||
os.makedirs(searcher_savedir, exist_ok=True)
|
||||
searcher.serialize(searcher_savedir)
|
||||
print(f'Saved trained searcher under "{searcher_savedir}"')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.path.append(os.getcwd())
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--database",
|
||||
"-d",
|
||||
default="data/rdm/retrieval_databases/openimages",
|
||||
type=str,
|
||||
help="path to folder containing the clip feature of the database",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--target_path",
|
||||
"-t",
|
||||
default="data/rdm/searchers/openimages",
|
||||
type=str,
|
||||
help="path to the target folder where the searcher shall be stored.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--knn",
|
||||
"-k",
|
||||
default=20,
|
||||
type=int,
|
||||
help="number of nearest neighbors, for which the searcher shall be optimized",
|
||||
)
|
||||
|
||||
opt, _ = parser.parse_known_args()
|
||||
|
||||
train_searcher(
|
||||
opt,
|
||||
)
|
||||
@ -1,316 +0,0 @@
|
||||
import argparse
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
from tqdm import tqdm, trange
|
||||
from itertools import islice
|
||||
from einops import rearrange
|
||||
from torchvision.utils import make_grid
|
||||
from pytorch_lightning import seed_everything
|
||||
from torch import autocast
|
||||
from contextlib import nullcontext
|
||||
|
||||
import k_diffusion as K
|
||||
import torch.nn as nn
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from ldm.invoke.devices import choose_torch_device
|
||||
|
||||
|
||||
def chunk(it, size):
|
||||
it = iter(it)
|
||||
return iter(lambda: tuple(islice(it, size)), ())
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
model.to(choose_torch_device())
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"--prompt",
|
||||
type=str,
|
||||
nargs="?",
|
||||
default="a painting of a virus monster playing guitar",
|
||||
help="the prompt to render",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip_grid",
|
||||
action="store_true",
|
||||
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip_save",
|
||||
action="store_true",
|
||||
help="do not save individual samples. For speed measurements.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ddim_steps",
|
||||
type=int,
|
||||
default=50,
|
||||
help="number of ddim sampling steps",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--plms",
|
||||
action="store_true",
|
||||
help="use plms sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--klms",
|
||||
action="store_true",
|
||||
help="use klms sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--laion400m",
|
||||
action="store_true",
|
||||
help="uses the LAION400M model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fixed_code",
|
||||
action="store_true",
|
||||
help="if enabled, uses the same starting code across samples ",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ddim_eta",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n_iter",
|
||||
type=int,
|
||||
default=2,
|
||||
help="sample this often",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--H",
|
||||
type=int,
|
||||
default=512,
|
||||
help="image height, in pixel space",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--W",
|
||||
type=int,
|
||||
default=512,
|
||||
help="image width, in pixel space",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--C",
|
||||
type=int,
|
||||
default=4,
|
||||
help="latent channels",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--f",
|
||||
type=int,
|
||||
default=8,
|
||||
help="downsampling factor",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n_samples",
|
||||
type=int,
|
||||
default=3,
|
||||
help="how many samples to produce for each given prompt. A.k.a. batch size",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n_rows",
|
||||
type=int,
|
||||
default=0,
|
||||
help="rows in the grid (default: n_samples)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scale",
|
||||
type=float,
|
||||
default=7.5,
|
||||
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--from-file",
|
||||
type=str,
|
||||
help="if specified, load prompts from this file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
type=str,
|
||||
default="configs/stable-diffusion/v1-inference.yaml",
|
||||
help="path to config which constructs model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ckpt",
|
||||
type=str,
|
||||
default="models/ldm/stable-diffusion-v1/model.ckpt",
|
||||
help="path to checkpoint of model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=42,
|
||||
help="the seed (for reproducible sampling)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
|
||||
)
|
||||
opt = parser.parse_args()
|
||||
|
||||
if opt.laion400m:
|
||||
print("Falling back to LAION 400M model...")
|
||||
opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
|
||||
opt.ckpt = "models/ldm/text2img-large/model.ckpt"
|
||||
opt.outdir = "outputs/txt2img-samples-laion400m"
|
||||
|
||||
config = OmegaConf.load(f"{opt.config}")
|
||||
model = load_model_from_config(config, f"{opt.ckpt}")
|
||||
|
||||
seed_everything(opt.seed)
|
||||
|
||||
device = torch.device(choose_torch_device())
|
||||
model = model.to(device)
|
||||
|
||||
# for klms
|
||||
model_wrap = K.external.CompVisDenoiser(model)
|
||||
|
||||
class CFGDenoiser(nn.Module):
|
||||
def __init__(self, model):
|
||||
super().__init__()
|
||||
self.inner_model = model
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigma] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
||||
return uncond + (cond - uncond) * cond_scale
|
||||
|
||||
if opt.plms:
|
||||
sampler = PLMSSampler(model)
|
||||
else:
|
||||
sampler = DDIMSampler(model)
|
||||
|
||||
os.makedirs(opt.outdir, exist_ok=True)
|
||||
outpath = opt.outdir
|
||||
|
||||
batch_size = opt.n_samples
|
||||
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
|
||||
if not opt.from_file:
|
||||
prompt = opt.prompt
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
|
||||
else:
|
||||
print(f"reading prompts from {opt.from_file}")
|
||||
with open(opt.from_file, "r") as f:
|
||||
data = f.read().splitlines()
|
||||
if len(data) >= batch_size:
|
||||
data = list(chunk(data, batch_size))
|
||||
else:
|
||||
while len(data) < batch_size:
|
||||
data.append(data[-1])
|
||||
data = [data]
|
||||
|
||||
sample_path = os.path.join(outpath, "samples")
|
||||
os.makedirs(sample_path, exist_ok=True)
|
||||
base_count = len(os.listdir(sample_path))
|
||||
grid_count = len(os.listdir(outpath)) - 1
|
||||
|
||||
start_code = None
|
||||
if opt.fixed_code:
|
||||
shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]
|
||||
if device.type == "mps":
|
||||
start_code = torch.randn(shape, device="cpu").to(device)
|
||||
else:
|
||||
torch.randn(shape, device=device)
|
||||
|
||||
precision_scope = autocast if opt.precision == "autocast" else nullcontext
|
||||
if device.type in ["mps", "cpu"]:
|
||||
precision_scope = nullcontext # have to use f32 on mps
|
||||
with torch.no_grad():
|
||||
with precision_scope(device.type):
|
||||
with model.ema_scope():
|
||||
all_samples = list()
|
||||
for n in trange(opt.n_iter, desc="Sampling"):
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
uc = None
|
||||
if opt.scale != 1.0:
|
||||
uc = model.get_learned_conditioning(batch_size * [""])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
c = model.get_learned_conditioning(prompts)
|
||||
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
|
||||
|
||||
if not opt.klms:
|
||||
samples_ddim, _ = sampler.sample(
|
||||
S=opt.ddim_steps,
|
||||
conditioning=c,
|
||||
batch_size=opt.n_samples,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt.scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt.ddim_eta,
|
||||
x_T=start_code,
|
||||
)
|
||||
else:
|
||||
sigmas = model_wrap.get_sigmas(opt.ddim_steps)
|
||||
if start_code:
|
||||
x = start_code
|
||||
else:
|
||||
x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0] # for GPU draw
|
||||
model_wrap_cfg = CFGDenoiser(model_wrap)
|
||||
extra_args = {"cond": c, "uncond": uc, "cond_scale": opt.scale}
|
||||
samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
|
||||
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
if not opt.skip_save:
|
||||
for x_sample in x_samples_ddim:
|
||||
x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
|
||||
Image.fromarray(x_sample.astype(np.uint8)).save(
|
||||
os.path.join(sample_path, f"{base_count:05}.png")
|
||||
)
|
||||
base_count += 1
|
||||
|
||||
if not opt.skip_grid:
|
||||
all_samples.append(x_samples_ddim)
|
||||
|
||||
if not opt.skip_grid:
|
||||
# additionally, save as grid
|
||||
grid = torch.stack(all_samples, 0)
|
||||
grid = rearrange(grid, "n b c h w -> (n b) c h w")
|
||||
grid = make_grid(grid, nrow=n_rows)
|
||||
|
||||
# to image
|
||||
grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
|
||||
Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f"grid-{grid_count:04}.png"))
|
||||
grid_count += 1
|
||||
|
||||
print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user