mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
tidied up scripts directory by moving the original CompViz scripts into a subfolder
This commit is contained in:
Lincoln Stein
41
scripts/orig_scripts/download_first_stages.sh
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41
scripts/orig_scripts/download_first_stages.sh
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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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49
scripts/orig_scripts/download_models.sh
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49
scripts/orig_scripts/download_models.sh
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@ -0,0 +1,49 @@
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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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293
scripts/orig_scripts/img2img.py
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293
scripts/orig_scripts/img2img.py
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"""make variations of input image"""
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import argparse, os, sys, glob
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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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import time
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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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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.cuda()
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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.*image - 1.
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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(
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"--init-img",
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type=str,
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nargs="?",
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help="path to the input image"
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)
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parser.add_argument(
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"--outdir",
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type=str,
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nargs="?",
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help="dir to write results to",
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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",
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type=str,
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help="evaluate at this precision",
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choices=["full", "autocast"],
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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("cuda") if torch.cuda.is_available() else torch.device("cpu")
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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. <= opt.strength <= 1., '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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with torch.no_grad():
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with precision_scope("cuda"):
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with model.ema_scope():
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tic = time.time()
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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(z_enc, c, t_enc, 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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|
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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. * 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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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')
|
||||
grid = make_grid(grid, nrow=n_rows)
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# to image
|
||||
grid = 255. * 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'))
|
||||
grid_count += 1
|
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|
||||
toc = time.time()
|
||||
|
||||
print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
|
||||
f" \nEnjoy.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
398
scripts/orig_scripts/knn2img.py
Normal file
398
scripts/orig_scripts/knn2img.py
Normal file
@ -0,0 +1,398 @@
|
||||
import argparse, os, sys, glob
|
||||
import clip
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
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, repeat
|
||||
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. * 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 = 255. * 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} \nEnjoy.")
|
||||
429
scripts/orig_scripts/latent_imagenet_diffusion.ipynb
Normal file
429
scripts/orig_scripts/latent_imagenet_diffusion.ipynb
Normal file
File diff suppressed because one or more lines are too long
313
scripts/orig_scripts/sample_diffusion.py
Normal file
313
scripts/orig_scripts/sample_diffusion.py
Normal file
@ -0,0 +1,313 @@
|
||||
import argparse, os, sys, glob, datetime, yaml
|
||||
import torch
|
||||
import time
|
||||
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
|
||||
|
||||
rescale = lambda x: (x + 1.) / 2.
|
||||
|
||||
def custom_to_pil(x):
|
||||
x = x.detach().cpu()
|
||||
x = torch.clamp(x, -1., 1.)
|
||||
x = (x + 1.) / 2.
|
||||
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:
|
||||
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.")
|
||||
147
scripts/orig_scripts/train_searcher.py
Normal file
147
scripts/orig_scripts/train_searcher.py
Normal file
@ -0,0 +1,147 @@
|
||||
import os, 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,)
|
||||
318
scripts/orig_scripts/txt2img.py
Normal file
318
scripts/orig_scripts/txt2img.py
Normal file
@ -0,0 +1,318 @@
|
||||
import argparse, os, sys, glob
|
||||
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 time
|
||||
from pytorch_lightning import seed_everything
|
||||
from torch import autocast
|
||||
from contextlib import contextmanager, nullcontext
|
||||
|
||||
import accelerate
|
||||
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
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
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"
|
||||
|
||||
seed_everything(opt.seed)
|
||||
|
||||
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)
|
||||
|
||||
#for klms
|
||||
model_wrap = K.external.CompVisDenoiser(model)
|
||||
accelerator = accelerate.Accelerator()
|
||||
device = accelerator.device
|
||||
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()
|
||||
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
|
||||
|
||||
start_code = None
|
||||
if opt.fixed_code:
|
||||
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
|
||||
|
||||
precision_scope = autocast if opt.precision=="autocast" else nullcontext
|
||||
with torch.no_grad():
|
||||
with precision_scope("cuda"):
|
||||
with model.ema_scope():
|
||||
tic = time.time()
|
||||
all_samples = list()
|
||||
for n in trange(opt.n_iter, desc="Sampling", disable =not accelerator.is_main_process):
|
||||
for prompts in tqdm(data, desc="data", disable =not accelerator.is_main_process):
|
||||
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, disable=not accelerator.is_main_process)
|
||||
|
||||
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 opt.klms:
|
||||
x_sample = accelerator.gather(x_samples_ddim)
|
||||
|
||||
if not opt.skip_save:
|
||||
for x_sample in x_samples_ddim:
|
||||
x_sample = 255. * 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. * 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
|
||||
|
||||
toc = time.time()
|
||||
|
||||
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