411 lines
16 KiB
Python
411 lines
16 KiB
Python
import PIL
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import gradio as gr
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import argparse, os, sys, glob
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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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import time
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from pytorch_lightning import seed_everything
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from torch import autocast
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import torch.nn as nn
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from contextlib import contextmanager, nullcontext
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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 k_diffusion.sampling import sample_lms
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from k_diffusion.external import CompVisDenoiser
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parser = argparse.ArgumentParser()
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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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"--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_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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"--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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"--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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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="cuda")
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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('cuda')
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model.eval()
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return model
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def load_img_pil(img_pil):
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image = img_pil.convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h})")
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w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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print(f"cropped image to size ({w}, {h})")
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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 load_img(path):
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return load_img_pil(Image.open(path))
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class CFGDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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return uncond + (cond - uncond) * cond_scale
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config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
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model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = model.half().to(device)
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def reshape_c_uc(c, uc):
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# I have no idea how to generate an empty tensor that's valid for the model,
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# so I'm gonna just pass in an empty prompt and hope it works!
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padding = model.get_learned_conditioning(["" for _ in range(c.shape[0])])
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while c.shape[1] != uc.shape[1]:
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if c.shape[1] > uc.shape[1]:
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uc = torch.cat([uc, padding], dim=1)
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else:
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c = torch.cat([c, padding], dim=1)
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return c, uc
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def dream(prompt: str, ddim_steps: int, sampler: str, fixed_code: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
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torch.cuda.empty_cache()
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opt.H = height
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opt.W = width
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rng_seed = seed_everything(seed)
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if sampler == 'plms':
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sampler = PLMSSampler(model)
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if sampler == 'ddim':
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sampler = DDIMSampler(model)
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if sampler == 'k_lms':
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model_wrap = CompVisDenoiser(model)
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opt.outdir = "outputs/txt2img-samples"
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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batch_size = 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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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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start_code = None
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if fixed_code:
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start_code = torch.randn([n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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precision_scope = autocast if opt.precision=="autocast" else nullcontext
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output_images = []
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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(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 cfg_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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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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if uc is not None:
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c, uc = reshape_c_uc(c, uc)
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if sampler == 'k_lms':
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sigmas = model_wrap.get_sigmas(ddim_steps)
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model_wrap_cfg = CFGDenoiser(model_wrap)
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x = torch.randn([n_samples, *shape], device=device) * sigmas[0]
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extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
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samples_ddim = sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=False)
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else:
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samples_ddim, _ = sampler.sample(S=ddim_steps,
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conditioning=c,
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batch_size=n_samples,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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eta=ddim_eta,
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x_T=start_code)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 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_ddim:
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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}-{rng_seed}_{prompt.replace(' ', '_')[:128]}.png"))
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output_images.append(Image.fromarray(x_sample.astype(np.uint8)))
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base_count += 1
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if not opt.skip_grid:
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all_samples.append(x_samples_ddim)
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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. * 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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toc = time.time()
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del sampler
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return output_images, rng_seed
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def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
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torch.cuda.empty_cache()
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rng_seed = seed_everything(seed)
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sampler = DDIMSampler(model)
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opt.outdir = "outputs/img2img-samples"
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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batch_size = 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 = 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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image = init_img.convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h})")
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w, h = map(lambda x: x - x % 32, (width, height)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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print(f"cropped image to size ({w}, {h})")
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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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output_images = []
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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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init_image = 2.*image - 1.
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init_image = init_image.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=ddim_steps, ddim_eta=ddim_eta, verbose=False)
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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t_enc = int(denoising_strength * ddim_steps)
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print(f"target t_enc is {t_enc} steps")
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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(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 cfg_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=cfg_scale,
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unconditional_conditioning=uc,)
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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. * 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}-{rng_seed}_{prompt.replace(' ', '_')[:128]}.png"))
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output_images.append(Image.fromarray(x_sample.astype(np.uint8)))
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base_count += 1
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all_samples.append(x_samples)
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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. * 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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Image.fromarray(grid.astype(np.uint8))
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grid_count += 1
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toc = time.time()
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del sampler
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return output_images, rng_seed
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dream_interface = gr.Interface(
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dream,
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inputs=[
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gr.Textbox(placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Dropdown(choices=['plms', 'ddim', 'k_lms'], value='k_lms', label='Sampler'),
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gr.Checkbox(label='Enable Fixed Code sampling', value=False),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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gr.Slider(minimum=1, maximum=8, step=1, label='Sampling iterations', value=2),
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gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
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gr.Slider(minimum=1.0, maximum=20.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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],
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outputs=[
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gr.Gallery(),
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gr.Number(label='Seed')
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],
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title="Stable Diffusion Text-to-Image",
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description="Generate images from text with Stable Diffusion",
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)
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img2img_interface = gr.Interface(
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translation,
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inputs=[
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gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
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gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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gr.Slider(minimum=1, maximum=8, step=1, label='Sampling iterations', value=2),
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gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
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gr.Slider(minimum=1.0, maximum=20.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Width", value=512),
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],
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outputs=[
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gr.Gallery(),
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gr.Number(label='Seed')
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],
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title="Stable Diffusion Image-to-Image",
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description="Generate images from images with Stable Diffusion",
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)
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demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"])
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demo.launch()
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