import argparse, os, sys, glob, random import torch import numpy as np import copy 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 from ldm.util import instantiate_from_config def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) def load_model_from_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"] return sd config = "optimizedSD/v1-inference.yaml" ckpt = "models/ldm/stable-diffusion-v1/model.ckpt" device = "cuda" 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( "--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=1, 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=5, 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( "--seed", type=int, default=42, help="the seed (for reproducible sampling)", ) parser.add_argument( "--small_batch", action='store_true', help="Reduce inference time when generate a smaller batch of images", ) parser.add_argument( "--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast" ) opt = parser.parse_args() tic = time.time() os.makedirs(opt.outdir, exist_ok=True) outpath = opt.outdir sample_path = os.path.join(outpath, "samples", "_".join(opt.prompt.split())[:255]) os.makedirs(sample_path, exist_ok=True) base_count = len(os.listdir(sample_path)) grid_count = len(os.listdir(outpath)) - 1 seed_everything(opt.seed) sd = load_model_from_config(f"{ckpt}") li = [] lo = [] for key, value in sd.items(): sp = key.split('.') if(sp[0]) == 'model': if('input_blocks' in sp): li.append(key) elif('middle_block' in sp): li.append(key) elif('time_embed' in sp): li.append(key) else: lo.append(key) for key in li: sd['model1.' + key[6:]] = sd.pop(key) for key in lo: sd['model2.' + key[6:]] = sd.pop(key) config = OmegaConf.load(f"{config}") config.modelUNet.params.ddim_steps = opt.ddim_steps if opt.small_batch: config.modelUNet.params.small_batch = True else: config.modelUNet.params.small_batch = False model = instantiate_from_config(config.modelUNet) _, _ = model.load_state_dict(sd, strict=False) model.eval() modelCS = instantiate_from_config(config.modelCondStage) _, _ = modelCS.load_state_dict(sd, strict=False) modelCS.eval() modelFS = instantiate_from_config(config.modelFirstStage) _, _ = modelFS.load_state_dict(sd, strict=False) modelFS.eval() if opt.precision == "autocast": model.half() modelCS.half() 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) 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)) precision_scope = autocast if opt.precision=="autocast" else nullcontext with torch.no_grad(): all_samples = list() for n in trange(opt.n_iter, desc="Sampling"): for prompts in tqdm(data, desc="data"): with precision_scope("cuda"): modelCS.to(device) uc = None if opt.scale != 1.0: uc = modelCS.get_learned_conditioning(batch_size * [""]) if isinstance(prompts, tuple): prompts = list(prompts) c = modelCS.get_learned_conditioning(prompts) shape = [opt.C, opt.H // opt.f, opt.W // opt.f] mem = torch.cuda.memory_allocated()/1e6 modelCS.to("cpu") while(torch.cuda.memory_allocated()/1e6 >= mem): time.sleep(1) samples_ddim = model.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) modelFS.to(device) print("saving images") for i in range(batch_size): x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0)) x_sample = 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[0].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 mem = torch.cuda.memory_allocated()/1e6 modelFS.to("cpu") while(torch.cuda.memory_allocated()/1e6 >= mem): time.sleep(1) # if not opt.skip_grid: # all_samples.append(x_samples_ddim) del samples_ddim print("memory_final = ", torch.cuda.memory_allocated()/1e6) # if not 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() time_taken = (toc-tic)/60.0 print(("Your samples are ready in {0:.2f} minutes and waiting for you here \n" + sample_path).format(time_taken))