405 lines
17 KiB
Python
405 lines
17 KiB
Python
import PIL
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import argparse, os, sys, glob
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import torch
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import torch.nn as nn
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import numpy as np
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import gradio as gr
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from omegaconf import OmegaConf
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from PIL import Image
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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 contextmanager, nullcontext
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import mimetypes
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import random
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import k_diffusion as K
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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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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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parser = argparse.ArgumentParser()
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parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
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parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
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parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
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parser.add_argument("--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)",)
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parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default='./GFPGAN')
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opt = parser.parse_args()
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GFPGAN_dir = opt.gfpgan_dir
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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_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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def load_GFPGAN():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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sys.path.append(os.path.abspath(GFPGAN_dir))
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from gfpgan import GFPGANer
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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GFPGAN = None
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if os.path.exists(GFPGAN_dir):
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try:
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GFPGAN = load_GFPGAN()
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print("Loaded GFPGAN")
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except Exception:
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import traceback
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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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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 image_grid(imgs, rows):
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cols = len(imgs) // rows
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w, h = imgs[0].size
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grid = Image.new('RGB', size=(cols * w, rows * h))
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for i, img in enumerate(imgs):
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use_GFPGAN: 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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outpath = opt.outdir or "outputs/txt2img-samples"
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if seed == -1:
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seed = random.randrange(4294967294)
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seed = int(seed)
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is_PLMS = sampler_name == 'PLMS'
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is_DDIM = sampler_name == 'DDIM'
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is_Kdif = sampler_name == 'k-diffusion'
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sampler = None
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if is_PLMS:
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sampler = PLMSSampler(model)
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elif is_DDIM:
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sampler = DDIMSampler(model)
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elif is_Kdif:
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pass
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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model_wrap = K.external.CompVisDenoiser(model)
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os.makedirs(outpath, exist_ok=True)
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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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assert prompt is not None
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data = [batch_size * [prompt]]
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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, height // opt_f, width // 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(), precision_scope("cuda"), model.ema_scope():
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all_samples = []
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for n in range(n_iter):
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for batch_index, prompts in enumerate(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, height // opt_f, width // opt_f]
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current_seed = seed + n * len(data) + batch_index
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torch.manual_seed(current_seed)
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if is_Kdif:
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sigmas = model_wrap.get_sigmas(ddim_steps)
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x = torch.randn([n_samples, *shape], device=device) * sigmas[0] # for GPU draw
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model_wrap_cfg = CFGDenoiser(model_wrap)
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
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elif sampler is not None:
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, 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 or not opt.skip_grid:
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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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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
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output_images.append(image)
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base_count += 1
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if not opt.skip_grid:
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all_samples.append(x_sample)
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if not opt.skip_grid:
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# additionally, save as grid
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grid = image_grid(output_images, rows=n_rows)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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if sampler is not None:
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del sampler
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info = f"""
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{prompt}
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Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip()
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return output_images, seed, info
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dream_interface = gr.Interface(
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dream,
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inputs=[
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gr.Textbox(label="Prompt", 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.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
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gr.Checkbox(label='Enable Fixed Code sampling', value=False),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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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=16, step=1, label='Sampling iterations', value=1),
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gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, 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(label="Images"),
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gr.Number(label='Seed'),
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gr.Textbox(label="Copy-paste generation parameters"),
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],
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title="Stable Diffusion Text-to-Image K",
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description="Generate images from text with Stable Diffusion (using K-LMS)",
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allow_flagging="never"
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)
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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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outpath = opt.outdir or "outputs/img2img-samples"
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if seed == -1:
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seed = random.randrange(4294967294)
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sampler = DDIMSampler(model)
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model_wrap = K.external.CompVisDenoiser(model)
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os.makedirs(outpath, exist_ok=True)
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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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assert prompt is not None
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data = [batch_size * [prompt]]
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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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seedit = 0
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image = init_img.convert("RGB")
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w, h = image.size
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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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x0 = init_latent
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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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all_samples = list()
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for n in range(n_iter):
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for batch_index, prompts in enumerate(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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sigmas = model_wrap.get_sigmas(ddim_steps)
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current_seed = seed + n * len(data) + batch_index
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torch.manual_seed(current_seed)
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noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw
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xi = x0 + noise
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sigma_sched = sigmas[ddim_steps - t_enc - 1:]
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# x = torch.randn([n_samples, *shape]).to(device) * sigmas[0] # for CPU draw
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model_wrap_cfg = CFGDenoiser(model_wrap)
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extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
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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 = Image.fromarray(x_sample.astype(np.uint8))
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image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
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output_images.append(image)
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base_count += 1
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seedit += 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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Image.fromarray(grid.astype(np.uint8))
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grid_count += 1
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del sampler
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return output_images, seed
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# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed
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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=50, 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=15.0, 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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