1019 lines
38 KiB
Python
1019 lines
38 KiB
Python
import argparse, os, sys, glob
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from collections import namedtuple
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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, ImageFont, ImageDraw, PngImagePlugin
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from itertools import islice
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from einops import rearrange, repeat
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from torch import autocast
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import mimetypes
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import random
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import math
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import html
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import time
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import json
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import traceback
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import k_diffusion.sampling
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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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import ldm.modules.encoders.modules
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except:
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pass
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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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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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invalid_filename_chars = '<>:"/\|?*\n'
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config_filename = "config.json"
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parser = argparse.ArgumentParser()
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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("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) # i disagree with where you're putting it but since all guidefags are doing it this way, there you go
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)")
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cmd_opts = parser.parse_args()
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css_hide_progressbar = """
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.wrap .m-12 svg { display:none!important; }
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.wrap .m-12::before { content:"Loading..." }
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.progress-bar { display:none!important; }
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.meta-text { display:none!important; }
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"""
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SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
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samplers = [
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*[SamplerData(x[0], lambda model: KDiffusionSampler(model, x[1])) for x in [
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('LMS', 'sample_lms'),
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('Heun', 'sample_heun'),
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('Euler', 'sample_euler'),
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('Euler ancestral', 'sample_euler_ancestral'),
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('DPM 2', 'sample_dpm_2'),
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('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
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] if hasattr(k_diffusion.sampling, x[1])],
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SamplerData('DDIM', lambda model: DDIMSampler(model)),
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SamplerData('PLMS', lambda model: PLMSSampler(model)),
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]
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class Options:
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data = None
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data_labels = {
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"outdir": ("", "Output dictectory; if empty, defaults to 'outputs/*'"),
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"samples_save": (True, "Save indiviual samples"),
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"samples_format": ('png', 'File format for indiviual samples'),
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"grid_save": (True, "Save image grids"),
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"grid_format": ('png', 'File format for grids'),
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"grid_extended_filename": (False, "Add extended info (seed, prompt) to filename when saving grid"),
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"n_rows": (-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", -1, 16),
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"jpeg_quality": (80, "Quality for saved jpeg images", 1, 100),
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"verify_input": (True, "Check input, and produce warning if it's too long"),
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"enable_pnginfo": (True, "Save text information about generation parameters as chunks to png files"),
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"prompt_matrix_add_to_start": (True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
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}
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def __init__(self):
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self.data = {k: v[0] for k, v in self.data_labels.items()}
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def __setattr__(self, key, value):
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if self.data is not None:
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if key in self.data:
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self.data[key] = value
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return super(Options, self).__setattr__(key, value)
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def __getattr__(self, item):
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if self.data is not None:
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if item in self.data:
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return self.data[item]
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if item in self.data_labels:
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return self.data_labels[item][0]
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return super(Options, self).__getattribute__(item)
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def save(self, filename):
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with open(filename, "w", encoding="utf8") as file:
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json.dump(self.data, file)
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def load(self, filename):
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with open(filename, "r", encoding="utf8") as file:
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self.data = json.load(file)
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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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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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class KDiffusionSampler:
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def __init__(self, m, funcname):
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self.model = m
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self.model_wrap = k_diffusion.external.CompVisDenoiser(m)
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self.funcname = funcname
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def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
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sigmas = self.model_wrap.get_sigmas(S)
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x = x_T * sigmas[0]
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model_wrap_cfg = CFGDenoiser(self.model_wrap)
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fun = getattr(k_diffusion.sampling, self.funcname)
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samples_ddim = fun(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
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return samples_ddim, None
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def create_random_tensors(shape, seeds):
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xs = []
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for seed in seeds:
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torch.manual_seed(seed)
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# randn results depend on device; gpu and cpu get different results for same seed;
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# the way I see it, it's better to do this on CPU, so that everyone gets same result;
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# but the original script had it like this so i do not dare change it for now because
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# it will break everyone's seeds.
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xs.append(torch.randn(shape, device=device))
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x = torch.stack(xs)
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return x
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def torch_gc():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
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prompt = sanitize_filename_part(prompt)
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if short_filename:
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filename = f"{basename}.{extension}"
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else:
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filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
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if extension == 'png' and opts.enable_pnginfo:
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pnginfo = PngImagePlugin.PngInfo()
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pnginfo.add_text("parameters", info)
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else:
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pnginfo = None
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image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo)
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def plaintext_to_html(text):
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text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
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return text
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def load_GFPGAN():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(cmd_opts.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(cmd_opts.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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def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
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if force_n_rows is not None:
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rows = force_n_rows
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elif opts.n_rows > 0:
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rows = opts.n_rows
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elif opts.n_rows == 0:
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rows = batch_size
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else:
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rows = math.sqrt(len(imgs))
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rows = int(rows) if round_down else round(rows)
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cols = math.ceil(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), color='black')
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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 draw_prompt_matrix(im, width, height, all_prompts):
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def wrap(text, d, font, line_length):
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lines = ['']
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for word in text.split():
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line = f'{lines[-1]} {word}'.strip()
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if d.textlength(line, font=font) <= line_length:
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lines[-1] = line
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else:
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lines.append(word)
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return '\n'.join(lines)
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def draw_texts(pos, x, y, texts, sizes):
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for i, (text, size) in enumerate(zip(texts, sizes)):
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active = pos & (1 << i) != 0
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if not active:
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text = '\u0336'.join(text) + '\u0336'
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d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
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y += size[1] + line_spacing
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fontsize = (width + height) // 25
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line_spacing = fontsize // 2
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fnt = ImageFont.truetype("arial.ttf", fontsize)
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color_active = (0, 0, 0)
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color_inactive = (153, 153, 153)
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pad_top = height // 4
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pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
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cols = im.width // width
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rows = im.height // height
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prompts = all_prompts[1:]
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result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
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result.paste(im, (pad_left, pad_top))
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d = ImageDraw.Draw(result)
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boundary = math.ceil(len(prompts) / 2)
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prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
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prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]
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sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
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sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
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hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
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ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing
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for col in range(cols):
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x = pad_left + width * col + width / 2
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y = pad_top / 2 - hor_text_height / 2
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draw_texts(col, x, y, prompts_horiz, sizes_hor)
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for row in range(rows):
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x = pad_left / 2
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y = pad_top + height * row + height / 2 - ver_text_height / 2
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draw_texts(row, x, y, prompts_vert, sizes_ver)
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return result
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def resize_image(resize_mode, im, width, height):
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if resize_mode == 0:
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res = im.resize((width, height), resample=LANCZOS)
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elif resize_mode == 1:
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ratio = width / height
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src_ratio = im.width / im.height
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src_w = width if ratio > src_ratio else im.width * height // im.height
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src_h = height if ratio <= src_ratio else im.height * width // im.width
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resized = im.resize((src_w, src_h), resample=LANCZOS)
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res = Image.new("RGB", (width, height))
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res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
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else:
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ratio = width / height
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src_ratio = im.width / im.height
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src_w = width if ratio < src_ratio else im.width * height // im.height
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src_h = height if ratio >= src_ratio else im.height * width // im.width
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resized = im.resize((src_w, src_h), resample=LANCZOS)
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res = Image.new("RGB", (width, height))
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res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
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if ratio < src_ratio:
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fill_height = height // 2 - src_h // 2
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res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
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res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
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elif ratio > src_ratio:
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fill_width = width // 2 - src_w // 2
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res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
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res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
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return res
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def check_prompt_length(prompt, comments):
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"""this function tests if prompt is too long, and if so, adds a message to comments"""
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tokenizer = model.cond_stage_model.tokenizer
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max_length = model.cond_stage_model.max_length
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info = model.cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
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ovf = info['overflowing_tokens'][0]
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overflowing_count = ovf.shape[0]
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if overflowing_count == 0:
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return
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vocab = {v: k for k, v in tokenizer.get_vocab().items()}
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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def wrap_gradio_call(func):
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def f(*p1, **p2):
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t = time.perf_counter()
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res = list(func(*p1, **p2))
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elapsed = time.perf_counter() - t
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# last item is always HTML
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res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
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return tuple(res)
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return f
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GFPGAN = None
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if os.path.exists(cmd_opts.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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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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class TextInversionEmbeddings:
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ids_lookup = {}
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word_embeddings = {}
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word_embeddings_checksums = {}
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fixes = []
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used_custom_terms = []
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dir_mtime = None
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def load(self, dir, model):
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mt = os.path.getmtime(dir)
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if self.dir_mtime is not None and mt <= self.dir_mtime:
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return
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self.dir_mtime = mt
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self.ids_lookup.clear()
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self.word_embeddings.clear()
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tokenizer = model.cond_stage_model.tokenizer
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def const_hash(a):
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r = 0
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for v in a:
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r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
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return r
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def process_file(path, filename):
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name = os.path.splitext(filename)[0]
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data = torch.load(path)
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param_dict = data['string_to_param']
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1].reshape(768)
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self.word_embeddings[name] = emb
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self.word_embeddings_checksums[name] = f'{const_hash(emb)&0xffff:04x}'
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ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
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first_id = ids[0]
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if first_id not in self.ids_lookup:
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self.ids_lookup[first_id] = []
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self.ids_lookup[first_id].append((ids, name))
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for fn in os.listdir(dir):
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try:
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process_file(os.path.join(dir, fn), fn)
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except:
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print(f"Error loading emedding {fn}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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continue
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print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
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def hijack(self, m):
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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|
|
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
|
|
m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
|
|
|
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|
def __init__(self, wrapped, embeddings):
|
|
super().__init__()
|
|
self.wrapped = wrapped
|
|
self.embeddings = embeddings
|
|
self.tokenizer = wrapped.tokenizer
|
|
self.max_length = wrapped.max_length
|
|
|
|
def forward(self, text):
|
|
self.embeddings.fixes = []
|
|
self.embeddings.used_custom_terms = []
|
|
remade_batch_tokens = []
|
|
id_start = self.wrapped.tokenizer.bos_token_id
|
|
id_end = self.wrapped.tokenizer.eos_token_id
|
|
maxlen = self.wrapped.max_length - 2
|
|
|
|
cache = {}
|
|
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
|
|
for tokens in batch_tokens:
|
|
tuple_tokens = tuple(tokens)
|
|
|
|
if tuple_tokens in cache:
|
|
remade_tokens, fixes = cache[tuple_tokens]
|
|
else:
|
|
fixes = []
|
|
remade_tokens = []
|
|
|
|
i = 0
|
|
while i < len(tokens):
|
|
token = tokens[i]
|
|
|
|
possible_matches = self.embeddings.ids_lookup.get(token, None)
|
|
|
|
if possible_matches is None:
|
|
remade_tokens.append(token)
|
|
else:
|
|
found = False
|
|
for ids, word in possible_matches:
|
|
if tokens[i:i+len(ids)] == ids:
|
|
fixes.append((len(remade_tokens), word))
|
|
remade_tokens.append(777)
|
|
i += len(ids) - 1
|
|
found = True
|
|
self.embeddings.used_custom_terms.append((word, self.embeddings.word_embeddings_checksums[word]))
|
|
break
|
|
|
|
if not found:
|
|
remade_tokens.append(token)
|
|
|
|
i += 1
|
|
|
|
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
|
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
|
|
cache[tuple_tokens] = (remade_tokens, fixes)
|
|
|
|
remade_batch_tokens.append(remade_tokens)
|
|
self.embeddings.fixes.append(fixes)
|
|
|
|
tokens = torch.asarray(remade_batch_tokens).to(self.wrapped.device)
|
|
outputs = self.wrapped.transformer(input_ids=tokens)
|
|
z = outputs.last_hidden_state
|
|
return z
|
|
|
|
|
|
class EmbeddingsWithFixes(nn.Module):
|
|
def __init__(self, wrapped, embeddings):
|
|
super().__init__()
|
|
self.wrapped = wrapped
|
|
self.embeddings = embeddings
|
|
|
|
def forward(self, input_ids):
|
|
batch_fixes = self.embeddings.fixes
|
|
self.embeddings.fixes = []
|
|
|
|
inputs_embeds = self.wrapped(input_ids)
|
|
|
|
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
|
for offset, word in fixes:
|
|
tensor[offset] = self.embeddings.word_embeddings[word]
|
|
|
|
return inputs_embeds
|
|
|
|
|
|
def get_learned_conditioning_with_embeddings(model, prompts):
|
|
if os.path.exists(cmd_opts.embeddings_dir):
|
|
text_inversion_embeddings.load(cmd_opts.embeddings_dir, model)
|
|
|
|
return model.get_learned_conditioning(prompts)
|
|
|
|
|
|
def process_images(outpath, func_init, func_sample, prompt, seed, sampler_index, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False, extra_generation_params=None):
|
|
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
|
|
|
|
assert prompt is not None
|
|
torch_gc()
|
|
|
|
if seed == -1:
|
|
seed = random.randrange(4294967294)
|
|
seed = int(seed)
|
|
|
|
os.makedirs(outpath, exist_ok=True)
|
|
|
|
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
|
|
|
|
comments = []
|
|
|
|
prompt_matrix_parts = []
|
|
if prompt_matrix:
|
|
all_prompts = []
|
|
prompt_matrix_parts = prompt.split("|")
|
|
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
|
|
for combination_num in range(combination_count):
|
|
selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1<<n)]
|
|
|
|
if opts.prompt_matrix_add_to_start:
|
|
selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
|
|
else:
|
|
selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
|
|
|
|
all_prompts.append( ", ".join(selected_prompts))
|
|
|
|
n_iter = math.ceil(len(all_prompts) / batch_size)
|
|
all_seeds = len(all_prompts) * [seed]
|
|
|
|
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
|
|
else:
|
|
|
|
if opts.verify_input:
|
|
try:
|
|
check_prompt_length(prompt, comments)
|
|
except:
|
|
import traceback
|
|
print("Error verifying input:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
all_prompts = batch_size * n_iter * [prompt]
|
|
all_seeds = [seed + x for x in range(len(all_prompts))]
|
|
|
|
generation_params = {
|
|
"Steps": steps,
|
|
"Sampler": samplers[sampler_index].name,
|
|
"CFG scale": cfg_scale,
|
|
"Seed": seed,
|
|
"GFPGAN": ("GFPGAN" if use_GFPGAN and GFPGAN is not None else None)
|
|
}
|
|
|
|
if extra_generation_params is not None:
|
|
generation_params.update(extra_generation_params)
|
|
|
|
generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
|
|
|
|
def infotext():
|
|
return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
|
|
|
|
if os.path.exists(cmd_opts.embeddings_dir):
|
|
text_inversion_embeddings.load(cmd_opts.embeddings_dir, model)
|
|
|
|
output_images = []
|
|
with torch.no_grad(), autocast("cuda"), model.ema_scope():
|
|
init_data = func_init()
|
|
|
|
for n in range(n_iter):
|
|
prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
|
|
seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
|
|
|
|
uc = model.get_learned_conditioning(len(prompts) * [""])
|
|
c = model.get_learned_conditioning(prompts)
|
|
|
|
if len(text_inversion_embeddings.used_custom_terms) > 0:
|
|
comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in text_inversion_embeddings.used_custom_terms]))
|
|
|
|
# we manually generate all input noises because each one should have a specific seed
|
|
x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
|
|
|
|
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc)
|
|
|
|
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 prompt_matrix or opts.samples_save or opts.grid_save:
|
|
for i, x_sample in enumerate(x_samples_ddim):
|
|
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
|
x_sample = x_sample.astype(np.uint8)
|
|
|
|
if use_GFPGAN and GFPGAN is not None:
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
|
|
x_sample = restored_img
|
|
|
|
image = Image.fromarray(x_sample)
|
|
save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext())
|
|
|
|
output_images.append(image)
|
|
base_count += 1
|
|
|
|
if (prompt_matrix or opts.grid_save) and not do_not_save_grid:
|
|
grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
|
|
|
|
if prompt_matrix:
|
|
|
|
try:
|
|
grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
|
|
except Exception:
|
|
import traceback
|
|
print("Error creating prompt_matrix text:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
output_images.insert(0, grid)
|
|
|
|
save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
|
|
grid_count += 1
|
|
|
|
|
|
|
|
torch_gc()
|
|
return output_images, seed, infotext()
|
|
|
|
|
|
def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
|
|
outpath = opts.outdir or "outputs/txt2img-samples"
|
|
|
|
sampler = samplers[sampler_index].constructor(model)
|
|
|
|
def init():
|
|
pass
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning):
|
|
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x)
|
|
return samples_ddim
|
|
|
|
output_images, seed, info = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_index=sampler_index,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN
|
|
)
|
|
|
|
del sampler
|
|
|
|
return output_images, seed, plaintext_to_html(info)
|
|
|
|
|
|
class Flagging(gr.FlaggingCallback):
|
|
|
|
def setup(self, components, flagging_dir: str):
|
|
pass
|
|
|
|
def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
|
|
import csv
|
|
|
|
os.makedirs("log/images", exist_ok=True)
|
|
|
|
# those must match the "txt2img" function
|
|
prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data
|
|
|
|
filenames = []
|
|
|
|
with open("log/log.csv", "a", encoding="utf8", newline='') as file:
|
|
import time
|
|
import base64
|
|
|
|
at_start = file.tell() == 0
|
|
writer = csv.writer(file)
|
|
if at_start:
|
|
writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"])
|
|
|
|
filename_base = str(int(time.time() * 1000))
|
|
for i, filedata in enumerate(images):
|
|
filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
|
|
|
|
if filedata.startswith("data:image/png;base64,"):
|
|
filedata = filedata[len("data:image/png;base64,"):]
|
|
|
|
with open(filename, "wb") as imgfile:
|
|
imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
|
|
|
|
filenames.append(filename)
|
|
|
|
writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]])
|
|
|
|
print("Logged:", filenames[0])
|
|
|
|
|
|
txt2img_interface = gr.Interface(
|
|
wrap_gradio_call(txt2img),
|
|
inputs=[
|
|
gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
|
|
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
|
|
gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
|
|
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
|
|
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
|
|
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
|
|
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
|
|
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
|
|
gr.Number(label='Seed', value=-1),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
|
|
],
|
|
outputs=[
|
|
gr.Gallery(label="Images"),
|
|
gr.Number(label='Seed'),
|
|
gr.HTML(),
|
|
],
|
|
title="Stable Diffusion Text-to-Image",
|
|
flagging_callback=Flagging()
|
|
)
|
|
|
|
|
|
def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
|
|
outpath = opts.outdir or "outputs/img2img-samples"
|
|
|
|
sampler = KDiffusionSampler(model, 'sample_lms')
|
|
|
|
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
|
|
|
def init():
|
|
image = init_img.convert("RGB")
|
|
image = resize_image(resize_mode, image, width, height)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image)
|
|
|
|
init_image = 2. * image - 1.
|
|
init_image = init_image.to(device)
|
|
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
|
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
|
|
|
|
return init_latent,
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning):
|
|
t_enc = int(denoising_strength * ddim_steps)
|
|
|
|
x0, = init_data
|
|
|
|
sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
|
|
noise = x * sigmas[ddim_steps - t_enc - 1]
|
|
|
|
xi = x0 + noise
|
|
sigma_sched = sigmas[ddim_steps - t_enc - 1:]
|
|
model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
|
|
samples_ddim = k_diffusion.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
|
|
return samples_ddim
|
|
|
|
if loopback:
|
|
output_images, info = None, None
|
|
history = []
|
|
initial_seed = None
|
|
|
|
for i in range(n_iter):
|
|
output_images, seed, info = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_index=0,
|
|
batch_size=1,
|
|
n_iter=1,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN,
|
|
do_not_save_grid=True,
|
|
extra_generation_params = {"Denoising Strength": denoising_strength},
|
|
)
|
|
|
|
if initial_seed is None:
|
|
initial_seed = seed
|
|
|
|
init_img = output_images[0]
|
|
seed = seed + 1
|
|
denoising_strength = max(denoising_strength * 0.95, 0.1)
|
|
history.append(init_img)
|
|
|
|
grid_count = len(os.listdir(outpath)) - 1
|
|
grid = image_grid(history, batch_size, force_n_rows=1)
|
|
|
|
save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
|
|
|
|
output_images = history
|
|
seed = initial_seed
|
|
|
|
else:
|
|
output_images, seed, info = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_index=0,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN,
|
|
extra_generation_params = {"Denoising Strength": denoising_strength},
|
|
)
|
|
|
|
del sampler
|
|
|
|
return output_images, seed, plaintext_to_html(info)
|
|
|
|
|
|
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
|
|
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
|
|
|
img2img_interface = gr.Interface(
|
|
wrap_gradio_call(img2img),
|
|
inputs=[
|
|
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
|
|
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
|
|
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
|
|
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
|
|
gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
|
|
gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
|
|
gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
|
|
gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
|
|
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
|
|
gr.Number(label='Seed', value=-1),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
|
|
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
|
|
],
|
|
outputs=[
|
|
gr.Gallery(),
|
|
gr.Number(label='Seed'),
|
|
gr.HTML(),
|
|
],
|
|
allow_flagging="never",
|
|
)
|
|
|
|
|
|
def run_GFPGAN(image, strength):
|
|
image = image.convert("RGB")
|
|
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
|
|
res = Image.fromarray(restored_img)
|
|
|
|
if strength < 1.0:
|
|
res = Image.blend(image, res, strength)
|
|
|
|
return res, 0, ''
|
|
|
|
|
|
gfpgan_interface = gr.Interface(
|
|
run_GFPGAN,
|
|
inputs=[
|
|
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
|
|
],
|
|
outputs=[
|
|
gr.Image(label="Result"),
|
|
gr.Number(label='Seed', visible=False),
|
|
gr.HTML(),
|
|
],
|
|
description="Fix faces on images",
|
|
allow_flagging="never",
|
|
)
|
|
|
|
opts = Options()
|
|
if os.path.exists(config_filename):
|
|
opts.load(config_filename)
|
|
|
|
|
|
def run_settings(*args):
|
|
up = []
|
|
|
|
for key, value, comp in zip(opts.data_labels.keys(), args, settings_interface.input_components):
|
|
opts.data[key] = value
|
|
up.append(comp.update(value=value))
|
|
|
|
opts.save(config_filename)
|
|
|
|
return 'Settings saved.', ''
|
|
|
|
|
|
def create_setting_component(key):
|
|
def fun():
|
|
return opts.data[key] if key in opts.data else opts.data_labels[key][0]
|
|
|
|
labelinfo = opts.data_labels[key]
|
|
t = type(labelinfo[0])
|
|
label = labelinfo[1]
|
|
if t == str:
|
|
item = gr.Textbox(label=label, value=fun, lines=1)
|
|
elif t == int:
|
|
if len(labelinfo) == 4:
|
|
item = gr.Slider(minimum=labelinfo[2], maximum=labelinfo[3], step=1, label=label, value=fun)
|
|
else:
|
|
item = gr.Number(label=label, value=fun)
|
|
elif t == bool:
|
|
item = gr.Checkbox(label=label, value=fun)
|
|
else:
|
|
raise Exception(f'bad options item type: {str(t)} for key {key}')
|
|
|
|
return item
|
|
|
|
|
|
settings_interface = gr.Interface(
|
|
run_settings,
|
|
inputs=[create_setting_component(key) for key in opts.data_labels.keys()],
|
|
outputs=[
|
|
gr.Textbox(label='Result'),
|
|
gr.HTML(),
|
|
],
|
|
title=None,
|
|
description=None,
|
|
allow_flagging="never",
|
|
)
|
|
|
|
interfaces = [
|
|
(txt2img_interface, "txt2img"),
|
|
(img2img_interface, "img2img"),
|
|
(gfpgan_interface, "GFPGAN"),
|
|
(settings_interface, "Settings"),
|
|
]
|
|
|
|
config = OmegaConf.load(cmd_opts.config)
|
|
model = load_model_from_config(config, cmd_opts.ckpt)
|
|
|
|
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
|
model = (model if cmd_opts.no_half else model.half()).to(device)
|
|
text_inversion_embeddings = TextInversionEmbeddings()
|
|
|
|
if os.path.exists(cmd_opts.embeddings_dir):
|
|
text_inversion_embeddings.hijack(model)
|
|
|
|
if GFPGAN is None:
|
|
interfaces = [x for x in interfaces if x[0] != gfpgan_interface]
|
|
|
|
demo = gr.TabbedInterface(
|
|
interface_list=[x[0] for x in interfaces],
|
|
tab_names=[x[1] for x in interfaces],
|
|
css=("" if cmd_opts.no_progressbar_hiding else css_hide_progressbar) + """
|
|
.output-html p {margin: 0 0.5em;}
|
|
.performance { font-size: 0.85em; color: #444; }
|
|
"""
|
|
)
|
|
|
|
demo.launch()
|