1425 lines
56 KiB
Python
1425 lines
56 KiB
Python
import argparse
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import os
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import sys
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from collections import namedtuple
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from contextlib import nullcontext
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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 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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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 Exception:
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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'))
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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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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--lowvram", action='store_true', help="enamble optimizations for low vram")
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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 funcname=x[1]: KDiffusionSampler(funcname)) 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: VanillaStableDiffusionSampler(DDIMSampler)),
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SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)),
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]
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samplers_for_img2img = [x for x in samplers if x.name != 'DDIM' and x.name != 'PLMS']
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RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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realesrgan_models = [
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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
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netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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),
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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus anime 6B",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
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netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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),
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RealesrganModelInfo(
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name="Real-ESRGAN 2x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
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netscale=2, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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),
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]
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have_realesrgan = True
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except Exception:
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print("Error loading Real-ESRGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
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have_realesrgan = False
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sd_upscalers = {
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"RealESRGAN": lambda img: upscale_with_realesrgan(img, 2, 0),
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"Lanczos": lambda img: img.resize((img.width*2, img.height*2), resample=LANCZOS),
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"None": lambda img: img
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}
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class Options:
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None):
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self.default = default
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self.label = label
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self.component = component
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self.component_args = component_args
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data = None
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data_labels = {
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"outdir": OptionInfo("", "Output dictectory; if empty, defaults to 'outputs/*'"),
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"samples_save": OptionInfo(True, "Save indiviual samples"),
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"samples_format": OptionInfo('png', 'File format for indiviual samples'),
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"grid_save": OptionInfo(True, "Save image grids"),
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"grid_format": OptionInfo('png', 'File format for grids'),
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"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
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"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
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"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
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"sd_upscale_upscaler_index": OptionInfo("RealESRGAN", "Upscaler to use for SD upscale", gr.Radio, {"choices": list(sd_upscalers.keys())}),
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"sd_upscale_overlap": OptionInfo(64, "Overlap for tiles for SD upscale. The smaller it is, the less smooth transition from one tile to another", gr.Slider, {"minimum": 0, "maximum": 256, "step": 16}),
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}
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def __init__(self):
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self.data = {k: v.default 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].default
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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 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.eval()
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return model
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module_in_gpu = None
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def setup_for_low_vram(sd_model):
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parents = {}
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def send_me_to_gpu(module, _):
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"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
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we add this as forward_pre_hook to a lot of modules and this way all but one of them will
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be in CPU
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"""
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global module_in_gpu
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module = parents.get(module, module)
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if module_in_gpu == module:
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return
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if module_in_gpu is not None:
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module_in_gpu.to(cpu)
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module.to(gpu)
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module_in_gpu = module
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# see below for register_forward_pre_hook;
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# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
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# useless here, and we just replace those methods
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def first_stage_model_encode_wrap(self, encoder, x):
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send_me_to_gpu(self, None)
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return encoder(x)
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def first_stage_model_decode_wrap(self, decoder, z):
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send_me_to_gpu(self, None)
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return decoder(z)
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# remove three big modules, cond, first_stage, and unet from the model and then
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# send the model to GPU. Then put modules back. the modules will be in CPU.
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stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
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sd_model.to(device)
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
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# register hooks for those the first two models
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sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
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sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
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sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
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sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
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parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
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# the third remaining model is still too big for 4GB, so we also do the same for its submodules
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# so that only one of them is in GPU at a time
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diff_model = sd_model.model.diffusion_model
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stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
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sd_model.model.to(device)
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
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# install hooks for bits of third model
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diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
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for block in diff_model.input_blocks:
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block.register_forward_pre_hook(send_me_to_gpu)
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diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
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for block in diff_model.output_blocks:
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block.register_forward_pre_hook(send_me_to_gpu)
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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 save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
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if short_filename or prompt is None or seed is None:
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filename = f"{basename}"
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else:
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filename = f"{basename}-{seed}-{sanitize_filename_part(prompt)[:128]}"
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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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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os.makedirs(path, exist_ok=True)
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fullfn = os.path.join(path, f"{filename}.{extension}")
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image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
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target_side_length = 4000
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oversize = image.width > target_side_length or image.height > target_side_length
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if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
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ratio = image.width / image.height
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if oversize and ratio > 1:
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image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
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elif oversize:
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image.save(os.path.join(path, f"{filename}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
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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 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=1, rows=None):
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if rows is None:
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if 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 = 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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Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
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def split_grid(image, tile_w=512, tile_h=512, overlap=64):
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w = image.width
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h = image.height
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now = tile_w - overlap # non-overlap width
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noh = tile_h - overlap
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cols = math.ceil((w - overlap) / now)
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rows = math.ceil((h - overlap) / noh)
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grid = Grid([], tile_w, tile_h, w, h, overlap)
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for row in range(rows):
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row_images = []
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y = row * noh
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if y + tile_h >= h:
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y = h - tile_h
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for col in range(cols):
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x = col * now
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if x+tile_w >= w:
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x = w - tile_w
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tile = image.crop((x, y, x + tile_w, y + tile_h))
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row_images.append([x, tile_w, tile])
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grid.tiles.append([y, tile_h, row_images])
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return grid
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def combine_grid(grid):
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def make_mask_image(r):
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r = r * 255 / grid.overlap
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r = r.astype(np.uint8)
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return Image.fromarray(r, 'L')
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mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
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mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
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combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
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for y, h, row in grid.tiles:
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combined_row = Image.new("RGB", (grid.image_w, h))
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for x, w, tile in row:
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if x == 0:
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combined_row.paste(tile, (0, 0))
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continue
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combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
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combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
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if y == 0:
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combined_image.paste(combined_row, (0, 0))
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continue
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combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
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combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
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return combined_image
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class GridAnnotation:
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def __init__(self, text='', is_active=True):
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self.text = text
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self.is_active = is_active
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self.size = None
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def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
|
|
def wrap(drawing, text, font, line_length):
|
|
lines = ['']
|
|
for word in text.split():
|
|
line = f'{lines[-1]} {word}'.strip()
|
|
if drawing.textlength(line, font=font) <= line_length:
|
|
lines[-1] = line
|
|
else:
|
|
lines.append(word)
|
|
return lines
|
|
|
|
def draw_texts(drawing, draw_x, draw_y, lines):
|
|
for i, line in enumerate(lines):
|
|
drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
|
|
|
|
if not line.is_active:
|
|
drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)
|
|
|
|
draw_y += line.size[1] + line_spacing
|
|
|
|
fontsize = (width + height) // 25
|
|
line_spacing = fontsize // 2
|
|
fnt = ImageFont.truetype("arial.ttf", fontsize)
|
|
color_active = (0, 0, 0)
|
|
color_inactive = (153, 153, 153)
|
|
|
|
pad_left = width * 3 // 4 if len(hor_texts) > 1 else 0
|
|
|
|
cols = im.width // width
|
|
rows = im.height // height
|
|
|
|
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
|
|
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
|
|
|
|
calc_img = Image.new("RGB", (1, 1), "white")
|
|
calc_d = ImageDraw.Draw(calc_img)
|
|
|
|
for texts in hor_texts + ver_texts:
|
|
items = [] + texts
|
|
texts.clear()
|
|
|
|
for line in items:
|
|
wrapped = wrap(calc_d, line.text, fnt, width)
|
|
texts += [GridAnnotation(x, line.is_active) for x in wrapped]
|
|
|
|
for line in texts:
|
|
bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
|
|
line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
|
|
|
|
hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
|
|
ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
|
|
|
|
pad_top = max(hor_text_heights) + line_spacing * 2
|
|
|
|
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
|
|
result.paste(im, (pad_left, pad_top))
|
|
|
|
d = ImageDraw.Draw(result)
|
|
|
|
for col in range(cols):
|
|
x = pad_left + width * col + width / 2
|
|
y = pad_top / 2 - hor_text_heights[col] / 2
|
|
|
|
draw_texts(d, x, y, hor_texts[col])
|
|
|
|
for row in range(rows):
|
|
x = pad_left / 2
|
|
y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
|
|
|
|
draw_texts(d, x, y, ver_texts[row])
|
|
|
|
return result
|
|
|
|
|
|
def draw_prompt_matrix(im, width, height, all_prompts):
|
|
prompts = all_prompts[1:]
|
|
boundary = math.ceil(len(prompts) / 2)
|
|
|
|
prompts_horiz = prompts[:boundary]
|
|
prompts_vert = prompts[boundary:]
|
|
|
|
hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
|
|
ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
|
|
|
|
return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
|
|
|
|
|
|
def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
|
res = []
|
|
|
|
ver_texts = [[GridAnnotation(y_label(y))] for y in ys]
|
|
hor_texts = [[GridAnnotation(x_label(x))] for x in xs]
|
|
|
|
for y in ys:
|
|
for x in xs:
|
|
res.append(cell(x, y))
|
|
|
|
|
|
grid = image_grid(res, rows=len(ys))
|
|
grid = draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
|
|
|
return grid
|
|
|
|
def resize_image(resize_mode, im, width, height):
|
|
if resize_mode == 0:
|
|
res = im.resize((width, height), resample=LANCZOS)
|
|
elif resize_mode == 1:
|
|
ratio = width / height
|
|
src_ratio = im.width / im.height
|
|
|
|
src_w = width if ratio > src_ratio else im.width * height // im.height
|
|
src_h = height if ratio <= src_ratio else im.height * width // im.width
|
|
|
|
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
|
res = Image.new("RGB", (width, height))
|
|
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
|
else:
|
|
ratio = width / height
|
|
src_ratio = im.width / im.height
|
|
|
|
src_w = width if ratio < src_ratio else im.width * height // im.height
|
|
src_h = height if ratio >= src_ratio else im.height * width // im.width
|
|
|
|
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
|
res = Image.new("RGB", (width, height))
|
|
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
|
|
|
if ratio < src_ratio:
|
|
fill_height = height // 2 - src_h // 2
|
|
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
|
|
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
|
|
elif ratio > src_ratio:
|
|
fill_width = width // 2 - src_w // 2
|
|
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
|
|
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
|
|
|
|
return res
|
|
|
|
|
|
def wrap_gradio_call(func):
|
|
def f(*p1, **p2):
|
|
t = time.perf_counter()
|
|
res = list(func(*p1, **p2))
|
|
elapsed = time.perf_counter() - t
|
|
|
|
# last item is always HTML
|
|
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
|
|
|
|
return tuple(res)
|
|
|
|
return f
|
|
|
|
|
|
GFPGAN = None
|
|
if os.path.exists(cmd_opts.gfpgan_dir):
|
|
try:
|
|
GFPGAN = load_gfpgan()
|
|
print("Loaded GFPGAN")
|
|
except Exception:
|
|
print("Error loading GFPGAN:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
|
|
class StableDiffusionModelHijack:
|
|
ids_lookup = {}
|
|
word_embeddings = {}
|
|
word_embeddings_checksums = {}
|
|
fixes = None
|
|
comments = None
|
|
dir_mtime = None
|
|
|
|
def load_textual_inversion_embeddings(self, dirname, model):
|
|
mt = os.path.getmtime(dirname)
|
|
if self.dir_mtime is not None and mt <= self.dir_mtime:
|
|
return
|
|
|
|
self.dir_mtime = mt
|
|
self.ids_lookup.clear()
|
|
self.word_embeddings.clear()
|
|
|
|
tokenizer = model.cond_stage_model.tokenizer
|
|
|
|
def const_hash(a):
|
|
r = 0
|
|
for v in a:
|
|
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
|
|
return r
|
|
|
|
def process_file(path, filename):
|
|
name = os.path.splitext(filename)[0]
|
|
|
|
data = torch.load(path)
|
|
param_dict = data['string_to_param']
|
|
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
|
emb = next(iter(param_dict.items()))[1].reshape(768)
|
|
self.word_embeddings[name] = emb
|
|
self.word_embeddings_checksums[name] = f'{const_hash(emb)&0xffff:04x}'
|
|
|
|
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
|
|
|
|
first_id = ids[0]
|
|
if first_id not in self.ids_lookup:
|
|
self.ids_lookup[first_id] = []
|
|
self.ids_lookup[first_id].append((ids, name))
|
|
|
|
for fn in os.listdir(dirname):
|
|
try:
|
|
process_file(os.path.join(dirname, fn), fn)
|
|
except Exception:
|
|
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
continue
|
|
|
|
print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
|
|
|
|
def hijack(self, m):
|
|
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
|
|
|
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, hijack):
|
|
super().__init__()
|
|
self.wrapped = wrapped
|
|
self.hijack = hijack
|
|
self.tokenizer = wrapped.tokenizer
|
|
self.max_length = wrapped.max_length
|
|
self.token_mults = {}
|
|
|
|
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
|
for text, ident in tokens_with_parens:
|
|
mult = 1.0
|
|
for c in text:
|
|
if c == '[':
|
|
mult /= 1.1
|
|
if c == ']':
|
|
mult *= 1.1
|
|
if c == '(':
|
|
mult *= 1.1
|
|
if c == ')':
|
|
mult /= 1.1
|
|
|
|
if mult != 1.0:
|
|
self.token_mults[ident] = mult
|
|
|
|
def forward(self, text):
|
|
self.hijack.fixes = []
|
|
self.hijack.comments = []
|
|
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
|
|
used_custom_terms = []
|
|
|
|
cache = {}
|
|
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
|
|
batch_multipliers = []
|
|
for tokens in batch_tokens:
|
|
tuple_tokens = tuple(tokens)
|
|
|
|
if tuple_tokens in cache:
|
|
remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
|
else:
|
|
fixes = []
|
|
remade_tokens = []
|
|
multipliers = []
|
|
mult = 1.0
|
|
|
|
i = 0
|
|
while i < len(tokens):
|
|
token = tokens[i]
|
|
|
|
possible_matches = self.hijack.ids_lookup.get(token, None)
|
|
|
|
mult_change = self.token_mults.get(token)
|
|
if mult_change is not None:
|
|
mult *= mult_change
|
|
elif possible_matches is None:
|
|
remade_tokens.append(token)
|
|
multipliers.append(mult)
|
|
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)
|
|
multipliers.append(mult)
|
|
i += len(ids) - 1
|
|
found = True
|
|
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
|
|
break
|
|
|
|
if not found:
|
|
remade_tokens.append(token)
|
|
multipliers.append(mult)
|
|
|
|
i += 1
|
|
|
|
if len(remade_tokens) > maxlen - 2:
|
|
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
|
ovf = remade_tokens[maxlen - 2:]
|
|
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
|
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
|
|
|
self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
|
|
|
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, multipliers)
|
|
|
|
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
|
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
|
|
|
remade_batch_tokens.append(remade_tokens)
|
|
self.hijack.fixes.append(fixes)
|
|
batch_multipliers.append(multipliers)
|
|
|
|
if len(used_custom_terms) > 0:
|
|
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
|
|
|
tokens = torch.asarray(remade_batch_tokens).to(self.wrapped.device)
|
|
outputs = self.wrapped.transformer(input_ids=tokens)
|
|
z = outputs.last_hidden_state
|
|
|
|
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
|
batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device)
|
|
original_mean = z.mean()
|
|
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
|
new_mean = z.mean()
|
|
z *= original_mean / new_mean
|
|
|
|
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 = None
|
|
|
|
inputs_embeds = self.wrapped(input_ids)
|
|
|
|
if batch_fixes is not None:
|
|
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
|
for offset, word in fixes:
|
|
tensor[offset] = self.embeddings.word_embeddings[word]
|
|
|
|
return inputs_embeds
|
|
|
|
|
|
class StableDiffusionProcessing:
|
|
def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None):
|
|
self.outpath: str = outpath
|
|
self.prompt: str = prompt
|
|
self.seed: int = seed
|
|
self.sampler_index: int = sampler_index
|
|
self.batch_size: int = batch_size
|
|
self.n_iter: int = n_iter
|
|
self.steps: int = steps
|
|
self.cfg_scale: float = cfg_scale
|
|
self.width: int = width
|
|
self.height: int = height
|
|
self.prompt_matrix: bool = prompt_matrix
|
|
self.use_GFPGAN: bool = use_GFPGAN
|
|
self.do_not_save_samples: bool = do_not_save_samples
|
|
self.do_not_save_grid: bool = do_not_save_grid
|
|
self.extra_generation_params: dict = extra_generation_params
|
|
|
|
def init(self):
|
|
pass
|
|
|
|
def sample(self, x, conditioning, unconditional_conditioning):
|
|
raise NotImplementedError()
|
|
|
|
|
|
class VanillaStableDiffusionSampler:
|
|
def __init__(self, constructor):
|
|
self.sampler = constructor(sd_model)
|
|
|
|
def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
|
|
samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
|
|
return samples_ddim
|
|
|
|
|
|
class CFGDenoiser(nn.Module):
|
|
def __init__(self, model):
|
|
super().__init__()
|
|
self.inner_model = model
|
|
|
|
def forward(self, x, sigma, uncond, cond, cond_scale):
|
|
x_in = torch.cat([x] * 2)
|
|
sigma_in = torch.cat([sigma] * 2)
|
|
cond_in = torch.cat([uncond, cond])
|
|
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
|
return uncond + (cond - uncond) * cond_scale
|
|
|
|
|
|
class KDiffusionSampler:
|
|
def __init__(self, funcname):
|
|
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model)
|
|
self.funcname = funcname
|
|
self.func = getattr(k_diffusion.sampling, self.funcname)
|
|
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
|
|
|
def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning):
|
|
sigmas = self.model_wrap.get_sigmas(p.steps)
|
|
x = x * sigmas[0]
|
|
|
|
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
|
|
return samples_ddim
|
|
|
|
|
|
Processed = namedtuple('Processed', ['images','seed', 'info'])
|
|
|
|
|
|
def process_images(p: StableDiffusionProcessing) -> Processed:
|
|
"""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"""
|
|
|
|
prompt = p.prompt
|
|
model = sd_model
|
|
|
|
assert p.prompt is not None
|
|
torch_gc()
|
|
|
|
seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
|
|
|
|
sample_path = os.path.join(p.outpath, "samples")
|
|
base_count = len(os.listdir(sample_path))
|
|
grid_count = len(os.listdir(p.outpath)) - 1
|
|
|
|
comments = []
|
|
|
|
prompt_matrix_parts = []
|
|
if p.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))
|
|
|
|
p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
|
|
all_seeds = len(all_prompts) * [seed]
|
|
|
|
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
|
|
else:
|
|
all_prompts = p.batch_size * p.n_iter * [prompt]
|
|
all_seeds = [seed + x for x in range(len(all_prompts))]
|
|
|
|
generation_params = {
|
|
"Steps": p.steps,
|
|
"Sampler": samplers[p.sampler_index].name,
|
|
"CFG scale": p.cfg_scale,
|
|
"Seed": seed,
|
|
"GFPGAN": ("GFPGAN" if p.use_GFPGAN and GFPGAN is not None else None)
|
|
}
|
|
|
|
if p.extra_generation_params is not None:
|
|
generation_params.update(p.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):
|
|
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, model)
|
|
|
|
output_images = []
|
|
ema_scope = (nullcontext if cmd_opts.lowvram else model.ema_scope)
|
|
with torch.no_grad(), autocast("cuda"), ema_scope():
|
|
p.init()
|
|
|
|
for n in range(p.n_iter):
|
|
prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
|
seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
|
|
|
uc = model.get_learned_conditioning(len(prompts) * [""])
|
|
c = model.get_learned_conditioning(prompts)
|
|
|
|
if len(model_hijack.comments) > 0:
|
|
comments += model_hijack.comments
|
|
|
|
# we manually generate all input noises because each one should have a specific seed
|
|
x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds)
|
|
|
|
samples_ddim = p.sample(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 p.prompt_matrix or opts.samples_save or opts.grid_save:
|
|
for i, x_sample in enumerate(x_samples_ddim):
|
|
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
|
x_sample = x_sample.astype(np.uint8)
|
|
|
|
if p.use_GFPGAN and GFPGAN is not None:
|
|
torch_gc()
|
|
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)
|
|
|
|
if not p.do_not_save_samples:
|
|
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
|
|
|
|
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
|
|
if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
|
|
if p.prompt_matrix:
|
|
grid = image_grid(output_images, p.batch_size, rows=1 << ((len(prompt_matrix_parts)-1)//2))
|
|
|
|
try:
|
|
grid = draw_prompt_matrix(grid, p.width, p.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)
|
|
else:
|
|
grid = image_grid(output_images, p.batch_size)
|
|
|
|
save_image(grid, p.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 Processed(output_images, seed, infotext())
|
|
|
|
|
|
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|
sampler = None
|
|
|
|
def init(self):
|
|
self.sampler = samplers[self.sampler_index].constructor()
|
|
|
|
def sample(self, x, conditioning, unconditional_conditioning):
|
|
samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
|
|
return samples_ddim
|
|
|
|
def txt2img(prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
|
|
outpath = opts.outdir or "outputs/txt2img-samples"
|
|
|
|
p = StableDiffusionProcessingTxt2Img(
|
|
outpath=outpath,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_index=sampler_index,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=prompt_matrix,
|
|
use_GFPGAN=use_GFPGAN
|
|
)
|
|
|
|
if code != '' and cmd_opts.allow_code:
|
|
p.do_not_save_grid = True
|
|
p.do_not_save_samples = True
|
|
|
|
display_result_data = [[], -1, ""]
|
|
def display(imgs, s=display_result_data[1], i=display_result_data[2]):
|
|
display_result_data[0] = imgs
|
|
display_result_data[1] = s
|
|
display_result_data[2] = i
|
|
|
|
from types import ModuleType
|
|
compiled = compile(code, '', 'exec')
|
|
module = ModuleType("testmodule")
|
|
module.__dict__.update(globals())
|
|
module.p = p
|
|
module.display = display
|
|
exec(compiled, module.__dict__)
|
|
|
|
processed = Processed(*display_result_data)
|
|
else:
|
|
processed = process_images(p)
|
|
|
|
return processed.images, processed.seed, plaintext_to_html(processed.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, code, 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=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),
|
|
gr.Textbox(label="Python script", visible=cmd_opts.allow_code, lines=1)
|
|
],
|
|
outputs=[
|
|
gr.Gallery(label="Images"),
|
|
gr.Number(label='Seed'),
|
|
gr.HTML(),
|
|
],
|
|
title="Stable Diffusion Text-to-Image",
|
|
flagging_callback=Flagging()
|
|
)
|
|
|
|
|
|
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|
sampler = None
|
|
|
|
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
self.init_images = init_images
|
|
self.resize_mode: int = resize_mode
|
|
self.denoising_strength: float = denoising_strength
|
|
self.init_latent = None
|
|
|
|
def init(self):
|
|
self.sampler = samplers_for_img2img[self.sampler_index].constructor()
|
|
|
|
imgs = []
|
|
for img in self.init_images:
|
|
image = img.convert("RGB")
|
|
image = resize_image(self.resize_mode, image, self.width, self.height)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = np.moveaxis(image, 2, 0)
|
|
imgs.append(image)
|
|
|
|
if len(imgs) == 1:
|
|
batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
|
|
elif len(imgs) <= self.batch_size:
|
|
self.batch_size = len(imgs)
|
|
batch_images = np.array(imgs)
|
|
else:
|
|
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
|
|
|
|
image = torch.from_numpy(batch_images)
|
|
image = 2. * image - 1.
|
|
image = image.to(device)
|
|
|
|
self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image))
|
|
|
|
def sample(self, x, conditioning, unconditional_conditioning):
|
|
t_enc = int(self.denoising_strength * self.steps)
|
|
|
|
sigmas = self.sampler.model_wrap.get_sigmas(self.steps)
|
|
noise = x * sigmas[self.steps - t_enc - 1]
|
|
|
|
xi = self.init_latent + noise
|
|
sigma_sched = sigmas[self.steps - t_enc - 1:]
|
|
samples_ddim = self.sampler.func(self.sampler.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': self.cfg_scale}, disable=False)
|
|
return samples_ddim
|
|
|
|
|
|
def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: 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"
|
|
|
|
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
|
|
|
p = StableDiffusionProcessingImg2Img(
|
|
outpath=outpath,
|
|
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,
|
|
init_images=[init_img],
|
|
resize_mode=resize_mode,
|
|
denoising_strength=denoising_strength,
|
|
extra_generation_params={"Denoising Strength": denoising_strength}
|
|
)
|
|
|
|
if loopback:
|
|
output_images, info = None, None
|
|
history = []
|
|
initial_seed = None
|
|
initial_info = None
|
|
|
|
for i in range(n_iter):
|
|
p.n_iter = 1
|
|
p.batch_size = 1
|
|
p.do_not_save_grid = True
|
|
|
|
processed = process_images(p)
|
|
|
|
if initial_seed is None:
|
|
initial_seed = processed.seed
|
|
initial_info = processed.info
|
|
|
|
p.init_img = processed.images[0]
|
|
p.seed = processed.seed + 1
|
|
p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
|
|
history.append(processed.images[0])
|
|
|
|
grid_count = len(os.listdir(outpath)) - 1
|
|
grid = image_grid(history, batch_size, 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)
|
|
|
|
processed = Processed(history, initial_seed, initial_info)
|
|
|
|
elif sd_upscale:
|
|
initial_seed = None
|
|
initial_info = None
|
|
|
|
upscaler = sd_upscalers[opts.sd_upscale_upscaler_index]
|
|
img = upscaler(init_img)
|
|
|
|
torch_gc()
|
|
|
|
grid = split_grid(img, tile_w=width, tile_h=height, overlap=opts.sd_upscale_overlap)
|
|
|
|
p.n_iter = 1
|
|
p.do_not_save_grid = True
|
|
p.do_not_save_samples = True
|
|
|
|
work = []
|
|
work_results = []
|
|
|
|
for y, h, row in grid.tiles:
|
|
for tiledata in row:
|
|
work.append(tiledata[2])
|
|
|
|
batch_count = math.ceil(len(work) / p.batch_size)
|
|
print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
|
|
|
|
for i in range(batch_count):
|
|
p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
|
|
|
|
processed = process_images(p)
|
|
|
|
if initial_seed is None:
|
|
initial_seed = processed.seed
|
|
initial_info = processed.info
|
|
|
|
p.seed = processed.seed + 1
|
|
work_results += processed.images
|
|
|
|
image_index = 0
|
|
for y, h, row in grid.tiles:
|
|
for tiledata in row:
|
|
tiledata[2] = work_results[image_index]
|
|
image_index += 1
|
|
|
|
combined_image = combine_grid(grid)
|
|
|
|
grid_count = len(os.listdir(outpath)) - 1
|
|
save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
|
|
|
|
processed = Processed([combined_image], initial_seed, initial_info)
|
|
|
|
else:
|
|
processed = process_images(p)
|
|
|
|
return processed.images, processed.seed, plaintext_to_html(processed.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.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[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.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
|
|
gr.Checkbox(label='Stable Diffusion upscale', 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 upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
|
|
info = realesrgan_models[RealESRGAN_model_index]
|
|
|
|
model = info.model()
|
|
upsampler = RealESRGANer(
|
|
scale=info.netscale,
|
|
model_path=info.location,
|
|
model=model,
|
|
half=True
|
|
)
|
|
|
|
upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
|
|
|
|
image = Image.fromarray(upsampled)
|
|
return image
|
|
|
|
|
|
def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
|
|
torch_gc()
|
|
|
|
image = image.convert("RGB")
|
|
|
|
outpath = opts.outdir or "outputs/extras-samples"
|
|
|
|
if GFPGAN is not None and GFPGAN_strength > 0:
|
|
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 GFPGAN_strength < 1.0:
|
|
res = Image.blend(image, res, GFPGAN_strength)
|
|
|
|
image = res
|
|
|
|
if have_realesrgan and RealESRGAN_upscaling != 1.0:
|
|
image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
|
|
|
|
base_count = len(os.listdir(outpath))
|
|
save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
|
|
|
|
return image, 0, ''
|
|
|
|
|
|
extras_interface = gr.Interface(
|
|
wrap_gradio_call(run_extras),
|
|
inputs=[
|
|
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=GFPGAN is not None),
|
|
gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=have_realesrgan),
|
|
gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan_models], value=realesrgan_models[0].name, type="index", interactive=have_realesrgan),
|
|
],
|
|
outputs=[
|
|
gr.Image(label="Result"),
|
|
gr.Number(label='Seed', visible=False),
|
|
gr.HTML(),
|
|
],
|
|
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].default
|
|
|
|
info = opts.data_labels[key]
|
|
t = type(info.default)
|
|
|
|
if info.component is not None:
|
|
item = info.component(label=info.label, value=fun, **(info.component_args or {}))
|
|
elif t == str:
|
|
item = gr.Textbox(label=info.label, value=fun, lines=1)
|
|
elif t == int:
|
|
item = gr.Number(label=info.label, value=fun)
|
|
elif t == bool:
|
|
item = gr.Checkbox(label=info.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"),
|
|
(extras_interface, "Extras"),
|
|
(settings_interface, "Settings"),
|
|
]
|
|
|
|
sd_config = OmegaConf.load(cmd_opts.config)
|
|
sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
|
|
|
|
cpu = torch.device("cpu")
|
|
gpu = torch.device("cuda")
|
|
device = gpu if torch.cuda.is_available() else cpu
|
|
|
|
sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
|
|
|
|
if not cmd_opts.lowvram:
|
|
sd_model = sd_model.to(device)
|
|
|
|
else:
|
|
setup_for_low_vram(sd_model)
|
|
|
|
model_hijack = StableDiffusionModelHijack()
|
|
model_hijack.hijack(sd_model)
|
|
|
|
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.queue(concurrency_count=1)
|
|
demo.launch()
|