cmdr2

// contributed by @madrang

PLUGINS['IMAGE_INFO_BUTTONS'].push({ text: 'Double Size (Madrang)', on_click: getStartNewTaskHandler('img2img_X2') })
PLUGINS['IMAGE_INFO_BUTTONS'].push({ text: 'Redo (Madrang)', on_click: getStartNewTaskHandler('img2img') })

function getStartNewTaskHandler(mode) {
    return function(reqBody, img) {
        const newTaskRequest = getCurrentUserRequest()
        switch (mode) {
            case 'img2img':

cmdr2

Only in sd2: LICENSE-MODEL
Only in sd2/assets: stable-inpainting
Only in sd2/assets/stable-samples: depth2img
Only in sd2/assets/stable-samples/txt2img: 768
Only in sd2/assets/stable-samples: upscaling
Only in sd1/configs: autoencoder
Only in sd1/configs: latent-diffusion
Only in sd1/configs: retrieval-augmented-diffusion
Only in sd1/configs/stable-diffusion: v1-inference.yaml
Only in sd2/configs/stable-diffusion: v2-inference-v.yaml

cmdr2

model:
  base_learning_rate: 1.0e-4
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    parameterization: "v"
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000

cmdr2

import time

MODEL_PATH = "F:/models/stable-diffusion/sd-v1-4.ckpt"
CONFIG_PATH = "F:/models/stable-diffusion/v1-inference.yaml"

def diff():
    print('diffusers')
    import torch
    from transformers import logging as tr_logging
    tr_logging.set_verbosity_error() # suppress unnecessary logging

cmdr2

"""
Runs the desired Stable Diffusion models against the desired samplers. Saves the output images
to disk, along with the peak RAM and VRAM usage, as well as the sampling performance.
"""
import argparse
import os
import time
import traceback
from pathlib import Path

cmdr2

import time

MODEL_PATH = "F:/models/stable-diffusion/sd-v1-4.ckpt"
CONFIG_PATH = "F:/models/stable-diffusion/v1-inference.yaml"
DEVICE = "mps" # or "cuda" or "cpu"

def diff():
    print('diffusers')
    import torch
    from transformers import logging as tr_logging

cmdr2

from sdkit import Context
from sdkit.generate import generate_images
from sdkit.utils import load_tensor_file, save_tensor_file

from ldm.util import instantiate_from_config
from omegaconf import OmegaConf

import time

model_path = "/path/to/models/stable-diffusion/sd-v1-4.ckpt"

cmdr2

import torch
import tensorrt as trt
from polygraphy import cuda
import sys
from packaging import version
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.onnx_utils import OnnxRuntimeModel, ORT_TO_NP_TYPE
from dataclasses import dataclass
import numpy as np
import onnxruntime as ort

cmdr2

import sys
import os
import platform
from importlib.metadata import version as pkg_version

from sdkit.utils import log

from easydiffusion import app

# future home of scripts/check_modules.py

cmdr2

import pefile
import sys

if len(sys.argv) < 2:
    print("Error: No file path specified. Usage: python hexdump.py <file_path>")
    sys.exit(1)

file_path = sys.argv[1]

try: