parser.add_argument("--config",type=str,default=os.path.join(sd_path,"configs/stable-diffusion/v1-inference.yaml"),help="path to config which constructs model",)
parser.add_argument("--ckpt",type=str,default=os.path.join(sd_path,sd_model_file),help="path to checkpoint of model",)
parser.add_argument("--ckpt",type=str,default=sd_model_file,help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
parser.add_argument("--ckpt-dir",type=str,default=os.path.join(script_path,'models'),help="path to directory with stable diffusion checkpoints",)
"show_progress_every_n_steps":OptionInfo(0,"Show show image creation progress every N sampling steps. Set 0 to disable.",gr.Slider,{"minimum":0,"maximum":32,"step":1}),
"multiple_tqdm":OptionInfo(True,"Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
"memmon_poll_rate":OptionInfo(8,"VRAM usage polls per second during generation. Set to 0 to disable.",gr.Slider,{"minimum":0,"maximum":40,"step":1}),
"Style 2":"Style to apply; styles have components for both positive and negative prompts and apply to both",
"Apply style":"Insert selected styles into prompt fields",
"Create style":"Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
"Checkpoint name":"Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
// I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
// If gradio at some point stops sending outputs, this may break something
windowed_image+=np.average(_np_src_image)*np_mask_rgb# / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
src_fft=_fft2(windowed_image)# get feature statistics from masked src img
src_dist=np.absolute(src_fft)
src_phase=src_fft/src_dist
noise_window=_get_gaussian_window(width,height,mode=1)# start with simple gaussian noise
AxisOptionImg2Img("Denoising",float,apply_field("denoising_strength"),format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones