automatically switch to 32-bit float VAE if the generated picture has NaNs.

 * added `[none]` filename token.

 * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)

 * add always_discard_next_to_last_sigma option to XYZ plot

 * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.

### Extensions and API:

 * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop

from typing import Any, Dict, List

import modules.sd_hijack

from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet

from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors

from modules.sd_hijack import model_hijack

from modules.shared import opts, cmd_opts, state

import modules.shared as shared

    return x

def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):

    samples = []

    for i in range(batch.shape[0]):

        sample = decode_first_stage(model, batch[i:i + 1])[0]

        if check_for_nans:

            try:

                devices.test_for_nans(sample, "vae")

            except devices.NansException as e:

                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:

                    raise e

                errors.print_error_explanation(

                    "A tensor with all NaNs was produced in VAE.\n"

                    "Web UI will now convert VAE into 32-bit float and retry.\n"

                    "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"

                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."

                )

                devices.dtype_vae = torch.float32

                model.first_stage_model.to(devices.dtype_vae)

                batch = batch.to(devices.dtype_vae)

                sample = decode_first_stage(model, batch[i:i + 1])[0]

        if target_device is not None:

            sample = sample.to(target_device)

        samples.append(sample)

    return samples

def decode_first_stage(model, x):

    x = model.decode_first_stage(x.to(devices.dtype_vae))

            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():

                samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)

            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]

            for x in x_samples_ddim:

                devices.test_for_nans(x, "vae")

            x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

            x_samples_ddim = torch.stack(x_samples_ddim).float()

            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

    "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),

    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),

    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),

    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),

    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),

}))