Merge branch 'dev' into sqlite-cache

        metadata = item.get("metadata") or {}

        keys = {

            'ss_output_name': "Output name:",

            'ss_sd_model_name': "Model:",

            'ss_clip_skip': "Clip skip:",

            'ss_network_module': "Kohya module:",

    var id = randomId();

    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {

    requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() {

    });

        env = os.environ.copy()

        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"

        stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env)

        stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()

        if stdout:

            print(stdout)

    except Exception as e:

def txt2img_image_conditioning(sd_model, x, width, height):

    if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models

        # The "masked-image" in this case will just be all zeros since the entire image is masked.

        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)

        # The "masked-image" in this case will just be all 0.5 since the entire image is masked.

        image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5

        image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))

        # Add the fake full 1s mask to the first dimension.

        else:

            decoded_samples = None

        current = shared.sd_model.sd_checkpoint_info

        try:

            if self.hr_checkpoint_info is not None:

                self.sampler = None

        with sd_models.SkipWritingToConfig():

            sd_models.reload_model_weights(info=self.hr_checkpoint_info)

        devices.torch_gc()

        return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)

        finally:

            self.sampler = None

            sd_models.reload_model_weights(info=current)

            devices.torch_gc()

    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):

        if shared.state.interrupted:

class ExtraNoiseParams:

    def __init__(self, noise, x):

    def __init__(self, noise, x, xi):

        self.noise = noise

        """Random noise generated by the seed"""

        self.x = x

        """Latent image representation of the image"""

        """Latent representation of the image"""

        self.xi = xi

        """Noisy latent representation of the image"""

class CFGDenoiserParams: