Merge branch 'dev' into master

          #     not to have GHA download an (at the time of writing) 4 GB cache

          #     of PyTorch and other dependencies.

      - name: Install Ruff

        run: pip install ruff==0.0.265

        run: pip install ruff==0.0.272

      - name: Run Ruff

        run: ruff .

  lint-js:

          --no-half

          --disable-opt-split-attention

          --use-cpu all

          --add-stop-route

          --api-server-stop

          2>&1 | tee output.txt &

      - name: Run tests

        run: |

          python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test

      - name: Kill test server

        if: always()

        run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10

        run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10

      - name: Show coverage

        run: |

          python -m coverage combine .coverage*

Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)

## Documentation

The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).

For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).

## Credits

Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.

from ldm.models.diffusion.ddim import DDIMSampler

from ldm.util import instantiate_from_config, ismap

from modules import shared, sd_hijack

from modules import shared, sd_hijack, devices

cached_ldsr_model: torch.nn.Module = None

        gc.collect()

        if torch.cuda.is_available:

            torch.cuda.empty_cache()

        devices.torch_gc()

        im_og = image

        width_og, height_og = im_og.size

        del model

        gc.collect()

        if torch.cuda.is_available:

            torch.cuda.empty_cache()

        devices.torch_gc()

        return a

import os

from basicsr.utils.download_util import load_file_from_url

from modules.modelloader import load_file_from_url

from modules.upscaler import Upscaler, UpscalerData

from ldsr_model_arch import LDSR

from modules import shared, script_callbacks, errors

        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):

            model = local_safetensors_path

        else:

            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)

            model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")

        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)

        yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")

        try:

        return LDSR(model, yaml)

        except Exception:

            errors.report("Error importing LDSR", exc_info=True)

        return None

    def do_upscale(self, img, path):

        try:

            ldsr = self.load_model(path)

        if ldsr is None:

            print("NO LDSR!")

        except Exception:

            errors.report(f"Failed loading LDSR model {path}", exc_info=True)

            return img

        ddim_steps = shared.opts.ldsr_steps

        return ldsr.super_resolution(img, ddim_steps, self.scale)