Merge branch 'master' into varsize

name: Feature request

description: Suggest an idea for this project

title: "[Feature Request]: "

labels: ["suggestion"]

labels: ["enhancement"]

body:

  - type: checkboxes

      - name: Checkout Code

        uses: actions/checkout@v3

      - name: Set up Python 3.10

        uses: actions/setup-python@v3

        uses: actions/setup-python@v4

        with:

          python-version: 3.10.6

      - uses: actions/cache@v2

        with:

          path: ~/.cache/pip

          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}

          restore-keys: |

            ${{ runner.os }}-pip-

          cache: pip

          cache-dependency-path: |

            **/requirements*txt

      - name: Install PyLint

        run: | 

          python -m pip install --upgrade pip

        uses: actions/setup-python@v4

        with:

          python-version: 3.10.6

      - uses: actions/cache@v3

        with:

          path: ~/.cache/pip

          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}

          restore-keys: ${{ runner.os }}-pip-

          cache: pip

          cache-dependency-path: |

            **/requirements*txt

      - name: Run tests

        run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test

      - name: Upload main app stdout-stderr

    "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",

    "Sampling method": "Which algorithm to use to produce the image",

	"GFPGAN": "Restore low quality faces using GFPGAN neural network",

	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",

	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",

	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",

	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 

    "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",

    "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.",

    "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a 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.",

    "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",

    "Weighted sum": "Result = A * (1 - M) + B * M",

    "Add difference": "Result = A + (B - C) * M",

    "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",

    "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",

    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",

    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality.",

    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.",

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.",

    "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",

    "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",

function setInactive(elem, inactive){

    console.log(elem)

    if(inactive){

        elem.classList.add('inactive')

    } else{

        elem.classList.remove('inactive')

    }

}

function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){

    console.log(enable, width, height, hr_scale, hr_resize_x, hr_resize_y)

    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')

    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')

    hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')

    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""

    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)

    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)

    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)

    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]

}