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add docker-slim
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‎README.md

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@@ -146,11 +146,4 @@ If you don't want to deploy the load balancing server but still want to test the
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Finally, you can call your http API(test it using postman).
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# Part 2: Deploy using Runpod Serverless
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Check `handler.py` where I realized a demo handler for txt2img.
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deploy steps:
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- get a GPU server, download this repository and model files
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- install docker: https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository
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- cd to the project directory(which contains `Dockerfile`)
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- docker build && docker tag && docker push (these are not bash commands!)
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- create serverless Endpoint on runpod: https://runpod.io?ref=bz40v32s
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see `sd-docker-slim`

‎sd-docker-slim/Dockerfile

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FROM python:3.10-slim
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# install docker on ubuntu: https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository
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# build image: docker build -t sd-multi .
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# docker image ls
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# dockerhub create repo
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# login on ubuntu: docker login
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# tag: docker tag sd-multi wolverinn/sd_multi_demo:v1
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# push: docker push wolverinn/sd_multi_demo:v1
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WORKDIR /
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COPY requirements.txt /
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# COPY torch-1.13.1+cu117-cp38-cp38-linux_x86_64.whl .
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RUN apt-get update && apt-get install -y libgl1-mesa-glx && \
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apt-get install -y libglib2.0-0 && \
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apt-get install -y git && \
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rm -rf /var/lib/apt/lists/*
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RUN pip3 install runpod==0.9.12 && \
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pip3 install -r requirements.txt && \
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# pip3 install torch-1.13.1+cu117-cp38-cp38-linux_x86_64.whl && \
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pip3 install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 && \
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pip3 install torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 && \
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# apt-get remove -y git && \
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# rm -rf torch-1.13.1+cu117-cp38-cp38-linux_x86_64.whl && \
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pip3 cache purge
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COPY . /
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RUN chmod +x docker_entrypoint.sh
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# COPY . /
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ENTRYPOINT ["/docker_entrypoint.sh"]
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CMD [ "python", "-u", "/handler.py" ]

‎sd-docker-slim/README.md

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# stable-diffusion docker slim
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# Features
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- deploy on runpod serverless
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- text2img, img2img, list models
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- upload models at any time, takes effect immediately
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# Deploy Steps
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## Build and upload docker
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- get a Ubuntu machine with GPU, download the project files
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- install docker on ubuntu: https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository
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- cd to the directory with `Dockerfile`
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- to make your custom docker, modify `handler.py` for uploading outputs & supporting more API ...
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- `docker build -t sd-multi .`
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- `docker login`
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- docker tag and then docker push
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## Create a storage on runpod
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- upload model files to storage volume. under `/workspace` you should upload the whole `models` directory. The directory structure should look like this:
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```
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/workspace
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- /models
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- /VAE
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- /Lora
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- /Stable-diffusion
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...
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```
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## Deploy on runpod serverless
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check the official guide: https://docs.runpod.io/docs/template-creation
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# Test your API
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check out `test_runpod.py`
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## API definition
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checkout `idl.yaml`

‎sd-docker-slim/config.json

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{
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"samples_save": true,
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"samples_format": "png",
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"samples_filename_pattern": "",
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"save_images_add_number": true,
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"grid_save": true,
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"grid_format": "png",
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"grid_extended_filename": false,
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"grid_only_if_multiple": true,
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"grid_prevent_empty_spots": false,
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"n_rows": -1,
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"enable_pnginfo": true,
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"save_txt": false,
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"save_images_before_face_restoration": false,
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"save_images_before_highres_fix": false,
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"save_images_before_color_correction": false,
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"jpeg_quality": 80,
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"export_for_4chan": true,
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"img_downscale_threshold": 4.0,
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"target_side_length": 4000,
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"use_original_name_batch": true,
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"use_upscaler_name_as_suffix": false,
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"save_selected_only": true,
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"do_not_add_watermark": false,
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"temp_dir": "",
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"clean_temp_dir_at_start": false,
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"outdir_samples": "",
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"outdir_txt2img_samples": "outputs/txt2img-images",
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"outdir_img2img_samples": "outputs/img2img-images",
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"outdir_extras_samples": "outputs/extras-images",
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"outdir_grids": "",
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"outdir_txt2img_grids": "outputs/txt2img-grids",
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"outdir_img2img_grids": "outputs/img2img-grids",
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"outdir_save": "log/images",
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"save_to_dirs": true,
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"grid_save_to_dirs": true,
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"use_save_to_dirs_for_ui": false,
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"directories_filename_pattern": "[date]",
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"directories_max_prompt_words": 8,
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"ESRGAN_tile": 192,
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"ESRGAN_tile_overlap": 8,
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"realesrgan_enabled_models": [
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"R-ESRGAN 4x+",
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"R-ESRGAN 4x+ Anime6B"
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],
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"upscaler_for_img2img": null,
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"face_restoration_model": "CodeFormer",
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"code_former_weight": 0.5,
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"face_restoration_unload": false,
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"show_warnings": false,
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"memmon_poll_rate": 8,
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"samples_log_stdout": false,
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"multiple_tqdm": true,
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"print_hypernet_extra": false,
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"unload_models_when_training": false,
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"pin_memory": false,
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"save_optimizer_state": false,
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"save_training_settings_to_txt": true,
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"dataset_filename_word_regex": "",
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"dataset_filename_join_string": " ",
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"training_image_repeats_per_epoch": 1,
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"training_write_csv_every": 500,
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"training_xattention_optimizations": false,
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"training_enable_tensorboard": false,
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"training_tensorboard_save_images": false,
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"training_tensorboard_flush_every": 120,
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"sd_checkpoint_cache": 0,
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"sd_vae_checkpoint_cache": 0,
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"sd_vae": "Automatic",
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"sd_vae_as_default": true,
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"inpainting_mask_weight": 1.0,
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"initial_noise_multiplier": 1.0,
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"img2img_color_correction": false,
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"img2img_fix_steps": false,
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"img2img_background_color": "#ffffff",
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"enable_quantization": false,
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"enable_emphasis": true,
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"enable_batch_seeds": true,
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"comma_padding_backtrack": 20,
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"CLIP_stop_at_last_layers": 1,
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"upcast_attn": false,
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"use_old_emphasis_implementation": false,
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"use_old_karras_scheduler_sigmas": false,
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"no_dpmpp_sde_batch_determinism": false,
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"use_old_hires_fix_width_height": false,
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"interrogate_keep_models_in_memory": false,
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"interrogate_return_ranks": false,
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"interrogate_clip_num_beams": 1,
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"interrogate_clip_min_length": 24,
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"interrogate_clip_max_length": 48,
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"interrogate_clip_dict_limit": 1500,
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"interrogate_clip_skip_categories": [],
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"interrogate_deepbooru_score_threshold": 0.5,
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"deepbooru_sort_alpha": true,
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"deepbooru_use_spaces": false,
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"deepbooru_escape": true,
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"deepbooru_filter_tags": "",
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"extra_networks_default_view": "cards",
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"extra_networks_default_multiplier": 1.0,
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"sd_hypernetwork": "None",
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"return_grid": true,
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"do_not_show_images": false,
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"add_model_hash_to_info": true,
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"add_model_name_to_info": true,
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"disable_weights_auto_swap": true,
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"send_seed": true,
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"send_size": true,
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"font": "",
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"js_modal_lightbox": true,
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"js_modal_lightbox_initially_zoomed": true,
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"show_progress_in_title": true,
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"samplers_in_dropdown": true,
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"dimensions_and_batch_together": true,
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"keyedit_precision_attention": 0.1,
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"keyedit_precision_extra": 0.05,
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"quicksettings": "sd_model_checkpoint",
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"ui_reorder": "inpaint, sampler, checkboxes, hires_fix, dimensions, cfg, seed, batch, override_settings, scripts",
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"ui_extra_networks_tab_reorder": "",
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"localization": "zh_CN",
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"show_progressbar": true,
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"live_previews_enable": true,
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"show_progress_grid": true,
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"show_progress_every_n_steps": 10,
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"show_progress_type": "Approx NN",
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"live_preview_content": "Prompt",
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"live_preview_refresh_period": 1000,
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"hide_samplers": [],
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"eta_ddim": 0.0,
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"eta_ancestral": 1.0,
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"ddim_discretize": "uniform",
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"s_churn": 0.0,
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"s_tmin": 0.0,
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"s_noise": 1.0,
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"eta_noise_seed_delta": 0,
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"always_discard_next_to_last_sigma": false,
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"postprocessing_enable_in_main_ui": [],
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"postprocessing_operation_order": [],
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"upscaling_max_images_in_cache": 5,
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"disabled_extensions": [],
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"ldsr_steps": 100,
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"ldsr_cached": false,
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"SWIN_tile": 192,
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"SWIN_tile_overlap": 8,
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"sd_lora": "None",
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"lora_apply_to_outputs": false
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}

‎sd-docker-slim/configs/alt-diffusion-inference.yaml

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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: modules.xlmr.BertSeriesModelWithTransformation
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params:
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name: "XLMR-Large"

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