Delete all

.pre-commit-config.yaml

@@ -1,47 +0,0 @@
-exclude: patch
-repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - id: check-shebang-scripts-are-executable
-  - id: check-toml
-  - id: check-yaml
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: mixed-line-ending
-    args: ['--fix=lf']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.12.0
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-      additional_dependencies: ['types-python-slugify']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
-- repo: https://github.com/kynan/nbstripout
-  rev: 0.6.0
-  hooks:
-    - id: nbstripout
-      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
-- repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.6.4
-  hooks:
-    - id: nbqa-isort
-    - id: nbqa-yapf

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 hysts
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

LICENSE.ControlNet

@@ -1,201 +0,0 @@
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-
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-      outstanding shares, or (iii) beneficial ownership of such entity.
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README.md

@@ -1,14 +0,0 @@
----
-title: ControlNet
-emoji: 🌖
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.22.1
-python_version: 3.10.9
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,150 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import pathlib
-import shlex
-import subprocess
-
-import gradio as gr
-
-if os.getenv('SYSTEM') == 'spaces':
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'), stdin=f, cwd='ControlNet')
-
-base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/'
-names = [
-    'body_pose_model.pth',
-    'dpt_hybrid-midas-501f0c75.pt',
-    'hand_pose_model.pth',
-    'mlsd_large_512_fp32.pth',
-    'mlsd_tiny_512_fp32.pth',
-    'network-bsds500.pth',
-    'upernet_global_small.pth',
-]
-for name in names:
-    command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}'
-    out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}')
-    if out_path.exists():
-        continue
-    subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/')
-
-from app_canny import create_demo as create_demo_canny
-from app_depth import create_demo as create_demo_depth
-from app_fake_scribble import create_demo as create_demo_fake_scribble
-from app_hed import create_demo as create_demo_hed
-from app_hough import create_demo as create_demo_hough
-from app_normal import create_demo as create_demo_normal
-from app_pose import create_demo as create_demo_pose
-from app_scribble import create_demo as create_demo_scribble
-from app_scribble_interactive import \
-    create_demo as create_demo_scribble_interactive
-from app_seg import create_demo as create_demo_seg
-from model import Model, download_all_controlnet_weights
-
-DESCRIPTION = '# [ControlNet](https://github.com/lllyasviel/ControlNet)'
-
-SPACE_ID = os.getenv('SPACE_ID')
-ALLOW_CHANGING_BASE_MODEL = SPACE_ID != 'hysts/ControlNet'
-
-if SPACE_ID is not None:
-    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
-
-MAX_IMAGES = int(os.getenv('MAX_IMAGES', '3'))
-DEFAULT_NUM_IMAGES = min(MAX_IMAGES, int(os.getenv('DEFAULT_NUM_IMAGES', '1')))
-
-if os.getenv('SYSTEM') == 'spaces':
-    download_all_controlnet_weights()
-
-DEFAULT_MODEL_ID = os.getenv('DEFAULT_MODEL_ID',
-                             'runwayml/stable-diffusion-v1-5')
-model = Model(base_model_id=DEFAULT_MODEL_ID, task_name='canny')
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Tabs():
-        with gr.TabItem('Canny'):
-            create_demo_canny(model.process_canny,
-                              max_images=MAX_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Hough'):
-            create_demo_hough(model.process_hough,
-                              max_images=MAX_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('HED'):
-            create_demo_hed(model.process_hed,
-                            max_images=MAX_IMAGES,
-                            default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Scribble'):
-            create_demo_scribble(model.process_scribble,
-                                 max_images=MAX_IMAGES,
-                                 default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Scribble Interactive'):
-            create_demo_scribble_interactive(
-                model.process_scribble_interactive,
-                max_images=MAX_IMAGES,
-                default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Fake Scribble'):
-            create_demo_fake_scribble(model.process_fake_scribble,
-                                      max_images=MAX_IMAGES,
-                                      default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Pose'):
-            create_demo_pose(model.process_pose,
-                             max_images=MAX_IMAGES,
-                             default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Segmentation'):
-            create_demo_seg(model.process_seg,
-                            max_images=MAX_IMAGES,
-                            default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Depth'):
-            create_demo_depth(model.process_depth,
-                              max_images=MAX_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Normal map'):
-            create_demo_normal(model.process_normal,
-                               max_images=MAX_IMAGES,
-                               default_num_images=DEFAULT_NUM_IMAGES)
-
-    with gr.Accordion(label='Base model', open=False):
-        with gr.Row():
-            with gr.Column():
-                current_base_model = gr.Text(label='Current base model')
-            with gr.Column(scale=0.3):
-                check_base_model_button = gr.Button('Check current base model')
-        with gr.Row():
-            with gr.Column():
-                new_base_model_id = gr.Text(
-                    label='New base model',
-                    max_lines=1,
-                    placeholder='runwayml/stable-diffusion-v1-5',
-                    info=
-                    'The base model must be compatible with Stable Diffusion v1.5.',
-                    interactive=ALLOW_CHANGING_BASE_MODEL)
-            with gr.Column(scale=0.3):
-                change_base_model_button = gr.Button(
-                    'Change base model', interactive=ALLOW_CHANGING_BASE_MODEL)
-        if not ALLOW_CHANGING_BASE_MODEL:
-            gr.Markdown(
-                '''The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>'''
-            )
-
-    gr.Markdown('''### Related Spaces
-
-- [Space using Anything-v4.0 as base model](https://huggingface.co/spaces/hysts/ControlNet-with-Anything-v4)
-- https://huggingface.co/spaces/jonigata/PoseMaker2
-- https://huggingface.co/spaces/diffusers/controlnet-openpose
-- https://huggingface.co/spaces/diffusers/controlnet-canny
-''')
-
-    check_base_model_button.click(fn=lambda: model.base_model_id,
-                                  outputs=current_base_model,
-                                  queue=False)
-    new_base_model_id.submit(fn=model.set_base_model,
-                             inputs=new_base_model_id,
-                             outputs=current_base_model)
-    change_base_model_button.click(fn=model.set_base_model,
-                                   inputs=new_base_model_id,
-                                   outputs=current_base_model)
-
-demo.queue(api_open=False).launch(file_directories=['/tmp'])

app_canny.py

@@ -1,91 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_canny2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Canny Edge Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    canny_low_threshold = gr.Slider(
-                        label='Canny low threshold',
-                        minimum=1,
-                        maximum=255,
-                        value=100,
-                        step=1)
-                    canny_high_threshold = gr.Slider(
-                        label='Canny high threshold',
-                        minimum=1,
-                        maximum=255,
-                        value=200,
-                        step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            canny_low_threshold,
-            canny_high_threshold,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='canny')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_canny)
-    demo.queue().launch()

app_depth.py

@@ -1,86 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_depth2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Depth Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    is_depth_image = gr.Checkbox(label='Is depth image',
-                                                 value=False)
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='Depth Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=384,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            is_depth_image,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='depth')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_depth)
-    demo.queue().launch()

app_fake_scribble.py

@@ -1,83 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_fake_scribble2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Fake Scribble Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='HED Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='fake_scribble')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_fake_scribble)
-    demo.queue().launch()

app_hed.py

@@ -1,83 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hed2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with HED Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='HED Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='hed')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_hed)
-    demo.queue().launch()

app_hough.py

@@ -1,97 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_hough2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Hough Line Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='Hough Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    mlsd_value_threshold = gr.Slider(
-                        label='Hough value threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=2.0,
-                        value=0.1,
-                        step=0.01)
-                    mlsd_distance_threshold = gr.Slider(
-                        label='Hough distance threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=20.0,
-                        value=0.1,
-                        step=0.01)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            mlsd_value_threshold,
-            mlsd_distance_threshold,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='hough')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_hough)
-    demo.queue().launch()

app_normal.py

@@ -1,93 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_normal2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Normal Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    is_normal_image = gr.Checkbox(label='Is normal image',
-                                                  value=False)
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='Normal Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=384,
-                                                  step=1)
-                    bg_threshold = gr.Slider(
-                        label='Normal background threshold',
-                        minimum=0.0,
-                        maximum=1.0,
-                        value=0.4,
-                        step=0.01)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            bg_threshold,
-            is_normal_image,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='normal')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_normal)
-    demo.queue().launch()

app_pose.py

@@ -1,89 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_pose2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Human Pose')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    is_pose_image = gr.Checkbox(label='Is pose image',
-                                                value=False)
-                    gr.Markdown(
-                        'You can use [PoseMaker2](https://huggingface.co/spaces/jonigata/PoseMaker2) to create pose images.'
-                    )
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(label='OpenPose Resolution',
-                                                  minimum=128,
-                                                  maximum=512,
-                                                  value=512,
-                                                  step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            is_pose_image,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='pose')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_pose)
-    demo.queue().launch()

app_scribble.py

@@ -1,77 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_scribble2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Scribble Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='scribble')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_scribble)
-    demo.queue().launch()

app_scribble_interactive.py

@@ -1,103 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_scribble2image_interactive.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-import numpy as np
-
-
-def create_canvas(w, h):
-    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown(
-                '## Control Stable Diffusion with Interactive Scribbles')
-        with gr.Row():
-            with gr.Column():
-                canvas_width = gr.Slider(label='Canvas Width',
-                                         minimum=256,
-                                         maximum=512,
-                                         value=512,
-                                         step=1)
-                canvas_height = gr.Slider(label='Canvas Height',
-                                          minimum=256,
-                                          maximum=512,
-                                          value=512,
-                                          step=1)
-                create_button = gr.Button(label='Start',
-                                          value='Open drawing canvas!')
-                input_image = gr.Image(source='upload',
-                                       type='numpy',
-                                       tool='sketch')
-                gr.Markdown(
-                    value=
-                    'Do not forget to change your brush width to make it thinner. (Gradio do not allow developers to set brush width so you need to do it manually.) '
-                    'Just click on the small pencil icon in the upper right corner of the above block.'
-                )
-                create_button.click(fn=create_canvas,
-                                    inputs=[canvas_width, canvas_height],
-                                    outputs=input_image,
-                                    queue=False)
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process, inputs=inputs, outputs=result)
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_scribble_interactive)
-    demo.queue().launch()

app_seg.py

@@ -1,87 +0,0 @@
-# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_seg2image.py
-# The original license file is LICENSE.ControlNet in this repo.
-import gradio as gr
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown('## Control Stable Diffusion with Segmentation Maps')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(source='upload', type='numpy')
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button(label='Run')
-                with gr.Accordion('Advanced options', open=False):
-                    is_segmentation_map = gr.Checkbox(
-                        label='Is segmentation map', value=False)
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image Resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    detect_resolution = gr.Slider(
-                        label='Segmentation Resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance Scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=-1,
-                                     maximum=2147483647,
-                                     step=1,
-                                     randomize=True)
-                    a_prompt = gr.Textbox(
-                        label='Added Prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative Prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output',
-                                    show_label=False,
-                                    elem_id='gallery').style(grid=2,
-                                                             height='auto')
-        inputs = [
-            input_image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            detect_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            is_segmentation_map,
-        ]
-        prompt.submit(fn=process, inputs=inputs, outputs=result)
-        run_button.click(fn=process,
-                         inputs=inputs,
-                         outputs=result,
-                         api_name='seg')
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model()
-    demo = create_demo(model.process_seg)
-    demo.queue().launch()

model.py

@@ -1,644 +0,0 @@
-# This file is adapted from gradio_*.py in https://github.com/lllyasviel/ControlNet/tree/f4748e3630d8141d7765e2bd9b1e348f47847707
-# The original license file is LICENSE.ControlNet in this repo.
-from __future__ import annotations
-
-import gc
-import pathlib
-import sys
-
-import cv2
-import numpy as np
-import PIL.Image
-import torch
-from diffusers import (ControlNetModel, DiffusionPipeline,
-                       StableDiffusionControlNetPipeline,
-                       UniPCMultistepScheduler)
-
-repo_dir = pathlib.Path(__file__).parent
-submodule_dir = repo_dir / 'ControlNet'
-sys.path.append(submodule_dir.as_posix())
-
-from annotator.canny import apply_canny
-from annotator.hed import apply_hed, nms
-from annotator.midas import apply_midas
-from annotator.mlsd import apply_mlsd
-from annotator.openpose import apply_openpose
-from annotator.uniformer import apply_uniformer
-from annotator.util import HWC3, resize_image
-
-CONTROLNET_MODEL_IDS = {
-    'canny': 'lllyasviel/sd-controlnet-canny',
-    'hough': 'lllyasviel/sd-controlnet-mlsd',
-    'hed': 'lllyasviel/sd-controlnet-hed',
-    'scribble': 'lllyasviel/sd-controlnet-scribble',
-    'pose': 'lllyasviel/sd-controlnet-openpose',
-    'seg': 'lllyasviel/sd-controlnet-seg',
-    'depth': 'lllyasviel/sd-controlnet-depth',
-    'normal': 'lllyasviel/sd-controlnet-normal',
-}
-
-
-def download_all_controlnet_weights() -> None:
-    for model_id in CONTROLNET_MODEL_IDS.values():
-        ControlNetModel.from_pretrained(model_id)
-
-
-class Model:
-    def __init__(self,
-                 base_model_id: str = 'runwayml/stable-diffusion-v1-5',
-                 task_name: str = 'canny'):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.base_model_id = ''
-        self.task_name = ''
-        self.pipe = self.load_pipe(base_model_id, task_name)
-
-    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
-        if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
-                self, 'pipe'):
-            return self.pipe
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            base_model_id,
-            safety_checker=None,
-            controlnet=controlnet,
-            torch_dtype=torch.float16)
-        pipe.scheduler = UniPCMultistepScheduler.from_config(
-            pipe.scheduler.config)
-        pipe.enable_xformers_memory_efficient_attention()
-        pipe.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.base_model_id = base_model_id
-        self.task_name = task_name
-        return pipe
-
-    def set_base_model(self, base_model_id: str) -> str:
-        if not base_model_id or base_model_id == self.base_model_id:
-            return self.base_model_id
-        del self.pipe
-        torch.cuda.empty_cache()
-        gc.collect()
-        try:
-            self.pipe = self.load_pipe(base_model_id, self.task_name)
-        except Exception:
-            self.pipe = self.load_pipe(self.base_model_id, self.task_name)
-        return self.base_model_id
-
-    def load_controlnet_weight(self, task_name: str) -> None:
-        if task_name == self.task_name:
-            return
-        if 'controlnet' in self.pipe.__dict__:
-            del self.pipe.controlnet
-        torch.cuda.empty_cache()
-        gc.collect()
-        model_id = CONTROLNET_MODEL_IDS[task_name]
-        controlnet = ControlNetModel.from_pretrained(model_id,
-                                                     torch_dtype=torch.float16)
-        controlnet.to(self.device)
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.pipe.controlnet = controlnet
-        self.task_name = task_name
-
-    def get_prompt(self, prompt: str, additional_prompt: str) -> str:
-        if not prompt:
-            prompt = additional_prompt
-        else:
-            prompt = f'{prompt}, {additional_prompt}'
-        return prompt
-
-    @torch.autocast('cuda')
-    def run_pipe(
-        self,
-        prompt: str,
-        negative_prompt: str,
-        control_image: PIL.Image.Image,
-        num_images: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        if seed == -1:
-            seed = np.random.randint(0, np.iinfo(np.int64).max)
-        generator = torch.Generator().manual_seed(seed)
-        return self.pipe(prompt=prompt,
-                         negative_prompt=negative_prompt,
-                         guidance_scale=guidance_scale,
-                         num_images_per_prompt=num_images,
-                         num_inference_steps=num_steps,
-                         generator=generator,
-                         image=control_image).images
-
-    @staticmethod
-    def preprocess_canny(
-        input_image: np.ndarray,
-        image_resolution: int,
-        low_threshold: int,
-        high_threshold: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image), image_resolution)
-        control_image = apply_canny(image, low_threshold, high_threshold)
-        control_image = HWC3(control_image)
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_canny(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        low_threshold: int,
-        high_threshold: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_canny(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            low_threshold=low_threshold,
-            high_threshold=high_threshold,
-        )
-        self.load_controlnet_weight('canny')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_hough(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        value_threshold: float,
-        distance_threshold: float,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_mlsd(
-            resize_image(input_image, detect_resolution), value_threshold,
-            distance_threshold)
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_NEAREST)
-
-        vis_control_image = 255 - cv2.dilate(
-            control_image, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_hough(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        value_threshold: float,
-        distance_threshold: float,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_hough(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            value_threshold=value_threshold,
-            distance_threshold=distance_threshold,
-        )
-        self.load_controlnet_weight('hough')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_hed(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_hed(resize_image(input_image, detect_resolution))
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_LINEAR)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_hed(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_hed(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-        )
-        self.load_controlnet_weight('hed')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_scribble(
-        input_image: np.ndarray,
-        image_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image), image_resolution)
-        control_image = np.zeros_like(image, dtype=np.uint8)
-        control_image[np.min(image, axis=2) < 127] = 255
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_scribble(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_scribble(
-            input_image=input_image,
-            image_resolution=image_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_scribble_interactive(
-        input_image: np.ndarray,
-        image_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        image = resize_image(HWC3(input_image['mask'][:, :, 0]),
-                             image_resolution)
-        control_image = np.zeros_like(image, dtype=np.uint8)
-        control_image[np.min(image, axis=2) > 127] = 255
-        vis_control_image = 255 - control_image
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_scribble_interactive(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_scribble_interactive(
-            input_image=input_image,
-            image_resolution=image_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_fake_scribble(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        control_image = apply_hed(resize_image(input_image, detect_resolution))
-        control_image = HWC3(control_image)
-        image = resize_image(input_image, image_resolution)
-        H, W = image.shape[:2]
-
-        control_image = cv2.resize(control_image, (W, H),
-                                   interpolation=cv2.INTER_LINEAR)
-        control_image = nms(control_image, 127, 3.0)
-        control_image = cv2.GaussianBlur(control_image, (0, 0), 3.0)
-        control_image[control_image > 4] = 255
-        control_image[control_image < 255] = 0
-
-        vis_control_image = 255 - control_image
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            vis_control_image)
-
-    @torch.inference_mode()
-    def process_fake_scribble(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_fake_scribble(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-        )
-        self.load_controlnet_weight('scribble')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_pose(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_pose_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_pose_image:
-            control_image, _ = apply_openpose(
-                resize_image(input_image, detect_resolution))
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_NEAREST)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_pose(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_pose_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_pose(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_pose_image=is_pose_image,
-        )
-        self.load_controlnet_weight('pose')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_seg(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_segmentation_map: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_segmentation_map:
-            control_image = apply_uniformer(
-                resize_image(input_image, detect_resolution))
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_NEAREST)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_seg(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_segmentation_map: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_seg(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_segmentation_map=is_segmentation_map,
-        )
-        self.load_controlnet_weight('seg')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_depth(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        is_depth_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_depth_image:
-            control_image, _ = apply_midas(
-                resize_image(input_image, detect_resolution))
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_LINEAR)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_depth(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        is_depth_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_depth(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            is_depth_image=is_depth_image,
-        )
-        self.load_controlnet_weight('depth')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results
-
-    @staticmethod
-    def preprocess_normal(
-        input_image: np.ndarray,
-        image_resolution: int,
-        detect_resolution: int,
-        bg_threshold: float,
-        is_normal_image: bool,
-    ) -> tuple[PIL.Image.Image, PIL.Image.Image]:
-        input_image = HWC3(input_image)
-        if not is_normal_image:
-            _, control_image = apply_midas(resize_image(
-                input_image, detect_resolution),
-                                           bg_th=bg_threshold)
-            control_image = HWC3(control_image)
-            image = resize_image(input_image, image_resolution)
-            H, W = image.shape[:2]
-            control_image = cv2.resize(control_image, (W, H),
-                                       interpolation=cv2.INTER_LINEAR)
-        else:
-            control_image = resize_image(input_image, image_resolution)
-        return PIL.Image.fromarray(control_image), PIL.Image.fromarray(
-            control_image)
-
-    @torch.inference_mode()
-    def process_normal(
-        self,
-        input_image: np.ndarray,
-        prompt: str,
-        additional_prompt: str,
-        negative_prompt: str,
-        num_images: int,
-        image_resolution: int,
-        detect_resolution: int,
-        num_steps: int,
-        guidance_scale: float,
-        seed: int,
-        bg_threshold: float,
-        is_normal_image: bool,
-    ) -> list[PIL.Image.Image]:
-        control_image, vis_control_image = self.preprocess_normal(
-            input_image=input_image,
-            image_resolution=image_resolution,
-            detect_resolution=detect_resolution,
-            bg_threshold=bg_threshold,
-            is_normal_image=is_normal_image,
-        )
-        self.load_controlnet_weight('normal')
-        results = self.run_pipe(
-            prompt=self.get_prompt(prompt, additional_prompt),
-            negative_prompt=negative_prompt,
-            control_image=control_image,
-            num_images=num_images,
-            num_steps=num_steps,
-            guidance_scale=guidance_scale,
-            seed=seed,
-        )
-        return [vis_control_image] + results

notebooks/notebook.ipynb

@@ -1,80 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8CnkIPtjn8Dc"
-   },
-   "outputs": [],
-   "source": [
-    "!git clone --recursive https://huggingface.co/spaces/hysts/ControlNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "IZlaYNTWoFPK"
-   },
-   "outputs": [],
-   "source": [
-    "%cd ControlNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0zhLFnZUoWdp"
-   },
-   "outputs": [],
-   "source": [
-    "!cd ControlNet && git apply ../patch && cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P_fzYrLvoIcI"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install -q -r requirements.txt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GOfGng5Woktd"
-   },
-   "outputs": [],
-   "source": [
-    "import app"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7Cued230ol7T"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

patch

@@ -1,128 +0,0 @@
-diff --git a/annotator/hed/__init__.py b/annotator/hed/__init__.py
-index 42d8dc6..1587035 100644
---- a/annotator/hed/__init__.py
-+++ b/annotator/hed/__init__.py
-@@ -1,8 +1,12 @@
-+import pathlib
-+
- import numpy as np
- import cv2
- import torch
- from einops import rearrange
- 
-+root_dir = pathlib.Path(__file__).parents[2]
-+
- 
- class Network(torch.nn.Module):
-     def __init__(self):
-@@ -64,7 +68,7 @@ class Network(torch.nn.Module):
-             torch.nn.Sigmoid()
-         )
- 
--        self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load('./annotator/ckpts/network-bsds500.pth').items()})
-+        self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load(f'{root_dir}/annotator/ckpts/network-bsds500.pth').items()})
-     # end
- 
-     def forward(self, tenInput):
-diff --git a/annotator/midas/api.py b/annotator/midas/api.py
-index 9fa305e..d8594ea 100644
---- a/annotator/midas/api.py
-+++ b/annotator/midas/api.py
-@@ -1,5 +1,7 @@
- # based on https://github.com/isl-org/MiDaS
- 
-+import pathlib
-+
- import cv2
- import torch
- import torch.nn as nn
-@@ -10,10 +12,11 @@ from .midas.midas_net import MidasNet
- from .midas.midas_net_custom import MidasNet_small
- from .midas.transforms import Resize, NormalizeImage, PrepareForNet
- 
-+root_dir = pathlib.Path(__file__).parents[2]
- 
- ISL_PATHS = {
--    "dpt_large": "annotator/ckpts/dpt_large-midas-2f21e586.pt",
--    "dpt_hybrid": "annotator/ckpts/dpt_hybrid-midas-501f0c75.pt",
-+    "dpt_large": f"{root_dir}/annotator/ckpts/dpt_large-midas-2f21e586.pt",
-+    "dpt_hybrid": f"{root_dir}/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt",
-     "midas_v21": "",
-     "midas_v21_small": "",
- }
-diff --git a/annotator/mlsd/__init__.py b/annotator/mlsd/__init__.py
-index 75db717..f310fe6 100644
---- a/annotator/mlsd/__init__.py
-+++ b/annotator/mlsd/__init__.py
-@@ -1,3 +1,5 @@
-+import pathlib
-+
- import cv2
- import numpy as np
- import torch
-@@ -8,8 +10,9 @@ from .models.mbv2_mlsd_tiny import  MobileV2_MLSD_Tiny
- from .models.mbv2_mlsd_large import  MobileV2_MLSD_Large
- from .utils import  pred_lines
- 
-+root_dir = pathlib.Path(__file__).parents[2]
- 
--model_path = './annotator/ckpts/mlsd_large_512_fp32.pth'
-+model_path = f'{root_dir}/annotator/ckpts/mlsd_large_512_fp32.pth'
- model = MobileV2_MLSD_Large()
- model.load_state_dict(torch.load(model_path), strict=True)
- model = model.cuda().eval()
-diff --git a/annotator/openpose/__init__.py b/annotator/openpose/__init__.py
-index 47d50a5..2369eed 100644
---- a/annotator/openpose/__init__.py
-+++ b/annotator/openpose/__init__.py
-@@ -1,4 +1,5 @@
- import os
-+import pathlib
- os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
- 
- import torch
-@@ -7,8 +8,10 @@ from . import util
- from .body import Body
- from .hand import Hand
- 
--body_estimation = Body('./annotator/ckpts/body_pose_model.pth')
--hand_estimation = Hand('./annotator/ckpts/hand_pose_model.pth')
-+root_dir = pathlib.Path(__file__).parents[2]
-+
-+body_estimation = Body(f'{root_dir}/annotator/ckpts/body_pose_model.pth')
-+hand_estimation = Hand(f'{root_dir}/annotator/ckpts/hand_pose_model.pth')
- 
- 
- def apply_openpose(oriImg, hand=False):
-diff --git a/annotator/uniformer/__init__.py b/annotator/uniformer/__init__.py
-index 500e53c..4061dbe 100644
---- a/annotator/uniformer/__init__.py
-+++ b/annotator/uniformer/__init__.py
-@@ -1,9 +1,12 @@
-+import pathlib
-+
- from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
- from annotator.uniformer.mmseg.core.evaluation import get_palette
- 
-+root_dir = pathlib.Path(__file__).parents[2]
- 
--checkpoint_file = "annotator/ckpts/upernet_global_small.pth"
--config_file = 'annotator/uniformer/exp/upernet_global_small/config.py'
-+checkpoint_file = f"{root_dir}/annotator/ckpts/upernet_global_small.pth"
-+config_file = f'{root_dir}/annotator/uniformer/exp/upernet_global_small/config.py'
- model = init_segmentor(config_file, checkpoint_file).cuda()
- 
- 
-diff --git a/annotator/util.py b/annotator/util.py
-index 7cde937..10a6d58 100644
---- a/annotator/util.py
-+++ b/annotator/util.py
-@@ -25,7 +25,7 @@ def resize_image(input_image, resolution):
-     H, W, C = input_image.shape
-     H = float(H)
-     W = float(W)
--    k = float(resolution) / min(H, W)
-+    k = float(resolution) / max(H, W)
-     H *= k
-     W *= k
-     H = int(np.round(H / 64.0)) * 64

requirements.txt

@@ -1,22 +0,0 @@
-addict==2.4.0
-albumentations==1.3.0
-einops==0.6.0
-git+https://github.com/huggingface/accelerate@78151f8
-git+https://github.com/huggingface/diffusers@fa6d52d
-gradio==3.22.1
-imageio==2.25.0
-imageio-ffmpeg==0.4.8
-kornia==0.6.9
-omegaconf==2.3.0
-open-clip-torch==2.13.0
-opencv-contrib-python==4.7.0.68
-opencv-python-headless==4.7.0.68
-prettytable==3.6.0
-pytorch-lightning==1.9.0
-safetensors==0.2.8
-timm==0.6.12
-torch==1.13.1
-torchvision==0.14.1
-transformers==4.26.1
-xformers==0.0.16
-yapf==0.32.0

style.css

@@ -1,3 +0,0 @@
-h1 {
-  text-align: center;
-}