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.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    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: 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: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.5.1
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          ["types-python-slugify", "types-requests", "types-PyYAML"]
+  - repo: https://github.com/psf/black
+    rev: 23.9.1
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.6.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.0
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

LICENSE

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+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

@@ -0,0 +1,201 @@
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README.md

@@ -1,12 +1,15 @@
 ---
-title: Demo Ctn
-emoji: 🏃
+title: ControlNet++
+emoji: 📉
 colorFrom: yellow
 colorTo: green
 sdk: gradio
-sdk_version: 4.27.0
+sdk_version: 4.0.0
+python_version: 3.10.13
 app_file: app.py
 pinned: false
+license: mit
+suggested_hardware: t4-medium
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import gradio as gr
+import torch
+
+torch.jit.script = lambda f: f
+import spaces
+
+from app_depth import create_demo as create_demo_depth
+from model import Model
+from settings import ALLOW_CHANGING_BASE_MODEL, DEFAULT_MODEL_ID, SHOW_DUPLICATE_BUTTON
+from transformers.utils.hub import move_cache
+
+move_cache()
+
+DESCRIPTION = "# ControlNet"
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="depth")
+
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=SHOW_DUPLICATE_BUTTON,
+    )
+
+    with gr.Tabs():
+        with gr.TabItem("Depth"):
+            create_demo_depth(model.process_depth)
+
+    with gr.Accordion(label="Base model", open=False):
+        with gr.Row():
+            with gr.Column(scale=5):
+                current_base_model = gr.Text(label="Current base model")
+            with gr.Column(scale=1):
+                check_base_model_button = gr.Button("Check current base model")
+        with gr.Row():
+            with gr.Column(scale=5):
+                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=1):
+                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."""
+            )
+
+    check_base_model_button.click(
+        fn=lambda: model.base_model_id,
+        outputs=current_base_model,
+        queue=False,
+        api_name="check_base_model",
+    )
+    gr.on(
+        triggers=[new_base_model_id.submit, change_base_model_button.click],
+        fn=model.set_base_model,
+        inputs=new_base_model_id,
+        outputs=current_base_model,
+        api_name=False,
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

app_depth.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+
+import gradio as gr
+
+from settings import (
+    DEFAULT_IMAGE_RESOLUTION,
+    DEFAULT_NUM_IMAGES,
+    MAX_IMAGE_RESOLUTION,
+    MAX_NUM_IMAGES,
+    MAX_SEED,
+)
+from utils import randomize_seed_fn
+
+examples = []
+
+
+def create_demo(process):
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image()
+                prompt = gr.Textbox(label="Prompt")
+                run_button = gr.Button("Run")
+                with gr.Accordion("Advanced options", open=False):
+                    preprocessor_name = gr.Radio(
+                        label="Preprocessor",
+                        choices=["Midas", "DPT", "None"],
+                        type="value",
+                        value="DPT",
+                    )
+                    num_samples = gr.Slider(
+                        label="Number of images",
+                        minimum=1,
+                        maximum=MAX_NUM_IMAGES,
+                        value=DEFAULT_NUM_IMAGES,
+                        step=1,
+                    )
+                    image_resolution = gr.Slider(
+                        label="Image resolution",
+                        minimum=256,
+                        maximum=MAX_IMAGE_RESOLUTION,
+                        value=DEFAULT_IMAGE_RESOLUTION,
+                        step=256,
+                    )
+                    preprocess_resolution = gr.Slider(
+                        label="Preprocess resolution",
+                        minimum=128,
+                        maximum=512,
+                        value=384,
+                        step=1,
+                    )
+                    num_steps = gr.Slider(
+                        label="Number of steps",
+                        minimum=1,
+                        maximum=100,
+                        value=20,
+                        step=1,
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.1,
+                        maximum=30.0,
+                        value=7.5,
+                        step=0.1,
+                    )
+                    seed = gr.Slider(
+                        label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0
+                    )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                    a_prompt = gr.Textbox(
+                        label="Additional prompt",
+                        value="high-quality, extremely detailed, 4K",
+                    )
+                    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, columns=2, object_fit="scale-down"
+                )
+
+        gr.Examples(
+            examples=examples,
+            inputs=[
+                image,
+                prompt,
+                guidance_scale,
+                seed,
+            ],
+            outputs=result,
+            fn=process,
+        )
+
+        inputs = [
+            image,
+            prompt,
+            a_prompt,
+            n_prompt,
+            num_samples,
+            image_resolution,
+            preprocess_resolution,
+            num_steps,
+            guidance_scale,
+            seed,
+            preprocessor_name,
+        ]
+        prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=process,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        run_button.click(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=process,
+            inputs=inputs,
+            outputs=result,
+            api_name="depth",
+        )
+    return demo
+
+
+if __name__ == "__main__":
+    from model import Model
+
+    model = Model(task_name="depth")
+    demo = create_demo(model.process_depth)
+    demo.queue().launch()

app_openpose.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+import gradio as gr
+
+from settings import (
+    DEFAULT_IMAGE_RESOLUTION,
+    DEFAULT_NUM_IMAGES,
+    MAX_IMAGE_RESOLUTION,
+    MAX_NUM_IMAGES,
+    MAX_SEED,
+)
+from utils import randomize_seed_fn
+
+
+def create_demo(process):
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image()
+                prompt = gr.Textbox(label="Prompt")
+                run_button = gr.Button(label="Run")
+                with gr.Accordion("Advanced options", open=False):
+                    preprocessor_name = gr.Radio(
+                        label="Preprocessor", choices=["Openpose", "None"], type="value", value="Openpose"
+                    )
+                    num_samples = gr.Slider(
+                        label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
+                    )
+                    image_resolution = gr.Slider(
+                        label="Image resolution",
+                        minimum=256,
+                        maximum=MAX_IMAGE_RESOLUTION,
+                        value=DEFAULT_IMAGE_RESOLUTION,
+                        step=256,
+                    )
+                    preprocess_resolution = gr.Slider(
+                        label="Preprocess resolution", minimum=128, maximum=512, value=512, step=1
+                    )
+                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
+                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
+                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
+                    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, columns=2, object_fit="scale-down")
+        inputs = [
+            image,
+            prompt,
+            a_prompt,
+            n_prompt,
+            num_samples,
+            image_resolution,
+            preprocess_resolution,
+            num_steps,
+            guidance_scale,
+            seed,
+            preprocessor_name,
+        ]
+        prompt.submit(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=process,
+            inputs=inputs,
+            outputs=result,
+            api_name=False,
+        )
+        run_button.click(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=process,
+            inputs=inputs,
+            outputs=result,
+            api_name="openpose",
+        )
+    return demo
+
+
+if __name__ == "__main__":
+    from model import Model
+
+    model = Model(task_name="Openpose")
+    demo = create_demo(model.process_openpose)
+    demo.queue().launch()

checkpoints/depth/controlnet/config.json

@@ -0,0 +1,52 @@
+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.26.3",
+  "_name_or_path": "work_dirs/finetune/MultiGen20M_depth/ft_controlnet_sd15_depth_res512_bs256_lr1e-5_warmup100_iter5k_fp16ft0-200/checkpoint-5000",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": 1,
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

checkpoints/depth/controlnet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7450404d13ef888c9701433a3c17b2a86c021a6d042f9f5d2519602abd7f2f3
+size 1445157120

cv_utils.py

@@ -0,0 +1,17 @@
+import cv2
+import numpy as np
+
+
+def resize_image(input_image, resolution, interpolation=None):
+    H, W, C = input_image.shape
+    H = float(H)
+    W = float(W)
+    k = float(resolution) / max(H, W)
+    H *= k
+    W *= k
+    H = int(np.round(H / 64.0)) * 64
+    W = int(np.round(W / 64.0)) * 64
+    if interpolation is None:
+        interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
+    img = cv2.resize(input_image, (W, H), interpolation=interpolation)
+    return img

depth_estimator.py

@@ -0,0 +1,25 @@
+import numpy as np
+import PIL.Image
+from controlnet_aux.util import HWC3
+from transformers import pipeline
+
+from cv_utils import resize_image
+
+
+class DepthEstimator:
+    def __init__(self):
+        self.model = pipeline("depth-estimation")
+
+    def __call__(self, image: np.ndarray, **kwargs) -> PIL.Image.Image:
+        detect_resolution = kwargs.pop("detect_resolution", 512)
+        image_resolution = kwargs.pop("image_resolution", 512)
+        image = np.array(image)
+        image = HWC3(image)
+        image = resize_image(image, resolution=detect_resolution)
+        image = PIL.Image.fromarray(image)
+        image = self.model(image)
+        image = image["depth"]
+        image = np.array(image)
+        image = HWC3(image)
+        image = resize_image(image, resolution=image_resolution)
+        return PIL.Image.fromarray(image)

image_segmentor.py

@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+import PIL.Image
+import torch
+from controlnet_aux.util import HWC3, ade_palette
+from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
+
+from cv_utils import resize_image
+
+
+class ImageSegmentor:
+    def __init__(self):
+        self.image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
+        self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
+
+    @torch.no_grad()
+    def __call__(self, image: np.ndarray, **kwargs) -> PIL.Image.Image:
+        detect_resolution = kwargs.pop("detect_resolution", 512)
+        image_resolution = kwargs.pop("image_resolution", 512)
+        image = HWC3(image)
+        image = resize_image(image, resolution=detect_resolution)
+        image = PIL.Image.fromarray(image)
+
+        pixel_values = self.image_processor(image, return_tensors="pt").pixel_values
+        outputs = self.image_segmentor(pixel_values)
+        seg = self.image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+        color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+        for label, color in enumerate(ade_palette()):
+            color_seg[seg == label, :] = color
+        color_seg = color_seg.astype(np.uint8)
+
+        color_seg = resize_image(color_seg, resolution=image_resolution, interpolation=cv2.INTER_NEAREST)
+        return PIL.Image.fromarray(color_seg)

model.py

@@ -0,0 +1,436 @@
+from __future__ import annotations
+
+import gc
+
+import numpy as np
+import PIL.Image
+import spaces
+import torch
+from controlnet_aux.util import HWC3
+from diffusers import (
+    ControlNetModel,
+    DiffusionPipeline,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+
+from cv_utils import resize_image
+from preprocessor import Preprocessor
+from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
+
+CONTROLNET_MODEL_IDS = {
+    "depth": "checkpoints/depth/controlnet",
+}
+
+
+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 = "depth",
+    ):
+        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)
+        self.preprocessor = Preprocessor()
+
+    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")
+            and self.pipe is not None
+        ):
+            return self.pipe
+        model_id = CONTROLNET_MODEL_IDS[task_name]
+        controlnet = ControlNetModel.from_pretrained(
+            model_id, torch_dtype=torch.float32
+        )
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            base_model_id,
+            safety_checker=None,
+            controlnet=controlnet,
+            torch_dtype=torch.float32,
+        )
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        # if self.device.type == "cuda":
+        #     pipe.disable_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 self.pipe is not None and hasattr(self.pipe, "controlnet"):
+            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.float32
+        )
+        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]:
+        self.pipe.to(self.device)
+        self.pipe.controlnet.to(self.device)
+        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
+
+    @torch.no_grad()
+    @spaces.GPU(enable_queue=True)
+    def process_canny(
+        self,
+        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]:
+        if image is None:
+            raise ValueError
+        if image_resolution > MAX_IMAGE_RESOLUTION:
+            raise ValueError
+        if num_images > MAX_NUM_IMAGES:
+            raise ValueError
+
+        self.preprocessor.load("Canny")
+        control_image = self.preprocessor(
+            image=image,
+            low_threshold=low_threshold,
+            high_threshold=high_threshold,
+            detect_resolution=image_resolution,
+        )
+
+        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,
+        )
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                low_threshold=low_threshold,
+                high_threshold=high_threshold,
+                detect_resolution=image_resolution,
+            )
+            for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
+
+    @torch.no_grad()
+    @spaces.GPU(enable_queue=True)
+    def process_softedge(
+        self,
+        image: np.ndarray,
+        prompt: str,
+        additional_prompt: str,
+        negative_prompt: str,
+        num_images: int,
+        image_resolution: int,
+        preprocess_resolution: int,
+        num_steps: int,
+        guidance_scale: float,
+        seed: int,
+        preprocessor_name: str,
+    ) -> list[PIL.Image.Image]:
+        if image is None:
+            raise ValueError
+        if image_resolution > MAX_IMAGE_RESOLUTION:
+            raise ValueError
+        if num_images > MAX_NUM_IMAGES:
+            raise ValueError
+
+        if preprocessor_name == "None":
+            image = HWC3(image)
+            image = resize_image(image, resolution=image_resolution)
+            control_image = PIL.Image.fromarray(image)
+        elif preprocessor_name in ["HED", "HED safe"]:
+            safe = "safe" in preprocessor_name
+            self.preprocessor.load("HED")
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+                scribble=safe,
+            )
+        elif preprocessor_name in ["PidiNet", "PidiNet safe"]:
+            safe = "safe" in preprocessor_name
+            self.preprocessor.load("PidiNet")
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+                safe=safe,
+            )
+        else:
+            raise ValueError
+        self.load_controlnet_weight("softedge")
+        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,
+        )
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+                scribble=safe,
+            )
+            for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
+
+    @torch.no_grad()
+    @spaces.GPU(enable_queue=True)
+    def process_segmentation(
+        self,
+        image: np.ndarray,
+        prompt: str,
+        additional_prompt: str,
+        negative_prompt: str,
+        num_images: int,
+        image_resolution: int,
+        preprocess_resolution: int,
+        num_steps: int,
+        guidance_scale: float,
+        seed: int,
+        preprocessor_name: str,
+    ) -> list[PIL.Image.Image]:
+        if image is None:
+            raise ValueError
+        if image_resolution > MAX_IMAGE_RESOLUTION:
+            raise ValueError
+        if num_images > MAX_NUM_IMAGES:
+            raise ValueError
+
+        if preprocessor_name == "None":
+            image = HWC3(image)
+            image = resize_image(image, resolution=image_resolution)
+            control_image = PIL.Image.fromarray(image)
+        else:
+            self.preprocessor.load(preprocessor_name)
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+        self.load_controlnet_weight("segmentation")
+        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,
+        )
+        self.preprocessor.load("UPerNet")
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=np.array(x),
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+            for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
+
+    @torch.no_grad()
+    @spaces.GPU(enable_queue=True)
+    def process_depth(
+        self,
+        image: np.ndarray,
+        prompt: str,
+        additional_prompt: str,
+        negative_prompt: str,
+        num_images: int,
+        image_resolution: int,
+        preprocess_resolution: int,
+        num_steps: int,
+        guidance_scale: float,
+        seed: int,
+        preprocessor_name: str,
+    ) -> list[PIL.Image.Image]:
+        if image is None:
+            raise ValueError
+        if image_resolution > MAX_IMAGE_RESOLUTION:
+            raise ValueError
+        if num_images > MAX_NUM_IMAGES:
+            raise ValueError
+
+        if preprocessor_name == "None":
+            image = HWC3(image)
+            image = resize_image(image, resolution=image_resolution)
+            control_image = PIL.Image.fromarray(image)
+        else:
+            self.preprocessor.load(preprocessor_name)
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+        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,
+        )
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+            for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
+
+    @torch.no_grad()
+    @spaces.GPU(enable_queue=True)
+    def process_lineart(
+        self,
+        image: np.ndarray,
+        prompt: str,
+        additional_prompt: str,
+        negative_prompt: str,
+        num_images: int,
+        image_resolution: int,
+        preprocess_resolution: int,
+        num_steps: int,
+        guidance_scale: float,
+        seed: int,
+        preprocessor_name: str,
+    ) -> list[PIL.Image.Image]:
+        if image is None:
+            raise ValueError
+        if image_resolution > MAX_IMAGE_RESOLUTION:
+            raise ValueError
+        if num_images > MAX_NUM_IMAGES:
+            raise ValueError
+
+        if preprocessor_name in ["None", "None (anime)"]:
+            image = 255 - HWC3(image)
+            image = resize_image(image, resolution=image_resolution)
+            control_image = PIL.Image.fromarray(image)
+        elif preprocessor_name in ["Lineart", "Lineart coarse"]:
+            coarse = "coarse" in preprocessor_name
+            self.preprocessor.load("Lineart")
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+                coarse=coarse,
+            )
+        elif preprocessor_name == "Lineart (anime)":
+            self.preprocessor.load("LineartAnime")
+            control_image = self.preprocessor(
+                image=image,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+        # NOTE: We still use the general lineart model
+        if "anime" in preprocessor_name:
+            self.load_controlnet_weight("lineart_anime")
+        else:
+            self.load_controlnet_weight("lineart")
+        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,
+        )
+        self.preprocessor.load("Lineart")
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            )
+            for x in results
+        ]
+
+        control_image = PIL.Image.fromarray(
+            (255 - np.array(control_image)).astype(np.uint8)
+        )
+        conditions_of_generated_imgs = [
+            PIL.Image.fromarray((255 - np.array(x)).astype(np.uint8))
+            for x in conditions_of_generated_imgs
+        ]
+
+        return [control_image] * num_images + results + conditions_of_generated_imgs

notebooks/notebook.ipynb

@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8CnkIPtjn8Dc"
+   },
+   "outputs": [],
+   "source": [
+    "!git clone --recursive https://huggingface.co/spaces/hysts/ControlNet-v1-1\n",
+    "%cd ControlNet-v1-1\n",
+    "%pip install -q -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "GOfGng5Woktd"
+   },
+   "outputs": [],
+   "source": [
+    "from app import demo\n",
+    "\n",
+    "demo.queue().launch()"
+   ]
+  },
+  {
+   "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
+}

preprocessor.py

@@ -0,0 +1,112 @@
+import gc
+
+import numpy as np
+import PIL.Image
+import torch
+import torchvision
+from controlnet_aux import (
+    CannyDetector,
+    ContentShuffleDetector,
+    HEDdetector,
+    LineartAnimeDetector,
+    LineartDetector,
+    MidasDetector,
+    MLSDdetector,
+    NormalBaeDetector,
+    OpenposeDetector,
+    PidiNetDetector,
+)
+from controlnet_aux.util import HWC3
+
+from cv_utils import resize_image
+from depth_estimator import DepthEstimator
+from image_segmentor import ImageSegmentor
+
+from kornia.core import Tensor
+from kornia.filters import canny
+
+
+class Canny:
+
+    def __call__(
+        self,
+        images: np.array,
+        low_threshold: float = 0.1,
+        high_threshold: float = 0.2,
+        kernel_size: tuple[int, int] | int = (5, 5),
+        sigma: tuple[float, float] | Tensor = (1, 1),
+        hysteresis: bool = True,
+        eps: float = 1e-6
+    ) -> torch.Tensor:
+
+        assert low_threshold is not None, "low_threshold must be provided"
+        assert high_threshold is not None, "high_threshold must be provided"
+
+        images = torch.from_numpy(images).permute(2, 0, 1).unsqueeze(0) / 255.0
+
+        images_tensor = canny(images, low_threshold, high_threshold, kernel_size, sigma, hysteresis, eps)[1]
+        images_tensor = (images_tensor[0][0].numpy() * 255).astype(np.uint8)
+        return images_tensor
+
+
+class Preprocessor:
+    MODEL_ID = "lllyasviel/Annotators"
+
+    def __init__(self):
+        self.model = None
+        self.name = ""
+
+    def load(self, name: str) -> None:
+        if name == self.name:
+            return
+        if name == "HED":
+            self.model = HEDdetector.from_pretrained(self.MODEL_ID)
+        elif name == "Midas":
+            self.model = MidasDetector.from_pretrained(self.MODEL_ID)
+        elif name == "MLSD":
+            self.model = MLSDdetector.from_pretrained(self.MODEL_ID)
+        elif name == "Openpose":
+            self.model = OpenposeDetector.from_pretrained(self.MODEL_ID)
+        elif name == "PidiNet":
+            self.model = PidiNetDetector.from_pretrained(self.MODEL_ID)
+        elif name == "NormalBae":
+            self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID)
+        elif name == "Lineart":
+            self.model = LineartDetector.from_pretrained(self.MODEL_ID)
+        elif name == "LineartAnime":
+            self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID)
+        elif name == "Canny":
+            self.model = Canny()
+        elif name == "ContentShuffle":
+            self.model = ContentShuffleDetector()
+        elif name == "DPT":
+            self.model = DepthEstimator()
+        elif name == "UPerNet":
+            self.model = ImageSegmentor()
+        else:
+            raise ValueError
+        torch.cuda.empty_cache()
+        gc.collect()
+        self.name = name
+
+    def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
+        if self.name == "Canny":
+            if "detect_resolution" in kwargs:
+                detect_resolution = kwargs.pop("detect_resolution")
+                image = np.array(image)
+                image = HWC3(image)
+                image = resize_image(image, resolution=detect_resolution)
+            image = self.model(image, **kwargs)
+            return PIL.Image.fromarray(image).convert('RGB')
+        elif self.name == "Midas":
+            detect_resolution = kwargs.pop("detect_resolution", 512)
+            image_resolution = kwargs.pop("image_resolution", 512)
+            image = np.array(image)
+            image = HWC3(image)
+            image = resize_image(image, resolution=detect_resolution)
+            image = self.model(image, **kwargs)
+            image = HWC3(image)
+            image = resize_image(image, resolution=image_resolution)
+            return PIL.Image.fromarray(image)
+        else:
+            return self.model(image, **kwargs)

requirements.txt

@@ -0,0 +1,12 @@
+controlnet-aux
+torch==2.0.1
+torchvision==0.15.2
+diffusers
+transformers
+safetensors
+accelerate
+kornia
+huggingface_hub
+gradio==4.0.0
+mediapipe
+spaces

settings.py

@@ -0,0 +1,15 @@
+import os
+
+import numpy as np
+
+DEFAULT_MODEL_ID = os.getenv("DEFAULT_MODEL_ID", "runwayml/stable-diffusion-v1-5")
+
+MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "4"))
+DEFAULT_NUM_IMAGES = min(MAX_NUM_IMAGES, int(os.getenv("DEFAULT_NUM_IMAGES", "2")))
+MAX_IMAGE_RESOLUTION = int(os.getenv("MAX_IMAGE_RESOLUTION", "768"))
+DEFAULT_IMAGE_RESOLUTION = min(MAX_IMAGE_RESOLUTION, int(os.getenv("DEFAULT_IMAGE_RESOLUTION", "512")))
+
+ALLOW_CHANGING_BASE_MODEL = os.getenv("SPACE_ID") != "hysts/ControlNet-v1-1"
+SHOW_DUPLICATE_BUTTON = os.getenv("SHOW_DUPLICATE_BUTTON") == "1"
+
+MAX_SEED = np.iinfo(np.int32).max

style.css

@@ -0,0 +1,10 @@
+h1 {
+  text-align: center;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
+}

utils.py

@@ -0,0 +1,9 @@
+import random
+
+from settings import MAX_SEED
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed