update

.gitignore

@@ -3,4 +3,5 @@ data
 # examples
 .gradio
 model_cache
-output
+output
+test.png

app_texnet.py

@@ -45,7 +45,7 @@ def apply_texture(src_mesh:str, texture:str, tag:str)->str:
 
     return mesh_copy
 
-def image_to_temp_path(img_like, tag):
+def image_to_temp_path(img_like, tag, out_dir=None):
     """
     Convert various image-like objects (str, PIL.Image, list, tuple) to temp PNG path.
     Returns the path to the saved image file.
@@ -62,13 +62,15 @@ def image_to_temp_path(img_like, tag):
 
     # If it's a PIL Image
     if isinstance(img_like, Image.Image):
-        temp_path = os.path.join(tempfile.mkdtemp(), f"{tag}.png")
+        temp_path = os.path.join(tempfile.mkdtemp() if out_dir is None else out_dir, f"{tag}.png")
+        os.makedirs(os.path.dirname(temp_path), exist_ok=True)
         img_like.save(temp_path)
         return temp_path
 
     # if it's numpy array
     if isinstance(img_like, np.ndarray):
-        temp_path = os.path.join(tempfile.mkdtemp(), f"{tag}.png")
+        temp_path = os.path.join(tempfile.mkdtemp() if out_dir is None else out_dir, f"{tag}.png")
+        os.makedirs(os.path.dirname(temp_path), exist_ok=True)
         img_like = Image.fromarray(img_like)
         img_like.save(temp_path)
         return temp_path

install.sh

@@ -0,0 +1,17 @@
+conda activate base
+conda remove -n matgen-plus --all
+
+
+conda create -n matgen-plus python=3.11
+conda activate matgen-plus
+
+pip install diffusers["torch"] transformers accelerate xformers
+pip install gradio
+pip install controlnet-aux
+
+# text2tex
+conda install pytorch3d -c pytorch -c conda-forge
+conda install -c conda-forge open-clip-torch pytorch-lightning
+pip install trimesh xatlas scikit-learn opencv-python omegaconf
+
+bash run.sh

model.py

@@ -30,6 +30,8 @@ from app_texnet import image_to_temp_path
 import os
 import time
 import tempfile
+from text2tex.scripts.generate_texture import text2tex_call, init_args
+from glob import glob
 
 CONTROLNET_MODEL_IDS = {
     # "Openpose": "lllyasviel/control_v11p_sd15_openpose",
@@ -202,53 +204,97 @@ class Model:
             raise ValueError
         if num_images > MAX_NUM_IMAGES:
             raise ValueError
+            
+        prompt_nospace = prompt.replace(' ', '_')
 
-        self.preprocessor.load("texnet")
-        control_image = self.preprocessor(
-            image=image, low_threshold=low_threshold, high_threshold=high_threshold, image_resolution=image_resolution, output_type="pil"
-        )
-
-        self.load_controlnet_weight("texnet")
-        tex_coarse = 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,
-        )
-
-        # use img2img pipeline
-        self.pipe_backup = self.pipe
-        self.pipe = self.pipe_base
-
-        # refine
+        # self.preprocessor.load("texnet")
+        # control_image = self.preprocessor(
+        #     image=image, low_threshold=low_threshold, high_threshold=high_threshold, image_resolution=image_resolution, output_type="pil"
+        # )
+
+        # self.load_controlnet_weight("texnet")
+        # tex_coarse = 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,
+        # )
+
+        # # use img2img pipeline
+        # self.pipe_backup = self.pipe
+        # self.pipe = self.pipe_base
+
+        # # refine
         tex_fine = []
-        for result_coarse in tex_coarse:
-            # clean up GPU cache
-            torch.cuda.empty_cache()
-            gc.collect()
-
-            # masking
-            mask = (np.array(control_image).sum(axis=-1) == 0)[...,None]
-            image_masked = PIL.Image.fromarray(np.where(mask, control_image, result_coarse))
-            image_blurry = transforms.GaussianBlur(kernel_size=5, sigma=1)(image_masked)
-            result_fine = self.run_pipe(
-                # prompt=prompt,
-                prompt=self.get_prompt(prompt, additional_prompt),
-                negative_prompt=negative_prompt,
-                control_image=image_blurry,
-                num_images=1,
-                num_steps=num_steps,
-                guidance_scale=guidance_scale,
-                seed=seed,
-            )[0]
-            result_fine = PIL.Image.fromarray(np.where(mask, control_image, result_fine))
-            tex_fine.append(result_fine)
+        mesh_fine = []
+        # for result_coarse in tex_coarse:
+        #     # clean up GPU cache
+        #     torch.cuda.empty_cache()
+        #     gc.collect()
+
+        #     # masking
+        #     mask = (np.array(control_image).sum(axis=-1) == 0)[...,None]
+        #     image_masked = PIL.Image.fromarray(np.where(mask, control_image, result_coarse))
+        #     image_blurry = transforms.GaussianBlur(kernel_size=5, sigma=1)(image_masked)
+        #     result_fine = self.run_pipe(
+        #         # prompt=prompt,
+        #         prompt=self.get_prompt(prompt, additional_prompt),
+        #         negative_prompt=negative_prompt,
+        #         control_image=image_blurry,
+        #         num_images=1,
+        #         num_steps=num_steps,
+        #         guidance_scale=guidance_scale,
+        #         seed=seed,
+        #     )[0]
+        #     result_fine = PIL.Image.fromarray(np.where(mask, control_image, result_fine))
+        #     tex_fine.append(result_fine)
+
+        temp_out_path = tempfile.mkdtemp()
+        temp_out_path = 'output'
+            
+        # put text2tex here,
+        args = init_args()
+        args.input_dir = f'examples/{obj_name}/'
+        args.output_dir = os.path.join(temp_out_path, f'{obj_name}/{prompt_nospace}')
+        args.obj_name = obj_name
+        args.obj_file = 'mesh.obj'
+        args.prompt = f'{prompt} {obj_name}'
+        args.add_view_to_prompt = True
+        args.ddim_steps = 5
+        # args.ddim_steps = 50
+        args.new_strength = 1.0
+        args.update_strength = 0.3
+        args.view_threshold = 0.1
+        args.blend = 0
+        args.dist = 1
+        args.num_viewpoints = 2
+        # args.num_viewpoints = 36
+        args.viewpoint_mode = 'predefined'
+        args.use_principle = True
+        args.update_steps = 2
+        # args.update_steps = 20
+        args.update_mode = 'heuristic'
+        args.seed = 42
+        args.post_process = True
+        args.device = '2080'
+        args.uv_size = 1000
+        args.image_size = 512
+        # args.image_size = 768
+        args.use_objaverse = True  # assume the mesh is normalized with y-axis as up
+        output_dir = text2tex_call(args)
+
+        # get the texture and mesh with underscore '_post', which is the id of the last mesh, should be good for the visual
+        post_idx = glob(os.path.join(output_dir, 'update', 'mesh', "*_post.png"))[0].split('/')[-1].split('_')[0]
+
+        tex_fine.append(PIL.Image.open(os.path.join(output_dir, 'update', 'mesh', f"{post_idx}.png")).convert("RGB"))
+        mesh_fine.append(os.path.join(output_dir, 'update', 'mesh', f"{post_idx}.obj"))
+        torch.cuda.empty_cache()
 
         # restore the original pipe
-        self.pipe = self.pipe_backup
+        # self.pipe = self.pipe_backup
 
         # use rgb2x for now for generating the texture
         def rgb2x(
@@ -315,11 +361,18 @@ class Model:
         # Load rgb2x pipeline
         _, preds, prompts = rgb2x(self.pipe_rgb2x, torchvision.transforms.PILToTensor()(tex_fine[0]).to(self.pipe.device), inference_step=num_steps, num_samples=num_images)
 
-        base_color_path = image_to_temp_path(tex_fine[0].rotate(90), "base_color")
-        normal_map_path = image_to_temp_path(preds[0].rotate(90), "normal_map")
-        roughness_path = image_to_temp_path(preds[1].rotate(90), "roughness")
-        metallic_path = image_to_temp_path(preds[2].rotate(90), "metallic")
-        prompt_nospace = prompt.replace(' ', '_')
+        intrinsic_dir = os.path.join(output_dir, 'intrinsic')
+        use_text2tex = True
+        if use_text2tex:
+            base_color_path = image_to_temp_path(tex_fine[0], "base_color", out_dir=intrinsic_dir)
+            normal_map_path = image_to_temp_path(preds[0], "normal_map", out_dir=intrinsic_dir)
+            roughness_path = image_to_temp_path(preds[1], "roughness", out_dir=intrinsic_dir)
+            metallic_path = image_to_temp_path(preds[2], "metallic", out_dir=intrinsic_dir)
+        else:
+            base_color_path = image_to_temp_path(tex_fine[0].rotate(90), "base_color", out_dir=intrinsic_dir)
+            normal_map_path = image_to_temp_path(preds[0].rotate(90), "normal_map", out_dir=intrinsic_dir)
+            roughness_path = image_to_temp_path(preds[1].rotate(90), "roughness", out_dir=intrinsic_dir)
+            metallic_path = image_to_temp_path(preds[2].rotate(90), "metallic", out_dir=intrinsic_dir)
         current_timecode = time.strftime("%Y%m%d_%H%M%S")
         # output_blend_path = os.path.join(os.getcwd(), "output", f"{obj_name}_{prompt_nospace}_{current_timecode}.blend")  # replace with desired output path
         output_blend_path = os.path.join(tempfile.mkdtemp(), f"{obj_name}_{prompt_nospace}_{current_timecode}.blend")  # replace with desired output path
@@ -345,7 +398,8 @@ class Model:
         run_blend_generation(
             blender_path=self.blender_path,
             generate_script_path="rgb2x/generate_blend.py",
-            obj_path=f"examples/{obj_name}/mesh.obj",  # replace with actual mesh path
+            # obj_path=f"examples/{obj_name}/mesh.obj",  # replace with actual mesh path
+            obj_path=mesh_fine[0],  # replace with actual mesh path
             base_color_path=base_color_path,
             normal_map_path=normal_map_path,
             roughness_path=roughness_path,

requirements.txt

@@ -5,4 +5,12 @@ torch
 transformers
 xformers
 controlnet-aux # for controlnet
-spaces # no need to specify here
+spaces # no need to specify here
+
+# for text2tex
+pytorch3d
+trimesh
+scikit-learn
+opencv-python
+matplotlib
+omegaconf

settings.py

@@ -19,4 +19,5 @@ MAX_SEED = np.iinfo(np.int32).max
 # setup CUDA
 # disable the following when deployting to hugging face
 # if os.getenv("CUDA_VISIBLE_DEVICES") is None:
-    # os.environ["CUDA_VISIBLE_DEVICES"] = "7"
+#     os.environ["CUDA_VISIBLE_DEVICES"] = "7"
+#     os.environ["GRADIO_SERVER_PORT"] = "7864"

text2tex/lib/camera_helper.py

@@ -0,0 +1,231 @@
+import torch
+
+import numpy as np
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+from pytorch3d.renderer import (
+    PerspectiveCameras,
+    look_at_view_transform
+)
+
+# customized
+import sys
+sys.path.append(".")
+
+from lib.constants import VIEWPOINTS
+
+# ---------------- UTILS ----------------------
+
+def degree_to_radian(d):
+    return d * np.pi / 180
+
+def radian_to_degree(r):
+    return 180 * r / np.pi
+
+def xyz_to_polar(xyz):
+    """ assume y-axis is the up axis """
+    
+    x, y, z = xyz
+    
+    theta = 180 * np.arccos(z) / np.pi
+    phi = 180 * np.arccos(y) / np.pi
+
+    return theta, phi
+
+def polar_to_xyz(theta, phi, dist):
+    """ assume y-axis is the up axis """
+
+    theta = degree_to_radian(theta)
+    phi = degree_to_radian(phi)
+
+    x = np.sin(phi) * np.sin(theta) * dist
+    y = np.cos(phi) * dist
+    z = np.sin(phi) * np.cos(theta) * dist
+
+    return [x, y, z]
+
+
+# ---------------- VIEWPOINTS ----------------------
+
+
+def filter_viewpoints(pre_viewpoints: dict, viewpoints: dict):
+    """ return the binary mask of viewpoints to be filtered """
+
+    filter_mask = [0 for _ in viewpoints.keys()]
+    for i, v in viewpoints.items():
+        x_v, y_v, z_v = polar_to_xyz(v["azim"], 90 - v["elev"], v["dist"])
+
+        for _, pv in pre_viewpoints.items():
+            x_pv, y_pv, z_pv = polar_to_xyz(pv["azim"], 90 - pv["elev"], pv["dist"])
+            sim = cosine_similarity(
+                np.array([[x_v, y_v, z_v]]),
+                np.array([[x_pv, y_pv, z_pv]])
+            )[0, 0]
+
+            if sim > 0.9:
+                filter_mask[i] = 1
+
+    return filter_mask
+
+
+def init_viewpoints(mode, sample_space, init_dist, init_elev, principle_directions, 
+    use_principle=True, use_shapenet=False, use_objaverse=False):
+
+    if mode == "predefined":
+
+        (
+            dist_list, 
+            elev_list, 
+            azim_list, 
+            sector_list
+        ) = init_predefined_viewpoints(sample_space, init_dist, init_elev)
+
+    elif mode == "hemisphere":
+
+        (
+            dist_list, 
+            elev_list, 
+            azim_list, 
+            sector_list
+        ) = init_hemisphere_viewpoints(sample_space, init_dist)
+
+    else:
+        raise NotImplementedError()
+
+    # punishments for views -> in case always selecting the same view
+    view_punishments = [1 for _ in range(len(dist_list))]
+
+    if use_principle:
+
+        (
+            dist_list, 
+            elev_list, 
+            azim_list, 
+            sector_list,
+            view_punishments
+        ) = init_principle_viewpoints(
+            principle_directions, 
+            dist_list, 
+            elev_list, 
+            azim_list, 
+            sector_list,
+            view_punishments,
+            use_shapenet,
+            use_objaverse
+        )
+
+    return dist_list, elev_list, azim_list, sector_list, view_punishments
+
+
+def init_principle_viewpoints(
+    principle_directions, 
+    dist_list, 
+    elev_list, 
+    azim_list, 
+    sector_list,
+    view_punishments,
+    use_shapenet=False,
+    use_objaverse=False
+):
+
+    if use_shapenet:
+        key = "shapenet"
+
+        pre_elev_list = [v for v in VIEWPOINTS[key]["elev"]]
+        pre_azim_list = [v for v in VIEWPOINTS[key]["azim"]]
+        pre_sector_list = [v for v in VIEWPOINTS[key]["sector"]]
+
+        num_principle = 10
+        pre_dist_list = [dist_list[0] for _ in range(num_principle)]
+        pre_view_punishments = [0 for _ in range(num_principle)]
+
+    elif use_objaverse:
+        key = "objaverse"
+
+        pre_elev_list = [v for v in VIEWPOINTS[key]["elev"]]
+        pre_azim_list = [v for v in VIEWPOINTS[key]["azim"]]
+        pre_sector_list = [v for v in VIEWPOINTS[key]["sector"]]
+
+        num_principle = 10
+        pre_dist_list = [dist_list[0] for _ in range(num_principle)]
+        pre_view_punishments = [0 for _ in range(num_principle)]
+    else:
+        num_principle = 6
+        pre_elev_list = [v for v in VIEWPOINTS[num_principle]["elev"]]
+        pre_azim_list = [v for v in VIEWPOINTS[num_principle]["azim"]]
+        pre_sector_list = [v for v in VIEWPOINTS[num_principle]["sector"]]
+        pre_dist_list = [dist_list[0] for _ in range(num_principle)]
+        pre_view_punishments = [0 for _ in range(num_principle)]
+
+    dist_list = pre_dist_list + dist_list
+    elev_list = pre_elev_list + elev_list
+    azim_list = pre_azim_list + azim_list
+    sector_list = pre_sector_list + sector_list
+    view_punishments = pre_view_punishments + view_punishments
+
+    return dist_list, elev_list, azim_list, sector_list, view_punishments
+
+
+def init_predefined_viewpoints(sample_space, init_dist, init_elev):
+    
+    viewpoints = VIEWPOINTS[sample_space]
+
+    assert sample_space == len(viewpoints["sector"])
+
+    dist_list = [init_dist for _ in range(sample_space)] # always the same dist
+    elev_list = [viewpoints["elev"][i] for i in range(sample_space)]
+    azim_list = [viewpoints["azim"][i] for i in range(sample_space)]
+    sector_list = [viewpoints["sector"][i] for i in range(sample_space)]
+
+    return dist_list, elev_list, azim_list, sector_list
+
+
+def init_hemisphere_viewpoints(sample_space, init_dist):
+    """
+        y is up-axis
+    """
+
+    num_points = 2 * sample_space
+    ga = np.pi * (3. - np.sqrt(5.))  # golden angle in radians
+
+    flags = []
+    elev_list = [] # degree
+    azim_list = [] # degree
+
+    for i in range(num_points):
+        y = 1 - (i / float(num_points - 1)) * 2  # y goes from 1 to -1
+
+        # only take the north hemisphere
+        if y >= 0: 
+            flags.append(True)
+        else:
+            flags.append(False)
+
+        theta = ga * i  # golden angle increment
+
+        elev_list.append(radian_to_degree(np.arcsin(y)))
+        azim_list.append(radian_to_degree(theta))
+
+        radius = np.sqrt(1 - y * y)  # radius at y
+        x = np.cos(theta) * radius
+        z = np.sin(theta) * radius
+
+    elev_list = [elev_list[i] for i in range(len(elev_list)) if flags[i]]
+    azim_list = [azim_list[i] for i in range(len(azim_list)) if flags[i]]
+
+    dist_list = [init_dist for _ in elev_list]
+    sector_list = ["good" for _ in elev_list] # HACK don't define sector names for now
+
+    return dist_list, elev_list, azim_list, sector_list
+
+
+# ---------------- CAMERAS ----------------------
+
+
+def init_camera(dist, elev, azim, image_size, device):
+    R, T = look_at_view_transform(dist, elev, azim)
+    image_size = torch.tensor([image_size, image_size]).unsqueeze(0)
+    cameras = PerspectiveCameras(R=R, T=T, device=device, image_size=image_size)
+
+    return cameras

text2tex/lib/constants.py

@@ -0,0 +1,648 @@
+PALETTE = {
+    0: [255, 255, 255], # white  -  background
+    1: [204, 50, 50],   # red    -  old
+    2: [231, 180, 22],  # yellow -  update
+    3: [45, 201, 55]    # green  -  new
+}
+
+QUAD_WEIGHTS = {
+    0: 0, # background
+    1: 0.1,   # old
+    2: 0.5,  # update
+    3: 1    # new
+}
+
+VIEWPOINTS = {
+    1: {
+        "azim": [
+            0
+        ],
+        "elev": [
+            0
+        ],
+        "sector": [
+            "front"
+        ]
+    },
+    2: {
+        "azim": [
+            0,
+            30
+        ],
+        "elev": [
+            0,
+            0
+        ],
+        "sector": [
+            "front",
+            "front"
+        ]
+    },
+    4: {
+        "azim": [
+            45,
+            315,
+            135,
+            225,
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            0,
+        ],
+        "sector": [
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+        ]
+    },
+    6: {
+        "azim": [
+            0,
+            90,
+            270,
+            0,
+            180,
+            0
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            90,
+            0,
+            -90
+        ],
+        "sector": [
+            "front",
+            "right",
+            "left",
+            "top",
+            "back",
+            "bottom",
+        ]
+    },
+    "shapenet": {
+        "azim": [
+            270,
+            315,
+            225,
+            0,
+            180,
+            45,
+            135,
+            90,
+            270,
+            270
+        ],
+        "elev": [
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            90,
+            -90
+        ],
+        "sector": [
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+            "top",
+            "bottom",
+        ]
+    },
+    "objaverse": {
+        "azim": [
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+            0,
+            0
+        ],
+        "elev": [
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            90,
+            -90
+        ],
+        "sector": [
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+            "top",
+            "bottom",
+        ]
+    },
+    12: {
+        "azim": [
+            45,
+            315,
+            135,
+            225,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            0,
+
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+        ],
+        "sector": [
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+        ]
+    },
+    20: {
+        "azim": [
+            45,
+            315,
+            135,
+            225,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            0,
+
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+        ],
+        "sector": [
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+        ]
+    },
+    36: {
+        "azim": [
+            45,
+            315,
+            135,
+            225,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5,
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            0,
+
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+        ],
+        "sector": [
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+
+            "top front",
+            "top right",
+            "top left",
+            "top right",
+            "top left",
+            "top right",
+            "top left",
+            "top back",
+
+            "front right",
+            "front left",
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+            "back right",
+            "back left",
+
+            "front right",
+            "front left",
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+            "back right",
+            "back left",
+        ]
+    },
+    68: {
+        "azim": [
+            45,
+            315,
+            135,
+            225,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            0,
+            45,
+            315,
+            90,
+            270,
+            135,
+            225,
+            180,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5,
+
+            22.5,
+            337.5,
+            67.5,
+            292.5,
+            112.5,
+            247.5,
+            157.5,
+            202.5
+        ],
+        "elev": [
+            0,
+            0,
+            0,
+            0,
+
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+            30,
+
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+            60,
+
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+            15,
+
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+            45,
+
+            -30,
+            -30,
+            -30,
+            -30,
+            -30,
+            -30,
+            -30,
+            -30,
+
+            -60,
+            -60,
+            -60,
+            -60,
+            -60,
+            -60,
+            -60,
+            -60,
+
+            -15,
+            -15,
+            -15,
+            -15,
+            -15,
+            -15,
+            -15,
+            -15,
+
+            -45,
+            -45,
+            -45,
+            -45,
+            -45,
+            -45,
+            -45,
+            -45,
+        ],
+        "sector": [
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+
+            "top front",
+            "top right",
+            "top left",
+            "top right",
+            "top left",
+            "top right",
+            "top left",
+            "top back",
+
+            "front right",
+            "front left",
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+            "back right",
+            "back left",
+
+            "front right",
+            "front left",
+            "front right",
+            "front left",
+            "back right",
+            "back left",
+            "back right",
+            "back left",
+
+            "front",
+            "front right",
+            "front left",
+            "right",
+            "left",
+            "back right",
+            "back left",
+            "back",
+
+            "bottom front",
+            "bottom right",
+            "bottom left",
+            "bottom right",
+            "bottom left",
+            "bottom right",
+            "bottom left",
+            "bottom back",
+
+            "bottom front right",
+            "bottom front left",
+            "bottom front right",
+            "bottom front left",
+            "bottom back right",
+            "bottom back left",
+            "bottom back right",
+            "bottom back left",
+
+            "bottom front right",
+            "bottom front left",
+            "bottom front right",
+            "bottom front left",
+            "bottom back right",
+            "bottom back left",
+            "bottom back right",
+            "bottom back left",
+        ]
+    }
+}

text2tex/lib/diffusion_helper.py

@@ -0,0 +1,189 @@
+import torch
+
+import cv2
+import numpy as np
+
+from PIL import Image
+from torchvision import transforms
+
+# Stable Diffusion 2
+from diffusers import (
+    StableDiffusionInpaintPipeline,
+    StableDiffusionPipeline, 
+    EulerDiscreteScheduler
+)
+
+# customized
+import sys
+sys.path.append(".")
+
+from models.ControlNet.gradio_depth2image import init_model, process
+
+
+def get_controlnet_depth():
+    print("=> initializing ControlNet Depth...")
+    model, ddim_sampler = init_model()
+
+    return model, ddim_sampler
+
+
+def get_inpainting(device):
+    print("=> initializing Inpainting...")
+
+    model = StableDiffusionInpaintPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-2-inpainting",
+        torch_dtype=torch.float16,
+    ).to(device)
+
+    return model
+
+def get_text2image(device):
+    print("=> initializing Inpainting...")
+
+    model_id = "stabilityai/stable-diffusion-2"
+    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+    model = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16).to(device)
+
+    return model
+
+
+@torch.no_grad()
+def apply_controlnet_depth(model, ddim_sampler, 
+    init_image, prompt, strength, ddim_steps,
+    generate_mask_image, keep_mask_image, depth_map_np, 
+    a_prompt, n_prompt, guidance_scale, seed, eta, num_samples,
+    device, blend=0, save_memory=False):
+    """
+        Use Stable Diffusion 2 to generate image
+
+        Arguments:
+            args: input arguments
+            model: Stable Diffusion 2 model
+            init_image_tensor: input image, torch.FloatTensor of shape (1, H, W, 3)
+            mask_tensor: depth map of the input image, torch.FloatTensor of shape (1, H, W, 1)
+            depth_map_np: depth map of the input image, torch.FloatTensor of shape (1, H, W)
+    """
+
+    print("=> generating ControlNet Depth RePaint image...")
+
+
+    # Stable Diffusion 2 receives PIL.Image
+    # NOTE Stable Diffusion 2 returns a PIL.Image object
+    # image and mask_image should be PIL images.
+    # The mask structure is white for inpainting and black for keeping as is
+    diffused_image_np = process(
+        model, ddim_sampler,
+        np.array(init_image), prompt, a_prompt, n_prompt, num_samples,
+        ddim_steps, guidance_scale, seed, eta, 
+        strength=strength, detected_map=depth_map_np, unknown_mask=np.array(generate_mask_image), save_memory=save_memory
+    )[0]
+
+    init_image = init_image.convert("RGB")
+    diffused_image = Image.fromarray(diffused_image_np).convert("RGB")
+
+    if blend > 0 and transforms.ToTensor()(keep_mask_image).sum() > 0:
+        print("=> blending the generated region...")
+        kernel_size = 3
+        kernel = np.ones((kernel_size, kernel_size), np.uint8)
+
+        keep_image_np = np.array(init_image).astype(np.uint8)
+        keep_image_np_dilate = cv2.dilate(keep_image_np, kernel, iterations=1)
+
+        keep_mask_np = np.array(keep_mask_image).astype(np.uint8)
+        keep_mask_np_dilate = cv2.dilate(keep_mask_np, kernel, iterations=1)
+
+        generate_image_np = np.array(diffused_image).astype(np.uint8)
+
+        overlap_mask_np = np.array(generate_mask_image).astype(np.uint8)
+        overlap_mask_np *= keep_mask_np_dilate
+        print("=> blending {} pixels...".format(np.sum(overlap_mask_np)))
+
+        overlap_keep = keep_image_np_dilate[overlap_mask_np == 1]
+        overlap_generate = generate_image_np[overlap_mask_np == 1]
+
+        overlap_np = overlap_keep * blend + overlap_generate * (1 - blend)
+
+        generate_image_np[overlap_mask_np == 1] = overlap_np
+
+        diffused_image = Image.fromarray(generate_image_np.astype(np.uint8)).convert("RGB")
+
+    init_image_masked = init_image
+    diffused_image_masked = diffused_image
+
+    return diffused_image, init_image_masked, diffused_image_masked
+
+
+@torch.no_grad()
+def apply_inpainting(model, 
+    init_image, mask_image_tensor, prompt, height, width, device):
+    """
+        Use Stable Diffusion 2 to generate image
+
+        Arguments:
+            args: input arguments
+            model: Stable Diffusion 2 model
+            init_image_tensor: input image, torch.FloatTensor of shape (1, H, W, 3)
+            mask_tensor: depth map of the input image, torch.FloatTensor of shape (1, H, W, 1)
+            depth_map_tensor: depth map of the input image, torch.FloatTensor of shape (1, H, W)
+    """
+
+    print("=> generating Inpainting image...")
+
+    mask_image = mask_image_tensor[0].cpu()
+    mask_image = mask_image.permute(2, 0, 1)
+    mask_image = transforms.ToPILImage()(mask_image).convert("L")
+
+    # NOTE Stable Diffusion 2 returns a PIL.Image object
+    # image and mask_image should be PIL images.
+    # The mask structure is white for inpainting and black for keeping as is
+    diffused_image = model(
+        prompt=prompt, 
+        image=init_image.resize((512, 512)), 
+        mask_image=mask_image.resize((512, 512)), 
+        height=512, 
+        width=512
+    ).images[0].resize((height, width))
+
+    return diffused_image
+
+
+@torch.no_grad()
+def apply_inpainting_postprocess(model, 
+    init_image, mask_image_tensor, prompt, height, width, device):
+    """
+        Use Stable Diffusion 2 to generate image
+
+        Arguments:
+            args: input arguments
+            model: Stable Diffusion 2 model
+            init_image_tensor: input image, torch.FloatTensor of shape (1, H, W, 3)
+            mask_tensor: depth map of the input image, torch.FloatTensor of shape (1, H, W, 1)
+            depth_map_tensor: depth map of the input image, torch.FloatTensor of shape (1, H, W)
+    """
+
+    print("=> generating Inpainting image...")
+
+    mask_image = mask_image_tensor[0].cpu()
+    mask_image = mask_image.permute(2, 0, 1)
+    mask_image = transforms.ToPILImage()(mask_image).convert("L")
+
+    # NOTE Stable Diffusion 2 returns a PIL.Image object
+    # image and mask_image should be PIL images.
+    # The mask structure is white for inpainting and black for keeping as is
+    diffused_image = model(
+        prompt=prompt, 
+        image=init_image.resize((512, 512)), 
+        mask_image=mask_image.resize((512, 512)), 
+        height=512, 
+        width=512
+    ).images[0].resize((height, width))
+
+    diffused_image_tensor = torch.from_numpy(np.array(diffused_image)).to(device)
+
+    init_images_tensor = torch.from_numpy(np.array(init_image)).to(device)
+    
+    init_images_tensor = diffused_image_tensor * mask_image_tensor[0] + init_images_tensor * (1 - mask_image_tensor[0])
+    init_image = Image.fromarray(init_images_tensor.cpu().numpy().astype(np.uint8)).convert("RGB")
+
+    return init_image
+

text2tex/lib/io_helper.py

@@ -0,0 +1,78 @@
+# common utils
+import os
+import json
+
+# numpy
+import numpy as np
+
+# visualization
+import matplotlib
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+
+matplotlib.use("Agg")
+
+from pytorch3d.io import save_obj
+
+from torchvision import transforms
+
+
+def save_depth(fragments, output_dir, init_image, view_idx):
+    print("=> saving depth...")
+    width, height = init_image.size
+    dpi = 100
+    figsize = width / float(dpi), height / float(dpi)
+
+    depth_np = fragments.zbuf[0].cpu().numpy()
+
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_axes([0, 0, 1, 1])
+    # Hide spines, ticks, etc.
+    ax.axis('off')
+    # Display the image.
+    ax.imshow(depth_np, cmap='gray')
+
+    plt.savefig(os.path.join(output_dir, "{}.png".format(view_idx)), bbox_inches='tight', pad_inches=0)
+    np.save(os.path.join(output_dir, "{}.npy".format(view_idx)), depth_np[..., 0])
+
+
+def save_backproject_obj(output_dir, obj_name, 
+    verts, faces, verts_uvs, faces_uvs, projected_texture, 
+    device):
+    print("=> saving OBJ file...")
+    texture_map = transforms.ToTensor()(projected_texture).to(device)
+    texture_map = texture_map.permute(1, 2, 0)
+    obj_path = os.path.join(output_dir, obj_name)
+
+    save_obj(
+        obj_path,
+        verts=verts,
+        faces=faces,
+        decimal_places=5,
+        verts_uvs=verts_uvs,
+        faces_uvs=faces_uvs,
+        texture_map=texture_map
+    )
+
+
+def save_args(args, output_dir):
+    with open(os.path.join(output_dir, "args.json"), "w") as f:
+        json.dump(
+            {k: v for k, v in vars(args).items()},
+            f,
+            indent=4
+        )
+
+
+def save_viewpoints(args, output_dir, dist_list, elev_list, azim_list, view_list):
+    with open(os.path.join(output_dir, "viewpoints.json"), "w") as f:
+        json.dump(
+            {
+                "dist": dist_list,
+                "elev": elev_list,
+                "azim": azim_list,
+                "view": view_list
+            },
+            f,
+            indent=4
+        )

text2tex/lib/mesh_helper.py

@@ -0,0 +1,148 @@
+import os
+import torch
+import trimesh
+import xatlas
+
+import numpy as np
+
+from sklearn.decomposition import PCA
+
+from torchvision import transforms
+
+from tqdm import tqdm
+
+from pytorch3d.io import (
+    load_obj,
+    load_objs_as_meshes
+)
+
+
+def compute_principle_directions(model_path, num_points=20000):
+    mesh = trimesh.load_mesh(model_path, force="mesh")
+    pc, _ = trimesh.sample.sample_surface_even(mesh, num_points)
+
+    pc -= np.mean(pc, axis=0, keepdims=True)
+
+    principle_directions = PCA(n_components=3).fit(pc).components_
+    
+    return principle_directions
+
+
+def init_mesh(input_path, cache_path, device):
+    print("=> parameterizing target mesh...")
+
+    mesh = trimesh.load_mesh(input_path, force='mesh')
+    try:
+        vertices, faces = mesh.vertices, mesh.faces
+    except AttributeError:
+        print("multiple materials in {} are not supported".format(input_path))
+        exit()
+
+    vmapping, indices, uvs = xatlas.parametrize(vertices, faces)
+    xatlas.export(str(cache_path), vertices[vmapping], indices, uvs)
+
+    print("=> loading target mesh...")
+
+    # principle_directions = compute_principle_directions(cache_path)
+    principle_directions = None
+    
+    _, faces, aux = load_obj(cache_path, device=device)
+    mesh = load_objs_as_meshes([cache_path], device=device)
+
+    num_verts = mesh.verts_packed().shape[0]
+
+    # make sure mesh center is at origin
+    bbox = mesh.get_bounding_boxes()
+    mesh_center = bbox.mean(dim=2).repeat(num_verts, 1)
+    mesh = apply_offsets_to_mesh(mesh, -mesh_center)
+
+    # make sure mesh size is normalized
+    box_size = bbox[..., 1] - bbox[..., 0]
+    box_max = box_size.max(dim=1, keepdim=True)[0].repeat(num_verts, 3)
+    mesh = apply_scale_to_mesh(mesh, 1 / box_max)
+
+    return mesh, mesh.verts_packed(), faces, aux, principle_directions, mesh_center, box_max
+
+
+def apply_offsets_to_mesh(mesh, offsets):
+    new_mesh = mesh.offset_verts(offsets)
+
+    return new_mesh
+
+def apply_scale_to_mesh(mesh, scale):
+    new_mesh = mesh.scale_verts(scale)
+
+    return new_mesh
+
+
+def adjust_uv_map(faces, aux, init_texture, uv_size):
+    """
+        adjust UV map to be compatiable with multiple textures.
+        UVs for different materials will be decomposed and placed horizontally
+
+        +-----+-----+-----+--
+        |  1  |  2  |  3  |
+        +-----+-----+-----+--
+
+    """
+
+    textures_ids = faces.textures_idx
+    materials_idx = faces.materials_idx
+    verts_uvs = aux.verts_uvs
+
+    num_materials = torch.unique(materials_idx).shape[0]
+
+    new_verts_uvs = verts_uvs.clone()
+    for material_id in range(num_materials):
+        # apply offsets to horizontal axis
+        faces_ids = textures_ids[materials_idx == material_id].unique()
+        new_verts_uvs[faces_ids, 0] += material_id
+
+    new_verts_uvs[:, 0] /= num_materials
+
+    init_texture_tensor = transforms.ToTensor()(init_texture)
+    init_texture_tensor = torch.cat([init_texture_tensor for _ in range(num_materials)], dim=-1)
+    init_texture = transforms.ToPILImage()(init_texture_tensor).resize((uv_size, uv_size))
+
+    return new_verts_uvs, init_texture
+
+
+@torch.no_grad()
+def update_face_angles(mesh, cameras, fragments):
+    def get_angle(x, y):
+        x = torch.nn.functional.normalize(x)
+        y = torch.nn.functional.normalize(y)
+        inner_product = (x * y).sum(dim=1)
+        x_norm = x.pow(2).sum(dim=1).pow(0.5)
+        y_norm = y.pow(2).sum(dim=1).pow(0.5)
+        cos = inner_product / (x_norm * y_norm)
+        angle = torch.acos(cos)
+        angle = angle * 180 / 3.14159
+
+        return angle
+
+    # face normals
+    face_normals = mesh.faces_normals_padded()[0]
+
+    # view vector (object center -> camera center)
+    camera_center = cameras.get_camera_center()
+
+    face_angles = get_angle(
+        face_normals, 
+        camera_center.repeat(face_normals.shape[0], 1)
+    ) # (F)
+
+    face_angles_rev = get_angle(
+        face_normals, 
+        -camera_center.repeat(face_normals.shape[0], 1)
+    ) # (F)
+
+    face_angles = torch.minimum(face_angles, face_angles_rev)
+
+    # Indices of unique visible faces
+    visible_map = fragments.pix_to_face.unique()  # (num_visible_faces)
+    invisible_mask = torch.ones_like(face_angles)
+    invisible_mask[visible_map] = 0
+    face_angles[invisible_mask == 1] = 10000.  # angles of invisible faces are ignored
+
+    return face_angles

text2tex/lib/projection_helper.py

@@ -0,0 +1,464 @@
+import os
+import torch
+
+import cv2
+import random
+
+import numpy as np
+
+from torchvision import transforms
+
+from pytorch3d.renderer import TexturesUV
+from pytorch3d.ops import interpolate_face_attributes
+
+from PIL import Image
+
+from tqdm import tqdm
+
+# customized
+import sys
+sys.path.append(".")
+
+from lib.camera_helper import init_camera
+from lib.render_helper import init_renderer, render
+from lib.shading_helper import (
+    BlendParams,
+    init_soft_phong_shader, 
+    init_flat_texel_shader,
+)
+from lib.vis_helper import visualize_outputs, visualize_quad_mask
+from lib.constants import *
+
+
+def get_all_4_locations(values_y, values_x):
+    y_0 = torch.floor(values_y)
+    y_1 = torch.ceil(values_y)
+    x_0 = torch.floor(values_x)
+    x_1 = torch.ceil(values_x)
+
+    return torch.cat([y_0, y_0, y_1, y_1], 0).long(), torch.cat([x_0, x_1, x_0, x_1], 0).long()
+
+
+def compose_quad_mask(new_mask_image, update_mask_image, old_mask_image, device):
+    """
+        compose quad mask:
+            -> 0: background
+            -> 1: old
+            -> 2: update
+            -> 3: new
+    """
+
+    new_mask_tensor = transforms.ToTensor()(new_mask_image).to(device)
+    update_mask_tensor = transforms.ToTensor()(update_mask_image).to(device)
+    old_mask_tensor = transforms.ToTensor()(old_mask_image).to(device)
+
+    all_mask_tensor = new_mask_tensor + update_mask_tensor + old_mask_tensor
+
+    quad_mask_tensor = torch.zeros_like(all_mask_tensor)
+    quad_mask_tensor[old_mask_tensor == 1] = 1
+    quad_mask_tensor[update_mask_tensor == 1] = 2
+    quad_mask_tensor[new_mask_tensor == 1] = 3
+
+    return old_mask_tensor, update_mask_tensor, new_mask_tensor, all_mask_tensor, quad_mask_tensor
+
+
+def compute_view_heat(similarity_tensor, quad_mask_tensor):
+    num_total_pixels = quad_mask_tensor.reshape(-1).shape[0]
+    heat = 0
+    for idx in QUAD_WEIGHTS:
+        heat += (quad_mask_tensor == idx).sum() * QUAD_WEIGHTS[idx] / num_total_pixels
+
+    return heat
+
+
+def select_viewpoint(selected_view_ids, view_punishments,
+    mode, dist_list, elev_list, azim_list, sector_list, view_idx,
+    similarity_texture_cache, exist_texture,
+    mesh, faces, verts_uvs,
+    image_size, faces_per_pixel,
+    init_image_dir, mask_image_dir, normal_map_dir, depth_map_dir, similarity_map_dir,
+    device, use_principle=False
+):
+    if mode == "sequential":
+        
+        num_views = len(dist_list)
+
+        dist = dist_list[view_idx % num_views]
+        elev = elev_list[view_idx % num_views]
+        azim = azim_list[view_idx % num_views]
+        sector = sector_list[view_idx % num_views]
+        
+        selected_view_ids.append(view_idx % num_views)
+
+    elif mode == "heuristic":
+
+        if use_principle and view_idx < 6:
+
+            selected_view_idx = view_idx
+
+        else:
+
+            selected_view_idx = None
+            max_heat = 0
+
+            print("=> selecting next view...")
+            view_heat_list = []
+            for sample_idx in tqdm(range(len(dist_list))):
+
+                view_heat, *_ = render_one_view_and_build_masks(dist_list[sample_idx], elev_list[sample_idx], azim_list[sample_idx], 
+                    sample_idx, sample_idx, view_punishments,
+                    similarity_texture_cache, exist_texture,
+                    mesh, faces, verts_uvs,
+                    image_size, faces_per_pixel,
+                    init_image_dir, mask_image_dir, normal_map_dir, depth_map_dir, similarity_map_dir,
+                    device)
+
+                if view_heat > max_heat:
+                    selected_view_idx = sample_idx
+                    max_heat = view_heat
+
+                view_heat_list.append(view_heat.item())
+
+            print(view_heat_list)
+            print("select view {} with heat {}".format(selected_view_idx, max_heat))
+
+ 
+        dist = dist_list[selected_view_idx]
+        elev = elev_list[selected_view_idx]
+        azim = azim_list[selected_view_idx]
+        sector = sector_list[selected_view_idx]
+
+        selected_view_ids.append(selected_view_idx)
+
+        view_punishments[selected_view_idx] *= 0.01
+
+    elif mode == "random":
+
+        selected_view_idx = random.choice(range(len(dist_list)))
+
+        dist = dist_list[selected_view_idx]
+        elev = elev_list[selected_view_idx]
+        azim = azim_list[selected_view_idx]
+        sector = sector_list[selected_view_idx]
+        
+        selected_view_ids.append(selected_view_idx)
+
+    else:
+        raise NotImplementedError()
+
+    return dist, elev, azim, sector, selected_view_ids, view_punishments
+
+
+@torch.no_grad()
+def build_backproject_mask(mesh, faces, verts_uvs, 
+    cameras, reference_image, faces_per_pixel, 
+    image_size, uv_size, device):
+    # construct pixel UVs
+    renderer_scaled = init_renderer(cameras,
+        shader=init_soft_phong_shader(
+            camera=cameras,
+            blend_params=BlendParams(),
+            device=device),
+        image_size=image_size, 
+        faces_per_pixel=faces_per_pixel
+    )
+    fragments_scaled = renderer_scaled.rasterizer(mesh)
+
+    # get UV coordinates for each pixel
+    faces_verts_uvs = verts_uvs[faces.textures_idx]
+
+    pixel_uvs = interpolate_face_attributes(
+        fragments_scaled.pix_to_face, fragments_scaled.bary_coords, faces_verts_uvs
+    )  # NxHsxWsxKx2
+    pixel_uvs = pixel_uvs.permute(0, 3, 1, 2, 4).reshape(-1, 2)
+
+    texture_locations_y, texture_locations_x = get_all_4_locations(
+        (1 - pixel_uvs[:, 1]).reshape(-1) * (uv_size - 1),
+        pixel_uvs[:, 0].reshape(-1) * (uv_size - 1)
+    )
+
+    K = faces_per_pixel
+
+    texture_values = torch.from_numpy(np.array(reference_image.resize((image_size, image_size)))).float() / 255.
+    texture_values = texture_values.to(device).unsqueeze(0).expand([4, -1, -1, -1]).unsqueeze(0).expand([K, -1, -1, -1, -1])
+
+    # texture
+    texture_tensor = torch.zeros(uv_size, uv_size, 3).to(device)
+    texture_tensor[texture_locations_y, texture_locations_x, :] = texture_values.reshape(-1, 3)
+
+    return texture_tensor[:, :, 0]
+
+
+@torch.no_grad()
+def build_diffusion_mask(mesh_stuff, 
+    renderer, exist_texture, similarity_texture_cache, target_value, device, image_size, 
+    smooth_mask=False, view_threshold=0.01):
+
+    mesh, faces, verts_uvs = mesh_stuff
+    mask_mesh = mesh.clone() # NOTE in-place operation - DANGER!!!
+
+    # visible mask => the whole region
+    exist_texture_expand = exist_texture.unsqueeze(0).unsqueeze(-1).expand(-1, -1, -1, 3).to(device)
+    mask_mesh.textures = TexturesUV(
+        maps=torch.ones_like(exist_texture_expand),
+        faces_uvs=faces.textures_idx[None, ...],
+        verts_uvs=verts_uvs[None, ...],
+        sampling_mode="nearest"
+    )
+    # visible_mask_tensor, *_ = render(mask_mesh, renderer)
+    visible_mask_tensor, _, similarity_map_tensor, *_ = render(mask_mesh, renderer)
+    # faces that are too rotated away from the viewpoint will be treated as invisible
+    valid_mask_tensor = (similarity_map_tensor >= view_threshold).float()
+    visible_mask_tensor *= valid_mask_tensor
+
+    # nonexist mask <=> new mask
+    exist_texture_expand = exist_texture.unsqueeze(0).unsqueeze(-1).expand(-1, -1, -1, 3).to(device)
+    mask_mesh.textures = TexturesUV(
+        maps=1 - exist_texture_expand,
+        faces_uvs=faces.textures_idx[None, ...],
+        verts_uvs=verts_uvs[None, ...],
+        sampling_mode="nearest"
+    )
+    new_mask_tensor, *_ = render(mask_mesh, renderer)
+    new_mask_tensor *= valid_mask_tensor
+
+    # exist mask => visible mask - new mask
+    exist_mask_tensor = visible_mask_tensor - new_mask_tensor
+    exist_mask_tensor[exist_mask_tensor < 0] = 0 # NOTE dilate can lead to overflow
+
+    # all update mask
+    mask_mesh.textures = TexturesUV(
+        maps=(
+            similarity_texture_cache.argmax(0) == target_value
+            # # only consider the views that have already appeared before
+            # similarity_texture_cache[0:target_value+1].argmax(0) == target_value
+        ).float().unsqueeze(0).unsqueeze(-1).expand(-1, -1, -1, 3).to(device),
+        faces_uvs=faces.textures_idx[None, ...],
+        verts_uvs=verts_uvs[None, ...],
+        sampling_mode="nearest"
+    )
+    all_update_mask_tensor, *_ = render(mask_mesh, renderer)
+
+    # current update mask => intersection between all update mask and exist mask
+    update_mask_tensor = exist_mask_tensor * all_update_mask_tensor
+
+    # keep mask => exist mask - update mask
+    old_mask_tensor = exist_mask_tensor - update_mask_tensor
+
+    # convert
+    new_mask = new_mask_tensor[0].cpu().float().permute(2, 0, 1)
+    new_mask = transforms.ToPILImage()(new_mask).convert("L")
+
+    update_mask = update_mask_tensor[0].cpu().float().permute(2, 0, 1)
+    update_mask = transforms.ToPILImage()(update_mask).convert("L")
+
+    old_mask = old_mask_tensor[0].cpu().float().permute(2, 0, 1)
+    old_mask = transforms.ToPILImage()(old_mask).convert("L")
+
+    exist_mask = exist_mask_tensor[0].cpu().float().permute(2, 0, 1)
+    exist_mask = transforms.ToPILImage()(exist_mask).convert("L")
+
+    return new_mask, update_mask, old_mask, exist_mask
+
+
+@torch.no_grad()
+def render_one_view(mesh,
+    dist, elev, azim,
+    image_size, faces_per_pixel,
+    device):
+
+    # render the view
+    cameras = init_camera(
+        dist, elev, azim,
+        image_size, device
+    )
+    renderer = init_renderer(cameras,
+        shader=init_soft_phong_shader(
+            camera=cameras,
+            blend_params=BlendParams(),
+            device=device),
+        image_size=image_size, 
+        faces_per_pixel=faces_per_pixel
+    )
+
+    init_images_tensor, normal_maps_tensor, similarity_tensor, depth_maps_tensor, fragments = render(mesh, renderer)
+    
+    return (
+        cameras, renderer,
+        init_images_tensor, normal_maps_tensor, similarity_tensor, depth_maps_tensor, fragments
+    )
+
+
+@torch.no_grad()
+def build_similarity_texture_cache_for_all_views(mesh, faces, verts_uvs,
+    dist_list, elev_list, azim_list,
+    image_size, image_size_scaled, uv_size, faces_per_pixel,
+    device):
+
+    num_candidate_views = len(dist_list)
+    similarity_texture_cache = torch.zeros(num_candidate_views, uv_size, uv_size).to(device)
+
+    print("=> building similarity texture cache for all views...")
+    for i in tqdm(range(num_candidate_views)):
+        cameras, _, _, _, similarity_tensor, _, _ = render_one_view(mesh,
+            dist_list[i], elev_list[i], azim_list[i],
+            image_size, faces_per_pixel, device)
+
+        similarity_texture_cache[i] = build_backproject_mask(mesh, faces, verts_uvs, 
+            cameras, transforms.ToPILImage()(similarity_tensor[0, :, :, 0]).convert("RGB"), faces_per_pixel,
+            image_size_scaled, uv_size, device)
+
+    return similarity_texture_cache
+
+
+@torch.no_grad()
+def render_one_view_and_build_masks(dist, elev, azim, 
+    selected_view_idx, view_idx, view_punishments,
+    similarity_texture_cache, exist_texture,
+    mesh, faces, verts_uvs,
+    image_size, faces_per_pixel,
+    init_image_dir, mask_image_dir, normal_map_dir, depth_map_dir, similarity_map_dir,
+    device, save_intermediate=False, smooth_mask=False, view_threshold=0.01):
+    
+    # render the view
+    (
+        cameras, renderer,
+        init_images_tensor, normal_maps_tensor, similarity_tensor, depth_maps_tensor, fragments
+    ) = render_one_view(mesh,
+        dist, elev, azim,
+        image_size, faces_per_pixel,
+        device
+    )
+    
+    init_image = init_images_tensor[0].cpu()
+    init_image = init_image.permute(2, 0, 1)
+    init_image = transforms.ToPILImage()(init_image).convert("RGB")
+
+    normal_map = normal_maps_tensor[0].cpu()
+    normal_map = normal_map.permute(2, 0, 1)
+    normal_map = transforms.ToPILImage()(normal_map).convert("RGB")
+
+    depth_map = depth_maps_tensor[0].cpu().numpy()
+    depth_map = Image.fromarray(depth_map).convert("L")
+
+    similarity_map = similarity_tensor[0, :, :, 0].cpu()
+    similarity_map = transforms.ToPILImage()(similarity_map).convert("L")
+
+
+    flat_renderer = init_renderer(cameras,
+        shader=init_flat_texel_shader(
+            camera=cameras,
+            device=device),
+        image_size=image_size,
+        faces_per_pixel=faces_per_pixel
+    )
+    new_mask_image, update_mask_image, old_mask_image, exist_mask_image = build_diffusion_mask(
+        (mesh, faces, verts_uvs), 
+        flat_renderer, exist_texture, similarity_texture_cache, selected_view_idx, device, image_size, 
+        smooth_mask=smooth_mask, view_threshold=view_threshold
+    )
+    # NOTE the view idx is the absolute idx in the sample space (i.e. `selected_view_idx`)
+    # it should match with `similarity_texture_cache`
+
+    (
+        old_mask_tensor, 
+        update_mask_tensor, 
+        new_mask_tensor, 
+        all_mask_tensor, 
+        quad_mask_tensor
+    ) = compose_quad_mask(new_mask_image, update_mask_image, old_mask_image, device)
+
+    view_heat = compute_view_heat(similarity_tensor, quad_mask_tensor)
+    view_heat *= view_punishments[selected_view_idx]
+
+    # save intermediate results
+    if save_intermediate:
+        init_image.save(os.path.join(init_image_dir, "{}.png".format(view_idx)))
+        normal_map.save(os.path.join(normal_map_dir, "{}.png".format(view_idx)))
+        depth_map.save(os.path.join(depth_map_dir, "{}.png".format(view_idx)))
+        similarity_map.save(os.path.join(similarity_map_dir, "{}.png".format(view_idx)))
+
+        new_mask_image.save(os.path.join(mask_image_dir, "{}_new.png".format(view_idx)))
+        update_mask_image.save(os.path.join(mask_image_dir, "{}_update.png".format(view_idx)))
+        old_mask_image.save(os.path.join(mask_image_dir, "{}_old.png".format(view_idx)))
+        exist_mask_image.save(os.path.join(mask_image_dir, "{}_exist.png".format(view_idx)))
+
+        visualize_quad_mask(mask_image_dir, quad_mask_tensor, view_idx, view_heat, device)
+
+    return (
+        view_heat,
+        renderer, cameras, fragments,
+        init_image, normal_map, depth_map, 
+        init_images_tensor, normal_maps_tensor, depth_maps_tensor, similarity_tensor, 
+        old_mask_image, update_mask_image, new_mask_image, 
+        old_mask_tensor, update_mask_tensor, new_mask_tensor, all_mask_tensor, quad_mask_tensor
+    )
+
+
+
+@torch.no_grad()
+def backproject_from_image(mesh, faces, verts_uvs, cameras, 
+    reference_image, new_mask_image, update_mask_image, 
+    init_texture, exist_texture,
+    image_size, uv_size, faces_per_pixel,
+    device):
+
+    # construct pixel UVs
+    renderer_scaled = init_renderer(cameras,
+        shader=init_soft_phong_shader(
+            camera=cameras,
+            blend_params=BlendParams(),
+            device=device),
+        image_size=image_size, 
+        faces_per_pixel=faces_per_pixel
+    )
+    fragments_scaled = renderer_scaled.rasterizer(mesh)
+
+    # get UV coordinates for each pixel
+    faces_verts_uvs = verts_uvs[faces.textures_idx]
+
+    pixel_uvs = interpolate_face_attributes(
+        fragments_scaled.pix_to_face, fragments_scaled.bary_coords, faces_verts_uvs
+    )  # NxHsxWsxKx2
+    pixel_uvs = pixel_uvs.permute(0, 3, 1, 2, 4).reshape(pixel_uvs.shape[-2], pixel_uvs.shape[1], pixel_uvs.shape[2], 2)
+
+    # the update mask has to be on top of the diffusion mask
+    new_mask_image_tensor = transforms.ToTensor()(new_mask_image).to(device).unsqueeze(-1)
+    update_mask_image_tensor = transforms.ToTensor()(update_mask_image).to(device).unsqueeze(-1)
+    
+    project_mask_image_tensor = torch.logical_or(update_mask_image_tensor, new_mask_image_tensor).float()
+    project_mask_image = project_mask_image_tensor * 255.
+    project_mask_image = Image.fromarray(project_mask_image[0, :, :, 0].cpu().numpy().astype(np.uint8))
+    
+    project_mask_image_scaled = project_mask_image.resize(
+        (image_size, image_size),
+        Image.Resampling.NEAREST
+    )
+    project_mask_image_tensor_scaled = transforms.ToTensor()(project_mask_image_scaled).to(device)
+
+    pixel_uvs_masked = pixel_uvs[project_mask_image_tensor_scaled == 1]
+
+    texture_locations_y, texture_locations_x = get_all_4_locations(
+        (1 - pixel_uvs_masked[:, 1]).reshape(-1) * (uv_size - 1), 
+        pixel_uvs_masked[:, 0].reshape(-1) * (uv_size - 1)
+    )
+    
+    K = pixel_uvs.shape[0]
+    project_mask_image_tensor_scaled = project_mask_image_tensor_scaled[:, None, :, :, None].repeat(1, 4, 1, 1, 3)
+
+    texture_values = torch.from_numpy(np.array(reference_image.resize((image_size, image_size))))
+    texture_values = texture_values.to(device).unsqueeze(0).expand([4, -1, -1, -1]).unsqueeze(0).expand([K, -1, -1, -1, -1])
+    
+    texture_values_masked = texture_values.reshape(-1, 3)[project_mask_image_tensor_scaled.reshape(-1, 3) == 1].reshape(-1, 3)
+
+    # texture
+    texture_tensor = torch.from_numpy(np.array(init_texture)).to(device)
+    texture_tensor[texture_locations_y, texture_locations_x, :] = texture_values_masked
+    
+    init_texture = Image.fromarray(texture_tensor.cpu().numpy().astype(np.uint8))
+
+    # update texture cache
+    exist_texture[texture_locations_y, texture_locations_x] = 1
+
+    return init_texture, project_mask_image, exist_texture
+

text2tex/lib/render_helper.py

@@ -0,0 +1,108 @@
+import os
+import torch
+
+import cv2
+
+import numpy as np
+
+from PIL import Image
+
+from torchvision import transforms
+from pytorch3d.ops import interpolate_face_attributes
+from pytorch3d.renderer import (
+    RasterizationSettings,
+    MeshRendererWithFragments,
+    MeshRasterizer,
+)
+
+# customized
+import sys
+sys.path.append(".")
+
+
+def init_renderer(camera, shader, image_size, faces_per_pixel):
+    raster_settings = RasterizationSettings(image_size=image_size, faces_per_pixel=faces_per_pixel)
+    renderer = MeshRendererWithFragments(
+        rasterizer=MeshRasterizer(
+            cameras=camera,
+            raster_settings=raster_settings
+        ),
+        shader=shader
+    )
+
+    return renderer
+
+
+@torch.no_grad()
+def render(mesh, renderer, pad_value=10):
+    def phong_normal_shading(meshes, fragments) -> torch.Tensor:
+        faces = meshes.faces_packed()  # (F, 3)
+        vertex_normals = meshes.verts_normals_packed()  # (V, 3)
+        faces_normals = vertex_normals[faces]
+        pixel_normals = interpolate_face_attributes(
+            fragments.pix_to_face, fragments.bary_coords, faces_normals
+        )
+
+        return pixel_normals
+
+    def similarity_shading(meshes, fragments):
+        faces = meshes.faces_packed()  # (F, 3)
+        vertex_normals = meshes.verts_normals_packed()  # (V, 3)
+        faces_normals = vertex_normals[faces]
+        vertices = meshes.verts_packed()  # (V, 3)
+        face_positions = vertices[faces]
+        view_directions = torch.nn.functional.normalize((renderer.shader.cameras.get_camera_center().reshape(1, 1, 3) - face_positions), p=2, dim=2)
+        cosine_similarity = torch.nn.CosineSimilarity(dim=2)(faces_normals, view_directions)
+        pixel_similarity = interpolate_face_attributes(
+            fragments.pix_to_face, fragments.bary_coords, cosine_similarity.unsqueeze(-1)
+        )
+
+        return pixel_similarity
+
+    def get_relative_depth_map(fragments, pad_value=pad_value):
+        absolute_depth = fragments.zbuf[..., 0] # B, H, W
+        no_depth = -1
+
+        depth_min, depth_max = absolute_depth[absolute_depth != no_depth].min(), absolute_depth[absolute_depth != no_depth].max()
+        target_min, target_max = 50, 255
+
+        depth_value = absolute_depth[absolute_depth != no_depth]
+        depth_value = depth_max - depth_value # reverse values
+
+        depth_value /= (depth_max - depth_min)
+        depth_value = depth_value * (target_max - target_min) + target_min
+
+        relative_depth = absolute_depth.clone()
+        relative_depth[absolute_depth != no_depth] = depth_value
+        relative_depth[absolute_depth == no_depth] = pad_value # not completely black
+
+        return relative_depth
+
+
+    images, fragments = renderer(mesh)
+    normal_maps = phong_normal_shading(mesh, fragments).squeeze(-2)
+    similarity_maps = similarity_shading(mesh, fragments).squeeze(-2) # -1 - 1
+    depth_maps = get_relative_depth_map(fragments)
+
+    # normalize similarity mask to 0 - 1 
+    similarity_maps = torch.abs(similarity_maps) # 0 - 1
+    
+    # HACK erode, eliminate isolated dots
+    non_zero_similarity = (similarity_maps > 0).float()
+    non_zero_similarity = (non_zero_similarity * 255.).cpu().numpy().astype(np.uint8)[0]
+    non_zero_similarity = cv2.erode(non_zero_similarity, kernel=np.ones((3, 3), np.uint8), iterations=2)
+    non_zero_similarity = torch.from_numpy(non_zero_similarity).to(similarity_maps.device).unsqueeze(0) / 255.
+    similarity_maps = non_zero_similarity.unsqueeze(-1) * similarity_maps 
+
+    return images, normal_maps, similarity_maps, depth_maps, fragments
+
+
+@torch.no_grad()
+def check_visible_faces(mesh, fragments):
+    pix_to_face = fragments.pix_to_face
+
+    # Indices of unique visible faces
+    visible_map = pix_to_face.unique()  # (num_visible_faces)
+
+    return visible_map
+

text2tex/lib/shading_helper.py

@@ -0,0 +1,45 @@
+from typing import NamedTuple, Sequence
+
+from pytorch3d.renderer.mesh.shader import ShaderBase
+from pytorch3d.renderer import (
+    AmbientLights,
+    SoftPhongShader
+)
+
+
+class BlendParams(NamedTuple):
+    sigma: float = 1e-4
+    gamma: float = 1e-4
+    background_color: Sequence = (1, 1, 1)
+
+
+class FlatTexelShader(ShaderBase):
+
+    def __init__(self, device="cpu", cameras=None, lights=None, materials=None, blend_params=None):
+        super().__init__(device, cameras, lights, materials, blend_params)
+
+    def forward(self, fragments, meshes, **_kwargs):
+        texels = meshes.sample_textures(fragments)
+        texels[(fragments.pix_to_face == -1), :] = 0
+        return texels.squeeze(-2)
+
+
+def init_soft_phong_shader(camera, blend_params, device):
+    lights = AmbientLights(device=device)
+    shader = SoftPhongShader(
+        cameras=camera,
+        lights=lights,
+        device=device,
+        blend_params=blend_params
+    )
+
+    return shader
+
+
+def init_flat_texel_shader(camera, device):
+    shader=FlatTexelShader(
+        cameras=camera,
+        device=device
+    )
+    
+    return shader

text2tex/lib/vis_helper.py

@@ -0,0 +1,209 @@
+import os
+import torch
+
+import numpy as np
+
+# visualization
+import matplotlib
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+
+matplotlib.use("Agg")
+
+from PIL import Image
+
+# GIF
+import imageio.v2 as imageio
+
+# customized
+import sys
+sys.path.append(".")
+
+from lib.constants import *
+from lib.camera_helper import polar_to_xyz
+
+def visualize_quad_mask(mask_image_dir, quad_mask_tensor, view_idx, view_score, device):
+    quad_mask_tensor = quad_mask_tensor.unsqueeze(-1).repeat(1, 1, 1, 3)
+    quad_mask_image_tensor = torch.zeros_like(quad_mask_tensor)
+    
+    for idx in PALETTE:
+        selected = quad_mask_tensor[quad_mask_tensor == idx].reshape(-1, 3)
+        selected = torch.FloatTensor(PALETTE[idx]).to(device).unsqueeze(0).repeat(selected.shape[0], 1)
+
+        quad_mask_image_tensor[quad_mask_tensor == idx] = selected.reshape(-1)
+
+    quad_mask_image_np = quad_mask_image_tensor[0].cpu().numpy().astype(np.uint8)
+    quad_mask_image = Image.fromarray(quad_mask_image_np).convert("RGB")
+    quad_mask_image.save(os.path.join(mask_image_dir, "{}_quad_{:.5f}.png".format(view_idx, view_score)))
+
+
+def visualize_outputs(output_dir, init_image_dir, mask_image_dir, inpainted_image_dir, num_views):
+    # subplot settings
+    num_col = 3
+    num_row = 1
+    subplot_size = 4
+
+    summary_image_dir = os.path.join(output_dir, "summary")
+    os.makedirs(summary_image_dir, exist_ok=True)
+
+    # graph settings
+    print("=> visualizing results...")
+    for view_idx in range(num_views):
+        plt.switch_backend("agg")
+        fig = plt.figure(dpi=100)
+        fig.set_size_inches(subplot_size * num_col, subplot_size * (num_row + 1))
+        fig.set_facecolor('white')
+
+        # rendering
+        plt.subplot2grid((num_row, num_col), (0, 0))
+        plt.imshow(Image.open(os.path.join(init_image_dir, "{}.png".format(view_idx))))
+        plt.text(0, 0, "Rendering", fontsize=16, color='black', backgroundcolor='white')
+        plt.axis('off')
+
+        # mask
+        plt.subplot2grid((num_row, num_col), (0, 1))
+        plt.imshow(Image.open(os.path.join(mask_image_dir, "{}_project.png".format(view_idx))))
+        plt.text(0, 0, "Project Mask", fontsize=16, color='black', backgroundcolor='white')
+        plt.set_cmap(cm.Greys_r)
+        plt.axis('off')
+
+        # inpainted
+        plt.subplot2grid((num_row, num_col), (0, 2))
+        plt.imshow(Image.open(os.path.join(inpainted_image_dir, "{}.png".format(view_idx))))
+        plt.text(0, 0, "Inpainted", fontsize=16, color='black', backgroundcolor='white')
+        plt.axis('off')
+
+        
+        plt.savefig(os.path.join(summary_image_dir, "{}.png".format(view_idx)), bbox_inches="tight")
+        fig.clf()
+
+    # generate GIF
+    images = [imageio.imread(os.path.join(summary_image_dir, "{}.png".format(view_idx)))for view_idx in range(num_views)]
+    imageio.mimsave(os.path.join(summary_image_dir, "output.gif"), images, duration=1)
+
+    print("=> done!")
+
+
+def visualize_principle_viewpoints(output_dir, dist_list, elev_list, azim_list):
+    theta_list = [e for e in azim_list]
+    phi_list = [90 - e for e in elev_list]
+    DIST = dist_list[0]
+
+    xyz_list = [polar_to_xyz(theta, phi, DIST) for theta, phi in zip(theta_list, phi_list)]
+
+    xyz_np = np.array(xyz_list)
+    color_np = np.array([[0, 0, 0]]).repeat(xyz_np.shape[0], 0)
+
+    fig = plt.figure()
+    ax = plt.axes(projection='3d')
+    SCALE = 0.8
+    ax.set_xlim((-DIST, DIST))
+    ax.set_ylim((-DIST, DIST))
+    ax.set_zlim((-SCALE * DIST, SCALE * DIST))
+
+    ax.scatter(xyz_np[:, 0], xyz_np[:, 2], xyz_np[:, 1], s=100, c=color_np, depthshade=True, label="Principle views")
+    ax.scatter([0], [0], [0], c=[[1, 0, 0]], s=100, depthshade=True, label="Object center")
+
+    # draw hemisphere
+    # theta inclination angle
+    # phi azimuthal angle
+    n_theta = 50    # number of values for theta
+    n_phi = 200     # number of values for phi
+    r = DIST        #radius of sphere
+
+    # theta, phi = np.mgrid[0.0:0.5*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
+    theta, phi = np.mgrid[0.0:1*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
+
+    x = r*np.sin(theta)*np.cos(phi)
+    y = r*np.sin(theta)*np.sin(phi)
+    z = r*np.cos(theta)
+
+    ax.plot_surface(x, y, z, rstride=1, cstride=1, alpha=0.25, linewidth=1)
+
+    # Make the grid
+    ax.quiver(
+        xyz_np[:, 0], 
+        xyz_np[:, 2], 
+        xyz_np[:, 1], 
+        -xyz_np[:, 0], 
+        -xyz_np[:, 2], 
+        -xyz_np[:, 1],
+        normalize=True,
+        length=0.3
+    )
+
+    ax.set_xlabel('X Label')
+    ax.set_ylabel('Z Label')
+    ax.set_zlabel('Y Label')
+
+    ax.view_init(30, 35)
+    ax.legend()
+
+    plt.show()
+
+    plt.savefig(os.path.join(output_dir, "principle_viewpoints.png"))
+
+
+
+def visualize_refinement_viewpoints(output_dir, selected_view_ids, dist_list, elev_list, azim_list):
+    theta_list = [azim_list[i] for i in selected_view_ids]
+    phi_list = [90 - elev_list[i] for i in selected_view_ids]
+    DIST = dist_list[0]
+
+    xyz_list = [polar_to_xyz(theta, phi, DIST) for theta, phi in zip(theta_list, phi_list)]
+
+    xyz_np = np.array(xyz_list)
+    color_np = np.array([[0, 0, 0]]).repeat(xyz_np.shape[0], 0)
+
+    fig = plt.figure()
+    ax = plt.axes(projection='3d')
+    SCALE = 0.8
+    ax.set_xlim((-DIST, DIST))
+    ax.set_ylim((-DIST, DIST))
+    ax.set_zlim((-SCALE * DIST, SCALE * DIST))
+
+    ax.scatter(xyz_np[:, 0], xyz_np[:, 2], xyz_np[:, 1], c=color_np, depthshade=True, label="Refinement views")
+    ax.scatter([0], [0], [0], c=[[1, 0, 0]], s=100, depthshade=True, label="Object center")
+
+    # draw hemisphere
+    # theta inclination angle
+    # phi azimuthal angle
+    n_theta = 50    # number of values for theta
+    n_phi = 200     # number of values for phi
+    r = DIST        #radius of sphere
+
+    # theta, phi = np.mgrid[0.0:0.5*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j]
+    theta, phi = np.mgrid[0.0:1*np.pi:n_theta*1j, 0.0:2.0*np.pi:n_phi*1j] 
+
+    x = r*np.sin(theta)*np.cos(phi)
+    y = r*np.sin(theta)*np.sin(phi)
+    z = r*np.cos(theta)
+
+    ax.plot_surface(x, y, z, rstride=1, cstride=1, alpha=0.25, linewidth=1)
+
+    # Make the grid
+    ax.quiver(
+        xyz_np[:, 0], 
+        xyz_np[:, 2], 
+        xyz_np[:, 1], 
+        -xyz_np[:, 0], 
+        -xyz_np[:, 2], 
+        -xyz_np[:, 1],
+        normalize=True,
+        length=0.3
+    )
+
+    ax.set_xlabel('X Label')
+    ax.set_ylabel('Z Label')
+    ax.set_zlabel('Y Label')
+
+    ax.view_init(30, 35)
+    ax.legend()
+
+    plt.show()
+
+    plt.savefig(os.path.join(output_dir, "refinement_viewpoints.png"))
+
+    fig.clear()
+
+        

text2tex/models/ControlNet/.gitignore

@@ -0,0 +1,143 @@
+.idea/
+
+training/
+lightning_logs/
+image_log/
+
+*.pth
+*.pt
+*.ckpt
+*.safetensors
+
+gradio_pose2image_private.py
+gradio_canny2image_private.py
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

text2tex/models/ControlNet/LICENSE

@@ -0,0 +1,201 @@
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text2tex/models/ControlNet/README.md

@@ -0,0 +1,234 @@
+# ControlNet
+
+Official implementation of [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543).
+
+ControlNet is a neural network structure to control diffusion models by adding extra conditions.
+
+![img](github_page/he.png)
+
+It copys the weights of neural network blocks into a "locked" copy and a "trainable" copy. 
+
+The "trainable" one learns your condition. The "locked" one preserves your model. 
+
+Thanks to this, training with small dataset of image pairs will not destroy the production-ready diffusion models.
+
+The "zero convolution" is 1×1 convolution with both weight and bias initialized as zeros. 
+
+Before training, all zero convolutions output zeros, and ControlNet will not cause any distortion.
+
+No layer is trained from scratch. You are still fine-tuning. Your original model is safe. 
+
+This allows training on small-scale or even personal devices.
+
+This is also friendly to merge/replacement/offsetting of models/weights/blocks/layers.
+
+### FAQ
+
+**Q:** But wait, if the weight of a conv layer is zero, the gradient will also be zero, and the network will not learn anything. Why "zero convolution" works?
+
+**A:** This is not true. [See an explanation here](docs/faq.md).
+
+# Stable Diffusion + ControlNet
+
+By repeating the above simple structure 14 times, we can control stable diffusion in this way:
+
+![img](github_page/sd.png)
+
+Note that the way we connect layers is computational efficient. The original SD encoder does not need to store gradients (the locked original SD Encoder Block 1234 and Middle). The required GPU memory is not much larger than original SD, although many layers are added. Great!
+
+# Production-Ready Pretrained Models
+
+First create a new conda environment
+
+    conda env create -f environment.yaml
+    conda activate control
+
+All models and detectors can be downloaded from [our Hugging Face page](https://huggingface.co/lllyasviel/ControlNet). Make sure that SD models are put in "ControlNet/models" and detectors are put in "ControlNet/annotator/ckpts". Make sure that you download all necessary pretrained weights and detector models from that Hugging Face page, including HED edge detection model, Midas depth estimation model, Openpose, and so on. 
+
+We provide 9 Gradio apps with these models.
+
+All test images can be found at the folder "test_imgs".
+
+### News
+
+2023/02/12 - Now you can play with any community model by [Transferring the ControlNet](https://github.com/lllyasviel/ControlNet/discussions/12).
+
+2023/02/11 - [Low VRAM mode](docs/low_vram.md) is added. Please use this mode if you are using 8GB GPU(s) or if you want larger batch size.
+
+## ControlNet with Canny Edge
+
+Stable Diffusion 1.5 + ControlNet (using simple Canny edge detection)
+
+    python gradio_canny2image.py
+
+The Gradio app also allows you to change the Canny edge thresholds. Just try it for more details.
+
+Prompt: "bird"
+![p](github_page/p1.png)
+
+Prompt: "cute dog"
+![p](github_page/p2.png)
+
+## ControlNet with M-LSD Lines
+
+Stable Diffusion 1.5 + ControlNet (using simple M-LSD straight line detection)
+
+    python gradio_hough2image.py
+
+The Gradio app also allows you to change the M-LSD thresholds. Just try it for more details.
+
+Prompt: "room"
+![p](github_page/p3.png)
+
+Prompt: "building"
+![p](github_page/p4.png)
+
+## ControlNet with HED Boundary
+
+Stable Diffusion 1.5 + ControlNet (using soft HED Boundary)
+
+    python gradio_hed2image.py
+
+The soft HED Boundary will preserve many details in input images, making this app suitable for recoloring and stylizing. Just try it for more details.
+
+Prompt: "oil painting of handsome old man, masterpiece"
+![p](github_page/p5.png)
+
+Prompt: "Cyberpunk robot"
+![p](github_page/p6.png)
+
+## ControlNet with User Scribbles
+
+Stable Diffusion 1.5 + ControlNet (using Scribbles)
+
+    python gradio_scribble2image.py
+
+Note that the UI is based on Gradio, and Gradio is somewhat difficult to customize. Right now you need to draw scribbles outside the UI (using your favorite drawing software, for example, MS Paint) and then import the scribble image to Gradio. 
+
+Prompt: "turtle"
+![p](github_page/p7.png)
+
+Prompt: "hot air balloon"
+![p](github_page/p8.png)
+
+### Interactive Interface
+
+We actually provide an interactive interface
+
+    python gradio_scribble2image_interactive.py
+
+However, because gradio is very [buggy](https://github.com/gradio-app/gradio/issues/3166) and difficult to customize, right now, user need to first set canvas width and heights and then click "Open drawing canvas" to get a drawing area. Please do not upload image to that drawing canvas. Also, the drawing area is very small; it should be bigger. But I failed to find out how to make it larger. Again, gradio is really buggy.
+
+The below dog sketch is drawn by me. Perhaps we should draw a better dog for showcase.
+
+Prompt: "dog in a room"
+![p](github_page/p20.png)
+
+## ControlNet with Fake Scribbles
+
+Stable Diffusion 1.5 + ControlNet (using fake scribbles)
+
+    python gradio_fake_scribble2image.py
+
+Sometimes we are lazy, and we do not want to draw scribbles. This script use the exactly same scribble-based model but use a simple algorithm to synthesize scribbles from input images.
+
+Prompt: "bag"
+![p](github_page/p9.png)
+
+Prompt: "shose" (Note that "shose" is a typo; it should be "shoes". But it still seems to work.)
+![p](github_page/p10.png)
+
+## ControlNet with Human Pose
+
+Stable Diffusion 1.5 + ControlNet (using human pose)
+
+    python gradio_pose2image.py
+
+Apparently, this model deserves a better UI to directly manipulate pose skeleton. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then the Openpose will detect the pose for you.
+
+Prompt: "Chief in the kitchen"
+![p](github_page/p11.png)
+
+Prompt: "An astronaut on the moon"
+![p](github_page/p12.png)
+
+## ControlNet with Semantic Segmentation
+
+Stable Diffusion 1.5 + ControlNet (using semantic segmentation)
+
+    python gradio_seg2image.py
+
+This model use ADE20K's segmentation protocol. Again, this model deserves a better UI to directly draw the segmentations. However, again, Gradio is somewhat difficult to customize. Right now you need to input an image and then a model called Uniformer will detect the segmentations for you. Just try it for more details.
+
+Prompt: "House"
+![p](github_page/p13.png)
+
+Prompt: "River"
+![p](github_page/p14.png)
+
+## ControlNet with Depth
+
+Stable Diffusion 1.5 + ControlNet (using depth map)
+
+    python gradio_depth2image.py
+
+Great! Now SD 1.5 also have a depth control. FINALLY. So many possibilities (considering SD1.5 has much more community models than SD2).
+
+Note that different from Stability's model, the ControlNet receive the full 512×512 depth map, rather than 64×64 depth. Note that Stability's SD2 depth model use 64*64 depth maps. This means that the ControlNet will preserve more details in the depth map.
+
+This is always a strength because if users do not want to preserve more details, they can simply use another SD to post-process an i2i. But if they want to preserve more details, ControlNet becomes their only choice. Again, SD2 uses 64×64 depth, we use 512×512.
+
+Prompt: "Stormtrooper's lecture"
+![p](github_page/p15.png)
+
+## ControlNet with Normal Map
+
+Stable Diffusion 1.5 + ControlNet (using normal map)
+
+    python gradio_normal2image.py
+
+This model use normal map. Rightnow in the APP, the normal is computed from the midas depth map and a user threshold (to determine how many area is background with identity normal face to viewer, tune the "Normal background threshold" in the gradio app to get a feeling).
+
+Prompt: "Cute toy"
+![p](github_page/p17.png)
+
+Prompt: "Plaster statue of Abraham Lincoln"
+![p](github_page/p18.png)
+
+Compared to depth model, this model seems to be a bit better at preserving the geometry. This is intuitive: minor details are not salient in depth maps, but are salient in normal maps. Below is the depth result with same inputs. You can see that the hairstyle of the man in the input image is modified by depth model, but preserved by the normal model. 
+
+Prompt: "Plaster statue of Abraham Lincoln"
+![p](github_page/p19.png)
+
+## ControlNet with Anime Line Drawing
+
+We also trained a relatively simple ControlNet for anime line drawings. This tool may be useful for artistic creations. (Although the image details in the results is a bit modified, since it still diffuse latent images.)
+
+This model is not available right now. We need to evaluate the potential risks before releasing this model. Nevertheless, you may be interested in [transferring the ControlNet to any community model](https://github.com/lllyasviel/ControlNet/discussions/12).
+
+![p](github_page/p21.png)
+
+# Annotate Your Own Data
+
+We provide simple python scripts to process images.
+
+[See a gradio example here](docs/annotator.md).
+
+# Train with Your Own Data
+
+Training a ControlNet is as easy as (or even easier than) training a simple pix2pix. 
+
+[See the steps here](docs/train.md).
+
+# Citation
+
+    @misc{zhang2023adding,
+      title={Adding Conditional Control to Text-to-Image Diffusion Models}, 
+      author={Lvmin Zhang and Maneesh Agrawala},
+      year={2023},
+      eprint={2302.05543},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+    }
+
+[Arxiv Link](https://arxiv.org/abs/2302.05543)

text2tex/models/ControlNet/annotator/canny/__init__.py

@@ -0,0 +1,5 @@
+import cv2
+
+
+def apply_canny(img, low_threshold, high_threshold):
+    return cv2.Canny(img, low_threshold, high_threshold)

text2tex/models/ControlNet/annotator/ckpts/ckpts.txt

@@ -0,0 +1 @@
+Weights here.

text2tex/models/ControlNet/annotator/hed/__init__.py

@@ -0,0 +1,127 @@
+import numpy as np
+import cv2
+import torch
+from einops import rearrange
+
+
+class Network(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.netVggOne = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False)
+        )
+
+        self.netVggTwo = torch.nn.Sequential(
+            torch.nn.MaxPool2d(kernel_size=2, stride=2),
+            torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False)
+        )
+
+        self.netVggThr = torch.nn.Sequential(
+            torch.nn.MaxPool2d(kernel_size=2, stride=2),
+            torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False)
+        )
+
+        self.netVggFou = torch.nn.Sequential(
+            torch.nn.MaxPool2d(kernel_size=2, stride=2),
+            torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False)
+        )
+
+        self.netVggFiv = torch.nn.Sequential(
+            torch.nn.MaxPool2d(kernel_size=2, stride=2),
+            torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False),
+            torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
+            torch.nn.ReLU(inplace=False)
+        )
+
+        self.netScoreOne = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0)
+        self.netScoreTwo = torch.nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0)
+        self.netScoreThr = torch.nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
+        self.netScoreFou = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
+        self.netScoreFiv = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
+
+        self.netCombine = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0),
+            torch.nn.Sigmoid()
+        )
+
+        self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load('./annotator/ckpts/network-bsds500.pth').items()})
+    # end
+
+    def forward(self, tenInput):
+        tenInput = tenInput * 255.0
+        tenInput = tenInput - torch.tensor(data=[104.00698793, 116.66876762, 122.67891434], dtype=tenInput.dtype, device=tenInput.device).view(1, 3, 1, 1)
+
+        tenVggOne = self.netVggOne(tenInput)
+        tenVggTwo = self.netVggTwo(tenVggOne)
+        tenVggThr = self.netVggThr(tenVggTwo)
+        tenVggFou = self.netVggFou(tenVggThr)
+        tenVggFiv = self.netVggFiv(tenVggFou)
+
+        tenScoreOne = self.netScoreOne(tenVggOne)
+        tenScoreTwo = self.netScoreTwo(tenVggTwo)
+        tenScoreThr = self.netScoreThr(tenVggThr)
+        tenScoreFou = self.netScoreFou(tenVggFou)
+        tenScoreFiv = self.netScoreFiv(tenVggFiv)
+
+        tenScoreOne = torch.nn.functional.interpolate(input=tenScoreOne, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
+        tenScoreTwo = torch.nn.functional.interpolate(input=tenScoreTwo, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
+        tenScoreThr = torch.nn.functional.interpolate(input=tenScoreThr, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
+        tenScoreFou = torch.nn.functional.interpolate(input=tenScoreFou, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
+        tenScoreFiv = torch.nn.functional.interpolate(input=tenScoreFiv, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
+
+        return self.netCombine(torch.cat([ tenScoreOne, tenScoreTwo, tenScoreThr, tenScoreFou, tenScoreFiv ], 1))
+    # end
+# end
+
+
+netNetwork = Network().cuda().eval()
+
+
+def apply_hed(input_image):
+    assert input_image.ndim == 3
+    input_image = input_image[:, :, ::-1].copy()
+    with torch.no_grad():
+        image_hed = torch.from_numpy(input_image).float().cuda()
+        image_hed = image_hed / 255.0
+        image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
+        edge = netNetwork(image_hed)[0]
+        edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
+        return edge[0]
+
+
+def nms(x, t, s):
+    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
+
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+
+    y = np.zeros_like(x)
+
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+
+    z = np.zeros_like(y, dtype=np.uint8)
+    z[y > t] = 255
+    return z

text2tex/models/ControlNet/annotator/midas/__init__.py

@@ -0,0 +1,36 @@
+import cv2
+import numpy as np
+import torch
+
+from einops import rearrange
+from .api import MiDaSInference
+
+model = MiDaSInference(model_type="dpt_hybrid").cuda()
+
+
+def apply_midas(input_image, a=np.pi * 2.0, bg_th=0.1):
+    assert input_image.ndim == 3
+    image_depth = input_image
+    with torch.no_grad():
+        image_depth = torch.from_numpy(image_depth).float().cuda()
+        image_depth = image_depth / 127.5 - 1.0
+        image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
+        depth = model(image_depth)[0]
+
+        depth_pt = depth.clone()
+        depth_pt -= torch.min(depth_pt)
+        depth_pt /= torch.max(depth_pt)
+        depth_pt = depth_pt.cpu().numpy()
+        depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
+
+        depth_np = depth.cpu().numpy()
+        x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
+        y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
+        z = np.ones_like(x) * a
+        x[depth_pt < bg_th] = 0
+        y[depth_pt < bg_th] = 0
+        normal = np.stack([x, y, z], axis=2)
+        normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
+        normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
+
+        return depth_image, normal_image

text2tex/models/ControlNet/annotator/midas/api.py

@@ -0,0 +1,165 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose
+
+from .midas.dpt_depth import DPTDepthModel
+from .midas.midas_net import MidasNet
+from .midas.midas_net_custom import MidasNet_small
+from .midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+import os
+import sys
+
+BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")
+
+ISL_PATHS = {
+    "dpt_large": BASE_DIR+"/annotator/ckpts/dpt_large-midas-2f21e586.pt",
+    "dpt_hybrid": BASE_DIR+"/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt",
+    "midas_v21": "",
+    "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def load_midas_transform(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load transform only
+    if model_type == "dpt_large":  # DPT-Large
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    elif model_type == "midas_v21_small":
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    else:
+        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return transform
+
+
+def load_model(model_type):
+    # https://github.com/isl-org/MiDaS/blob/master/run.py
+    # load network
+    model_path = ISL_PATHS[model_type]
+    if model_type == "dpt_large":  # DPT-Large
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitl16_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "dpt_hybrid":  # DPT-Hybrid
+        model = DPTDepthModel(
+            path=model_path,
+            backbone="vitb_rn50_384",
+            non_negative=True,
+        )
+        net_w, net_h = 384, 384
+        resize_mode = "minimal"
+        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    elif model_type == "midas_v21":
+        model = MidasNet(model_path, non_negative=True)
+        net_w, net_h = 384, 384
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    elif model_type == "midas_v21_small":
+        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+                               non_negative=True, blocks={'expand': True})
+        net_w, net_h = 256, 256
+        resize_mode = "upper_bound"
+        normalization = NormalizeImage(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+
+    else:
+        print(f"model_type '{model_type}' not implemented, use: --model_type large")
+        assert False
+
+    transform = Compose(
+        [
+            Resize(
+                net_w,
+                net_h,
+                resize_target=None,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=32,
+                resize_method=resize_mode,
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            normalization,
+            PrepareForNet(),
+        ]
+    )
+
+    return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+    MODEL_TYPES_TORCH_HUB = [
+        "DPT_Large",
+        "DPT_Hybrid",
+        "MiDaS_small"
+    ]
+    MODEL_TYPES_ISL = [
+        "dpt_large",
+        "dpt_hybrid",
+        "midas_v21",
+        "midas_v21_small",
+    ]
+
+    def __init__(self, model_type):
+        super().__init__()
+        assert (model_type in self.MODEL_TYPES_ISL)
+        model, _ = load_model(model_type)
+        self.model = model
+        self.model.train = disabled_train
+
+    def forward(self, x):
+        with torch.no_grad():
+            prediction = self.model(x)
+        return prediction
+

text2tex/models/ControlNet/annotator/midas/midas/base_model.py

@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device('cpu'))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)

text2tex/models/ControlNet/annotator/midas/midas/blocks.py

@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained, hooks=hooks, use_readout=use_readout
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
+    elif backbone == "efficientnet_lite3":
+        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+        
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand==True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+
+    return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+    efficientnet = torch.hub.load(
+        "rwightman/gen-efficientnet-pytorch",
+        "tf_efficientnet_lite3",
+        pretrained=use_pretrained,
+        exportable=exportable
+    )
+    return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+    pretrained = nn.Module()
+
+    pretrained.layer1 = nn.Sequential(
+        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+    )
+    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+    return pretrained
+    
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+    """Interpolation module.
+    """
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+

text2tex/models/ControlNet/annotator/midas/midas/dpt_depth.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+    FeatureFusionBlock,
+    FeatureFusionBlock_custom,
+    Interpolate,
+    _make_encoder,
+    forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+
+class DPT(BaseModel):
+    def __init__(
+        self,
+        head,
+        features=256,
+        backbone="vitb_rn50_384",
+        readout="project",
+        channels_last=False,
+        use_bn=False,
+    ):
+
+        super(DPT, self).__init__()
+
+        self.channels_last = channels_last
+
+        hooks = {
+            "vitb_rn50_384": [0, 1, 8, 11],
+            "vitb16_384": [2, 5, 8, 11],
+            "vitl16_384": [5, 11, 17, 23],
+        }
+
+        # Instantiate backbone and reassemble blocks
+        self.pretrained, self.scratch = _make_encoder(
+            backbone,
+            features,
+            False, # Set to true of you want to train from scratch, uses ImageNet weights
+            groups=1,
+            expand=False,
+            exportable=False,
+            hooks=hooks[backbone],
+            use_readout=readout,
+        )
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        self.scratch.output_conv = head
+
+
+    def forward(self, x):
+        if self.channels_last == True:
+            x.contiguous(memory_format=torch.channels_last)
+
+        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return out
+
+
+class DPTDepthModel(DPT):
+    def __init__(self, path=None, non_negative=True, **kwargs):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+           self.load(path)
+
+    def forward(self, x):
+        return super().forward(x).squeeze(dim=1)
+

text2tex/models/ControlNet/annotator/midas/midas/midas_net.py

@@ -0,0 +1,76 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=256, non_negative=True):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet, self).__init__()
+
+        use_pretrained = False if path is None else True
+
+        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
+
+        self.scratch.refinenet4 = FeatureFusionBlock(features)
+        self.scratch.refinenet3 = FeatureFusionBlock(features)
+        self.scratch.refinenet2 = FeatureFusionBlock(features)
+        self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+        )
+
+        if path:
+            self.load(path)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)

text2tex/models/ControlNet/annotator/midas/midas/midas_net_custom.py

@@ -0,0 +1,128 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
+
+
+class MidasNet_small(BaseModel):
+    """Network for monocular depth estimation.
+    """
+
+    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
+        blocks={'expand': True}):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet_small, self).__init__()
+
+        use_pretrained = False if path else True
+                
+        self.channels_last = channels_last
+        self.blocks = blocks
+        self.backbone = backbone
+
+        self.groups = 1
+
+        features1=features
+        features2=features
+        features3=features
+        features4=features
+        self.expand = False
+        if "expand" in self.blocks and self.blocks['expand'] == True:
+            self.expand = True
+            features1=features
+            features2=features*2
+            features3=features*4
+            features4=features*8
+
+        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
+  
+        self.scratch.activation = nn.ReLU(False)    
+
+        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
+
+        
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
+            self.scratch.activation,
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+        
+        if path:
+            self.load(path)
+
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+        if self.channels_last==True:
+            print("self.channels_last = ", self.channels_last)
+            x.contiguous(memory_format=torch.channels_last)
+
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)
+
+
+
+def fuse_model(m):
+    prev_previous_type = nn.Identity()
+    prev_previous_name = ''
+    previous_type = nn.Identity()
+    previous_name = ''
+    for name, module in m.named_modules():
+        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
+            # print("FUSED ", prev_previous_name, previous_name, name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
+        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
+            # print("FUSED ", prev_previous_name, previous_name)
+            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
+        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
+        #    print("FUSED ", previous_name, name)
+        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
+
+        prev_previous_type = previous_type
+        prev_previous_name = previous_name
+        previous_type = type(module)
+        previous_name = name

text2tex/models/ControlNet/annotator/midas/midas/transforms.py

@@ -0,0 +1,234 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "disparity" in sample:
+            disparity = sample["disparity"].astype(np.float32)
+            sample["disparity"] = np.ascontiguousarray(disparity)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        return sample

text2tex/models/ControlNet/annotator/midas/midas/vit.py

@@ -0,0 +1,491 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+class Slice(nn.Module):
+    def __init__(self, start_index=1):
+        super(Slice, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+    def __init__(self, start_index=1):
+        super(AddReadout, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        if self.start_index == 2:
+            readout = (x[:, 0] + x[:, 1]) / 2
+        else:
+            readout = x[:, 0]
+        return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+    def __init__(self, in_features, start_index=1):
+        super(ProjectReadout, self).__init__()
+        self.start_index = start_index
+
+        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+    def forward(self, x):
+        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+        features = torch.cat((x[:, self.start_index :], readout), -1)
+
+        return self.project(features)
+
+
+class Transpose(nn.Module):
+    def __init__(self, dim0, dim1):
+        super(Transpose, self).__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x):
+        x = x.transpose(self.dim0, self.dim1)
+        return x
+
+
+def forward_vit(pretrained, x):
+    b, c, h, w = x.shape
+
+    glob = pretrained.model.forward_flex(x)
+
+    layer_1 = pretrained.activations["1"]
+    layer_2 = pretrained.activations["2"]
+    layer_3 = pretrained.activations["3"]
+    layer_4 = pretrained.activations["4"]
+
+    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+    unflatten = nn.Sequential(
+        nn.Unflatten(
+            2,
+            torch.Size(
+                [
+                    h // pretrained.model.patch_size[1],
+                    w // pretrained.model.patch_size[0],
+                ]
+            ),
+        )
+    )
+
+    if layer_1.ndim == 3:
+        layer_1 = unflatten(layer_1)
+    if layer_2.ndim == 3:
+        layer_2 = unflatten(layer_2)
+    if layer_3.ndim == 3:
+        layer_3 = unflatten(layer_3)
+    if layer_4.ndim == 3:
+        layer_4 = unflatten(layer_4)
+
+    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+    return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+    posemb_tok, posemb_grid = (
+        posemb[:, : self.start_index],
+        posemb[0, self.start_index :],
+    )
+
+    gs_old = int(math.sqrt(len(posemb_grid)))
+
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+    return posemb
+
+
+def forward_flex(self, x):
+    b, c, h, w = x.shape
+
+    pos_embed = self._resize_pos_embed(
+        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+    )
+
+    B = x.shape[0]
+
+    if hasattr(self.patch_embed, "backbone"):
+        x = self.patch_embed.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+
+    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+    if getattr(self, "dist_token", None) is not None:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        dist_token = self.dist_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+    else:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+
+    x = x + pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+        x = blk(x)
+
+    x = self.norm(x)
+
+    return x
+
+
+activations = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activations[name] = output
+
+    return hook
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+    if use_readout == "ignore":
+        readout_oper = [Slice(start_index)] * len(features)
+    elif use_readout == "add":
+        readout_oper = [AddReadout(start_index)] * len(features)
+    elif use_readout == "project":
+        readout_oper = [
+            ProjectReadout(vit_features, start_index) for out_feat in features
+        ]
+    else:
+        assert (
+            False
+        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+    return readout_oper
+
+
+def _make_vit_b16_backbone(
+    model,
+    features=[96, 192, 384, 768],
+    size=[384, 384],
+    hooks=[2, 5, 8, 11],
+    vit_features=768,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    # 32, 48, 136, 384
+    pretrained.act_postprocess1 = nn.Sequential(
+        readout_oper[0],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[0],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[0],
+            out_channels=features[0],
+            kernel_size=4,
+            stride=4,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess2 = nn.Sequential(
+        readout_oper[1],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[1],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[1],
+            out_channels=features[1],
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+    hooks = [5, 11, 17, 23] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[256, 512, 1024, 1024],
+        hooks=hooks,
+        vit_features=1024,
+        use_readout=use_readout,
+    )
+
+
+def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+    )
+
+
+def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
+    model = timm.create_model(
+        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+    )
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        start_index=2,
+    )
+
+
+def _make_vit_b_rn50_backbone(
+    model,
+    features=[256, 512, 768, 768],
+    size=[384, 384],
+    hooks=[0, 1, 8, 11],
+    vit_features=768,
+    use_vit_only=False,
+    use_readout="ignore",
+    start_index=1,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+
+    if use_vit_only == True:
+        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    else:
+        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+            get_activation("1")
+        )
+        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+            get_activation("2")
+        )
+
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    if use_vit_only == True:
+        pretrained.act_postprocess1 = nn.Sequential(
+            readout_oper[0],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[0],
+                out_channels=features[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+
+        pretrained.act_postprocess2 = nn.Sequential(
+            readout_oper[1],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[1],
+                out_channels=features[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+    else:
+        pretrained.act_postprocess1 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+        pretrained.act_postprocess2 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
+):
+    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_b_rn50_backbone(
+        model,
+        features=[256, 512, 768, 768],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+    )

text2tex/models/ControlNet/annotator/midas/utils.py

@@ -0,0 +1,189 @@
+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+    """Read pfm file.
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        tuple: (data, scale)
+    """
+    with open(path, "rb") as file:
+
+        color = None
+        width = None
+        height = None
+        scale = None
+        endian = None
+
+        header = file.readline().rstrip()
+        if header.decode("ascii") == "PF":
+            color = True
+        elif header.decode("ascii") == "Pf":
+            color = False
+        else:
+            raise Exception("Not a PFM file: " + path)
+
+        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+        if dim_match:
+            width, height = list(map(int, dim_match.groups()))
+        else:
+            raise Exception("Malformed PFM header.")
+
+        scale = float(file.readline().decode("ascii").rstrip())
+        if scale < 0:
+            # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            # big-endian
+            endian = ">"
+
+        data = np.fromfile(file, endian + "f")
+        shape = (height, width, 3) if color else (height, width)
+
+        data = np.reshape(data, shape)
+        data = np.flipud(data)
+
+        return data, scale
+
+
+def write_pfm(path, image, scale=1):
+    """Write pfm file.
+
+    Args:
+        path (str): pathto file
+        image (array): data
+        scale (int, optional): Scale. Defaults to 1.
+    """
+
+    with open(path, "wb") as file:
+        color = None
+
+        if image.dtype.name != "float32":
+            raise Exception("Image dtype must be float32.")
+
+        image = np.flipud(image)
+
+        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
+            color = True
+        elif (
+            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+        ):  # greyscale
+            color = False
+        else:
+            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+        file.write("PF\n" if color else "Pf\n".encode())
+        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+        endian = image.dtype.byteorder
+
+        if endian == "<" or endian == "=" and sys.byteorder == "little":
+            scale = -scale
+
+        file.write("%f\n".encode() % scale)
+
+        image.tofile(file)
+
+
+def read_image(path):
+    """Read image and output RGB image (0-1).
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        array: RGB image (0-1)
+    """
+    img = cv2.imread(path)
+
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+    return img
+
+
+def resize_image(img):
+    """Resize image and make it fit for network.
+
+    Args:
+        img (array): image
+
+    Returns:
+        tensor: data ready for network
+    """
+    height_orig = img.shape[0]
+    width_orig = img.shape[1]
+
+    if width_orig > height_orig:
+        scale = width_orig / 384
+    else:
+        scale = height_orig / 384
+
+    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+    img_resized = (
+        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+    )
+    img_resized = img_resized.unsqueeze(0)
+
+    return img_resized
+
+
+def resize_depth(depth, width, height):
+    """Resize depth map and bring to CPU (numpy).
+
+    Args:
+        depth (tensor): depth
+        width (int): image width
+        height (int): image height
+
+    Returns:
+        array: processed depth
+    """
+    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+    depth_resized = cv2.resize(
+        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+    )
+
+    return depth_resized
+
+def write_depth(path, depth, bits=1):
+    """Write depth map to pfm and png file.
+
+    Args:
+        path (str): filepath without extension
+        depth (array): depth
+    """
+    write_pfm(path + ".pfm", depth.astype(np.float32))
+
+    depth_min = depth.min()
+    depth_max = depth.max()
+
+    max_val = (2**(8*bits))-1
+
+    if depth_max - depth_min > np.finfo("float").eps:
+        out = max_val * (depth - depth_min) / (depth_max - depth_min)
+    else:
+        out = np.zeros(depth.shape, dtype=depth.type)
+
+    if bits == 1:
+        cv2.imwrite(path + ".png", out.astype("uint8"))
+    elif bits == 2:
+        cv2.imwrite(path + ".png", out.astype("uint16"))
+
+    return

text2tex/models/ControlNet/annotator/mlsd/__init__.py

@@ -0,0 +1,30 @@
+import cv2
+import numpy as np
+import torch
+import os
+
+from einops import rearrange
+from .models.mbv2_mlsd_tiny import  MobileV2_MLSD_Tiny
+from .models.mbv2_mlsd_large import  MobileV2_MLSD_Large
+from .utils import  pred_lines
+
+
+model_path = './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()
+
+
+def apply_mlsd(input_image, thr_v, thr_d):
+    assert input_image.ndim == 3
+    img = input_image
+    img_output = np.zeros_like(img)
+    try:
+        with torch.no_grad():
+            lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
+            for line in lines:
+                x_start, y_start, x_end, y_end = [int(val) for val in line]
+                cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
+    except Exception as e:
+        pass
+    return img_output[:, :, 0]

text2tex/models/ControlNet/annotator/mlsd/models/mbv2_mlsd_large.py

@@ -0,0 +1,292 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+from  torch.nn import  functional as F
+
+
+class BlockTypeA(nn.Module):
+    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
+        super(BlockTypeA, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c2, out_c2, kernel_size=1),
+            nn.BatchNorm2d(out_c2),
+            nn.ReLU(inplace=True)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c1, out_c1, kernel_size=1),
+            nn.BatchNorm2d(out_c1),
+            nn.ReLU(inplace=True)
+        )
+        self.upscale = upscale
+
+    def forward(self, a, b):
+        b = self.conv1(b)
+        a = self.conv2(a)
+        if self.upscale:
+             b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
+        return torch.cat((a, b), dim=1)
+
+
+class BlockTypeB(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeB, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_c),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.conv1(x) + x
+        x = self.conv2(x)
+        return x
+
+class BlockTypeC(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeC, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=5, dilation=5),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        return x
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class ConvBNReLU(nn.Sequential):
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        self.channel_pad = out_planes - in_planes
+        self.stride = stride
+        #padding = (kernel_size - 1) // 2
+
+        # TFLite uses slightly different padding than PyTorch
+        if stride == 2:
+            padding = 0
+        else:
+            padding = (kernel_size - 1) // 2
+
+        super(ConvBNReLU, self).__init__(
+            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_planes),
+            nn.ReLU6(inplace=True)
+        )
+        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+
+
+    def forward(self, x):
+        # TFLite uses  different padding
+        if self.stride == 2:
+            x = F.pad(x, (0, 1, 0, 1), "constant", 0)
+            #print(x.shape)
+
+        for module in self:
+            if not isinstance(module, nn.MaxPool2d):
+                x = module(x)
+        return x
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            # pw
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend([
+            # dw
+            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(oup),
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, pretrained=True):
+        """
+        MobileNet V2 main class
+        Args:
+            num_classes (int): Number of classes
+            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
+            inverted_residual_setting: Network structure
+            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
+            Set to 1 to turn off rounding
+            block: Module specifying inverted residual building block for mobilenet
+        """
+        super(MobileNetV2, self).__init__()
+
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        width_mult = 1.0
+        round_nearest = 8
+
+        inverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            #[6, 160, 3, 2],
+            #[6, 320, 1, 1],
+        ]
+
+        # only check the first element, assuming user knows t,c,n,s are required
+        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
+            raise ValueError("inverted_residual_setting should be non-empty "
+                             "or a 4-element list, got {}".format(inverted_residual_setting))
+
+        # building first layer
+        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
+        features = [ConvBNReLU(4, input_channel, stride=2)]
+        # building inverted residual blocks
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = _make_divisible(c * width_mult, round_nearest)
+            for i in range(n):
+                stride = s if i == 0 else 1
+                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+                input_channel = output_channel
+
+        self.features = nn.Sequential(*features)
+        self.fpn_selected = [1, 3, 6, 10, 13]
+        # weight initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.zeros_(m.bias)
+        if pretrained:
+           self._load_pretrained_model()
+
+    def _forward_impl(self, x):
+        # This exists since TorchScript doesn't support inheritance, so the superclass method
+        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
+        fpn_features = []
+        for i, f in enumerate(self.features):
+            if i > self.fpn_selected[-1]:
+                break
+            x = f(x)
+            if i in self.fpn_selected:
+                fpn_features.append(x)
+
+        c1, c2, c3, c4, c5 = fpn_features
+        return c1, c2, c3, c4, c5
+
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+    def _load_pretrained_model(self):
+        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
+        model_dict = {}
+        state_dict = self.state_dict()
+        for k, v in pretrain_dict.items():
+            if k in state_dict:
+                model_dict[k] = v
+        state_dict.update(model_dict)
+        self.load_state_dict(state_dict)
+
+
+class MobileV2_MLSD_Large(nn.Module):
+    def __init__(self):
+        super(MobileV2_MLSD_Large, self).__init__()
+
+        self.backbone = MobileNetV2(pretrained=False)
+        ## A, B
+        self.block15 = BlockTypeA(in_c1= 64, in_c2= 96,
+                                  out_c1= 64, out_c2=64,
+                                  upscale=False)
+        self.block16 = BlockTypeB(128, 64)
+
+        ## A, B
+        self.block17 = BlockTypeA(in_c1 = 32,  in_c2 = 64,
+                                  out_c1= 64,  out_c2= 64)
+        self.block18 = BlockTypeB(128, 64)
+
+        ## A, B
+        self.block19 = BlockTypeA(in_c1=24, in_c2=64,
+                                  out_c1=64, out_c2=64)
+        self.block20 = BlockTypeB(128, 64)
+
+        ## A, B, C
+        self.block21 = BlockTypeA(in_c1=16, in_c2=64,
+                                  out_c1=64, out_c2=64)
+        self.block22 = BlockTypeB(128, 64)
+
+        self.block23 = BlockTypeC(64, 16)
+
+    def forward(self, x):
+        c1, c2, c3, c4, c5 = self.backbone(x)
+
+        x = self.block15(c4, c5)
+        x = self.block16(x)
+
+        x = self.block17(c3, x)
+        x = self.block18(x)
+
+        x = self.block19(c2, x)
+        x = self.block20(x)
+
+        x = self.block21(c1, x)
+        x = self.block22(x)
+        x = self.block23(x)
+        x = x[:, 7:, :, :]
+
+        return x

text2tex/models/ControlNet/annotator/mlsd/models/mbv2_mlsd_tiny.py

@@ -0,0 +1,275 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+from  torch.nn import  functional as F
+
+
+class BlockTypeA(nn.Module):
+    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
+        super(BlockTypeA, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c2, out_c2, kernel_size=1),
+            nn.BatchNorm2d(out_c2),
+            nn.ReLU(inplace=True)
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c1, out_c1, kernel_size=1),
+            nn.BatchNorm2d(out_c1),
+            nn.ReLU(inplace=True)
+        )
+        self.upscale = upscale
+
+    def forward(self, a, b):
+        b = self.conv1(b)
+        a = self.conv2(a)
+        b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
+        return torch.cat((a, b), dim=1)
+
+
+class BlockTypeB(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeB, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_c),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.conv1(x) + x
+        x = self.conv2(x)
+        return x
+
+class BlockTypeC(nn.Module):
+    def __init__(self, in_c, out_c):
+        super(BlockTypeC, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=5, dilation=5),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
+            nn.BatchNorm2d(in_c),
+            nn.ReLU()
+        )
+        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        return x
+
+def _make_divisible(v, divisor, min_value=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    :param v:
+    :param divisor:
+    :param min_value:
+    :return:
+    """
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class ConvBNReLU(nn.Sequential):
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        self.channel_pad = out_planes - in_planes
+        self.stride = stride
+        #padding = (kernel_size - 1) // 2
+
+        # TFLite uses slightly different padding than PyTorch
+        if stride == 2:
+            padding = 0
+        else:
+            padding = (kernel_size - 1) // 2
+
+        super(ConvBNReLU, self).__init__(
+            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_planes),
+            nn.ReLU6(inplace=True)
+        )
+        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+
+
+    def forward(self, x):
+        # TFLite uses  different padding
+        if self.stride == 2:
+            x = F.pad(x, (0, 1, 0, 1), "constant", 0)
+            #print(x.shape)
+
+        for module in self:
+            if not isinstance(module, nn.MaxPool2d):
+                x = module(x)
+        return x
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            # pw
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend([
+            # dw
+            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(oup),
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, pretrained=True):
+        """
+        MobileNet V2 main class
+        Args:
+            num_classes (int): Number of classes
+            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
+            inverted_residual_setting: Network structure
+            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
+            Set to 1 to turn off rounding
+            block: Module specifying inverted residual building block for mobilenet
+        """
+        super(MobileNetV2, self).__init__()
+
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        width_mult = 1.0
+        round_nearest = 8
+
+        inverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            #[6, 96, 3, 1],
+            #[6, 160, 3, 2],
+            #[6, 320, 1, 1],
+        ]
+
+        # only check the first element, assuming user knows t,c,n,s are required
+        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
+            raise ValueError("inverted_residual_setting should be non-empty "
+                             "or a 4-element list, got {}".format(inverted_residual_setting))
+
+        # building first layer
+        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
+        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
+        features = [ConvBNReLU(4, input_channel, stride=2)]
+        # building inverted residual blocks
+        for t, c, n, s in inverted_residual_setting:
+            output_channel = _make_divisible(c * width_mult, round_nearest)
+            for i in range(n):
+                stride = s if i == 0 else 1
+                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+                input_channel = output_channel
+        self.features = nn.Sequential(*features)
+
+        self.fpn_selected = [3, 6, 10]
+        # weight initialization
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out')
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.ones_(m.weight)
+                nn.init.zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.zeros_(m.bias)
+
+        #if pretrained:
+        #    self._load_pretrained_model()
+
+    def _forward_impl(self, x):
+        # This exists since TorchScript doesn't support inheritance, so the superclass method
+        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
+        fpn_features = []
+        for i, f in enumerate(self.features):
+            if i > self.fpn_selected[-1]:
+                break
+            x = f(x)
+            if i in self.fpn_selected:
+                fpn_features.append(x)
+
+        c2, c3, c4 = fpn_features
+        return c2, c3, c4
+
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+    def _load_pretrained_model(self):
+        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
+        model_dict = {}
+        state_dict = self.state_dict()
+        for k, v in pretrain_dict.items():
+            if k in state_dict:
+                model_dict[k] = v
+        state_dict.update(model_dict)
+        self.load_state_dict(state_dict)
+
+
+class MobileV2_MLSD_Tiny(nn.Module):
+    def __init__(self):
+        super(MobileV2_MLSD_Tiny, self).__init__()
+
+        self.backbone = MobileNetV2(pretrained=True)
+
+        self.block12 = BlockTypeA(in_c1= 32, in_c2= 64,
+                                  out_c1= 64, out_c2=64)
+        self.block13 = BlockTypeB(128, 64)
+
+        self.block14 = BlockTypeA(in_c1 = 24,  in_c2 = 64,
+                                  out_c1= 32,  out_c2= 32)
+        self.block15 = BlockTypeB(64, 64)
+
+        self.block16 = BlockTypeC(64, 16)
+
+    def forward(self, x):
+        c2, c3, c4 = self.backbone(x)
+
+        x = self.block12(c3, c4)
+        x = self.block13(x)
+        x = self.block14(c2, x)
+        x = self.block15(x)
+        x = self.block16(x)
+        x = x[:, 7:, :, :]
+        #print(x.shape)
+        x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True)
+
+        return x

text2tex/models/ControlNet/annotator/mlsd/utils.py

@@ -0,0 +1,580 @@
+'''
+modified by  lihaoweicv
+pytorch version
+'''
+
+'''
+M-LSD
+Copyright 2021-present NAVER Corp.
+Apache License v2.0
+'''
+
+import os
+import numpy as np
+import cv2
+import torch
+from  torch.nn import  functional as F
+
+
+def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
+    '''
+    tpMap:
+    center: tpMap[1, 0, :, :]
+    displacement: tpMap[1, 1:5, :, :]
+    '''
+    b, c, h, w = tpMap.shape
+    assert  b==1, 'only support bsize==1'
+    displacement = tpMap[:, 1:5, :, :][0]
+    center = tpMap[:, 0, :, :]
+    heat = torch.sigmoid(center)
+    hmax = F.max_pool2d( heat, (ksize, ksize), stride=1, padding=(ksize-1)//2)
+    keep = (hmax == heat).float()
+    heat = heat * keep
+    heat = heat.reshape(-1, )
+
+    scores, indices = torch.topk(heat, topk_n, dim=-1, largest=True)
+    yy = torch.floor_divide(indices, w).unsqueeze(-1)
+    xx = torch.fmod(indices, w).unsqueeze(-1)
+    ptss = torch.cat((yy, xx),dim=-1)
+
+    ptss   = ptss.detach().cpu().numpy()
+    scores = scores.detach().cpu().numpy()
+    displacement = displacement.detach().cpu().numpy()
+    displacement = displacement.transpose((1,2,0))
+    return  ptss, scores, displacement
+
+
+def pred_lines(image, model,
+               input_shape=[512, 512],
+               score_thr=0.10,
+               dist_thr=20.0):
+    h, w, _ = image.shape
+    h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
+
+    resized_image = np.concatenate([cv2.resize(image, (input_shape[1], input_shape[0]), interpolation=cv2.INTER_AREA),
+                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
+
+    resized_image = resized_image.transpose((2,0,1))
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    batch_image = (batch_image / 127.5) - 1.0
+
+    batch_image = torch.from_numpy(batch_image).float().cuda()
+    outputs = model(batch_image)
+    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
+    start = vmap[:, :, :2]
+    end = vmap[:, :, 2:]
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    segments_list = []
+    for center, score in zip(pts, pts_score):
+        y, x = center
+        distance = dist_map[y, x]
+        if score > score_thr and distance > dist_thr:
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            x_start = x + disp_x_start
+            y_start = y + disp_y_start
+            x_end = x + disp_x_end
+            y_end = y + disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+
+    lines = 2 * np.array(segments_list)  # 256 > 512
+    lines[:, 0] = lines[:, 0] * w_ratio
+    lines[:, 1] = lines[:, 1] * h_ratio
+    lines[:, 2] = lines[:, 2] * w_ratio
+    lines[:, 3] = lines[:, 3] * h_ratio
+
+    return lines
+
+
+def pred_squares(image,
+                 model,
+                 input_shape=[512, 512],
+                 params={'score': 0.06,
+                         'outside_ratio': 0.28,
+                         'inside_ratio': 0.45,
+                         'w_overlap': 0.0,
+                         'w_degree': 1.95,
+                         'w_length': 0.0,
+                         'w_area': 1.86,
+                         'w_center': 0.14}):
+    '''
+    shape = [height, width]
+    '''
+    h, w, _ = image.shape
+    original_shape = [h, w]
+
+    resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA),
+                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
+    resized_image = resized_image.transpose((2, 0, 1))
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    batch_image = (batch_image / 127.5) - 1.0
+
+    batch_image = torch.from_numpy(batch_image).float().cuda()
+    outputs = model(batch_image)
+
+    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
+    start = vmap[:, :, :2]  # (x, y)
+    end = vmap[:, :, 2:]  # (x, y)
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    junc_list = []
+    segments_list = []
+    for junc, score in zip(pts, pts_score):
+        y, x = junc
+        distance = dist_map[y, x]
+        if score > params['score'] and distance > 20.0:
+            junc_list.append([x, y])
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            d_arrow = 1.0
+            x_start = x + d_arrow * disp_x_start
+            y_start = y + d_arrow * disp_y_start
+            x_end = x + d_arrow * disp_x_end
+            y_end = y + d_arrow * disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+
+    segments = np.array(segments_list)
+
+    ####### post processing for squares
+    # 1. get unique lines
+    point = np.array([[0, 0]])
+    point = point[0]
+    start = segments[:, :2]
+    end = segments[:, 2:]
+    diff = start - end
+    a = diff[:, 1]
+    b = -diff[:, 0]
+    c = a * start[:, 0] + b * start[:, 1]
+
+    d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
+    theta = np.arctan2(diff[:, 0], diff[:, 1]) * 180 / np.pi
+    theta[theta < 0.0] += 180
+    hough = np.concatenate([d[:, None], theta[:, None]], axis=-1)
+
+    d_quant = 1
+    theta_quant = 2
+    hough[:, 0] //= d_quant
+    hough[:, 1] //= theta_quant
+    _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
+
+    acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
+    idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
+    yx_indices = hough[indices, :].astype('int32')
+    acc_map[yx_indices[:, 0], yx_indices[:, 1]] = counts
+    idx_map[yx_indices[:, 0], yx_indices[:, 1]] = indices
+
+    acc_map_np = acc_map
+    # acc_map = acc_map[None, :, :, None]
+    #
+    # ### fast suppression using tensorflow op
+    # acc_map = tf.constant(acc_map, dtype=tf.float32)
+    # max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5, 5), strides=1, padding='same')(acc_map)
+    # acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
+    # flatten_acc_map = tf.reshape(acc_map, [1, -1])
+    # topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
+    # _, h, w, _ = acc_map.shape
+    # y = tf.expand_dims(topk_indices // w, axis=-1)
+    # x = tf.expand_dims(topk_indices % w, axis=-1)
+    # yx = tf.concat([y, x], axis=-1)
+
+    ### fast suppression using pytorch op
+    acc_map = torch.from_numpy(acc_map_np).unsqueeze(0).unsqueeze(0)
+    _,_, h, w = acc_map.shape
+    max_acc_map = F.max_pool2d(acc_map,kernel_size=5, stride=1, padding=2)
+    acc_map = acc_map * ( (acc_map == max_acc_map).float() )
+    flatten_acc_map = acc_map.reshape([-1, ])
+
+    scores, indices = torch.topk(flatten_acc_map, len(pts), dim=-1, largest=True)
+    yy = torch.div(indices, w, rounding_mode='floor').unsqueeze(-1)
+    xx = torch.fmod(indices, w).unsqueeze(-1)
+    yx = torch.cat((yy, xx), dim=-1)
+
+    yx = yx.detach().cpu().numpy()
+
+    topk_values = scores.detach().cpu().numpy()
+    indices = idx_map[yx[:, 0], yx[:, 1]]
+    basis = 5 // 2
+
+    merged_segments = []
+    for yx_pt, max_indice, value in zip(yx, indices, topk_values):
+        y, x = yx_pt
+        if max_indice == -1 or value == 0:
+            continue
+        segment_list = []
+        for y_offset in range(-basis, basis + 1):
+            for x_offset in range(-basis, basis + 1):
+                indice = idx_map[y + y_offset, x + x_offset]
+                cnt = int(acc_map_np[y + y_offset, x + x_offset])
+                if indice != -1:
+                    segment_list.append(segments[indice])
+                if cnt > 1:
+                    check_cnt = 1
+                    current_hough = hough[indice]
+                    for new_indice, new_hough in enumerate(hough):
+                        if (current_hough == new_hough).all() and indice != new_indice:
+                            segment_list.append(segments[new_indice])
+                            check_cnt += 1
+                        if check_cnt == cnt:
+                            break
+        group_segments = np.array(segment_list).reshape([-1, 2])
+        sorted_group_segments = np.sort(group_segments, axis=0)
+        x_min, y_min = sorted_group_segments[0, :]
+        x_max, y_max = sorted_group_segments[-1, :]
+
+        deg = theta[max_indice]
+        if deg >= 90:
+            merged_segments.append([x_min, y_max, x_max, y_min])
+        else:
+            merged_segments.append([x_min, y_min, x_max, y_max])
+
+    # 2. get intersections
+    new_segments = np.array(merged_segments)  # (x1, y1, x2, y2)
+    start = new_segments[:, :2]  # (x1, y1)
+    end = new_segments[:, 2:]  # (x2, y2)
+    new_centers = (start + end) / 2.0
+    diff = start - end
+    dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
+
+    # ax + by = c
+    a = diff[:, 1]
+    b = -diff[:, 0]
+    c = a * start[:, 0] + b * start[:, 1]
+    pre_det = a[:, None] * b[None, :]
+    det = pre_det - np.transpose(pre_det)
+
+    pre_inter_y = a[:, None] * c[None, :]
+    inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
+    pre_inter_x = c[:, None] * b[None, :]
+    inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
+    inter_pts = np.concatenate([inter_x[:, :, None], inter_y[:, :, None]], axis=-1).astype('int32')
+
+    # 3. get corner information
+    # 3.1 get distance
+    '''
+    dist_segments:
+        | dist(0), dist(1), dist(2), ...|
+    dist_inter_to_segment1:
+        | dist(inter,0), dist(inter,0), dist(inter,0), ... |
+        | dist(inter,1), dist(inter,1), dist(inter,1), ... |
+        ...
+    dist_inter_to_semgnet2:
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        ...
+    '''
+
+    dist_inter_to_segment1_start = np.sqrt(
+        np.sum(((inter_pts - start[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment1_end = np.sqrt(
+        np.sum(((inter_pts - end[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_start = np.sqrt(
+        np.sum(((inter_pts - start[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_end = np.sqrt(
+        np.sum(((inter_pts - end[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
+
+    # sort ascending
+    dist_inter_to_segment1 = np.sort(
+        np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1),
+        axis=-1)  # [n_batch, n_batch, 2]
+    dist_inter_to_segment2 = np.sort(
+        np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1),
+        axis=-1)  # [n_batch, n_batch, 2]
+
+    # 3.2 get degree
+    inter_to_start = new_centers[:, None, :] - inter_pts
+    deg_inter_to_start = np.arctan2(inter_to_start[:, :, 1], inter_to_start[:, :, 0]) * 180 / np.pi
+    deg_inter_to_start[deg_inter_to_start < 0.0] += 360
+    inter_to_end = new_centers[None, :, :] - inter_pts
+    deg_inter_to_end = np.arctan2(inter_to_end[:, :, 1], inter_to_end[:, :, 0]) * 180 / np.pi
+    deg_inter_to_end[deg_inter_to_end < 0.0] += 360
+
+    '''
+    B -- G
+    |    |
+    C -- R
+    B : blue / G: green / C: cyan / R: red
+
+    0 -- 1
+    |    |
+    3 -- 2
+    '''
+    # rename variables
+    deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
+    # sort deg ascending
+    deg_sort = np.sort(np.concatenate([deg1_map[:, :, None], deg2_map[:, :, None]], axis=-1), axis=-1)
+
+    deg_diff_map = np.abs(deg1_map - deg2_map)
+    # we only consider the smallest degree of intersect
+    deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
+
+    # define available degree range
+    deg_range = [60, 120]
+
+    corner_dict = {corner_info: [] for corner_info in range(4)}
+    inter_points = []
+    for i in range(inter_pts.shape[0]):
+        for j in range(i + 1, inter_pts.shape[1]):
+            # i, j > line index, always i < j
+            x, y = inter_pts[i, j, :]
+            deg1, deg2 = deg_sort[i, j, :]
+            deg_diff = deg_diff_map[i, j]
+
+            check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
+
+            outside_ratio = params['outside_ratio']  # over ratio >>> drop it!
+            inside_ratio = params['inside_ratio']  # over ratio >>> drop it!
+            check_distance = ((dist_inter_to_segment1[i, j, 1] >= dist_segments[i] and \
+                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * outside_ratio) or \
+                              (dist_inter_to_segment1[i, j, 1] <= dist_segments[i] and \
+                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * inside_ratio)) and \
+                             ((dist_inter_to_segment2[i, j, 1] >= dist_segments[j] and \
+                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * outside_ratio) or \
+                              (dist_inter_to_segment2[i, j, 1] <= dist_segments[j] and \
+                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * inside_ratio))
+
+            if check_degree and check_distance:
+                corner_info = None
+
+                if (deg1 >= 0 and deg1 <= 45 and deg2 >= 45 and deg2 <= 120) or \
+                        (deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
+                    corner_info, color_info = 0, 'blue'
+                elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
+                    corner_info, color_info = 1, 'green'
+                elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
+                    corner_info, color_info = 2, 'black'
+                elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
+                        (deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
+                    corner_info, color_info = 3, 'cyan'
+                else:
+                    corner_info, color_info = 4, 'red'  # we don't use it
+                    continue
+
+                corner_dict[corner_info].append([x, y, i, j])
+                inter_points.append([x, y])
+
+    square_list = []
+    connect_list = []
+    segments_list = []
+    for corner0 in corner_dict[0]:
+        for corner1 in corner_dict[1]:
+            connect01 = False
+            for corner0_line in corner0[2:]:
+                if corner0_line in corner1[2:]:
+                    connect01 = True
+                    break
+            if connect01:
+                for corner2 in corner_dict[2]:
+                    connect12 = False
+                    for corner1_line in corner1[2:]:
+                        if corner1_line in corner2[2:]:
+                            connect12 = True
+                            break
+                    if connect12:
+                        for corner3 in corner_dict[3]:
+                            connect23 = False
+                            for corner2_line in corner2[2:]:
+                                if corner2_line in corner3[2:]:
+                                    connect23 = True
+                                    break
+                            if connect23:
+                                for corner3_line in corner3[2:]:
+                                    if corner3_line in corner0[2:]:
+                                        # SQUARE!!!
+                                        '''
+                                        0 -- 1
+                                        |    |
+                                        3 -- 2
+                                        square_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            ...
+                                        connect_list:
+                                            order: 01 > 12 > 23 > 30
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            ...
+                                        segments_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            ...
+                                        '''
+                                        square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
+                                        connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
+                                        segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
+
+    def check_outside_inside(segments_info, connect_idx):
+        # return 'outside or inside', min distance, cover_param, peri_param
+        if connect_idx == segments_info[0]:
+            check_dist_mat = dist_inter_to_segment1
+        else:
+            check_dist_mat = dist_inter_to_segment2
+
+        i, j = segments_info
+        min_dist, max_dist = check_dist_mat[i, j, :]
+        connect_dist = dist_segments[connect_idx]
+        if max_dist > connect_dist:
+            return 'outside', min_dist, 0, 1
+        else:
+            return 'inside', min_dist, -1, -1
+
+    top_square = None
+
+    try:
+        map_size = input_shape[0] / 2
+        squares = np.array(square_list).reshape([-1, 4, 2])
+        score_array = []
+        connect_array = np.array(connect_list)
+        segments_array = np.array(segments_list).reshape([-1, 4, 2])
+
+        # get degree of corners:
+        squares_rollup = np.roll(squares, 1, axis=1)
+        squares_rolldown = np.roll(squares, -1, axis=1)
+        vec1 = squares_rollup - squares
+        normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
+        vec2 = squares_rolldown - squares
+        normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
+        inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1)  # [n_squares, 4]
+        squares_degree = np.arccos(inner_products) * 180 / np.pi  # [n_squares, 4]
+
+        # get square score
+        overlap_scores = []
+        degree_scores = []
+        length_scores = []
+
+        for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
+            '''
+            0 -- 1
+            |    |
+            3 -- 2
+
+            # segments: [4, 2]
+            # connects: [4]
+            '''
+
+            ###################################### OVERLAP SCORES
+            cover = 0
+            perimeter = 0
+            # check 0 > 1 > 2 > 3
+            square_length = []
+
+            for start_idx in range(4):
+                end_idx = (start_idx + 1) % 4
+
+                connect_idx = connects[start_idx]  # segment idx of segment01
+                start_segments = segments[start_idx]
+                end_segments = segments[end_idx]
+
+                start_point = square[start_idx]
+                end_point = square[end_idx]
+
+                # check whether outside or inside
+                start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments,
+                                                                                                      connect_idx)
+                end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
+
+                cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
+                perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
+
+                square_length.append(
+                    dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
+
+            overlap_scores.append(cover / perimeter)
+            ######################################
+            ###################################### DEGREE SCORES
+            '''
+            deg0 vs deg2
+            deg1 vs deg3
+            '''
+            deg0, deg1, deg2, deg3 = degree
+            deg_ratio1 = deg0 / deg2
+            if deg_ratio1 > 1.0:
+                deg_ratio1 = 1 / deg_ratio1
+            deg_ratio2 = deg1 / deg3
+            if deg_ratio2 > 1.0:
+                deg_ratio2 = 1 / deg_ratio2
+            degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
+            ######################################
+            ###################################### LENGTH SCORES
+            '''
+            len0 vs len2
+            len1 vs len3
+            '''
+            len0, len1, len2, len3 = square_length
+            len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
+            len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
+            length_scores.append((len_ratio1 + len_ratio2) / 2)
+
+            ######################################
+
+        overlap_scores = np.array(overlap_scores)
+        overlap_scores /= np.max(overlap_scores)
+
+        degree_scores = np.array(degree_scores)
+        # degree_scores /= np.max(degree_scores)
+
+        length_scores = np.array(length_scores)
+
+        ###################################### AREA SCORES
+        area_scores = np.reshape(squares, [-1, 4, 2])
+        area_x = area_scores[:, :, 0]
+        area_y = area_scores[:, :, 1]
+        correction = area_x[:, -1] * area_y[:, 0] - area_y[:, -1] * area_x[:, 0]
+        area_scores = np.sum(area_x[:, :-1] * area_y[:, 1:], axis=-1) - np.sum(area_y[:, :-1] * area_x[:, 1:], axis=-1)
+        area_scores = 0.5 * np.abs(area_scores + correction)
+        area_scores /= (map_size * map_size)  # np.max(area_scores)
+        ######################################
+
+        ###################################### CENTER SCORES
+        centers = np.array([[256 // 2, 256 // 2]], dtype='float32')  # [1, 2]
+        # squares: [n, 4, 2]
+        square_centers = np.mean(squares, axis=1)  # [n, 2]
+        center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
+        center_scores = center2center / (map_size / np.sqrt(2.0))
+
+        '''
+        score_w = [overlap, degree, area, center, length]
+        '''
+        score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
+        score_array = params['w_overlap'] * overlap_scores \
+                      + params['w_degree'] * degree_scores \
+                      + params['w_area'] * area_scores \
+                      - params['w_center'] * center_scores \
+                      + params['w_length'] * length_scores
+
+        best_square = []
+
+        sorted_idx = np.argsort(score_array)[::-1]
+        score_array = score_array[sorted_idx]
+        squares = squares[sorted_idx]
+
+    except Exception as e:
+        pass
+
+    '''return list
+    merged_lines, squares, scores
+    '''
+
+    try:
+        new_segments[:, 0] = new_segments[:, 0] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:, 1] = new_segments[:, 1] * 2 / input_shape[0] * original_shape[0]
+        new_segments[:, 2] = new_segments[:, 2] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:, 3] = new_segments[:, 3] * 2 / input_shape[0] * original_shape[0]
+    except:
+        new_segments = []
+
+    try:
+        squares[:, :, 0] = squares[:, :, 0] * 2 / input_shape[1] * original_shape[1]
+        squares[:, :, 1] = squares[:, :, 1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        squares = []
+        score_array = []
+
+    try:
+        inter_points = np.array(inter_points)
+        inter_points[:, 0] = inter_points[:, 0] * 2 / input_shape[1] * original_shape[1]
+        inter_points[:, 1] = inter_points[:, 1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        inter_points = []
+
+    return new_segments, squares, score_array, inter_points

text2tex/models/ControlNet/annotator/openpose/__init__.py

@@ -0,0 +1,29 @@
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+import torch
+import numpy as np
+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')
+
+
+def apply_openpose(oriImg, hand=False):
+    oriImg = oriImg[:, :, ::-1].copy()
+    with torch.no_grad():
+        candidate, subset = body_estimation(oriImg)
+        canvas = np.zeros_like(oriImg)
+        canvas = util.draw_bodypose(canvas, candidate, subset)
+        if hand:
+            hands_list = util.handDetect(candidate, subset, oriImg)
+            all_hand_peaks = []
+            for x, y, w, is_left in hands_list:
+                peaks = hand_estimation(oriImg[y:y+w, x:x+w, :])
+                peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
+                peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
+                all_hand_peaks.append(peaks)
+            canvas = util.draw_handpose(canvas, all_hand_peaks)
+        return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())

text2tex/models/ControlNet/annotator/openpose/body.py

@@ -0,0 +1,219 @@
+import cv2
+import numpy as np
+import math
+import time
+from scipy.ndimage.filters import gaussian_filter
+import matplotlib.pyplot as plt
+import matplotlib
+import torch
+from torchvision import transforms
+
+from . import util
+from .model import bodypose_model
+
+class Body(object):
+    def __init__(self, model_path):
+        self.model = bodypose_model()
+        if torch.cuda.is_available():
+            self.model = self.model.cuda()
+            print('cuda')
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def __call__(self, oriImg):
+        # scale_search = [0.5, 1.0, 1.5, 2.0]
+        scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre1 = 0.1
+        thre2 = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            if torch.cuda.is_available():
+                data = data.cuda()
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
+            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
+            paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
+            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+            paf_avg += + paf / len(multiplier)
+
+        all_peaks = []
+        peak_counter = 0
+
+        for part in range(18):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+            map_left = np.zeros(one_heatmap.shape)
+            map_left[1:, :] = one_heatmap[:-1, :]
+            map_right = np.zeros(one_heatmap.shape)
+            map_right[:-1, :] = one_heatmap[1:, :]
+            map_up = np.zeros(one_heatmap.shape)
+            map_up[:, 1:] = one_heatmap[:, :-1]
+            map_down = np.zeros(one_heatmap.shape)
+            map_down[:, :-1] = one_heatmap[:, 1:]
+
+            peaks_binary = np.logical_and.reduce(
+                (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
+            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]))  # note reverse
+            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+            peak_id = range(peak_counter, peak_counter + len(peaks))
+            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
+
+            all_peaks.append(peaks_with_score_and_id)
+            peak_counter += len(peaks)
+
+        # find connection in the specified sequence, center 29 is in the position 15
+        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+                   [1, 16], [16, 18], [3, 17], [6, 18]]
+        # the middle joints heatmap correpondence
+        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
+                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
+                  [55, 56], [37, 38], [45, 46]]
+
+        connection_all = []
+        special_k = []
+        mid_num = 10
+
+        for k in range(len(mapIdx)):
+            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+            candA = all_peaks[limbSeq[k][0] - 1]
+            candB = all_peaks[limbSeq[k][1] - 1]
+            nA = len(candA)
+            nB = len(candB)
+            indexA, indexB = limbSeq[k]
+            if (nA != 0 and nB != 0):
+                connection_candidate = []
+                for i in range(nA):
+                    for j in range(nB):
+                        vec = np.subtract(candB[j][:2], candA[i][:2])
+                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+                        norm = max(0.001, norm)
+                        vec = np.divide(vec, norm)
+
+                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
+                                            np.linspace(candA[i][1], candB[j][1], num=mid_num)))
+
+                        vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
+                                          for I in range(len(startend))])
+                        vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
+                                          for I in range(len(startend))])
+
+                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
+                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
+                            0.5 * oriImg.shape[0] / norm - 1, 0)
+                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
+                        criterion2 = score_with_dist_prior > 0
+                        if criterion1 and criterion2:
+                            connection_candidate.append(
+                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
+
+                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
+                connection = np.zeros((0, 5))
+                for c in range(len(connection_candidate)):
+                    i, j, s = connection_candidate[c][0:3]
+                    if (i not in connection[:, 3] and j not in connection[:, 4]):
+                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
+                        if (len(connection) >= min(nA, nB)):
+                            break
+
+                connection_all.append(connection)
+            else:
+                special_k.append(k)
+                connection_all.append([])
+
+        # last number in each row is the total parts number of that person
+        # the second last number in each row is the score of the overall configuration
+        subset = -1 * np.ones((0, 20))
+        candidate = np.array([item for sublist in all_peaks for item in sublist])
+
+        for k in range(len(mapIdx)):
+            if k not in special_k:
+                partAs = connection_all[k][:, 0]
+                partBs = connection_all[k][:, 1]
+                indexA, indexB = np.array(limbSeq[k]) - 1
+
+                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
+                    found = 0
+                    subset_idx = [-1, -1]
+                    for j in range(len(subset)):  # 1:size(subset,1):
+                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
+                            subset_idx[found] = j
+                            found += 1
+
+                    if found == 1:
+                        j = subset_idx[0]
+                        if subset[j][indexB] != partBs[i]:
+                            subset[j][indexB] = partBs[i]
+                            subset[j][-1] += 1
+                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+                    elif found == 2:  # if found 2 and disjoint, merge them
+                        j1, j2 = subset_idx
+                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
+                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
+                            subset[j1][:-2] += (subset[j2][:-2] + 1)
+                            subset[j1][-2:] += subset[j2][-2:]
+                            subset[j1][-2] += connection_all[k][i][2]
+                            subset = np.delete(subset, j2, 0)
+                        else:  # as like found == 1
+                            subset[j1][indexB] = partBs[i]
+                            subset[j1][-1] += 1
+                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+
+                    # if find no partA in the subset, create a new subset
+                    elif not found and k < 17:
+                        row = -1 * np.ones(20)
+                        row[indexA] = partAs[i]
+                        row[indexB] = partBs[i]
+                        row[-1] = 2
+                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
+                        subset = np.vstack([subset, row])
+        # delete some rows of subset which has few parts occur
+        deleteIdx = []
+        for i in range(len(subset)):
+            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+                deleteIdx.append(i)
+        subset = np.delete(subset, deleteIdx, axis=0)
+
+        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
+        # candidate: x, y, score, id
+        return candidate, subset
+
+if __name__ == "__main__":
+    body_estimation = Body('../model/body_pose_model.pth')
+
+    test_image = '../images/ski.jpg'
+    oriImg = cv2.imread(test_image)  # B,G,R order
+    candidate, subset = body_estimation(oriImg)
+    canvas = util.draw_bodypose(oriImg, candidate, subset)
+    plt.imshow(canvas[:, :, [2, 1, 0]])
+    plt.show()

text2tex/models/ControlNet/annotator/openpose/hand.py

@@ -0,0 +1,86 @@
+import cv2
+import json
+import numpy as np
+import math
+import time
+from scipy.ndimage.filters import gaussian_filter
+import matplotlib.pyplot as plt
+import matplotlib
+import torch
+from skimage.measure import label
+
+from .model import handpose_model
+from . import util
+
+class Hand(object):
+    def __init__(self, model_path):
+        self.model = handpose_model()
+        if torch.cuda.is_available():
+            self.model = self.model.cuda()
+            print('cuda')
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def __call__(self, oriImg):
+        scale_search = [0.5, 1.0, 1.5, 2.0]
+        # scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 22))
+        # paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            if torch.cuda.is_available():
+                data = data.cuda()
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                output = self.model(data).cpu().numpy()
+                # output = self.model(data).numpy()q
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+            heatmap_avg += heatmap / len(multiplier)
+
+        all_peaks = []
+        for part in range(21):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
+            # 全部小于阈值
+            if np.sum(binary) == 0:
+                all_peaks.append([0, 0])
+                continue
+            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
+            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
+            label_img[label_img != max_index] = 0
+            map_ori[label_img == 0] = 0
+
+            y, x = util.npmax(map_ori)
+            all_peaks.append([x, y])
+        return np.array(all_peaks)
+
+if __name__ == "__main__":
+    hand_estimation = Hand('../model/hand_pose_model.pth')
+
+    # test_image = '../images/hand.jpg'
+    test_image = '../images/hand.jpg'
+    oriImg = cv2.imread(test_image)  # B,G,R order
+    peaks = hand_estimation(oriImg)
+    canvas = util.draw_handpose(oriImg, peaks, True)
+    cv2.imshow('', canvas)
+    cv2.waitKey(0)

text2tex/models/ControlNet/annotator/openpose/model.py

@@ -0,0 +1,219 @@
+import torch
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+def make_layers(block, no_relu_layers):
+    layers = []
+    for layer_name, v in block.items():
+        if 'pool' in layer_name:
+            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
+                                    padding=v[2])
+            layers.append((layer_name, layer))
+        else:
+            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
+                               kernel_size=v[2], stride=v[3],
+                               padding=v[4])
+            layers.append((layer_name, conv2d))
+            if layer_name not in no_relu_layers:
+                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
+
+    return nn.Sequential(OrderedDict(layers))
+
+class bodypose_model(nn.Module):
+    def __init__(self):
+        super(bodypose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
+                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
+                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
+                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
+        blocks = {}
+        block0 = OrderedDict([
+                      ('conv1_1', [3, 64, 3, 1, 1]),
+                      ('conv1_2', [64, 64, 3, 1, 1]),
+                      ('pool1_stage1', [2, 2, 0]),
+                      ('conv2_1', [64, 128, 3, 1, 1]),
+                      ('conv2_2', [128, 128, 3, 1, 1]),
+                      ('pool2_stage1', [2, 2, 0]),
+                      ('conv3_1', [128, 256, 3, 1, 1]),
+                      ('conv3_2', [256, 256, 3, 1, 1]),
+                      ('conv3_3', [256, 256, 3, 1, 1]),
+                      ('conv3_4', [256, 256, 3, 1, 1]),
+                      ('pool3_stage1', [2, 2, 0]),
+                      ('conv4_1', [256, 512, 3, 1, 1]),
+                      ('conv4_2', [512, 512, 3, 1, 1]),
+                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
+                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
+                  ])
+
+
+        # Stage 1
+        block1_1 = OrderedDict([
+                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
+                    ])
+
+        block1_2 = OrderedDict([
+                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
+                    ])
+        blocks['block1_1'] = block1_1
+        blocks['block1_2'] = block1_2
+
+        self.model0 = make_layers(block0, no_relu_layers)
+
+        # Stages 2 - 6
+        for i in range(2, 7):
+            blocks['block%d_1' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
+                ])
+
+            blocks['block%d_2' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_1 = blocks['block1_1']
+        self.model2_1 = blocks['block2_1']
+        self.model3_1 = blocks['block3_1']
+        self.model4_1 = blocks['block4_1']
+        self.model5_1 = blocks['block5_1']
+        self.model6_1 = blocks['block6_1']
+
+        self.model1_2 = blocks['block1_2']
+        self.model2_2 = blocks['block2_2']
+        self.model3_2 = blocks['block3_2']
+        self.model4_2 = blocks['block4_2']
+        self.model5_2 = blocks['block5_2']
+        self.model6_2 = blocks['block6_2']
+
+
+    def forward(self, x):
+
+        out1 = self.model0(x)
+
+        out1_1 = self.model1_1(out1)
+        out1_2 = self.model1_2(out1)
+        out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+        out2_1 = self.model2_1(out2)
+        out2_2 = self.model2_2(out2)
+        out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+        out3_1 = self.model3_1(out3)
+        out3_2 = self.model3_2(out3)
+        out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+        out4_1 = self.model4_1(out4)
+        out4_2 = self.model4_2(out4)
+        out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+        out5_1 = self.model5_1(out5)
+        out5_2 = self.model5_2(out5)
+        out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+        out6_1 = self.model6_1(out6)
+        out6_2 = self.model6_2(out6)
+
+        return out6_1, out6_2
+
+class handpose_model(nn.Module):
+    def __init__(self):
+        super(handpose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
+                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
+        # stage 1
+        block1_0 = OrderedDict([
+                ('conv1_1', [3, 64, 3, 1, 1]),
+                ('conv1_2', [64, 64, 3, 1, 1]),
+                ('pool1_stage1', [2, 2, 0]),
+                ('conv2_1', [64, 128, 3, 1, 1]),
+                ('conv2_2', [128, 128, 3, 1, 1]),
+                ('pool2_stage1', [2, 2, 0]),
+                ('conv3_1', [128, 256, 3, 1, 1]),
+                ('conv3_2', [256, 256, 3, 1, 1]),
+                ('conv3_3', [256, 256, 3, 1, 1]),
+                ('conv3_4', [256, 256, 3, 1, 1]),
+                ('pool3_stage1', [2, 2, 0]),
+                ('conv4_1', [256, 512, 3, 1, 1]),
+                ('conv4_2', [512, 512, 3, 1, 1]),
+                ('conv4_3', [512, 512, 3, 1, 1]),
+                ('conv4_4', [512, 512, 3, 1, 1]),
+                ('conv5_1', [512, 512, 3, 1, 1]),
+                ('conv5_2', [512, 512, 3, 1, 1]),
+                ('conv5_3_CPM', [512, 128, 3, 1, 1])
+            ])
+
+        block1_1 = OrderedDict([
+            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
+            ('conv6_2_CPM', [512, 22, 1, 1, 0])
+        ])
+
+        blocks = {}
+        blocks['block1_0'] = block1_0
+        blocks['block1_1'] = block1_1
+
+        # stage 2-6
+        for i in range(2, 7):
+            blocks['block%d' % i] = OrderedDict([
+                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_0 = blocks['block1_0']
+        self.model1_1 = blocks['block1_1']
+        self.model2 = blocks['block2']
+        self.model3 = blocks['block3']
+        self.model4 = blocks['block4']
+        self.model5 = blocks['block5']
+        self.model6 = blocks['block6']
+
+    def forward(self, x):
+        out1_0 = self.model1_0(x)
+        out1_1 = self.model1_1(out1_0)
+        concat_stage2 = torch.cat([out1_1, out1_0], 1)
+        out_stage2 = self.model2(concat_stage2)
+        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+        out_stage3 = self.model3(concat_stage3)
+        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+        out_stage4 = self.model4(concat_stage4)
+        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+        out_stage5 = self.model5(concat_stage5)
+        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+        out_stage6 = self.model6(concat_stage6)
+        return out_stage6
+
+

text2tex/models/ControlNet/annotator/openpose/util.py

@@ -0,0 +1,164 @@
+import math
+import numpy as np
+import matplotlib
+import cv2
+
+
+def padRightDownCorner(img, stride, padValue):
+    h = img.shape[0]
+    w = img.shape[1]
+
+    pad = 4 * [None]
+    pad[0] = 0 # up
+    pad[1] = 0 # left
+    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+    img_padded = img
+    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
+    img_padded = np.concatenate((pad_up, img_padded), axis=0)
+    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
+    img_padded = np.concatenate((pad_left, img_padded), axis=1)
+    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
+    img_padded = np.concatenate((img_padded, pad_down), axis=0)
+    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
+    img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+    return img_padded, pad
+
+# transfer caffe model to pytorch which will match the layer name
+def transfer(model, model_weights):
+    transfered_model_weights = {}
+    for weights_name in model.state_dict().keys():
+        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
+    return transfered_model_weights
+
+# draw the body keypoint and lims
+def draw_bodypose(canvas, candidate, subset):
+    stickwidth = 4
+    limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+               [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+               [1, 16], [16, 18], [3, 17], [6, 18]]
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+    for i in range(18):
+        for n in range(len(subset)):
+            index = int(subset[n][i])
+            if index == -1:
+                continue
+            x, y = candidate[index][0:2]
+            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
+    for i in range(17):
+        for n in range(len(subset)):
+            index = subset[n][np.array(limbSeq[i]) - 1]
+            if -1 in index:
+                continue
+            cur_canvas = canvas.copy()
+            Y = candidate[index.astype(int), 0]
+            X = candidate[index.astype(int), 1]
+            mX = np.mean(X)
+            mY = np.mean(Y)
+            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+            cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
+            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
+    # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
+    # plt.imshow(canvas[:, :, [2, 1, 0]])
+    return canvas
+
+
+# image drawed by opencv is not good.
+def draw_handpose(canvas, all_hand_peaks, show_number=False):
+    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
+             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
+
+    for peaks in all_hand_peaks:
+        for ie, e in enumerate(edges):
+            if np.sum(np.all(peaks[e], axis=1)==0)==0:
+                x1, y1 = peaks[e[0]]
+                x2, y2 = peaks[e[1]]
+                cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)
+
+        for i, keyponit in enumerate(peaks):
+            x, y = keyponit
+            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+            if show_number:
+                cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
+    return canvas
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(candidate, subset, oriImg):
+    # right hand: wrist 4, elbow 3, shoulder 2
+    # left hand: wrist 7, elbow 6, shoulder 5
+    ratioWristElbow = 0.33
+    detect_result = []
+    image_height, image_width = oriImg.shape[0:2]
+    for person in subset.astype(int):
+        # if any of three not detected
+        has_left = np.sum(person[[5, 6, 7]] == -1) == 0
+        has_right = np.sum(person[[2, 3, 4]] == -1) == 0
+        if not (has_left or has_right):
+            continue
+        hands = []
+        #left hand
+        if has_left:
+            left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
+            x1, y1 = candidate[left_shoulder_index][:2]
+            x2, y2 = candidate[left_elbow_index][:2]
+            x3, y3 = candidate[left_wrist_index][:2]
+            hands.append([x1, y1, x2, y2, x3, y3, True])
+        # right hand
+        if has_right:
+            right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
+            x1, y1 = candidate[right_shoulder_index][:2]
+            x2, y2 = candidate[right_elbow_index][:2]
+            x3, y3 = candidate[right_wrist_index][:2]
+            hands.append([x1, y1, x2, y2, x3, y3, False])
+
+        for x1, y1, x2, y2, x3, y3, is_left in hands:
+            # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
+            # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
+            # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
+            # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
+            # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
+            # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
+            x = x3 + ratioWristElbow * (x3 - x2)
+            y = y3 + ratioWristElbow * (y3 - y2)
+            distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+            distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+            width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+            # x-y refers to the center --> offset to topLeft point
+            # handRectangle.x -= handRectangle.width / 2.f;
+            # handRectangle.y -= handRectangle.height / 2.f;
+            x -= width / 2
+            y -= width / 2  # width = height
+            # overflow the image
+            if x < 0: x = 0
+            if y < 0: y = 0
+            width1 = width
+            width2 = width
+            if x + width > image_width: width1 = image_width - x
+            if y + width > image_height: width2 = image_height - y
+            width = min(width1, width2)
+            # the max hand box value is 20 pixels
+            if width >= 20:
+                detect_result.append([int(x), int(y), int(width), is_left])
+
+    '''
+    return value: [[x, y, w, True if left hand else False]].
+    width=height since the network require squared input.
+    x, y is the coordinate of top left 
+    '''
+    return detect_result
+
+# get max index of 2d array
+def npmax(array):
+    arrayindex = array.argmax(1)
+    arrayvalue = array.max(1)
+    i = arrayvalue.argmax()
+    j = arrayindex[i]
+    return i, j

text2tex/models/ControlNet/annotator/uniformer/__init__.py

@@ -0,0 +1,13 @@
+from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
+from annotator.uniformer.mmseg.core.evaluation import get_palette
+
+
+checkpoint_file = "annotator/ckpts/upernet_global_small.pth"
+config_file = 'annotator/uniformer/exp/upernet_global_small/config.py'
+model = init_segmentor(config_file, checkpoint_file).cuda()
+
+
+def apply_uniformer(img):
+    result = inference_segmentor(model, img)
+    res_img = show_result_pyplot(model, img, result, get_palette('ade'), opacity=1)
+    return res_img

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/ade20k.py

@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'ADE20KDataset'
+data_root = 'data/ade/ADEChallengeData2016'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=True),
+    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/chase_db1.py

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+# dataset settings
+dataset_type = 'ChaseDB1Dataset'
+data_root = 'data/CHASE_DB1'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_scale = (960, 999)
+crop_size = (128, 128)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=40000,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            img_dir='images/training',
+            ann_dir='annotations/training',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/cityscapes.py

@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/train',
+        ann_dir='gtFine/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/cityscapes_769x769.py

@@ -0,0 +1,35 @@
+_base_ = './cityscapes.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (769, 769)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2049, 1025),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/drive.py

@@ -0,0 +1,59 @@
+# dataset settings
+dataset_type = 'DRIVEDataset'
+data_root = 'data/DRIVE'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_scale = (584, 565)
+crop_size = (64, 64)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=40000,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            img_dir='images/training',
+            ann_dir='annotations/training',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/hrf.py

@@ -0,0 +1,59 @@
+# dataset settings
+dataset_type = 'HRFDataset'
+data_root = 'data/HRF'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_scale = (2336, 3504)
+crop_size = (256, 256)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=40000,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            img_dir='images/training',
+            ann_dir='annotations/training',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

text2tex/models/ControlNet/annotator/uniformer/configs/_base_/datasets/pascal_context.py

@@ -0,0 +1,60 @@
+# dataset settings
+dataset_type = 'PascalContextDataset'
+data_root = 'data/VOCdevkit/VOC2010/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+img_scale = (520, 520)
+crop_size = (480, 480)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='JPEGImages',
+        ann_dir='SegmentationClassContext',
+        split='ImageSets/SegmentationContext/train.txt',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='JPEGImages',
+        ann_dir='SegmentationClassContext',
+        split='ImageSets/SegmentationContext/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='JPEGImages',
+        ann_dir='SegmentationClassContext',
+        split='ImageSets/SegmentationContext/val.txt',
+        pipeline=test_pipeline))