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run.py
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run.py
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import os
import sys
import folder_paths
import torch
comfy_path = os.path.dirname(folder_paths.__file__)
unique3d_path = f'{comfy_path}/custom_nodes/ComfyUI-Unique3D'
output_path = f'{comfy_path}/output/Unique3D/'
os.makedirs(output_path, exist_ok=True)
yaml_path = os.path.join(unique3d_path, 'app', 'custom_models', 'image2mvimage.yaml')
with open(yaml_path, 'r', encoding='utf-8') as file:
content = file.read()
content = content.replace('#YOUR_COMFYUI_PATH#', unique3d_path.replace(os.path.sep, '/'))
with open(yaml_path, 'w', encoding='utf-8') as file:
file.write(content)
sys.path.append(unique3d_path)
python_executable_path = sys.executable
print("python_executable_path: ", python_executable_path)
python_embeded_path = os.path.dirname(python_executable_path)
print("python_embeded_path: ", python_embeded_path)
python_scripts_path = os.path.join(python_embeded_path, 'Scripts')
os.environ['PATH'] = os.environ['PATH'] + ";" + python_scripts_path
from PIL import Image
from pytorch3d.structures import Meshes
import numpy as np
class Unique3DLoadPipeline:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ip_adapter": ([True, False],),
"plus_model": ([True, False],),
}
}
RETURN_TYPES = ("Unique3DPipeline",)
RETURN_NAMES = ("pipe",)
FUNCTION = "run"
CATEGORY = "Unique3D"
def run(self, ip_adapter, plus_model):
base_model = "runwayml/stable-diffusion-v1-5"
from .scripts.sd_model_zoo import load_common_sd15_pipe
pipe = load_common_sd15_pipe(
base_model=base_model, ip_adapter=ip_adapter, plus_model=plus_model,
controlnet=f"{unique3d_path}/ckpt/controlnet-tile")
return (pipe,)
class Unique3DRunMVPrediction:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"input_processing": ([True, False],),
},
}
RETURN_TYPES = ("PILS", "PIL",)
RETURN_NAMES = ("rgb_pils", "front_pil",)
FUNCTION = "run"
CATEGORY = "Unique3D"
def run(self, images, input_processing):
img_batch_np = images.cpu().detach().numpy().__mul__(255.).astype(np.uint8)
preview_img = Image.fromarray(img_batch_np[0])
if preview_img.size[0] <= 512:
from .scripts.refine_lr_to_sr import run_sr_fast
preview_img = run_sr_fast([preview_img])[0]
seed = -1
from .app.custom_models.mvimg_prediction import run_mvprediction
rgb_pils, front_pil = run_mvprediction(preview_img, input_processing, int(seed))
return (rgb_pils, front_pil,)
class Unique3DRunGeoReconstruct:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"rgb_pils": ("PILS",),
"front_pil": ("PIL",),
"pipe": ("Unique3DPipeline",),
"do_refine": ([True, False],),
"render_video": ([True, False],),
"predict_normal": ([True, False],),
"init_type": (["std", "thin", "ball"],),
},
}
RETURN_TYPES = ("IMAGE", "STRING",)
RETURN_NAMES = ("images", "mesh_path",)
FUNCTION = "run"
CATEGORY = "Unique3D"
def run(self, images, rgb_pils, front_pil, pipe, do_refine, render_video, predict_normal, init_type):
from .scripts.multiview_inference import geo_reconstruct
expansion_weight = 0.1
new_meshes, img_list, rm_normals = geo_reconstruct(
pipe, rgb_pils, None, front_pil, do_refine=do_refine, predict_normal=predict_normal,
expansion_weight=expansion_weight, init_type=init_type)
vertices = new_meshes.verts_packed()
vertices = vertices / 2 * 1.35
vertices[..., [0, 2]] = - vertices[..., [0, 2]]
new_meshes = Meshes(verts=[vertices], faces=new_meshes.faces_list(), textures=new_meshes.textures)
output_prefix = f'{output_path}/generated'
from .scripts.utils import save_glb_and_video
ret_mesh, video = save_glb_and_video(
output_prefix, new_meshes, with_timestamp=True, dist=3.5,
fov_in_degrees=2 / 1.35, cam_type="ortho", export_video=render_video)
return images, ret_mesh,
NODE_CLASS_MAPPINGS = {
"Unique3DRun - MVPrediction": Unique3DRunMVPrediction,
"Unique3DRun - Geo Reconstruct": Unique3DRunGeoReconstruct,
"Unique3DLoadPipeline": Unique3DLoadPipeline
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Unique3DRun - MVPrediction": Unique3DRunMVPrediction,
"Unique3DRun - Geo Reconstruct": Unique3DRunGeoReconstruct,
"Unique3DLoadPipeline": Unique3DLoadPipeline
}