Use diffusers
Browse files- app_image_to_3d.py +1 -3
- model.py +34 -117
- requirements.txt +2 -2
app_image_to_3d.py
CHANGED
@@ -24,9 +24,7 @@ def create_demo(model: Model) -> gr.Blocks:
|
|
24 |
|
25 |
with gr.Blocks() as demo:
|
26 |
with gr.Box():
|
27 |
-
image = gr.Image(label='Input image',
|
28 |
-
show_label=False,
|
29 |
-
type='filepath')
|
30 |
run_button = gr.Button('Run')
|
31 |
result = gr.Model3D(label='Result', show_label=False)
|
32 |
with gr.Accordion('Advanced options', open=False):
|
|
|
24 |
|
25 |
with gr.Blocks() as demo:
|
26 |
with gr.Box():
|
27 |
+
image = gr.Image(label='Input image', show_label=False, type='pil')
|
|
|
|
|
28 |
run_button = gr.Button('Run')
|
29 |
result = gr.Model3D(label='Result', show_label=False)
|
30 |
with gr.Accordion('Advanced options', open=False):
|
model.py
CHANGED
@@ -1,99 +1,33 @@
|
|
1 |
import tempfile
|
2 |
|
3 |
import numpy as np
|
|
|
4 |
import torch
|
5 |
import trimesh
|
6 |
-
from
|
7 |
-
from
|
8 |
-
from shap_e.models.download import load_config, load_model
|
9 |
-
from shap_e.models.nn.camera import (DifferentiableCameraBatch,
|
10 |
-
DifferentiableProjectiveCamera)
|
11 |
-
from shap_e.models.transmitter.base import Transmitter, VectorDecoder
|
12 |
-
from shap_e.rendering.torch_mesh import TorchMesh
|
13 |
-
from shap_e.util.collections import AttrDict
|
14 |
-
from shap_e.util.image_util import load_image
|
15 |
-
|
16 |
-
|
17 |
-
# Copied from https://github.com/openai/shap-e/blob/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/util/notebooks.py#L15-L42
|
18 |
-
def create_pan_cameras(size: int,
|
19 |
-
device: torch.device) -> DifferentiableCameraBatch:
|
20 |
-
origins = []
|
21 |
-
xs = []
|
22 |
-
ys = []
|
23 |
-
zs = []
|
24 |
-
for theta in np.linspace(0, 2 * np.pi, num=20):
|
25 |
-
z = np.array([np.sin(theta), np.cos(theta), -0.5])
|
26 |
-
z /= np.sqrt(np.sum(z**2))
|
27 |
-
origin = -z * 4
|
28 |
-
x = np.array([np.cos(theta), -np.sin(theta), 0.0])
|
29 |
-
y = np.cross(z, x)
|
30 |
-
origins.append(origin)
|
31 |
-
xs.append(x)
|
32 |
-
ys.append(y)
|
33 |
-
zs.append(z)
|
34 |
-
return DifferentiableCameraBatch(
|
35 |
-
shape=(1, len(xs)),
|
36 |
-
flat_camera=DifferentiableProjectiveCamera(
|
37 |
-
origin=torch.from_numpy(np.stack(origins,
|
38 |
-
axis=0)).float().to(device),
|
39 |
-
x=torch.from_numpy(np.stack(xs, axis=0)).float().to(device),
|
40 |
-
y=torch.from_numpy(np.stack(ys, axis=0)).float().to(device),
|
41 |
-
z=torch.from_numpy(np.stack(zs, axis=0)).float().to(device),
|
42 |
-
width=size,
|
43 |
-
height=size,
|
44 |
-
x_fov=0.7,
|
45 |
-
y_fov=0.7,
|
46 |
-
),
|
47 |
-
)
|
48 |
-
|
49 |
-
|
50 |
-
# Copied from https://github.com/openai/shap-e/blob/8625e7c15526d8510a2292f92165979268d0e945/shap_e/util/notebooks.py#LL64C1-L76C33
|
51 |
-
@torch.no_grad()
|
52 |
-
def decode_latent_mesh(
|
53 |
-
xm: Transmitter | VectorDecoder,
|
54 |
-
latent: torch.Tensor,
|
55 |
-
) -> TorchMesh:
|
56 |
-
decoded = xm.renderer.render_views(
|
57 |
-
AttrDict(cameras=create_pan_cameras(
|
58 |
-
2, latent.device)), # lowest resolution possible
|
59 |
-
params=(xm.encoder if isinstance(xm, Transmitter) else
|
60 |
-
xm).bottleneck_to_params(latent[None]),
|
61 |
-
options=AttrDict(rendering_mode='stf', render_with_direction=False),
|
62 |
-
)
|
63 |
-
return decoded.raw_meshes[0]
|
64 |
|
65 |
|
66 |
class Model:
|
67 |
def __init__(self):
|
68 |
self.device = torch.device(
|
69 |
'cuda' if torch.cuda.is_available() else 'cpu')
|
70 |
-
self.
|
71 |
-
|
72 |
-
self.
|
73 |
-
self.model_image = None
|
74 |
|
75 |
-
|
76 |
-
|
77 |
-
|
78 |
-
self.model_text = load_model(model_name, device=self.device)
|
79 |
-
elif model_name == 'image300M' and self.model_image is None:
|
80 |
-
self.model_image = load_model(model_name, device=self.device)
|
81 |
|
82 |
-
def to_glb(self,
|
83 |
-
|
84 |
-
delete=False,
|
85 |
-
mode='w+b')
|
86 |
-
decode_latent_mesh(self.xm, latent).tri_mesh().write_ply(ply_path)
|
87 |
-
|
88 |
-
mesh = trimesh.load(ply_path.name)
|
89 |
rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])
|
90 |
mesh = mesh.apply_transform(rot)
|
91 |
rot = trimesh.transformations.rotation_matrix(np.pi, [0, 1, 0])
|
92 |
mesh = mesh.apply_transform(rot)
|
93 |
-
|
94 |
mesh_path = tempfile.NamedTemporaryFile(suffix='.glb', delete=False)
|
95 |
mesh.export(mesh_path.name, file_type='glb')
|
96 |
-
|
97 |
return mesh_path.name
|
98 |
|
99 |
def run_text(self,
|
@@ -101,48 +35,31 @@ class Model:
|
|
101 |
seed: int = 0,
|
102 |
guidance_scale: float = 15.0,
|
103 |
num_steps: int = 64) -> str:
|
104 |
-
self.
|
105 |
-
|
106 |
-
|
107 |
-
|
108 |
-
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
use_fp16=True,
|
116 |
-
use_karras=True,
|
117 |
-
karras_steps=num_steps,
|
118 |
-
sigma_min=1e-3,
|
119 |
-
sigma_max=160,
|
120 |
-
s_churn=0,
|
121 |
-
)
|
122 |
-
return self.to_glb(latents[0])
|
123 |
|
124 |
def run_image(self,
|
125 |
-
|
126 |
seed: int = 0,
|
127 |
guidance_scale: float = 3.0,
|
128 |
num_steps: int = 64) -> str:
|
129 |
-
self.
|
130 |
-
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
clip_denoised=True,
|
141 |
-
use_fp16=True,
|
142 |
-
use_karras=True,
|
143 |
-
karras_steps=num_steps,
|
144 |
-
sigma_min=1e-3,
|
145 |
-
sigma_max=160,
|
146 |
-
s_churn=0,
|
147 |
-
)
|
148 |
-
return self.to_glb(latents[0])
|
|
|
1 |
import tempfile
|
2 |
|
3 |
import numpy as np
|
4 |
+
import PIL.Image
|
5 |
import torch
|
6 |
import trimesh
|
7 |
+
from diffusers import ShapEImg2ImgPipeline, ShapEPipeline
|
8 |
+
from diffusers.utils import export_to_ply
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
9 |
|
10 |
|
11 |
class Model:
|
12 |
def __init__(self):
|
13 |
self.device = torch.device(
|
14 |
'cuda' if torch.cuda.is_available() else 'cpu')
|
15 |
+
self.pipe = ShapEPipeline.from_pretrained('YiYiXu/shap-e',
|
16 |
+
torch_dtype=torch.float16)
|
17 |
+
self.pipe.to(self.device)
|
|
|
18 |
|
19 |
+
self.pipe_img = ShapEImg2ImgPipeline.from_pretrained(
|
20 |
+
'YiYiXu/shap-e-img2img', torch_dtype=torch.float16)
|
21 |
+
self.pipe_img.to(self.device)
|
|
|
|
|
|
|
22 |
|
23 |
+
def to_glb(self, ply_path: str) -> str:
|
24 |
+
mesh = trimesh.load(ply_path)
|
|
|
|
|
|
|
|
|
|
|
25 |
rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])
|
26 |
mesh = mesh.apply_transform(rot)
|
27 |
rot = trimesh.transformations.rotation_matrix(np.pi, [0, 1, 0])
|
28 |
mesh = mesh.apply_transform(rot)
|
|
|
29 |
mesh_path = tempfile.NamedTemporaryFile(suffix='.glb', delete=False)
|
30 |
mesh.export(mesh_path.name, file_type='glb')
|
|
|
31 |
return mesh_path.name
|
32 |
|
33 |
def run_text(self,
|
|
|
35 |
seed: int = 0,
|
36 |
guidance_scale: float = 15.0,
|
37 |
num_steps: int = 64) -> str:
|
38 |
+
generator = torch.Generator(device=self.device).manual_seed(seed)
|
39 |
+
images = self.pipe(prompt,
|
40 |
+
generator=generator,
|
41 |
+
guidance_scale=guidance_scale,
|
42 |
+
num_inference_steps=num_steps,
|
43 |
+
output_type='mesh').images
|
44 |
+
ply_path = tempfile.NamedTemporaryFile(suffix='.ply',
|
45 |
+
delete=False,
|
46 |
+
mode='w+b')
|
47 |
+
export_to_ply(images[0], ply_path.name)
|
48 |
+
return self.to_glb(ply_path.name)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
49 |
|
50 |
def run_image(self,
|
51 |
+
image: PIL.Image.Image,
|
52 |
seed: int = 0,
|
53 |
guidance_scale: float = 3.0,
|
54 |
num_steps: int = 64) -> str:
|
55 |
+
generator = torch.Generator(device=self.device).manual_seed(seed)
|
56 |
+
images = self.pipe_img(image,
|
57 |
+
generator=generator,
|
58 |
+
guidance_scale=guidance_scale,
|
59 |
+
num_inference_steps=num_steps,
|
60 |
+
output_type='mesh').images
|
61 |
+
ply_path = tempfile.NamedTemporaryFile(suffix='.ply',
|
62 |
+
delete=False,
|
63 |
+
mode='w+b')
|
64 |
+
export_to_ply(images[0], ply_path.name)
|
65 |
+
return self.to_glb(ply_path.name)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
requirements.txt
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
-
git+https://github.com/
|
2 |
gradio==3.36.1
|
3 |
torch==2.0.1
|
4 |
torchvision==0.15.2
|
5 |
-
trimesh==3.22.
|
|
|
1 |
+
git+https://github.com/huggingface/diffusers@shap-ee-mesh
|
2 |
gradio==3.36.1
|
3 |
torch==2.0.1
|
4 |
torchvision==0.15.2
|
5 |
+
trimesh==3.22.3
|