File size: 7,254 Bytes
7c1eee1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f8f1d3f
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f8f1d3f
 
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
ccbca0a
 
 
 
 
 
 
 
f8f1d3f
5ff1dba
f8f1d3f
 
 
 
 
 
5ff1dba
f8f1d3f
 
 
 
 
7c1eee1
 
 
 
 
 
 
f8f1d3f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
 
 
 
 
 
 
 
3e3ca46
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
 
d75a844
7c1eee1
 
3e3ca46
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f8f1d3f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
52a119d
 
 
 
 
 
 
7c1eee1
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
from collections import namedtuple
from typing import List

ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"])
model_info = {}

def register_model_info(
    full_names: List[str], simple_name: str, link: str, description: str
):
    info = ModelInfo(simple_name, link, description)

    for full_name in full_names:
        model_info[full_name] = info

def get_model_info(name: str) -> ModelInfo:
    if name in model_info:
        return model_info[name]
    else:
        # To fix this, please use `register_model_info` to register your model
        return ModelInfo(
            name, "", "Register the description at fastchat/model/model_registry.py"
        )

def get_model_description_md(model_list):
    model_description_md = """
| | | |
| ---- | ---- | ---- |
"""
    ct = 0
    visited = set()
    for i, name in enumerate(model_list):
        minfo = get_model_info(name)
        if minfo.simple_name in visited:
            continue
        visited.add(minfo.simple_name)
        one_model_md = f"[{minfo.simple_name}]({minfo.link}): {minfo.description}"

        if ct % 3 == 0:
            model_description_md += "|"
        model_description_md += f" {one_model_md} |"
        if ct % 3 == 2:
            model_description_md += "\n"
        ct += 1
    return model_description_md

# regist text-to-shape generation models

register_model_info(
    ["dreamfusion"],
    "DreamFusion",
    "https://dreamfusion3d.github.io/",
    "Text-to-3D using 2D Diffusion and SDS Loss",
)

register_model_info(
    ["fantasia3d"],
    "Fantasia3D",
    "https://fantasia3d.github.io/",
    "Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation",
)

register_model_info(
    ["instant3d"],
    "Instant3D",
    "https://jiahao.ai/instant3d/",
    "Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model",
)

register_model_info(
    ["latent-nerf"],
    "Latent-NeRF",
    "https://github.com/eladrich/latent-nerf",
    "Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures",
)

register_model_info(
    ["magic3d"],
    "Magic3D",
    "https://research.nvidia.com/labs/dir/magic3d/",
    "High-Resolution Text-to-3D Content Creation",
)

register_model_info(
    ["geodream"],
    "GeoDream",
    "https://mabaorui.github.io/GeoDream_page/",
    "Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation",
)


register_model_info(
    ["lucid-dreamer"],
    "LucidDreamer",
    "https://github.com/EnVision-Research/LucidDreamer",
    "Towards High-Fidelity Text-to-3D Generation via Interval Score Matching",
)

register_model_info(
    ["mvdream"],
    "MVDream",
    "https://github.com/bytedance/MVDream",
    "Multi-view Diffusion for 3D Generation",
)


register_model_info(
    ["grm-t", "grm-i"],
    "GRM",
    "https://justimyhxu.github.io/projects/grm",
    "GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation",
)

register_model_info(
    ["point-e-t", "point-e-i"],
    "Point·E",
    "https://github.com/openai/point-e",
    "A System for Generating 3D Point Clouds from Complex Prompts",
)

register_model_info(
    ["shap-e-t", "shap-e-i"],
    "Shap-E",
    "https://github.com/openai/shap-e",
    "Generating Conditional 3D Implicit Functions",
)

register_model_info(
    ["prolificdreamer"],
    "ProlificDreamer",
    "https://ml.cs.tsinghua.edu.cn/prolificdreamer/",
    "High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation",
)

register_model_info(
    ["sjc"],
    "Score Jacobian Chaining",
    "https://pals.ttic.edu/p/score-jacobian-chaining",
    "Lifting Pretrained 2D Diffusion Models for 3D Generation",
)

# register_model_info(
#     [],
#     "",
#     "",
#     "",
# )


## regist image-to-shape generation models
register_model_info(
    ["dreamgaussian"],
    "DreamGaussian",
    "https://github.com/dreamgaussian/dreamgaussian",
    "Generative Gaussian Splatting for Efficient 3D Content Creation",
)


register_model_info(
    ["wonder3d"],
    "Wonder3D",
    "https://github.com/xxlong0/Wonder3D",
    "Single Image to 3D using Cross-Domain Diffusion",
)

register_model_info(
    ["dreamcraft3d"],
    "Dreamcraft3d",
    "https://github.com/deepseek-ai/DreamCraft3D",
    "Hierarchical 3d generation with bootstrapped diffusion prior",
)

register_model_info(
    ["syncdreamer"],
    "SyncDreamer",
    "https://github.com/liuyuan-pal/SyncDreamer",
    "Generating Multiview-consistent Images from a Single-view Image",
)

register_model_info(
    ["zero123"],
    "Zero-1-to-3",
    "https://github.com/cvlab-columbia/zero123",
    "Zero-shot One Image to 3D Object",
)

register_model_info(
    ["stable-zero123"],
    "Stable Zero123",
    "https://stability.ai/news/stable-zero123-3d-generation",
    "Quality 3D Object Generation from Single Images",
)

register_model_info(
    ["zero123-xl"],
    "Zero123-XL",
    "https://stability.ai/news/stable-zero123-3d-generation",
    "Quality 3D Object Generation from Single Images",
)

register_model_info(
    ["magic123"],
    "Magic123",
    "https://guochengqian.github.io/project/magic123/",
    "One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors",
)
    
register_model_info(
    ["imagedream"],
    "ImageDream",
    "https://github.com/bytedance/ImageDream",
    "Image-Prompt Multi-view Diffusion for 3D Generation",
)
register_model_info(
    ["make-it-3d"],
    "Make-It-3D",
    "https://github.com/junshutang/Make-It-3D",
    "High-Fidelity 3D Creation from A Single Image with Diffusion Prior",
)

register_model_info(
    ["triplane-gaussian"],
    "TriplaneGaussian",
    "https://github.com/VAST-AI-Research/TriplaneGaussian",
    "Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers",
)

register_model_info(
    ["free3d"],
    "Free3D",
    "https://github.com/lyndonzheng/Free3D",
    "Consistent Novel View Synthesis without 3D Representation",
)

register_model_info(
    ["escher-net"],
    "EscherNet",
    "https://github.com/kxhit/EscherNet",
    "A Generative Model for Scalable View Synthesis",
)

register_model_info(
    ["v3d"],
    "V3D",
    "https://github.com/heheyas/V3D",
    "Video Diffusion Models are Effective 3D Generators",
)

register_model_info(
    ["lgm"],
    "LGM",
    "https://github.com/3DTopia/LGM",
    "Large Multi-View Gaussian Model for High-Resolution 3D Content Creation",
)

register_model_info(
    ["gsgen"],
    "GSGEN",
    "https://github.com/gsgen3d/gsgen",
    "Text-to-3D using Gaussian Splatting",
)

register_model_info(
    ["openlrm"],
    "OpenLRM",
    "https://github.com/3DTopia/OpenLRM",
    "Open-Source Large Reconstruction Models",
)

register_model_info(
    ["hifa"],
    "HiFA",
    "https://github.com/JunzheJosephZhu/HiFA",
    "High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance",
)

register_model_info(
    ["instant-mesh"],
    "InstantMesh",
    "https://github.com/TencentARC/InstantMesh",
    "Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models",
)

# register_model_info(
#     [],
#     "",
#     "",
#     "",
# )