File size: 7,260 Bytes
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
94bd22c
e368cec
 
 
 
 
e049190
 
 
 
 
 
 
e368cec
94bd22c
e368cec
 
 
 
 
 
94bd22c
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
94bd22c
e368cec
 
 
 
 
 
94bd22c
e368cec
 
 
 
 
 
 
 
 
 
 
 
86da3fc
94bd22c
86da3fc
 
 
 
 
e368cec
94bd22c
e368cec
 
 
 
 
 
94bd22c
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
623aaf3
 
 
 
 
 
 
 
 
 
 
 
 
 
e368cec
944dd2b
e368cec
 
 
 
944dd2b
 
26dad4e
944dd2b
 
 
 
 
765fb5e
 
 
 
 
 
 
944dd2b
26dad4e
944dd2b
 
 
 
26dad4e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65622ab
e1b7db1
 
 
c3c53e2
e1b7db1
 
e368cec
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
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 image generation models

register_model_info(
    ["imagenhub_LCM_generation", "fal_LCM_text2image"],
    "LCM",
    "https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7",
    "Latent Consistency Models.",
)

register_model_info(
    ["fal_LCM(v1.5/XL)_text2image"],
    "LCM(v1.5/XL)",
    "https://fal.ai/models/fast-lcm-diffusion-turbo",
    "Latent Consistency Models (v1.5/XL)",
)

register_model_info(
    ["imagenhub_PlayGroundV2_generation", 'playground_PlayGroundV2_generation'],
    "Playground v2",
    "https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic",
    "Playground v2 – 1024px Aesthetic Model",
)

register_model_info(
    ["imagenhub_PlayGroundV2.5_generation", 'playground_PlayGroundV2.5_generation'],
    "Playground v2.5",
    "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic",
    "Playground v2.5 is the state-of-the-art open-source model in aesthetic quality",
)

register_model_info(
    ["imagenhub_OpenJourney_generation"],
    "Openjourney",
    "https://huggingface.co/prompthero/openjourney",
    "Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by PromptHero.",
)

register_model_info(
    ["imagenhub_SDXLTurbo_generation", "fal_SDXLTurbo_text2image"],
    "SDXLTurbo",
    "https://huggingface.co/stabilityai/sdxl-turbo",
    "SDXL-Turbo is a fast generative text-to-image model.",
)

register_model_info(
    ["imagenhub_SDXL_generation", "fal_SDXL_text2image"],
    "SDXL",
    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
    "SDXL is a Latent Diffusion Model that uses two fixed, pretrained text encoders.",
)

register_model_info(
    ["imagenhub_PixArtAlpha_generation"],
    "PixArtAlpha",
    "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS",
    "Pixart-α consists of pure transformer blocks for latent diffusion.",
)

register_model_info(
    ["imagenhub_PixArtSigma_generation", "fal_PixArtSigma_text2image"],
    "PixArtSigma",
    "https://github.com/PixArt-alpha/PixArt-sigma",
    "Improved version of Pixart-α.",
)

register_model_info(
    ["imagenhub_SDXLLightning_generation", "fal_SDXLLightning_text2image"],
    "SDXL-Lightning",
    "https://huggingface.co/ByteDance/SDXL-Lightning",
    "SDXL-Lightning is a lightning-fast text-to-image generation model.",
)

register_model_info(
    ["imagenhub_StableCascade_generation", "fal_StableCascade_text2image"],
    "StableCascade",
    "https://huggingface.co/stabilityai/stable-cascade",
    "StableCascade is built upon the Würstchen architecture and working at a much smaller latent space.",
)

# regist image edition models
register_model_info(
    ["imagenhub_CycleDiffusion_edition"],
    "CycleDiffusion",
    "https://github.com/ChenWu98/cycle-diffusion?tab=readme-ov-file",
    "A latent space for stochastic diffusion models.",
)

register_model_info(
    ["imagenhub_Pix2PixZero_edition"],
    "Pix2PixZero",
    "https://pix2pixzero.github.io/",
    "A zero-shot Image-to-Image translation model.",
)

register_model_info(
    ["imagenhub_Prompt2prompt_edition"],
    "Prompt2prompt",
    "https://prompt-to-prompt.github.io/",
    "Image Editing with Cross-Attention Control.",
)


register_model_info(
    ["imagenhub_InstructPix2Pix_edition"],
    "InstructPix2Pix",
    "https://www.timothybrooks.com/instruct-pix2pix",
    "An instruction-based image editing model.",
)

register_model_info(
    ["imagenhub_MagicBrush_edition"],
    "MagicBrush",
    "https://osu-nlp-group.github.io/MagicBrush/",
    "Manually Annotated Dataset for Instruction-Guided Image Editing.",
)

register_model_info(
    ["imagenhub_PNP_edition"],
    "PNP",
    "https://github.com/MichalGeyer/plug-and-play",
    "Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation.",
)

register_model_info(
    ["imagenhub_InfEdit_edition"],
    "InfEdit",
    "https://sled-group.github.io/InfEdit/",
    "Inversion-Free Image Editing with Natural Language.",
)

register_model_info(
    ["imagenhub_CosXLEdit_edition"],
    "CosXLEdit",
    "https://huggingface.co/stabilityai/cosxl",
    "An instruction-based image editing model from SDXL.",
)

register_model_info(
    ["fal_stable-cascade_text2image"],
    "StableCascade",
    "https://fal.ai/models/stable-cascade/api",
    "StableCascade is a generative model that can generate high-quality images from text prompts.",
)

register_model_info(
    ["fal_AnimateDiff_text2video"],
    "AnimateDiff",
    "https://fal.ai/models/fast-animatediff-t2v",
    "AnimateDiff is a text-driven models that produce diverse and personalized animated images.",
)

register_model_info(
    ["fal_StableVideoDiffusion_text2video"],
    "StableVideoDiffusion",
    "https://fal.ai/models/fal-ai/fast-svd/text-to-video/api",
    "Stable Video Diffusion empowers individuals to transform text and image inputs into vivid scenes.",
)

register_model_info(
    ["fal_AnimateDiffTurbo_text2video"],
    "AnimateDiff Turbo",
    "https://fal.ai/models/fast-animatediff-t2v-turbo",
    "AnimateDiff Turbo is a lightning version of AnimateDiff.",
)

register_model_info(
    ["videogenhub_LaVie_generation"],
    "LaVie",
    "https://github.com/Vchitect/LaVie",
    "LaVie is a video generation model with cascaded latent diffusion models.",
)

register_model_info(
    ["videogenhub_VideoCrafter2_generation"],
    "VideoCrafter2",
    "https://ailab-cvc.github.io/videocrafter2/",
    "VideoCrafter2 is a T2V model that disentangling motion from appearance.",
)

register_model_info(
    ["videogenhub_ModelScope_generation"],
    "ModelScope",
    "https://arxiv.org/abs/2308.06571",
    "ModelScope is a a T2V synthesis model that evolves from a T2I synthesis model.",
)

register_model_info(
    ["videogenhub_OpenSora_generation"],
    "OpenSora",
    "https://github.com/hpcaitech/Open-Sora",
    "A community-driven opensource implementation of Sora.",
)