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ShapE on Huggingface

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ title: Shap E
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+ emoji: 🚀
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+ colorFrom: yellow
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+ colorTo: green
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+ sdk: gradio
7
+ sdk_version: 3.28.2
8
+ app_file: app.py
9
+ pinned: true
10
+ license: creativeml-openrail-m
11
+ ---
12
+
13
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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1
+ import os
2
+ import gradio as gr
3
+ from PIL import Image
4
+ import torch
5
+ import matplotlib.pyplot as plt
6
+ import imageio
7
+ import numpy as np
8
+ import math
9
+ import argparse
10
+
11
+ import torch
12
+ import base64
13
+ import io
14
+ import os
15
+ from typing import Union
16
+
17
+
18
+ from shap_e.diffusion.sample import sample_latents
19
+ from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
20
+ from shap_e.models.download import load_model, load_config
21
+ from shap_e.util.notebooks import create_pan_cameras, decode_latent_images
22
+
23
+ from shap_e.models.nn.camera import DifferentiableCameraBatch, DifferentiableProjectiveCamera
24
+ from shap_e.models.transmitter.base import Transmitter, VectorDecoder
25
+ from shap_e.util.collections import AttrDict
26
+
27
+ import trimesh
28
+
29
+
30
+ state = ""
31
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
32
+
33
+ css = '''
34
+ .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
35
+ .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
36
+ #component-4, #component-3, #component-10{min-height: 0}
37
+ .duplicate-button img{margin: 0}
38
+ '''
39
+
40
+ def set_state(s):
41
+ print(s)
42
+ global state
43
+ state = s
44
+
45
+ def get_state():
46
+ return state
47
+
48
+ @torch.no_grad()
49
+ def decode_latent_images_foo(
50
+ xm: Union[Transmitter, VectorDecoder],
51
+ latent: torch.Tensor,
52
+ cameras: DifferentiableCameraBatch,
53
+ rendering_mode: str = "stf",
54
+ ):
55
+ decoded = xm.renderer.render_views(
56
+ AttrDict(cameras=cameras),
57
+ params=(xm.encoder if isinstance(xm, Transmitter) else xm).bottleneck_to_params(
58
+ latent[None]
59
+ ),
60
+ options=AttrDict(rendering_mode=rendering_mode, render_with_direction=False),
61
+ )
62
+ return decoded
63
+
64
+ def generate_3D(input, grid_size=64):
65
+ set_state('Entered generate function...')
66
+
67
+ # if input is a string, it's a text prompt
68
+ xm = load_model('transmitter', device=device)
69
+ diffusion = diffusion_from_config(load_config('diffusion'))
70
+ batch_size = 4
71
+
72
+ print(type(input))
73
+
74
+ if isinstance(input, Image.Image):
75
+ input = prepare_img(input)
76
+ model = load_model('image300M', device=device)
77
+ guidance_scale = 3.0
78
+ model_kwargs = dict(images=[input] * batch_size)
79
+ else:
80
+ model = load_model('text300M', device=device)
81
+ guidance_scale = 15.0
82
+ model_kwargs = dict(texts=[input] * batch_size)
83
+
84
+ print(input)
85
+
86
+ latents = sample_latents(
87
+ batch_size=batch_size,
88
+ model=model,
89
+ diffusion=diffusion,
90
+ guidance_scale=guidance_scale,
91
+ model_kwargs=model_kwargs,
92
+ progress=True,
93
+ clip_denoised=True,
94
+ use_fp16=True,
95
+ use_karras=True,
96
+ karras_steps=64,
97
+ sigma_min=1e-3,
98
+ sigma_max=160,
99
+ s_churn=0,
100
+ )
101
+
102
+ render_mode = 'stf' # you can change this to 'stf'
103
+ size = grid_size # this is the size of the renders; higher values take longer to render.
104
+
105
+ cameras = create_pan_cameras(size, device)
106
+
107
+ x=decode_latent_images_foo(xm, latents[0], cameras, rendering_mode=render_mode)
108
+ mesh=x['meshes'][0]
109
+ rm=x['raw_meshes'][0]
110
+
111
+ rm.vertex_channels["R"]=mesh.vertex_colors[:,0]
112
+ rm.vertex_channels["G"]=mesh.vertex_colors[:,1]
113
+ rm.vertex_channels["B"]=mesh.vertex_colors[:,2]
114
+
115
+ tm=rm.tri_mesh()
116
+
117
+ with open("output/mesh.ply",'wb') as f:
118
+ tm.write_ply(f)
119
+
120
+
121
+ set_state('Converting to point cloud...')
122
+ # pc = sampler.output_to_point_clouds(samples)[0]
123
+
124
+ set_state('Converting to mesh...')
125
+ # save_ply(pc, 'output/mesh.ply', grid_size)
126
+
127
+ set_state('')
128
+
129
+ images = decode_latent_images(xm, latents[0], cameras, rendering_mode=render_mode)
130
+
131
+ # Convert images to gif
132
+ os.makedirs('output', exist_ok=True)
133
+ images[0].save(f'/tmp/mesh.mp4', save_all=True, append_images=images[1:], duration=100, loop=0)
134
+
135
+
136
+ return ply_to_glb('/tmp/mesh.ply', '/tmp/mesh.glb'), '/tmp/mesh.mp4', gr.update(value=['/tmp/mesh.glb', '/tmp/mesh.ply'], visible=True)
137
+
138
+ def prepare_img(img):
139
+
140
+ w, h = img.size
141
+ if w > h:
142
+ img = img.crop((w - h) / 2, 0, w - (w - h) / 2, h)
143
+ else:
144
+ img = img.crop((0, (h - w) / 2, w, h - (h - w) / 2))
145
+
146
+ # resize to 256x256
147
+ img = img.resize((256, 256))
148
+
149
+ return img
150
+
151
+
152
+ def ply_to_glb(ply_file, glb_file):
153
+ mesh = trimesh.load(ply_file)
154
+
155
+ # Save the mesh as a glb file using Trimesh
156
+ mesh.export(glb_file, file_type='glb')
157
+
158
+ return glb_file
159
+
160
+
161
+ # def save_ply(pc, file_name, grid_size):
162
+ # set_state('Creating SDF model...')
163
+ # sdf_name = 'sdf'
164
+ # sdf_model = model_from_config(MODEL_CONFIGS[sdf_name], device)
165
+ # sdf_model.eval()
166
+
167
+ # set_state('Loading SDF model...')
168
+ # sdf_model.load_state_dict(load_checkpoint(sdf_name, device))
169
+
170
+ # # Produce a mesh (with vertex colors)
171
+ # mesh = marching_cubes_mesh(
172
+ # pc=pc,
173
+ # model=sdf_model,
174
+ # batch_size=4096,
175
+ # grid_size=grid_size, # increase to 128 for resolution used in evals
176
+ # progress=True,
177
+ # )
178
+
179
+ # # Write the mesh to a PLY file to import into some other program.
180
+ # with open(file_name, 'wb') as f:
181
+ # mesh.write_ply(f)
182
+
183
+
184
+ block = gr.Blocks().queue(max_size=250, concurrency_count=6)
185
+ with block:
186
+ with gr.Box():
187
+ if(not torch.cuda.is_available()):
188
+ top_description = gr.HTML(f'''
189
+ <div style="text-align: center; max-width: 650px; margin: 0 auto;">
190
+ <div>
191
+ <img class="logo" src="file/images/mirage.png" alt="Mirage Logo"
192
+ style="margin: auto; max-width: 7rem;">
193
+ <br />
194
+ <h1 style="font-weight: 900; font-size: 2.5rem;">
195
+ Shap-E Web UI
196
+ </h1>
197
+ <br />
198
+ <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/MirageML/point-e?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
199
+ </div>
200
+ <br />
201
+ <p style="margin-bottom: 10px; font-size: 94%">
202
+ Generate 3D Assets in 1 minute with a prompt or image!
203
+ Based on the <a href="https://github.com/openai/shap-e">Shap-E</a> implementation
204
+ </p>
205
+ <br />
206
+ <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4 GPU</b> to it (via the Settings tab)</a> and run the training below. Other GPUs are not compatible for now. You will be billed by the minute from when you activate the GPU until when it is turned it off.</p>
207
+ </div>
208
+ ''')
209
+ else:
210
+ top_description = gr.HTML(f'''
211
+ <div style="text-align: center; max-width: 650px; margin: 0 auto;">
212
+ <div>
213
+ <img class="logo" src="file/images/mirage.png" alt="Mirage Logo"
214
+ style="margin: auto; max-width: 7rem;">
215
+ <br />
216
+ <h1 style="font-weight: 900; font-size: 2.5rem;">
217
+ Point-E Web UI
218
+ </h1>
219
+ <br />
220
+ <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/MirageML/point-e?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
221
+ </div>
222
+ <br />
223
+ <p style="margin-bottom: 10px; font-size: 94%">
224
+ Generate 3D Assets in 1 minute with a prompt or image!
225
+ Based on the <a href="https://github.com/openai/shap-e">Shap-E</a> implementation
226
+ </p>
227
+ </div>
228
+ ''')
229
+ with gr.Row():
230
+ with gr.Column():
231
+ with gr.Tab("Text to 3D"):
232
+ gr.Markdown("Uses Stable Diffusion to create an image from the prompt.")
233
+ prompt = gr.Textbox(label="Prompt", placeholder="A HD photo of a Corgi")
234
+ text_button = gr.Button(label="Generate")
235
+
236
+ with gr.Tab("Image to 3D"):
237
+ gr.Markdown("Best results with images of objects on an empty background.")
238
+ input_image = gr.Image(label="Image")
239
+ img_button = gr.Button(label="Generate")
240
+
241
+ # with gr.Accordion("Advanced options", open=False):
242
+ # model = gr.Radio(["base40M", "base300M", "base1B"], label="Model", value="base1B")
243
+ # scale = gr.Slider(
244
+ # label="Guidance Scale", minimum=1.0, maximum=10.0, value=3.0, step=0.1
245
+ # )
246
+
247
+ with gr.Column():
248
+ model_gif = gr.Model3D(label="3D Model GIF")
249
+ # btn_pc_to_obj = gr.Button(value="Convert to OBJ", visible=False)
250
+ model_3d = gr.Model3D(value=None)
251
+ file_out = gr.File(label="Files", visible=False)
252
+
253
+ if torch.cuda.is_available():
254
+ gr.Examples(
255
+ examples=[
256
+ ["images/pumpkin.png"],
257
+ ["images/fantasy_world.png"],
258
+ ],
259
+ inputs=[input_image],
260
+ outputs=[model_3d, model_gif, file_out],
261
+ fn=generate_3D,
262
+ cache_examples=True
263
+ )
264
+
265
+ img_button.click(fn=generate_3D, inputs=[input_image], outputs=[model_3d, model_gif, file_out])
266
+ text_button.click(fn=generate_3D, inputs=[prompt], outputs=[model_3d, model_gif, file_out])
267
+
268
+ block.launch(show_api=False)
images/fantasy_world.png ADDED
images/mirage.png ADDED
images/pumpkin.png ADDED
requirements.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ git+https://github.com/openai/shap-e@main
2
+ trimesh
3
+ diffusers
4
+ transformers
5
+ accelerate
6
+ imageio[ffmpeg]
7
+ imageio[pyav]
8
+ blobfile
9
+ ipywidgets
shap_e_model_cache/ViT-L-14.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836
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+ size 932768134
shap_e_model_cache/diffusion_config.yaml ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ mean_type: x_start
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+ schedule: exp
3
+ timesteps: 1024
shap_e_model_cache/diffusion_config.yaml.lock ADDED
File without changes
shap_e_model_cache/image_cond.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:cb8072c64bbbcf6910488814d212227de5db291780d4ea99c6152f9346cf12aa
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+ size 1263925407
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shap_e_model_cache/image_cond_config.yaml ADDED
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1
+ d_latent: 1048576
2
+ inner:
3
+ cond_drop_prob: 0.1
4
+ heads: 8
5
+ init_scale: 0.25
6
+ layers: 24
7
+ name: CLIPImageGridPointDiffusionTransformer
8
+ pos_emb_init_scale: 0.05
9
+ time_token_cond: true
10
+ use_pos_emb: true
11
+ width: 1024
12
+ latent_ctx: 1024
13
+ name: SplitVectorDiffusion
shap_e_model_cache/image_cond_config.yaml.lock ADDED
File without changes
shap_e_model_cache/text_cond.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e6b4fa599a7b3c3b16c222d5f5fe56f9db9289ff0b6575fbe5c11bc97106aad4
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+ size 1262868003
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shap_e_model_cache/text_cond_config.yaml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ d_latent: 1048576
2
+ inner:
3
+ cond_drop_prob: 0.1
4
+ heads: 16
5
+ init_scale: 0.25
6
+ layers: 24
7
+ name: CLIPImagePointDiffusionTransformer
8
+ pos_emb_init_scale: 0.05
9
+ time_token_cond: true
10
+ token_cond: true
11
+ use_pos_emb: true
12
+ width: 1024
13
+ latent_ctx: 1024
14
+ name: SplitVectorDiffusion
shap_e_model_cache/text_cond_config.yaml.lock ADDED
File without changes
shap_e_model_cache/transmitter.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:af02a0b85a8abdfb3919584b63c540ba175f6ad4790f574a7fef4617e5acdc3b
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+ size 1776048343
shap_e_model_cache/transmitter.pt.lock ADDED
File without changes
shap_e_model_cache/transmitter_config.yaml ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ encoder:
2
+ cross_attention_dataset: pcl_and_multiview_pcl
3
+ d_latent: 1024
4
+ data_ctx: 1024
5
+ fps_method: first
6
+ heads: 8
7
+ init_scale: 0.25
8
+ inner_batch_size:
9
+ - 16384
10
+ - 20
11
+ input_channels: 6
12
+ latent_bottleneck:
13
+ diffusion:
14
+ schedule: inv_parabola
15
+ schedule_args:
16
+ power: 5.0
17
+ timesteps: 1024
18
+ diffusion_prob: 0.1
19
+ name: clamp_diffusion_noise
20
+ layers: 12
21
+ max_depth: 9.0
22
+ max_unrolls: 1
23
+ min_unrolls: 1
24
+ name: PointCloudPerceiverChannelsEncoder
25
+ params_proj:
26
+ init_scale: 1.0
27
+ learned_scale: 0.0625
28
+ name: channels
29
+ use_ln: true
30
+ patch_size: 8
31
+ pointconv_hidden:
32
+ - 1024
33
+ - 1024
34
+ pointconv_padding_mode: circular
35
+ pointconv_patch_size: 8
36
+ pointconv_samples: 64
37
+ pointconv_stride: 4
38
+ pos_emb: nerf
39
+ use_depth: true
40
+ use_pointconv: true
41
+ width: 1024
42
+ name: Transmitter
43
+ renderer:
44
+ grid_size: 128
45
+ n_coarse_samples: 64
46
+ n_fine_samples: 128
47
+ name: NeRSTFRenderer
48
+ nerstf:
49
+ activation: swish
50
+ d_hidden: 256
51
+ density_activation: relu
52
+ init_scale: 0.25
53
+ initial_density_bias: 0.1
54
+ insert_direction_at: 4
55
+ meta_bias: false
56
+ meta_parameters: true
57
+ n_hidden_layers: 6
58
+ n_meta_layers: 4
59
+ name: MLPNeRSTFModel
60
+ posenc_version: nerf
61
+ separate_coarse_channels: true
62
+ separate_nerf_channels: true
63
+ trainable_meta: false
64
+ separate_shared_samples: true
65
+ void:
66
+ background:
67
+ - 0
68
+ - 0
69
+ - 0
70
+ name: VoidNeRFModel
71
+ volume:
72
+ bbox_max:
73
+ - 1.0
74
+ - 1.0
75
+ - 1.0
76
+ bbox_min:
77
+ - -1.0
78
+ - -1.0
79
+ - -1.0
80
+ name: BoundingBoxVolume
shap_e_model_cache/transmitter_config.yaml.lock ADDED
File without changes