Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -74,173 +74,6 @@ clevr_model.to(device)
|
|
74 |
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
|
75 |
device = th.device('cpu' if not th.cuda.is_available() else 'cuda')
|
76 |
|
77 |
-
# init stable diffusion model
|
78 |
-
pipe = ComposableStableDiffusionPipeline.from_pretrained(
|
79 |
-
"CompVis/stable-diffusion-v1-4",
|
80 |
-
).to(device)
|
81 |
-
|
82 |
-
pipe.safety_checker = None
|
83 |
-
|
84 |
-
# create model for CLEVR Objects
|
85 |
-
clevr_options = model_and_diffusion_defaults_for_clevr()
|
86 |
-
|
87 |
-
flags = {
|
88 |
-
"image_size": 128,
|
89 |
-
"num_channels": 192,
|
90 |
-
"num_res_blocks": 2,
|
91 |
-
"learn_sigma": True,
|
92 |
-
"use_scale_shift_norm": False,
|
93 |
-
"raw_unet": True,
|
94 |
-
"noise_schedule": "squaredcos_cap_v2",
|
95 |
-
"rescale_learned_sigmas": False,
|
96 |
-
"rescale_timesteps": False,
|
97 |
-
"num_classes": '2',
|
98 |
-
"dataset": "clevr_pos",
|
99 |
-
"use_fp16": has_cuda,
|
100 |
-
"timestep_respacing": '100'
|
101 |
-
}
|
102 |
-
|
103 |
-
for key, val in flags.items():
|
104 |
-
clevr_options[key] = val
|
105 |
-
|
106 |
-
clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options)
|
107 |
-
clevr_model.eval()
|
108 |
-
if has_cuda:
|
109 |
-
clevr_model.convert_to_fp16()
|
110 |
-
|
111 |
-
clevr_model.to(device)
|
112 |
-
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
|
113 |
-
print('total clevr_pos parameters', sum(x.numel() for x in clevr_model.parameters()))
|
114 |
-
|
115 |
-
print('creating base model...')
|
116 |
-
base_name = 'base40M-textvec'
|
117 |
-
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
|
118 |
-
base_model.eval()
|
119 |
-
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
|
120 |
-
|
121 |
-
print('creating upsample model...')
|
122 |
-
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
|
123 |
-
upsampler_model.eval()
|
124 |
-
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
|
125 |
-
|
126 |
-
print('downloading base checkpoint...')
|
127 |
-
base_model.load_state_dict(load_checkpoint(base_name, device))
|
128 |
-
|
129 |
-
print('downloading upsampler checkpoint...')
|
130 |
-
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
|
131 |
-
|
132 |
-
print('creating SDF model...')
|
133 |
-
name = 'sdf'
|
134 |
-
model = model_from_config(MODEL_CONFIGS[name], device)
|
135 |
-
model.eval()
|
136 |
-
|
137 |
-
print('loading SDF model...')
|
138 |
-
model.load_state_dict(load_checkpoint(name, device))
|
139 |
-
|
140 |
-
|
141 |
-
def compose_pointe(prompt, weights):
|
142 |
-
weight_list = [float(x.strip()) for x in weights.split('|')]
|
143 |
-
sampler = PointCloudSampler(
|
144 |
-
device=device,
|
145 |
-
models=[base_model, upsampler_model],
|
146 |
-
diffusions=[base_diffusion, upsampler_diffusion],
|
147 |
-
num_points=[1024, 4096 - 1024],
|
148 |
-
aux_channels=['R', 'G', 'B'],
|
149 |
-
guidance_scale=[weight_list, 0.0],
|
150 |
-
model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
|
151 |
-
)
|
152 |
-
|
153 |
-
def generate_pcd(prompt_list):
|
154 |
-
# Produce a sample from the model.
|
155 |
-
samples = None
|
156 |
-
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=prompt_list))):
|
157 |
-
samples = x
|
158 |
-
return samples
|
159 |
-
|
160 |
-
def generate_fig(samples):
|
161 |
-
pc = sampler.output_to_point_clouds(samples)[0]
|
162 |
-
return pc
|
163 |
-
|
164 |
-
|
165 |
-
# has_cuda = th.cuda.is_available()
|
166 |
-
device = th.device('cpu' if not th.cuda.is_available() else 'cuda')
|
167 |
-
|
168 |
-
# init stable diffusion model
|
169 |
-
pipe = ComposableStableDiffusionPipeline.from_pretrained(
|
170 |
-
"CompVis/stable-diffusion-v1-4",
|
171 |
-
).to(device)
|
172 |
-
|
173 |
-
pipe.safety_checker = None
|
174 |
-
|
175 |
-
# create model for CLEVR Objects
|
176 |
-
clevr_options = model_and_diffusion_defaults_for_clevr()
|
177 |
-
|
178 |
-
flags = {
|
179 |
-
"image_size": 128,
|
180 |
-
"num_channels": 192,
|
181 |
-
"num_res_blocks": 2,
|
182 |
-
"learn_sigma": True,
|
183 |
-
"use_scale_shift_norm": False,
|
184 |
-
"raw_unet": True,
|
185 |
-
"noise_schedule": "squaredcos_cap_v2",
|
186 |
-
"rescale_learned_sigmas": False,
|
187 |
-
"rescale_timesteps": False,
|
188 |
-
"num_classes": '2',
|
189 |
-
"dataset": "clevr_pos",
|
190 |
-
"use_fp16": has_cuda,
|
191 |
-
"timestep_respacing": '100'
|
192 |
-
}
|
193 |
-
|
194 |
-
for key, val in flags.items():
|
195 |
-
clevr_options[key] = val
|
196 |
-
|
197 |
-
clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options)
|
198 |
-
clevr_model.eval()
|
199 |
-
if has_cuda:
|
200 |
-
clevr_model.convert_to_fp16()
|
201 |
-
|
202 |
-
clevr_model.to(device)
|
203 |
-
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
|
204 |
-
device = th.device('cpu' if not th.cuda.is_available() else 'cuda')
|
205 |
-
|
206 |
-
# init stable diffusion model
|
207 |
-
pipe = ComposableStableDiffusionPipeline.from_pretrained(
|
208 |
-
"CompVis/stable-diffusion-v1-4",
|
209 |
-
).to(device)
|
210 |
-
|
211 |
-
pipe.safety_checker = None
|
212 |
-
|
213 |
-
# create model for CLEVR Objects
|
214 |
-
clevr_options = model_and_diffusion_defaults_for_clevr()
|
215 |
-
|
216 |
-
flags = {
|
217 |
-
"image_size": 128,
|
218 |
-
"num_channels": 192,
|
219 |
-
"num_res_blocks": 2,
|
220 |
-
"learn_sigma": True,
|
221 |
-
"use_scale_shift_norm": False,
|
222 |
-
"raw_unet": True,
|
223 |
-
"noise_schedule": "squaredcos_cap_v2",
|
224 |
-
"rescale_learned_sigmas": False,
|
225 |
-
"rescale_timesteps": False,
|
226 |
-
"num_classes": '2',
|
227 |
-
"dataset": "clevr_pos",
|
228 |
-
"use_fp16": has_cuda,
|
229 |
-
"timestep_respacing": '100'
|
230 |
-
}
|
231 |
-
|
232 |
-
for key, val in flags.items():
|
233 |
-
clevr_options[key] = val
|
234 |
-
|
235 |
-
clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options)
|
236 |
-
clevr_model.eval()
|
237 |
-
if has_cuda:
|
238 |
-
clevr_model.convert_to_fp16()
|
239 |
-
|
240 |
-
clevr_model.to(device)
|
241 |
-
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
|
242 |
-
print('total clevr_pos parameters', sum(x.numel() for x in clevr_model.parameters()))
|
243 |
-
|
244 |
print('creating base model...')
|
245 |
base_name = 'base40M-textvec'
|
246 |
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
|
|
|
74 |
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
|
75 |
device = th.device('cpu' if not th.cuda.is_available() else 'cuda')
|
76 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
77 |
print('creating base model...')
|
78 |
base_name = 'base40M-textvec'
|
79 |
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
|