abormal behavior

[dentistfrankchen]

I followed and copied the code on your main page, however it did not behave normally: ValueError Traceback (most recent call last) Cell In[5], line 4 1 from stable_diffusion_tf.stable_diffusion import StableDiffusion 2 from PIL import Image ----> 4 generator = StableDiffusion()

File ~/anaconda3/envs/art_intel/lib/python3.9/site-packages/stable_diffusion_tf/stable_diffusion.py:24, in StableDiffusion.init(self, img_height, img_width, jit_compile, download_weights) 21 self.img_width = img_width 22 self.tokenizer = SimpleTokenizer() ---> 24 text_encoder, diffusion_model, decoder, encoder = get_models(img_height, img_width, download_weights=download_weights) 25 self.text_encoder = text_encoder 26 self.diffusion_model = diffusion_model

File ~/anaconda3/envs/art_intel/lib/python3.9/site-packages/stable_diffusion_tf/stable_diffusion.py:238, in get_models(img_height, img_width, download_weights) 235 latent = keras.layers.Input((n_h, n_w, 4)) 236 unet = UNetModel() 237 diffusion_model = keras.models.Model( --> 238 [latent, t_emb, context], unet([latent, t_emb, context]) 239 ) 241 # Create decoder 242 latent = keras.layers.Input((n_h, n_w, 4))

File ~/anaconda3/envs/art_intel/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb

File /tmp/autograph_generated_file2lo7vt15.py:84, in outer_factory..inner_factory..tfcall(self, inputs) 82 layer = ag.Undefined('layer') 83 b = ag.Undefined('b') ---> 84 ag__.for_stmt(ag__.ld(self).input_blocks, None, loop_body_1, get_state_3, set_state_3, ('x',), {'iterate_names': 'b'}) 86 def get_state_4(): 87 return (x,)

File /tmp/autograph_generated_file2lo7vt15.py:80, in outer_factory..inner_factory..tfcall..loop_body_1(itr_1) 78 layer = itr 79 x = ag.converted_call(ag.ld(apply), (ag.ld(x), ag.ld(layer)), None, fscope) ---> 80 ag.for_stmt(ag.ld(b), None, loop_body, get_state_2, set_state_2, ('x',), {'iterate_names': 'layer'}) 81 ag.converted_call(ag__.ld(saved_inputs).append, (ag.ld(x),), None, fscope)

File /tmp/autograph_generated_file2lo7vt15.py:79, in outer_factory..inner_factory..tfcall..loop_body_1..loop_body(itr) 77 nonlocal x 78 layer = itr ---> 79 x = ag__.converted_call(ag.ld(apply), (ag.ld(x), ag__.ld(layer)), None, fscope)

File /tmp/autograph_generated_file2lo7vt15.py:48, in outer_factory..inner_factory..tfcall..apply(x, layer) 46 x = ag.converted_call(ag.ld(layer), (ag.ld(x),), None, fscope_1) 47 ag.if_stmt(ag.converted_call(ag.ld(isinstance), (ag.ld(layer), ag__.ld(SpatialTransformer)), None, fscope_1), if_body, else_body, get_state, set_state, ('x',), 1) ---> 48 ag.if_stmt(ag__.converted_call(ag.ld(isinstance), (ag.ld(layer), ag__.ld(ResBlock)), None, fscope_1), if_body_1, else_body_1, get_state_1, set_state_1, ('x',), 1) 49 try: 50 do_return_1 = True

File /tmp/autograph_generated_file2lo7vt15.py:28, in outer_factory..inner_factory..tfcall..apply..if_body_1() 26 def if_body_1(): 27 nonlocal x ---> 28 x = ag__.converted_call(ag.ld(layer), ([ag.ld(x), ag__.ld(emb)],), None, fscope_1)

File /tmp/autograph_generated_filem_kzpxnn.py:11, in outer_factory..inner_factory..tf_call(self, inputs) 9 retval = ag.UndefinedReturnValue() 10 (x, emb) = ag.ld(inputs) ---> 11 h = ag.converted_call(ag.ld(apply_seq), (ag.ld(x), ag.ld(self).in_layers), None, fscope) 12 emb_out = ag__.converted_call(ag.ld(apply_seq), (ag.ld(emb), ag.ld(self).emb_layers), None, fscope) 13 h = ag.ld(h) + ag.ld(emb_out)[:, None, None]

File /tmp/autograph_generated_file612zmgqy.py:23, in outer_factory..inner_factory..tfapply_seq(x, layers) 21 x = ag.converted_call(ag.ld(l), (ag.ld(x),), None, fscope) 22 l = ag.Undefined('l') ---> 23 ag.for_stmt(ag.ld(layers), None, loop_body, get_state, set_state, ('x',), {'iterate_names': 'l'}) 24 try: 25 do_return = True

File /tmp/autograph_generated_file612zmgqy.py:21, in outer_factory..inner_factory..tfapply_seq..loop_body(itr) 19 nonlocal x 20 l = itr ---> 21 x = ag__.converted_call(ag.ld(l), (ag.ld(x),), None, fscope)

File ~/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py:110, in GroupNormalization.build(self, input_shape) 108 self._check_if_input_shape_is_none(input_shape) 109 self._set_number_of_groups_for_instance_norm(input_shape) --> 110 self._check_size_of_dimensions(input_shape) 111 self._create_input_spec(input_shape) 113 self._add_gamma_weight(input_shape)

File ~/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py:227, in GroupNormalization._check_size_of_dimensions(self, input_shape) 225 dim = input_shape[self.axis] 226 if dim < self.groups: --> 227 raise ValueError( 228 "Number of groups (" + str(self.groups) + ") cannot be " 229 "more than the number of channels (" + str(dim) + ")." 230 ) 232 if dim % self.groups != 0: 233 raise ValueError( 234 "Number of groups (" + str(self.groups) + ") must be a " 235 "multiple of the number of channels (" + str(dim) + ")." 236 )

ValueError: Exception encountered when calling layer "u_net_model_1" (type UNetModel).

in user code:

File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/stable_diffusion_tf/diffusion_model.py", line 199, in apply  *
    x = layer([x, emb])
File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler  **
    raise e.with_traceback(filtered_tb) from None
File "/tmp/__autograph_generated_filem_kzpxnn.py", line 11, in tf__call
    h = ag__.converted_call(ag__.ld(apply_seq), (ag__.ld(x), ag__.ld(self).in_layers), None, fscope)
File "/tmp/__autograph_generated_file612zmgqy.py", line 23, in tf__apply_seq

ag.for_stmt(ag.ld(layers), None, loop_body, get_state, set_state, ('x',), {'iterate_names': 'l'}) File "/tmp/autograph_generated_file612zmgqy.py", line 21, in loop_body x = ag.converted_call(ag.ld(l), (ag.ld(x),), None, fscope) File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py", line 110, in build self._check_size_of_dimensions(input_shape) File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py", line 227, in _check_size_of_dimensions raise ValueError(

ValueError: Exception encountered when calling layer "res_block_22" "                 f"(type ResBlock).

in user code:

    File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/stable_diffusion_tf/diffusion_model.py", line 31, in call  *
        h = apply_seq(x, self.in_layers)
    File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/stable_diffusion_tf/layers.py", line 41, in apply_seq  *
        x = l(x)
    File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler  **
        raise e.with_traceback(filtered_tb) from None
    File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py", line 110, in build
        self._check_size_of_dimensions(input_shape)
    File "/home/ec2-user/anaconda3/envs/art_intel/lib/python3.9/site-packages/tensorflow_addons/layers/normalizations.py", line 227, in _check_size_of_dimensions
        raise ValueError(

    ValueError: Number of groups (32) cannot be more than the number of channels (4).

Call arguments received by layer "res_block_22" "                 f"(type ResBlock):
  • inputs=['tf.Tensor(shape=(None, 320, 125, 4), dtype=float32)', 'tf.Tensor(shape=(None, 1280), dtype=float32)']

Call arguments received by layer "u_net_model_1" (type UNetModel): • inputs=['tf.Tensor(shape=(None, 125, 125, 4), dtype=float32)', 'tf.Tensor(shape=(None, 320), dtype=float32)', 'tf.Tensor(shape=(None, 77, 768), dtype=float32)']

[WSINTRA]

What size did you try to make the image?

On May 1, 2023, at 09:22, dentistfrankchen @.***> wrote:

self._check_size_of_dimensions(input_shape

[dentistfrankchen]

@WSINTRA A 512x512 image. I have already tried the code on the main page and added img_height=..., img_width=..., but that did not work. I still got the same error.