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StableDiffusion Tensorflow to TF Lite

See original GitHub issue

Hi @LukeWood,

For fun, I tried converting stable Diffusion model from Tensorflow to TF lite, so that I can run it on coral/edge tpu.

I tried two approaches: I- Saved model approach: II- Go through h5

will try to document them as much as possible. (sorry in advance for the long traces)

for both:

!pip install git+https://github.com/divamgupta/stable-diffusion-tensorflow --upgrade 
!pip install tensorflow tensorflow_addons ftfy --upgrade

Using !pip install --upgrade keras-cv I was not able to save the model for both.

I- Saved model approach:

  1. Saved the model in a directory
from stable_diffusion_tf.stable_diffusion import StableDiffusion
model = StableDiffusion(
    img_height=512,
    img_width=512,
)
model.diffusion_model.save('/saved_model')
  1. lets try to load it:
import tensorflow as tf
model2 = tf.keras.models.load_model('/saved_model')
converter = tf.lite.TFLiteConverter.from_keras_model(model2)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
  1. The following error is thrown:
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.
WARNING:absl:Found untraced functions such as dense_328_layer_call_fn, dense_328_layer_call_and_return_conditional_losses, dense_329_layer_call_fn, dense_329_layer_call_and_return_conditional_losses, group_normalization_173_layer_call_fn while saving (showing 5 of 1200). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /tmp/tmpicdc9dmk/assets
INFO:tensorflow:Assets written to: /tmp/tmpicdc9dmk/assets
---------------------------------------------------------------------------
ConverterError                            Traceback (most recent call last)
<ipython-input-15-52d23a3e5390> in <module>
      2 model2 = tf.keras.models.load_model('mydata/ivo/pythalpha/saved_model')
      3 converter = tf.lite.TFLiteConverter.from_keras_model(model2)
----> 4 tflite_model = converter.convert()
      5 open("converted_model.tflite", "wb").write(tflite_model)

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/lite.py in wrapper(self, *args, **kwargs)
    931   def wrapper(self, *args, **kwargs):
    932     # pylint: disable=protected-access
--> 933     return self._convert_and_export_metrics(convert_func, *args, **kwargs)
    934     # pylint: enable=protected-access
    935 

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/lite.py in _convert_and_export_metrics(self, convert_func, *args, **kwargs)
    909     self._save_conversion_params_metric()
    910     start_time = time.process_time()
--> 911     result = convert_func(self, *args, **kwargs)
    912     elapsed_time_ms = (time.process_time() - start_time) * 1000
    913     if result:

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/lite.py in convert(self)
   1340         Invalid quantization parameters.
   1341     """
-> 1342     saved_model_convert_result = self._convert_as_saved_model()
   1343     if saved_model_convert_result:
   1344       return saved_model_convert_result

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/lite.py in _convert_as_saved_model(self)
   1322           self._convert_keras_to_saved_model(temp_dir))
   1323       if self.saved_model_dir:
-> 1324         return super(TFLiteKerasModelConverterV2,
   1325                      self).convert(graph_def, input_tensors, output_tensors)
   1326     finally:

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/lite.py in convert(self, graph_def, input_tensors, output_tensors)
   1133 
   1134     # Converts model.
-> 1135     result = _convert_graphdef(
   1136         input_data=graph_def,
   1137         input_tensors=input_tensors,

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/convert_phase.py in wrapper(*args, **kwargs)
    210         else:
    211           report_error_message(str(converter_error))
--> 212         raise converter_error from None  # Re-throws the exception.
    213       except Exception as error:
    214         report_error_message(str(error))

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/convert_phase.py in wrapper(*args, **kwargs)
    203     def wrapper(*args, **kwargs):
    204       try:
--> 205         return func(*args, **kwargs)
    206       except ConverterError as converter_error:
    207         if converter_error.errors:

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/convert.py in convert_graphdef(input_data, input_tensors, output_tensors, **kwargs)
    791       model_flags.output_arrays.append(util.get_tensor_name(output_tensor))
    792 
--> 793   data = convert(
    794       model_flags.SerializeToString(),
    795       conversion_flags.SerializeToString(),

/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/lite/python/convert.py in convert(model_flags_str, conversion_flags_str, input_data_str, debug_info_str, enable_mlir_converter)
    308       for error_data in _metrics_wrapper.retrieve_collected_errors():
    309         converter_error.append_error(error_data)
--> 310       raise converter_error
    311 
    312   return _run_deprecated_conversion_binary(model_flags_str,

ConverterError: /opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: error: 'tf.Conv2D' op is neither a custom op nor a flex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/opt/conda/envs/rapids/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py:1267:0: note: Error code: ERROR_NEEDS_FLEX_OPS
<unknown>:0: error: failed while converting: 'main': 
Some ops are not supported by the native TFLite runtime, you can enable TF kernels fallback using TF Select. See instructions: https://www.tensorflow.org/lite/guide/ops_select 
TF Select ops: Conv2D
Details:
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<1x1x1280x1280xf32>) -> (tensor<?x?x?x1280xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<1x1x320x320xf32>) -> (tensor<?x?x?x320xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<1x1x640x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x1280x1280xf32>) -> (tensor<?x?x?x1280xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x1280x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x1920x1280xf32>) -> (tensor<?x?x?x1280xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x1920x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x2560x1280xf32>) -> (tensor<?x?x?x1280xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x320x320xf32>) -> (tensor<?x?x?x320xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x320x4xf32>) -> (tensor<?x?x?x4xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x320x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x640x1280xf32>) -> (tensor<?x?x?x1280xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x640x320xf32>) -> (tensor<?x?x?x320xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x640x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x960x320xf32>) -> (tensor<?x?x?x320xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}
	tf.Conv2D(tensor<?x?x?x?xf32>, tensor<3x3x960x640xf32>) -> (tensor<?x?x?x640xf32>) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}

II- Go through h5

  1. Save the model with format h5
from stable_diffusion_tf.stable_diffusion import StableDiffusion
model = StableDiffusion(
    img_height=512,
    img_width=512,
)
model.diffusion_model.save('./stable_diffusion.h5', save_format='h5')
  1. Lets try to load it
import tensorflow as tf

model2 = tf.keras.models.load_model('stable_diffusion.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model2)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
  1. It seems that the TF 2.11.0 does not load h5 files anymore.
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-3-72d9a214a713>](https://localhost:8080/#) in <module>
      1 import tensorflow as tf
      2 
----> 3 model2 = tf.keras.models.load_model('stable_diffusion.h5')
      4 converter = tf.lite.TFLiteConverter.from_keras_model(model2)
      5 tflite_model = converter.convert()

1 frames
[/usr/local/lib/python3.7/dist-packages/keras/saving/legacy/serialization.py](https://localhost:8080/#) in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
    384     if cls is None:
    385         raise ValueError(
--> 386             f"Unknown {printable_module_name}: '{class_name}'. "
    387             "Please ensure you are using a `keras.utils.custom_object_scope` "
    388             "and that this object is included in the scope. See "

ValueError: Unknown layer: 'UNetModel'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
  1. Therefore, uninstall tf 2.11.0 and install tf 2.1.0
  2. Attempt to load the saved h5 file:
import tensorflow as tf

model2 = tf.keras.models.load_model('stable_diffusion.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model2)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

The load_model throws the following error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-72d9a214a713> in <module>
      1 import tensorflow as tf
      2 
----> 3 model2 = tf.keras.models.load_model('stable_diffusion.h5')
      4 converter = tf.lite.TFLiteConverter.from_keras_model(model2)
      5 tflite_model = converter.convert()

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/saving/hdf5_format.py in load_model_from_hdf5(filepath, custom_objects, compile)
    164     if model_config is None:
    165       raise ValueError('No model found in config file.')
--> 166     model_config = json.loads(model_config.decode('utf-8'))
    167     model = model_config_lib.model_from_config(model_config,
    168                                                custom_objects=custom_objects)

AttributeError: 'str' object has no attribute 'decode'

Issue Analytics

  • State:open
  • Created 10 months ago
  • Reactions:2
  • Comments:38 (18 by maintainers)

github_iconTop GitHub Comments

4reactions
freedomtancommented, Dec 20, 2022

@charbull FYR. I gave a try.

ideally, if we feed input tensors with correct ranges, we should be able to get quantized model. if that works, we can extend the representative_data_gen().

  • input 1: 77x768 encoded text vectors, we can use the text encoder to generate encoded text prompt
  • input 2: I borrowed timestep embeddeding from the keras model
  • input 3: noise, since this is noise, I guess we can use random number
model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)

prompt_1 = "A watercolor painting of a Golden Retriever at the beach"
encoding_1 = model.encode_text(prompt_1)


def get_timestep_embedding(timestep, batch_size, dim=320, max_period=10000):
    half = dim // 2
    freqs = tf.math.exp(
        -math.log(max_period) * tf.range(0, half, dtype=tf.float32) / half
    )

    args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
    embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
    embedding = tf.reshape(embedding, [1, -1])
    return tf.repeat(embedding, batch_size, axis=0)

def representative_data_gen():
  for i in range(1):
    em = get_timestep_embedding(i, 1)
    noise = tf.random.normal(1, 64, 64, 4)
    yield [encoding_1, em, noise]


converter = tf.lite.TFLiteConverter.from_keras_model(model.diffusion_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8  # or tf.uint8
converter.inference_output_type = tf.uint8  # or tf.uint8

converter.representative_dataset = representative_data_gen
tflite_model_int8 = converter.convert()

unfortunately, I got the following message. Dunno how to solve it yet.

Traceback (most recent call last):
  File "/hack/freedom/ml/notebooks/bar.py", line 45, in <module>
    tflite_model_int8 = converter.convert()
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 933, in wrapper
    return self._convert_and_export_metrics(convert_func, *args, **kwargs)
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 911, in _convert_and_export_metrics
    result = convert_func(self, *args, **kwargs)
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 1342, in convert
    saved_model_convert_result = self._convert_as_saved_model()
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 1325, in _convert_as_saved_model
    self).convert(graph_def, input_tensors, output_tensors)
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 1141, in convert
    return self._optimize_tflite_model(
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py", line 215, in wrapper
    raise error from None  # Re-throws the exception.
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py", line 205, in wrapper
    return func(*args, **kwargs)
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 871, in _optimize_tflite_model
    model = self._quantize(model, q_in_type, q_out_type, q_activations_type,
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/lite.py", line 608, in _quantize
    result = _calibrator.add_intermediate_tensors(result)
  File "/home/freedom/tf-master/lib/python3.10/site-packages/tensorflow/lite/python/optimize/calibrator.py", line 36, in add_intermediate_tensors
    return _calibration_wrapper.AddIntermediateTensors(model_content)
ValueError: Invalid model
3reactions
LukeWoodcommented, Nov 21, 2022

Thanks @charbull for the report! Will take a look at this. TFLite conversion would be an awesome addition.

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