Update app.py

app.py

@@ -59,43 +59,6 @@ pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
     dtype=jnp.bfloat16,
 )
 
-prompts = ["apple"] * 1
-
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-
-def generate_image(dense_class_vector=None, int_index=None, noise_seed_vector=None, truncation=0.4):
-    seed = int(noise_seed_vector.sum().item()) if noise_seed_vector is not None else None
-    noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1, seed=seed)
-    noise_vector = torch.from_numpy(noise_vector)
-    if int_index is not None:
-        class_vector = one_hot_from_int([int_index], batch_size=1)
-        class_vector = torch.from_numpy(class_vector)
-        dense_class_vector = gan_model.embeddings(class_vector)
-    else:
-        if isinstance(dense_class_vector, np.ndarray):
-            dense_class_vector = torch.tensor(dense_class_vector)
-        dense_class_vector = dense_class_vector.view(1, 128)
-
-    input_vector = torch.cat([noise_vector, dense_class_vector], dim=1)
-
-    # Generate an image
-    with torch.no_grad():
-        output = gan_model.generator(input_vector, truncation)
-    output = output.cpu().numpy()
-    output = output.transpose((0, 2, 3, 1))
-    output = ((output + 1.0) / 2.0) * 256
-    output.clip(0, 255, out=output)
-    output = np.asarray(np.uint8(output[0]), dtype=np.uint8)
-    return output
-
-
-
 def text_to_image(text):
     images = sd2_inference(pipeline, [text], params, seed = 42, num_inference_steps = 5 )
     img = images[0]