Update inference_code.py

inference_code.py

@@ -16,16 +16,20 @@ def generate_images(prompt):
     prng_seed = jax.random.PRNGKey(-1)
     num_inference_steps = 20
 
-    num_samples = jax.device_count()
-    prompt = num_samples * [prompt]
-    prompt_ids = pipeline.prepare_inputs(prompt)
+    images = pipeline(prompt, width=512, num_inference_steps=20, num_images_per_prompt=1).images
+    images = pipeline.numpy_to_pil(np.asarray(images.reshape((1,) + images.shape[-3:])))
 
-    # shard inputs and rng
-    params = replicate(_params)
-    prng_seed = jax.random.split(prng_seed, jax.device_count())
-    prompt_ids = shard(prompt_ids)
+    
+    # num_samples = jax.device_count()
+    # prompt = num_samples * [prompt]
+    # prompt_ids = pipeline.prepare_inputs(prompt)
 
-    images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-    images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+    # # shard inputs and rng
+    # params = replicate(_params)
+    # prng_seed = jax.random.split(prng_seed, jax.device_count())
+    # prompt_ids = shard(prompt_ids)
+
+    # images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+    # images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
 
     return images[0]