Optimize log_validation in train_controlnet_flax (#3110)

extract pipeline from log_validation

examples/controlnet/train_controlnet_flax.py

@@ -76,20 +76,11 @@ def image_grid(imgs, rows, cols):
     return grid
 
 
-def log_validation(controlnet, controlnet_params, tokenizer, args, rng, weight_dtype):
-    logger.info("Running validation... ")
+def log_validation(pipeline, pipeline_params, controlnet_params, tokenizer, args, rng, weight_dtype):
+    logger.info("Running validation...")
 
-    pipeline, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        tokenizer=tokenizer,
-        controlnet=controlnet,
-        safety_checker=None,
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-    params = jax_utils.replicate(params)
-    params["controlnet"] = controlnet_params
+    pipeline_params = pipeline_params.copy()
+    pipeline_params["controlnet"] = controlnet_params
 
     num_samples = jax.device_count()
     prng_seed = jax.random.split(rng, jax.device_count())
@@ -121,7 +112,7 @@ def log_validation(controlnet, controlnet_params, tokenizer, args, rng, weight_d
         images = pipeline(
             prompt_ids=prompt_ids,
             image=processed_image,
-            params=params,
+            params=pipeline_params,
             prng_seed=prng_seed,
             num_inference_steps=50,
             jit=True,
@@ -176,6 +167,7 @@ tags:
 - text-to-image
 - diffusers
 - controlnet
+- jax-diffusers-event
 inference: true
 ---
     """
@@ -800,6 +792,17 @@ def main():
         ]:
             controlnet_params[key] = unet_params[key]
 
+    pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
+        args.pretrained_model_name_or_path,
+        tokenizer=tokenizer,
+        controlnet=controlnet,
+        safety_checker=None,
+        dtype=weight_dtype,
+        revision=args.revision,
+        from_pt=args.from_pt,
+    )
+    pipeline_params = jax_utils.replicate(pipeline_params)
+
     # Optimization
     if args.scale_lr:
         args.learning_rate = args.learning_rate * total_train_batch_size
@@ -1073,7 +1076,7 @@ def main():
                 and global_step % args.validation_steps == 0
                 and jax.process_index() == 0
             ):
-                _ = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype)
+                _ = log_validation(pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype)
 
             if global_step % args.logging_steps == 0 and jax.process_index() == 0:
                 if args.report_to == "wandb":
@@ -1105,7 +1108,7 @@ def main():
         if args.validation_prompt is not None:
             if args.profile_validation:
                 jax.profiler.start_trace(args.output_dir)
-            image_logs = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype)
+            image_logs = log_validation(pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype)
             if args.profile_validation:
                 jax.profiler.stop_trace()
         else: