diff --git a/examples/controlnet/README_flux.md b/examples/controlnet/README_flux.md
index d8be36a6e17a..aa5fa251409e 100644
--- a/examples/controlnet/README_flux.md
+++ b/examples/controlnet/README_flux.md
@@ -6,7 +6,19 @@ Training script provided by LibAI, which is an institution dedicated to the prog
 > [!NOTE]
 > **Memory consumption**
 >
-> Flux can be quite expensive to run on consumer hardware devices and as a result, ControlNet training of it comes with higher memory requirements than usual. 
+> Flux can be quite expensive to run on consumer hardware devices and as a result, ControlNet training of it comes with higher memory requirements than usual.
+
+Here is a gpu memory consumption for reference, tested on a single A100 with 80G.
+
+| period | GPU |
+| - | - | 
+| load as float32 | ~70G |
+| mv transformer and vae to bf16 | ~48G |
+| pre compute txt embeddings | ~62G |
+| **offload te to cpu** | ~30G |
+| training | ~58G |
+| validation | ~71G |
+
 
 > **Gated access**
 >
@@ -98,8 +110,9 @@ accelerate launch train_controlnet_flux.py \
     --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
     --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
     --train_batch_size=1 \
-    --gradient_accumulation_steps=4 \
+    --gradient_accumulation_steps=16 \
     --report_to="wandb" \
+    --lr_scheduler="cosine" \
     --num_double_layers=4 \
     --num_single_layers=0 \
     --seed=42 \
diff --git a/examples/controlnet/train_controlnet_flux.py b/examples/controlnet/train_controlnet_flux.py
index 83965a73286d..edee3fbe557c 100644
--- a/examples/controlnet/train_controlnet_flux.py
+++ b/examples/controlnet/train_controlnet_flux.py
@@ -148,7 +148,7 @@ def log_validation(
                     pooled_prompt_embeds=pooled_prompt_embeds,
                     control_image=validation_image,
                     num_inference_steps=28,
-                    controlnet_conditioning_scale=0.7,
+                    controlnet_conditioning_scale=1,
                     guidance_scale=3.5,
                     generator=generator,
                 ).images[0]
@@ -1085,8 +1085,6 @@ def compute_embeddings(batch, proportion_empty_prompts, flux_controlnet_pipeline
         return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds, "text_ids": text_ids}
 
     train_dataset = get_train_dataset(args, accelerator)
-    text_encoders = [text_encoder_one, text_encoder_two]
-    tokenizers = [tokenizer_one, tokenizer_two]
     compute_embeddings_fn = functools.partial(
         compute_embeddings,
         flux_controlnet_pipeline=flux_controlnet_pipeline,
@@ -1103,7 +1101,8 @@ def compute_embeddings(batch, proportion_empty_prompts, flux_controlnet_pipeline
             compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=50
         )
 
-    del text_encoders, tokenizers, text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
+    text_encoder_one.to("cpu")
+    text_encoder_two.to("cpu")
     free_memory()
 
     # Then get the training dataset ready to be passed to the dataloader.