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fix issue that training flux controlnet was unstable and validation r… #11373

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merged 3 commits into from
Apr 21, 2025
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fix issue that training flux controlnet was unstable and validation r… #11373

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723168e
fix issue that training flux controlnet was unstable and validation r…
Apr 21, 2025
274a2be
del unused code pieces, fix grammar
Apr 21, 2025
96ea3cd
Merge branch 'main' into flux-controlnet-train
sayakpaul Apr 21, 2025
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17 changes: 15 additions & 2 deletions examples/controlnet/README_flux.md
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Expand Up @@ -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**
>
Expand Down Expand Up @@ -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 \
Expand Down
6 changes: 4 additions & 2 deletions examples/controlnet/train_controlnet_flux.py
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Expand Up @@ -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]
Expand Down Expand Up @@ -1103,7 +1103,9 @@ 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
# 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.
Expand Down
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