Upload scripts/training/train_flux_lora.py with huggingface_hub

scripts/training/train_flux_lora.py

@@ -131,13 +131,18 @@ def generate_samples(
     num_inference_steps=28, guidance_scale=3.5,
 ):
     from diffusers import FluxPipeline
-    import numpy as np
 
     output_dir = Path(output_dir) / "samples"
     output_dir.mkdir(parents=True, exist_ok=True)
 
     transformer.eval()
 
+    # Move all components to same device for inference
+    gen_device = train_device
+    vae.to(gen_device)
+    text_encoder.to(gen_device)
+    text_encoder_2.to(gen_device)
+
     try:
         pipe = FluxPipeline.from_pretrained(
             "black-forest-labs/FLUX.1-dev",
@@ -149,7 +154,7 @@ def generate_samples(
             tokenizer_2=tokenizer_2,
             torch_dtype=torch.bfloat16,
         )
-        pipe = pipe.to(train_device)
+        pipe = pipe.to(gen_device)
 
         for i, prompt in enumerate(prompts):
             image = pipe(
@@ -165,6 +170,11 @@ def generate_samples(
     except Exception as e:
         print(f"  WARNING: Sample generation failed: {e}")
 
+    # Move components back to encode_device for training
+    vae.to(encode_device)
+    text_encoder.to(encode_device)
+    text_encoder_2.to(encode_device)
+
     transformer.train()
     torch.cuda.empty_cache()