Upload scripts/training/train_flux_lora.py with huggingface_hub

scripts/training/train_flux_lora.py

@@ -128,7 +128,7 @@ def generate_samples(
     tokenizer, tokenizer_2,
     prompts, output_dir, global_step,
     encode_device, train_device,
-    num_inference_steps=4, guidance_scale=0.0,
+    num_inference_steps=28, guidance_scale=3.5,
 ):
     from diffusers import FluxPipeline
     import numpy as np
@@ -140,7 +140,7 @@ def generate_samples(
 
     try:
         pipe = FluxPipeline.from_pretrained(
-            "black-forest-labs/FLUX.1-schnell",
+            "black-forest-labs/FLUX.1-dev",
             transformer=transformer,
             vae=vae,
             text_encoder=text_encoder,
@@ -171,7 +171,7 @@ def generate_samples(
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model-name", default="black-forest-labs/FLUX.1-schnell")
+    parser.add_argument("--model-name", default="black-forest-labs/FLUX.1-dev")
     parser.add_argument("--data-dir", type=Path, required=True)
     parser.add_argument("--output-dir", type=Path, required=True)
     parser.add_argument("--cache-dir", default="/data0/models")