Create tr.py

tr.py

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+import os
+import torch
+from diffusers import FluxPipeline
+from huggingface_hub import hf_hub_download
+
+# Ensure 'output' folder exists, if not, create it
+output_folder = './output/flux_8step'
+os.makedirs(output_folder, exist_ok=True)
+
+# Load base model
+base_model_id = "trongg/Flux-Dev2Pro_nsfw_fluxtastic-v3"
+repo_name = "ByteDance/Hyper-SD"
+ckpt_name = "Hyper-FLUX.1-dev-8steps-lora.safetensors"
+pipe = FluxPipeline.from_pretrained(base_model_id)
+
+# Load and fuse LoRA weights
+pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
+pipe.fuse_lora(lora_scale=0.125)
+
+# Unload LoRA to return the model to its original state
+pipe.unload_lora_weights()
+
+# Save the transformer model in 'output' folder
+model = pipe.transformer
+model.save_pretrained(output_folder)