add : lora weights

app.py

@@ -21,10 +21,10 @@ good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfold
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
 
 # PONIX mode load
-# pipe.load_lora_weights('cwhuh/ponix-generator-v0.1.0', weight_name='pytorch_lora_weights.safetensors')
-# embedding_path = hf_hub_download(repo_id='cwhuh/ponix-generator-v0.1.0', filename='./ponix-generator-v0.1.0_emb.safetensors', repo_type="model")
-# state_dict = load_file(embedding_path)
-# pipe.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
+pipe.load_lora_weights('cwhuh/ponix-generator-v0.1.0', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='cwhuh/ponix-generator-v0.1.0', filename='./ponix-generator-v0.1.0_emb.safetensors', repo_type="model")
+state_dict = load_file(embedding_path)
+pipe.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
 
 torch.cuda.empty_cache()