add : ponix generator

app.py

@@ -7,12 +7,22 @@ from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, Autoe
 from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
 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)
+
 torch.cuda.empty_cache()
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -54,8 +64,7 @@ css="""
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+        gr.Markdown(f"""# [POSTECH] PONIX Generator
 [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
         """)