undo

app.py

@@ -10,11 +10,11 @@ from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = SanaSprintPipeline.from_pretrained(
-    "Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers",
-    torch_dtype=torch.bfloat16
-)
-pipe.to(device)
+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)
+torch.cuda.empty_cache()
+
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
@@ -54,7 +54,10 @@ css="""
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# Sana Sprint""")
+        gr.Markdown(f"""# FLUX.1 [dev]
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+[[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)]
+        """)
         
         with gr.Row():
             
@@ -107,7 +110,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=15,
                     step=0.1,
-                    value=1,
+                    value=3.5,
                 )
   
                 num_inference_steps = gr.Slider(