Update app.py

app.py

@@ -23,7 +23,7 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
 # pipe.fuse_lora(lora_scale=0.125)
 pipe.to(DEVICE)
 
-examples = [[Image.open("cat.jpg"), "a tiger"]]
+examples = [[Image.open("cat.jpg"), "a tiger", 0, 0.38, 0.5, 0.9, 28, 28, 789385745, False]]
 def reset_do_inversion():
         return True
     
@@ -110,19 +110,19 @@ Stochastic Rectified Differential Equations*](https://rf-inversion.github.io/dat
                 with gr.Row():          
                     start_timestep = gr.Slider(
                     label="start timestep",
-                    info = "lower gamma to ehnace the edits",
+                    info = "decrease to enhace fidelity to original image",
                     minimum=0.0,
                     maximum=1.0,
                     step=0.1,
-                    value=0.9,
+                    value=0.,
                 )
                     stop_timestep = gr.Slider(
                         label="stop timestep",
-                        info = "lower gamma to ehnace the edits",
+                        info = "increase to enhace fidelity to original image",
                         minimum=0.0,
                         maximum=1.0,
                         step=0.1,
-                        value=0.9,
+                        value=0.3,
                     )
                     eta = gr.Slider(
                         label="eta",
@@ -164,7 +164,7 @@ Stochastic Rectified Differential Equations*](https://rf-inversion.github.io/dat
                     minimum=0.0,
                     maximum=1.0,
                     step=0.1,
-                    value=0.9,
+                    value=0.5,
                 )
                 num_inversion_steps = gr.Slider(
                         label="num inversion steps",
@@ -218,7 +218,7 @@ Stochastic Rectified Differential Equations*](https://rf-inversion.github.io/dat
 
     gr.Examples(
                 examples=examples,
-                inputs=[input_image, prompt],
+                inputs=[input_image, prompt,start_timestep, stop_timestep, gamma, eta, num_inversion_steps, num_inference_steps, seed, randomize_seed ],
                 outputs=[result, inverted_latents, image_latents, latent_image_ids, do_inversion, seed],
                 fn=invert_and_edit,
             )