This PR allows the user to automatically randomize the seed

demo.py

@@ -136,8 +136,11 @@ def prepare_image(image, vae, transform_video, device, dtype=torch.float16):
     return image
 
 @spaces.GPU
-def gen_video(input_image, korean_prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed):
+def gen_video(input_image, korean_prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, randomize_seed, seed):
     english_prompt = translate_prompt(korean_prompt)
+
+    if randomize_seed:
+        seed = random.randint(1, int(1e8))
     torch.manual_seed(seed)
 
     scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_path, 
@@ -232,6 +235,7 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
                     sample_step_slider = gr.Slider(label="Sampling steps", value=50, minimum=10, maximum=250, step=1)
 
                 with gr.Row():
+                    randomize_seed_checkbox = gr.Checkbox(label = "Randomize seed", value = True, info = "If checked, result is always different")
                     seed_textbox = gr.Slider(label="Seed", value=100, minimum=1, maximum=int(1e8), step=1, interactive=True)
                 
                 with gr.Row():
@@ -264,6 +268,7 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
                     dct_coefficients,
                     noise_level,
                     motion_bucket_id,
+                    randomize_seed_checkbox,
                     seed_textbox,
                 ],
                 outputs=[result_video]