No more duration, only frames

app.py

@@ -27,9 +27,6 @@ FIXED_FPS = 24
 MIN_FRAMES_MODEL = 8
 MAX_FRAMES_MODEL = 121
 
-MIN_DURATION = round(MIN_FRAMES_MODEL/FIXED_FPS,1)
-MAX_DURATION = round(MAX_FRAMES_MODEL/FIXED_FPS,1)
-
 
 pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID,
     transformer=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
@@ -85,7 +82,7 @@ def get_duration(
     input_image,
     prompt,
     negative_prompt,
-    duration_seconds,
+    num_frames,
     guidance_scale,
     steps,
     seed,
@@ -99,7 +96,7 @@ def generate_video(
     input_image,
     prompt,
     negative_prompt=default_negative_prompt,
-    duration_seconds = MAX_DURATION,
+    num_frames = MAX_FRAMES_MODEL,
     guidance_scale = 3.5,
     steps = 28,
     seed = 42,
@@ -118,8 +115,8 @@ def generate_video(
         prompt (str): Text prompt describing the desired animation or motion.
         negative_prompt (str, optional): Negative prompt to avoid unwanted elements. 
             Defaults to default_negative_prompt (contains unwanted visual artifacts).
-        duration_seconds (float, optional): Duration of the generated video in seconds.
-            Defaults to 2. Clamped between MIN_FRAMES_MODEL/FIXED_FPS and MAX_FRAMES_MODEL/FIXED_FPS.
+        num_frames (int, optional): Number of frames.
+            Defaults to MAX_FRAMES_MODEL
         guidance_scale (float, optional): Controls adherence to the prompt. Higher values = more adherence.
             Defaults to 1.0. Range: 0.0-20.0.
         steps (int, optional): Number of inference steps. More steps = higher quality but slower.
@@ -140,15 +137,12 @@ def generate_video(
     
     Note:
         - The function automatically resizes the input image to the target dimensions
-        - Frame count is calculated as duration_seconds * FIXED_FPS (24)
         - Output dimensions are adjusted to be multiples of MOD_VALUE (32)
         - The function uses GPU acceleration via the @spaces.GPU decorator
-        - Generation time varies based on steps and duration (see get_duration function)
     """
     if input_image is None:
         raise gr.Error("Please upload an input image.")
     
-    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
     current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
     resized_image = resize_image(input_image)
 
@@ -178,7 +172,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)")
             prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
-            duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=MAX_DURATION, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
+            num_frames_input = gr.Slider(minimum=MIN_FRAMES_MODEL, maximum=MAX_FRAMES_MODEL, step=1, value=MAX_FRAMES_MODEL, label="Frames")
             
             with gr.Accordion("Advanced Settings", open=False):
                 negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
@@ -193,7 +187,7 @@ with gr.Blocks() as demo:
     
     ui_inputs = [
         input_image_component, prompt_input,
-        negative_prompt_input, duration_seconds_input,
+        negative_prompt_input, num_frames_input,
         guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
     ]
     generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])