Update app.py

app.py

@@ -4,12 +4,11 @@ import os
 import spaces
 import uuid
 
-from diffusers import AnimateDiffPipeline, EulerDiscreteScheduler
+from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
 from diffusers.utils import export_to_video
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
 from PIL import Image
-from transformers import CLIPFeatureExtractor
 
 # Constants
 bases = {
@@ -34,8 +33,12 @@ else:
 pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
 
+# Safety checkers
+from transformers import CLIPFeatureExtractor
+
 feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
 
+# Function 
 @spaces.GPU(duration=30, queue=False)
 def generate_image(prompt, base="Realistic", motion="", step=8, resolution="Square", progress=gr.Progress()):
     global step_loaded
@@ -49,8 +52,6 @@ def generate_image(prompt, base="Realistic", motion="", step=8, resolution="Squa
         width, height = 512, 512
     elif resolution == "Horizontal":
         width, height = 1280, 720
-    else:
-        width, height = 512, 512  # default fallback
 
     if step_loaded != step:
         repo = "ByteDance/AnimateDiff-Lightning"
@@ -70,36 +71,37 @@ def generate_image(prompt, base="Realistic", motion="", step=8, resolution="Squa
         motion_loaded = motion
 
     progress((0, step))
+    
     def progress_callback(i, t, z):
         progress((i+1, step))
 
-    output = pipe(
-        prompt=prompt,
-        guidance_scale=1.2,
-        num_inference_steps=step,
-        width=width,
-        height=height,
-        callback=progress_callback,
-        callback_steps=1
-    )
+    output = pipe(prompt=prompt, guidance_scale=1.2, num_inference_steps=step, width=width, height=height, callback=progress_callback, callback_steps=1)
 
     name = str(uuid.uuid4()).replace("-", "")
     path = f"/tmp/{name}.mp4"
     export_to_video(output.frames[0], path, fps=10)
     return path
 
+
 # Gradio Interface
 with gr.Blocks(css="style.css") as demo:
-    gr.HTML("<h1><center>Textual Imagination : A Text To Video Synthesis</center></h1>")
-    
+    gr.HTML(
+        "<h1><center>Textual Imagination : A Text To Video Synthesis</center></h1>"
+    )
     with gr.Group():
         with gr.Row():
-            prompt = gr.Textbox(label='Prompt')
-        
+            prompt = gr.Textbox(
+                label='Prompt'
+            )
         with gr.Row():
             select_base = gr.Dropdown(
                 label='Base model',
-                choices=["Cartoon", "Realistic", "3d", "Anime"],
+                choices=[
+                    "Cartoon", 
+                    "Realistic",
+                    "3d",
+                    "Anime",
+                ],
                 value=base_loaded,
                 interactive=True
             )
@@ -132,12 +134,17 @@ with gr.Blocks(css="style.css") as demo:
             )
             select_resolution = gr.Dropdown(
                 label='Resolution',
-                choices=["Square", "Horizontal"],
+                choices=[
+                    "Square", 
+                    "Horizontal",
+                ],
                 value="Square",
                 interactive=True
             )
-            submit = gr.Button(scale=1, variant='primary')
-
+            submit = gr.Button(
+                scale=1,
+                variant='primary'
+            )
     video = gr.Video(
         label='AnimateDiff-Lightning',
         autoplay=True,
@@ -147,15 +154,14 @@ with gr.Blocks(css="style.css") as demo:
     )
 
     gr.on(
-    triggers=[
-        submit.click,
-        prompt.submit
-    ],
-    fn=generate_image,
-    inputs=[prompt, select_base, select_motion, select_step, select_resolution],
-    outputs=[video],
-    queue=False
-)
-
+        triggers=[
+            submit.click,
+            prompt.submit
+        ],
+        fn=generate_image,
+        inputs=[prompt, select_base, select_motion, select_step, select_resolution],
+        outputs=[video],
+        queue=False
+    )
 
 demo.queue().launch()