Update app.py

app.py

@@ -21,38 +21,44 @@ step_loaded = None
 base_loaded = "Realistic"
 motion_loaded = None
 
-# Ensure model and scheduler are initialized in GPU-enabled function
+# Ensure GPU availability
 if not torch.cuda.is_available():
     raise NotImplementedError("No GPU detected!")
 
 device = "cuda"
 dtype = torch.float16
+
+# Load initial pipeline
+print("Loading AnimateDiff pipeline...")
 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")
+print("Pipeline loaded successfully.")
 
 # Safety checkers
 from transformers import CLIPFeatureExtractor
-
 feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
 
-# Function 
-@spaces.GPU(duration=30,queue=False)
+# Video Generation Function
+@spaces.GPU(duration=30, queue=False)
 def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Progress()):
     global step_loaded
     global base_loaded
     global motion_loaded
-    print(prompt, base, step)
+    print(f"Generating video for: Prompt='{prompt}', Base='{base}', Motion='{motion}', Steps='{step}'")
 
+    # Load step-specific model
     if step_loaded != step:
         repo = "ByteDance/AnimateDiff-Lightning"
         ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
         pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
         step_loaded = step
 
+    # Load base model
     if base_loaded != base:
         pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
         base_loaded = base
 
+    # Load motion adapter
     if motion_loaded != motion:
         pipe.unload_lora_weights()
         if motion != "":
@@ -60,37 +66,44 @@ def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Prog
             pipe.set_adapters(["motion"], [0.7])
         motion_loaded = motion
 
+    # Video parameters: 30-second duration
+    fps = 10
+    duration = 30  # seconds
+    total_frames = fps * duration  # 300 frames for 30s at 10 FPS
+
     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, callback=progress_callback, callback_steps=1)
+    def progress_callback(i, t, z):
+        progress((i + 1, step))
+
+    # Generate video frames
+    output_frames = []
+    for frame in range(total_frames):
+        output = pipe(
+            prompt=prompt,
+            guidance_scale=1.2,
+            num_inference_steps=step,
+            callback=progress_callback,
+            callback_steps=1
+        )
+        output_frames.extend(output.frames[0])  # Collect frames
 
+    # Export to video
     name = str(uuid.uuid4()).replace("-", "")
     path = f"/tmp/{name}.mp4"
-    export_to_video(output.frames[0], path, fps=10)
+    export_to_video(output_frames, path, fps=fps)
     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', placeholder="Enter your video description here...")
         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
             )
@@ -112,38 +125,27 @@ with gr.Blocks(css="style.css") as demo:
             )
             select_step = gr.Dropdown(
                 label='Inference steps',
-                choices=[
-                    ('1-Step', 1), 
-                    ('2-Step', 2),
-                    ('4-Step', 4),
-                    ('8-Step', 8),
-                ],
+                choices=[('1-Step', 1), ('2-Step', 2), ('4-Step', 4), ('8-Step', 8)],
                 value=4,
                 interactive=True
             )
-            submit = gr.Button(
-                scale=1,
-                variant='primary'
-            )
+            submit = gr.Button(scale=1, variant='primary')
+
     video = gr.Video(
-        label='AnimateDiff-Lightning',
+        label='Generated Video',
         autoplay=True,
         height=512,
         width=512,
         elem_id="video_output"
     )
 
-    gr.on(triggers=[
-            submit.click,
-            prompt.submit
-    ],
-        fn = generate_image,
-        inputs = [prompt, select_base, select_motion, select_step],
-        outputs = [video],
-        api_name = "instant_video",
-        queue = False
+    gr.on(
+        triggers=[submit.click, prompt.submit],
+        fn=generate_image,
+        inputs=[prompt, select_base, select_motion, select_step],
+        outputs=[video],
+        api_name="instant_video",
+        queue=False
     )
 
 demo.queue().launch()
-
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