Update app.py

app.py

@@ -1,11 +1,69 @@
+import gradio as gr
 import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
+from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler
+from diffusers.utils import export_to_gif
+import random
 
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
+def generate_gif(image, animation_type):
+    # Load the motion adapter
+    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
 
-prompt = "Spiderman is surfing"
-video_frames = pipe(prompt, num_inference_steps=25).frames
-video_path = export_to_video(video_frames)
+    # Load SD 1.5 based finetuned model
+    model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
+    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
+
+    # Scheduler setup
+    scheduler = DDIMScheduler(
+        clip_sample=False,
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="linear",
+        timestep_spacing="trailing",
+        steps_offset=1
+    )
+    pipe.scheduler = scheduler
+
+    # Enable memory savings
+    pipe.enable_vae_slicing()
+    pipe.enable_model_cpu_offload()
+
+    # Load ip_adapter
+    pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
+
+    # Load the selected motion adapter
+    pipe.load_lora_weights(f"guoyww/animatediff-motion-lora-{animation_type}", adapter_name=animation_type)
+
+    # Generate a random seed
+    seed = random.randint(0, 2**32 - 1)
+    prompt = "best quality, high quality, trending on artstation"
+
+    # Set adapter weights for the selected adapter
+    adapter_weight = [0.75]
+
+    pipe.set_adapters([animation_type], adapter_weights=adapter_weight)
+
+    # Generate GIF
+    output = pipe(
+        prompt=prompt,
+        num_frames=16,
+        guidance_scale=7.5,
+        num_inference_steps=30,
+        ip_adapter_image=image,
+        generator=torch.Generator("cpu").manual_seed(seed),
+    )
+    frames = output.frames[0]
+
+    gif_path = "output_animation.gif"
+    export_to_gif(frames, gif_path)
+    return gif_path
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_gif,
+    inputs=[gr.Image(type="pil"), gr.Radio(["zoom-out", "tilt-up", "pan-left"])],
+    outputs=gr.Image(type="pil", label="Generated GIF"),
+    title="AnimateDiff + IP Adapter Demo",
+    description="Upload an image and select an motion module type to generate a GIF!"
+)
+
+iface.launch(debug=True,share=True)