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| import gradio as gr | |
| import torch | |
| from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler | |
| from diffusers.utils import export_to_gif | |
| import random | |
| 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) | |
| # 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,photorealisric" | |
| # 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) |