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import gradio as gr |
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import torch |
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from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler |
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from diffusers.utils import export_to_gif |
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import random |
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def generate_gif(image, animation_type): |
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adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
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model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE" |
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pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) |
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scheduler = DDIMScheduler( |
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clip_sample=False, |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="linear", |
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timestep_spacing="trailing", |
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steps_offset=1 |
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) |
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pipe.scheduler = scheduler |
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pipe.enable_vae_slicing() |
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pipe.enable_model_cpu_offload() |
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pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin") |
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pipe.load_lora_weights(f"guoyww/animatediff-motion-lora-{animation_type}", adapter_name=animation_type) |
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seed = random.randint(0, 2**32 - 1) |
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prompt = "best quality, high quality, trending on artstation" |
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adapter_weight = [0.75] |
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pipe.set_adapters([animation_type], adapter_weights=adapter_weight) |
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output = pipe( |
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prompt=prompt, |
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num_frames=16, |
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guidance_scale=7.5, |
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num_inference_steps=30, |
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ip_adapter_image=image, |
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generator=torch.Generator("cpu").manual_seed(seed), |
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) |
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frames = output.frames[0] |
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gif_path = "output_animation.gif" |
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export_to_gif(frames, gif_path) |
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return gif_path |
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iface = gr.Interface( |
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fn=generate_gif, |
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inputs=[gr.Image(type="pil"), gr.Radio(["zoom-out", "tilt-up", "pan-left"])], |
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outputs=gr.Image(type="pil", label="Generated GIF"), |
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title="AnimateDiff + IP Adapter Demo", |
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description="Upload an image and select an motion module type to generate a GIF!" |
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) |
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iface.launch(debug=True,share=True) |
