Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
+from diffusers.utils import export_to_gif
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16
+
+# 加載模型
+step = 4  # Options: [1, 2, 4, 8]
+repo = "ByteDance/AnimateDiff-Lightning"
+ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
+base = "emilianJR/epiCRealism"
+
+adapter = MotionAdapter().to(device, dtype)
+adapter.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
+pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
+
+# 定義生成 GIF 的函數
+def text_to_gif(prompt):
+    output = pipe(prompt, guidance_scale=1.0, num_inference_steps=step)
+    gif_path = "animation.gif"
+    export_to_gif(output.frames[0], gif_path)
+    return gif_path
+
+# 設置 Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to GIF Generator using AnimateDiff")
+    prompt = gr.Textbox(label="Enter your prompt", placeholder="Describe the animation you want to create")
+    gif_output = gr.Image(label="Generated GIF")
+    generate_btn = gr.Button("Generate GIF")
+    
+    generate_btn.click(fn=text_to_gif, inputs=prompt, outputs=gif_output)
+
+# 啟動 Gradio 應用
+demo.launch()