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GenVideo_app.py

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+import gradio as gr
+import torch
+from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
+from diffusers.utils import export_to_gif
+from diffusers.utils import export_to_video
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import uuid
+import spaces
+
+# Available adapters (replace with your actual adapter names)
+adapter_options = {
+    "zoom-out":"guoyww/animatediff-motion-lora-zoom-out",
+    "zoom-in":"guoyww/animatediff-motion-lora-zoom-in",
+    "pan-left":"guoyww/animatediff-motion-lora-pan-left",
+    "pan-right":"guoyww/animatediff-motion-lora-pan-right",
+    "roll-clockwise":"guoyww/animatediff-motion-lora-rolling-clockwise",
+    "roll-anticlockwise":"guoyww/animatediff-motion-lora-rolling-anticlockwise",
+    "tilt-up":"guoyww/animatediff-motion-lora-tilt-up",
+    "tilt-down":"guoyww/animatediff-motion-lora-tilt-down"
+}
+
+def load_cached_examples():
+    examples = [
+        ["a cat playing with a ball of yarn", "blurry", 7.5, 12, ["zoom-in"]],
+        ["a dog running in a field", "dark, indoors", 8.0, 8, ["pan-left", "tilt-up"]],
+    ]
+    return examples
+
+device = "cuda"
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+
+pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to(device)
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe.scheduler = scheduler
+
+@spaces.GPU
+def generate_video(prompt,negative_prompt, guidance_scale, num_inference_steps, adapter_choices):
+
+    pipe.to(device)
+
+    # Set adapters based on user selection
+    if adapter_choices:
+        for i in range(len(adapter_choices)):
+            adapter_name = adapter_choices[i]
+            pipe.load_lora_weights(
+                adapter_options[adapter_name], adapter_name=adapter_name,
+            )
+        pipe.set_adapters(adapter_choices, adapter_weights=[1.0] * len(adapter_choices))
+        print(adapter_choices)
+
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_frames=16,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+    )
+    name = str(uuid.uuid4()).replace("-", "")
+    path = f"/tmp/{name}.mp4"
+    export_to_video(output.frames[0], path, fps=10)
+    return path
+
+
+
+iface = gr.Interface(
+    theme=gr.themes.Soft(primary_hue="cyan", secondary_hue="teal"),
+    fn=generate_video,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Textbox(label="Negative Prompt"),
+        gr.Slider(minimum=0.5, maximum=10, value=7.5, label="Guidance Scale"),
+        gr.Slider(minimum=4, maximum=24, step=4, value=4, label="Inference Steps"),
+        gr.CheckboxGroup(adapter_options.keys(), label="Adapter Choice",type='value'),
+    ],
+    outputs=gr.Video(label="Generated Video"),
+    examples = [
+        ["Urban ambiance, man walking, neon lights, rain, wet floor, high quality", "bad quality", 7.5, 24, []],
+        ["Nature, farms, mountains in background, drone shot, high quality","bad quality" ,8.0, 24, []],
+    ],
+    cache_examples=True
+)
+
+iface.launch()