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 import uuid import spaces 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) pipe.load_lora_weights( "diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out", ) pipe.load_lora_weights( "diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left", ) 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, guidance_scale, num_inference_steps, num_frames, adapter_choice): pipe.to(device) # Set adapters based on user selection if adapter_choice: pipe.set_adapters([adapter_choice], adapter_weights=[1.0]) output = pipe( prompt=prompt, negative_prompt="bad quality, worse quality", num_frames=num_frames, 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 # Available adapters (replace with your actual adapter names) adapter_options = ["zoom-out", "pan-left"] iface = gr.Interface( fn=generate_video, inputs=[ gr.Textbox(label="Enter your 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.Slider(minimum=16, maximum=64, step=1, value=16, label="Frames"), gr.Dropdown(choices=adapter_options, label="Select Adapter") # Add dropdown for adapter selection ], outputs=gr.Video(label="Generated Video"), ) iface.launch()