import gradio as gr import torch import os import spaces import uuid from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler from diffusers.utils import export_to_video from huggingface_hub import hf_hub_download from safetensors.torch import load_file from PIL import Image # Constants bases = { "Cartoon": "frankjoshua/toonyou_beta6", "Realistic": "emilianJR/epiCRealism", "3d": "Lykon/DreamShaper", "Anime": "Yntec/mistoonAnime2" } step_loaded = None base_loaded = "Realistic" motion_loaded = None # Ensure model and scheduler are initialized in GPU-enabled function if not torch.cuda.is_available(): raise NotImplementedError("No GPU detected!") device = "cuda" dtype = torch.float16 pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device) pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear") # Safety checkers from transformers import CLIPFeatureExtractor feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32") # Function @spaces.GPU(duration=60,queue=False) def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Progress()): global step_loaded global base_loaded global motion_loaded print(prompt, base, step) step = int(step) if step_loaded != step: repo = "ByteDance/AnimateDiff-Lightning" ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors" pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False) step_loaded = step if base_loaded != base: pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False) base_loaded = base if motion_loaded != motion: pipe.unload_lora_weights() if motion != "": pipe.load_lora_weights(motion, adapter_name="motion") pipe.set_adapters(["motion"], [0.7]) motion_loaded = motion progress((0, step)) def progress_callback(i, t, z): progress((i+1, step)) output = pipe(prompt=prompt, guidance_scale=1.2, num_inference_steps=step, callback=progress_callback, callback_steps=1) name = str(uuid.uuid4()).replace("-", "") path = f"/tmp/{name}.mp4" export_to_video(output.frames[0], path, fps=10) return path # Gradio Interface css = """ body {font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: #f4f4f9; color: #333;} h1 {color: #333; text-align: center; margin-bottom: 20px;} .gradio-container {max-width: 800px; margin: auto; padding: 20px; background: #fff; box-shadow: 0px 0px 20px rgba(0,0,0,0.1); border-radius: 10px;} .gr-input {margin-bottom: 15px;} .gr-button {width: 100%; background-color: #4CAF50; color: white; border: none; padding: 10px 20px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; border-radius: 5px; cursor: pointer; transition: background-color 0.3s;} .gr-button:hover {background-color: #45a049;} .gr-video {margin-top: 20px;} .gr-examples {margin-top: 30px;} .gr-examples .gr-example {display: inline-block; width: 100%; text-align: center; padding: 10px; background: #eaeaea; border-radius: 5px; margin-bottom: 10px;} .container {display: flex; flex-wrap: wrap;} .inputs, .output {padding: 20px;} .inputs {flex: 1; min-width: 300px;} .output {flex: 1; min-width: 300px;} @media (max-width: 768px) { .container {flex-direction: column-reverse;} } .svelte-1ybb3u7, .svelte-1clup3e {display: none !important;} """ with gr.Blocks(css=css) as demo: gr.HTML("

Instantāš” Text to Video

") with gr.Row(elem_id="container"): with gr.Column(elem_id="inputs"): prompt = gr.Textbox(label='Prompt', placeholder="Enter text to generate video...", elem_id="gr-input") select_base = gr.Dropdown( label='Base model', choices=["Cartoon", "Realistic", "3d", "Anime"], value=base_loaded, interactive=True, elem_id="gr-input" ) select_motion = gr.Dropdown( label='Motion', choices=[ ("Default", ""), ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"), ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"), ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"), ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"), ("Pan left", "guoyww/animatediff-motion-lora-pan-left"), ("Pan right", "guoyww/animatediff-motion-lora-pan-right"), ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"), ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"), ], value="guoyww/animatediff-motion-lora-zoom-in", interactive=True, elem_id="gr-input" ) select_step = gr.Dropdown( label='Inference steps', choices=[('1-Step', 1), ('2-Step', 2), ('4-Step', 4), ('8-Step', 8)], value=4, interactive=True, elem_id="gr-input" ) submit = gr.Button("Generate Video", variant='primary', elem_id="gr-button") with gr.Column(elem_id="output"): video = gr.Video(label='AnimateDiff-Lightning', autoplay=True, height=512, width=512, elem_id="gr-video") prompt.submit(fn=generate_image, inputs=[prompt, select_base, select_motion, select_step], outputs=video) submit.click(fn=generate_image, inputs=[prompt, select_base, select_motion, select_step], outputs=video, api_name="instant_video") gr.Examples( examples=[ ["Focus: Eiffel Tower (Animate: Clouds moving)"], ["Focus: Trees In forest (Animate: Lion running)"], ["Focus: Astronaut in Space"], ["Focus: Group of Birds in sky (Animate: Birds Moving) (Shot From distance)"], ["Focus: Statue of liberty (Shot from Drone) (Animate: Drone coming toward statue)"], ["Focus: Panda in Forest (Animate: Drinking Tea)"], ["Focus: Kids Playing (Season: Winter)"], ["Focus: Cars in Street (Season: Rain, Daytime) (Shot from Distance) (Movement: Cars running)"] ], fn=generate_image, inputs=[prompt], outputs=video, cache_examples=True, elem_id="gr-examples" ) demo.queue().launch(show_error=True)