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 MORE = """ ## TRY Other Models ### JARVIS: Your VOICE Assistant -> https://huggingface.co/spaces/KingNish/JARVIS ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-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) 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 with gr.Blocks(css="style.css") as demo: gr.HTML( "

Instant⚡Video

" + "

You may change the steps from 4 to 8, if you didn't get satisfied results.

" + "

First Video Generating takes time then Videos generate faster.

" + "

To get best results Make Sure to Write prompts in style as Given in Examples/p>" + "

Must Share you Best Results with Community - Click HERE

" ) with gr.Group(): with gr.Row(): prompt = gr.Textbox( label='Prompt' ) with gr.Row(): select_base = gr.Dropdown( label='Base model', choices=[ "Cartoon", "Realistic", "3d", "Anime", ], value=base_loaded, interactive=True ) 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 ) select_step = gr.Dropdown( label='Inference steps', choices=[ ('1-Step', 1), ('2-Step', 2), ('4-Step', 4), ('8-Step', 8), ], value=4, interactive=True ) submit = gr.Button( scale=1, variant='primary' ) video = gr.Video( label='AnimateDiff-Lightning', autoplay=True, height=512, width=512, elem_id="video_output" ) 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, ) gr.Examples( examples=[ ["Focus: Eiffel Tower (Animate: Clouds moving)"], #Atmosphere Movement Example ["Focus: Trees In forest (Animate: Lion running)"], #Object Movement Example ["Focus: Astronaut in Space"], #Normal ["Focus: Group of Birds in sky (Animate: Birds Moving) (Shot From distance)"], #Camera distance ["Focus: Statue of liberty (Shot from Drone) (Animate: Drone coming toward statue)"], #Camera Movement ["Focus: Panda in Forest (Animate: Drinking Tea)"], #Doing Something ["Focus: Kids Playing (Season: Winter)"], #Atmosphere or Season {"Focus: Cars in Street (Season: Rain, Daytime) (Shot from Distance) (Movement: Cars running)"} #Mixture ], fn=generate_image, inputs=[prompt], outputs=video, cache_examples=True, ) demo.queue().launch()