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| import gradio as gr | |
| import torch | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers.utils import export_to_video | |
| from video_diffusion.utils.scheduler_list import diff_scheduler_list, get_scheduler_list | |
| stable_model_list =["damo-vilab/text-to-video-ms-1.7b","cerspense/zeroscope_v2_576w","strangeman3107/animov-0.1.1"] | |
| class DamoText2VideoGenerator: | |
| def __init__(self): | |
| self.pipe = None | |
| def load_model(self, stable_model, scheduler): | |
| if self.pipe is None: | |
| self.pipe = DiffusionPipeline.from_pretrained( | |
| stable_model, torch_dtype=torch.float32 | |
| ) | |
| self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler) | |
| self.pipe.to("cpu") | |
| #self.pipe.enable_xformers_memory_efficient_attention() | |
| return self.pipe | |
| def generate_video( | |
| self, | |
| prompt: str, | |
| negative_prompt: str, | |
| stable_model:str, | |
| num_frames: int, | |
| num_inference_steps: int, | |
| guidance_scale: int, | |
| height: int, | |
| width: int, | |
| scheduler: str, | |
| ): | |
| pipe = self.load_model(stable_model=stable_model, scheduler=scheduler) | |
| video = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| num_frames=int(num_frames), | |
| height=height, | |
| width=width, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| ).frames | |
| video_path = export_to_video(video) | |
| return video_path | |
| def app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| dano_text2video_prompt = gr.Textbox(lines=1, placeholder="Prompt", show_label=False) | |
| dano_text2video_negative_prompt = gr.Textbox( | |
| lines=1, placeholder="Negative Prompt", show_label=False | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| dano_text2video_model_list = gr.Dropdown( | |
| choices=stable_model_list, | |
| label="Model List", | |
| value=stable_model_list[0], | |
| ) | |
| dano_text2video_num_inference_steps = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| value=50, | |
| step=1, | |
| label="Inference Steps", | |
| ) | |
| dano_text2video_guidance_scale = gr.Slider( | |
| minimum=1, | |
| maximum=15, | |
| value=7, | |
| step=1, | |
| label="Guidance Scale", | |
| ) | |
| dano_text2video_num_frames = gr.Slider( | |
| minimum=1, | |
| maximum=50, | |
| value=16, | |
| step=1, | |
| label="Number of Frames", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| dano_text2video_height = gr.Slider( | |
| minimum=128, | |
| maximum=1280, | |
| value=512, | |
| step=32, | |
| label="Height", | |
| ) | |
| dano_text2video_width = gr.Slider( | |
| minimum=128, | |
| maximum=1280, | |
| value=512, | |
| step=32, | |
| label="Width", | |
| ) | |
| damo_text2video_scheduler = gr.Dropdown( | |
| choices=diff_scheduler_list, | |
| label="Scheduler", | |
| value=diff_scheduler_list[6], | |
| ) | |
| dano_text2video_generate = gr.Button(value="Generator") | |
| with gr.Column(): | |
| dano_output = gr.Video(label="Output") | |
| dano_text2video_generate.click( | |
| fn=DamoText2VideoGenerator().generate_video, | |
| inputs=[ | |
| dano_text2video_prompt, | |
| dano_text2video_negative_prompt, | |
| dano_text2video_model_list, | |
| dano_text2video_num_frames, | |
| dano_text2video_num_inference_steps, | |
| dano_text2video_guidance_scale, | |
| dano_text2video_height, | |
| dano_text2video_width, | |
| damo_text2video_scheduler, | |
| ], | |
| outputs=dano_output, | |
| ) | |