from enum import Enum import gc import numpy as np import torch import decord from diffusers import StableDiffusionInstructPix2PixPipeline, StableDiffusionControlNetPipeline, ControlNetModel, UNet2DConditionModel from diffusers.schedulers import EulerAncestralDiscreteScheduler, DDIMScheduler from text_to_video.text_to_video_pipeline import TextToVideoPipeline import utils import gradio_utils decord.bridge.set_bridge('torch') class ModelType(Enum): Pix2Pix_Video = 1, Text2Video = 2, ControlNetCanny = 3, ControlNetCannyDB = 4, ControlNetPose = 5, class Model: def __init__(self, device, dtype, **kwargs): self.device = device self.dtype = dtype self.generator = torch.Generator(device=device) self.pipe_dict = { ModelType.Pix2Pix_Video: StableDiffusionInstructPix2PixPipeline, ModelType.Text2Video: TextToVideoPipeline, ModelType.ControlNetCanny: StableDiffusionControlNetPipeline, ModelType.ControlNetCannyDB: StableDiffusionControlNetPipeline, ModelType.ControlNetPose: StableDiffusionControlNetPipeline, } self.controlnet_attn_proc = utils.CrossFrameAttnProcessor(unet_chunk_size=2) self.pix2pix_attn_proc = utils.CrossFrameAttnProcessor(unet_chunk_size=3) self.text2video_attn_proc = utils.CrossFrameAttnProcessor(unet_chunk_size=2) self.pipe = None self.model_type = None self.states = {} def set_model(self, model_type: ModelType, model_id: str, **kwargs): if self.pipe is not None: del self.pipe torch.cuda.empty_cache() gc.collect() safety_checker = kwargs.pop('safety_checker', None) self.pipe = self.pipe_dict[model_type].from_pretrained(model_id, safety_checker=safety_checker, **kwargs).to(self.device).to(self.dtype) self.model_type = model_type def inference_chunk(self, frame_ids, **kwargs): if self.pipe is None: return image = kwargs.pop('image') prompt = np.array(kwargs.pop('prompt')) negative_prompt = np.array(kwargs.pop('negative_prompt', '')) latents = None if 'latents' in kwargs: latents = kwargs.pop('latents')[frame_ids] return self.pipe(image=image[frame_ids], prompt=prompt[frame_ids].tolist(), negative_prompt=negative_prompt[frame_ids].tolist(), latents=latents, generator=self.generator, **kwargs) def inference(self, split_to_chunks=False, chunk_size=8, **kwargs): if self.pipe is None: return seed = kwargs.pop('seed', 0) kwargs.pop('generator', '') # self.generator.manual_seed(seed) if split_to_chunks: assert 'image' in kwargs assert 'prompt' in kwargs image = kwargs.pop('image') prompt = kwargs.pop('prompt') negative_prompt = kwargs.pop('negative_prompt', '') f = image.shape[0] chunk_ids = np.arange(0, f, chunk_size - 1) result = [] for i in range(len(chunk_ids)): ch_start = chunk_ids[i] ch_end = f if i == len(chunk_ids) - 1 else chunk_ids[i + 1] frame_ids = [0] + list(range(ch_start, ch_end)) self.generator.manual_seed(seed) print(f'Processing chunk {i + 1} / {len(chunk_ids)}') result.append(self.inference_chunk(frame_ids=frame_ids, image=image, prompt=[prompt] * f, negative_prompt=[negative_prompt] * f, **kwargs).images[1:]) result = np.concatenate(result) return result else: return self.pipe(generator=self.generator, **kwargs).videos[0] def process_controlnet_canny(self, video_path, prompt, num_inference_steps=20, controlnet_conditioning_scale=1.0, guidance_scale=9.0, seed=42, eta=0.0, low_threshold=100, high_threshold=200, resolution=512): video_path = gradio_utils.edge_path_to_video_path(video_path) if self.model_type != ModelType.ControlNetCanny: controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") self.set_model(ModelType.ControlNetCanny, model_id="runwayml/stable-diffusion-v1-5", controlnet=controlnet) self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) self.pipe.unet.set_attn_processor(processor=self.controlnet_attn_proc) self.pipe.controlnet.set_attn_processor(processor=self.controlnet_attn_proc) # TODO: Check scheduler added_prompt = 'best quality, extremely detailed' negative_prompts = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' video, fps = utils.prepare_video(video_path, resolution, self.device, self.dtype, False) control = utils.pre_process_canny(video, low_threshold, high_threshold).to(self.device).to(self.dtype) f, _, h, w = video.shape self.generator.manual_seed(seed) latents = torch.randn((1, 4, h//8, w//8), dtype=self.dtype, device=self.device, generator=self.generator) latents = latents.repeat(f, 1, 1, 1) result = self.inference(image=control, prompt=prompt + ', ' + added_prompt, height=h, width=w, negative_prompt=negative_prompts, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, eta=eta, latents=latents, seed=seed, output_type='numpy', split_to_chunks=True, chunk_size=8, ) return utils.create_video(result, fps) def process_controlnet_pose(self, video_path, prompt, num_inference_steps=20, controlnet_conditioning_scale=1.0, guidance_scale=9.0, seed=42, eta=0.0, resolution=512): video_path = gradio_utils.motion_to_video_path(video_path) if self.model_type != ModelType.ControlNetPose: controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose") self.set_model(ModelType.ControlNetPose, model_id="runwayml/stable-diffusion-v1-5", controlnet=controlnet) self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) self.pipe.unet.set_attn_processor(processor=self.controlnet_attn_proc) self.pipe.controlnet.set_attn_processor(processor=self.controlnet_attn_proc) added_prompt = 'best quality, extremely detailed, HD, ultra-realistic, 8K, HQ, masterpiece, trending on artstation, art, smooth' negative_prompts = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer difits, cropped, worst quality, low quality, deformed body, bloated, ugly, unrealistic' video, fps = utils.prepare_video(video_path, resolution, self.device, self.dtype, False, output_fps=4) control = utils.pre_process_pose(video, apply_pose_detect=False).to(self.device).to(self.dtype) f, _, h, w = video.shape self.generator.manual_seed(seed) latents = torch.randn((1, 4, h//8, w//8), dtype=self.dtype, device=self.device, generator=self.generator) latents = latents.repeat(f, 1, 1, 1) result = self.inference(image=control, prompt=prompt + ', ' + added_prompt, height=h, width=w, negative_prompt=negative_prompts, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, eta=eta, latents=latents, seed=seed, output_type='numpy', split_to_chunks=True, chunk_size=8, ) return utils.create_gif(result, fps) # return utils.create_video(result, fps) def process_controlnet_canny_db(self, db_path, video_path, prompt, num_inference_steps=20, controlnet_conditioning_scale=1.0, guidance_scale=9.0, seed=42, eta=0.0, low_threshold=100, high_threshold=200, resolution=512): db_path = gradio_utils.get_model_from_db_selection(db_path) video_path = gradio_utils.get_video_from_canny_selection(video_path) # Load db and controlnet weights if 'db_path' not in self.states or db_path != self.states['db_path']: controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") self.set_model(ModelType.ControlNetCannyDB, model_id=db_path, controlnet=controlnet) self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) self.pipe.unet.set_attn_processor(processor=self.controlnet_attn_proc) self.pipe.controlnet.set_attn_processor(processor=self.controlnet_attn_proc) self.states['db_path'] = db_path added_prompt = 'best quality, extremely detailed' negative_prompts = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' video, fps = utils.prepare_video(video_path, resolution, self.device, self.dtype, False) control = utils.pre_process_canny(video, low_threshold, high_threshold).to(self.device).to(self.dtype) f, _, h, w = video.shape self.generator.manual_seed(seed) latents = torch.randn((1, 4, h//8, w//8), dtype=self.dtype, device=self.device, generator=self.generator) latents = latents.repeat(f, 1, 1, 1) result = self.inference(image=control, prompt=prompt + ', ' + added_prompt, height=h, width=w, negative_prompt=negative_prompts, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, eta=eta, latents=latents, seed=seed, output_type='numpy', split_to_chunks=True, chunk_size=8, ) return utils.create_gif(result, fps) def process_pix2pix(self, video, prompt, resolution=512, seed=0, start_t=0, end_t=-1, out_fps=-1): end_t = start_t+15 if self.model_type != ModelType.Pix2Pix_Video: self.set_model(ModelType.Pix2Pix_Video, model_id="timbrooks/instruct-pix2pix") self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config) self.pipe.unet.set_attn_processor(processor=self.pix2pix_attn_proc) video, fps = utils.prepare_video(video, resolution, self.device, self.dtype, True, start_t, end_t, out_fps) self.generator.manual_seed(seed) result = self.inference(image=video, prompt=prompt, seed=seed, output_type='numpy', num_inference_steps=50, image_guidance_scale=1.5, split_to_chunks=True, chunk_size=8, ) return utils.create_video(result, fps) def process_text2video(self, prompt, motion_field_strength_x=12,motion_field_strength_y=12, n_prompt="", resolution=512, seed=24, num_frames=8, fps=2, t0=881, t1=941, use_cf_attn=True, use_motion_field=True, smooth_bg=False, smooth_bg_strength=0.4 ): if self.model_type != ModelType.Text2Video: unet = UNet2DConditionModel.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder="unet") self.set_model(ModelType.Text2Video, model_id="runwayml/stable-diffusion-v1-5", unet=unet) self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) if use_cf_attn: self.pipe.unet.set_attn_processor(processor=self.text2video_attn_proc) self.generator.manual_seed(seed) added_prompt = "high quality, HD, 8K, trending on artstation, high focus, dramatic lighting" negative_prompts = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer difits, cropped, worst quality, low quality, deformed body, bloated, ugly, unrealistic' prompt = prompt.rstrip() if len(prompt) > 0 and (prompt[-1] == "," or prompt[-1] == "."): prompt = prompt.rstrip()[:-1] prompt = prompt.rstrip() prompt = prompt + ", "+added_prompt if len(n_prompt)>0: negative_prompt = [n_prompt] else: negative_prompt = None result = self.inference(prompt=[prompt], video_length=num_frames, height=resolution, width=resolution, num_inference_steps=50, guidance_scale=7.5, guidance_stop_step=1.0, t0=t0, t1=t1, motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y, use_motion_field=use_motion_field, smooth_bg=smooth_bg, smooth_bg_strength=smooth_bg_strength, seed=seed, output_type='numpy', negative_prompt = negative_prompt, ) return utils.create_video(result, fps)