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| 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 | |
| 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] | |
| if 'image' in kwargs: | |
| kwargs['image'] = kwargs['image'][frame_ids] | |
| if 'video_length' in kwargs: | |
| kwargs['video_length'] = len(frame_ids) | |
| if self.model_type == ModelType.Text2Video: | |
| kwargs["frame_ids"] = frame_ids | |
| return self.pipe(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) | |
| if seed < 0: | |
| seed = self.generator.seed() | |
| kwargs.pop('generator', '') | |
| if 'image' in kwargs: | |
| f = kwargs['image'].shape[0] | |
| else: | |
| f = kwargs['video_length'] | |
| assert 'prompt' in kwargs | |
| prompt = [kwargs.pop('prompt')] * f | |
| negative_prompt = [kwargs.pop('negative_prompt', '')] * f | |
| # Processing chunk-by-chunk | |
| if split_to_chunks: | |
| 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, | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| **kwargs).images[1:]) | |
| result = np.concatenate(result) | |
| return result | |
| else: | |
| return self.pipe(prompt=prompt, negative_prompt=negative_prompt, generator=self.generator, **kwargs).images | |
| def process_controlnet_canny(self, | |
| video_path, | |
| prompt, | |
| chunk_size=8, | |
| watermark=None, | |
| 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, | |
| use_cf_attn=True, | |
| save_path=None): | |
| 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) | |
| if use_cf_attn: | |
| 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' | |
| 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=chunk_size, | |
| ) | |
| return utils.create_video(result, fps, path=save_path, watermark=gradio_utils.logo_name_to_path(watermark)) | |
| def process_controlnet_pose(self, | |
| video_path, | |
| prompt, | |
| chunk_size=8, | |
| watermark=None, | |
| num_inference_steps=20, | |
| controlnet_conditioning_scale=1.0, | |
| guidance_scale=9.0, | |
| seed=42, | |
| eta=0.0, | |
| resolution=512, | |
| use_cf_attn=True, | |
| save_path=None): | |
| 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) | |
| if use_cf_attn: | |
| self.pipe.unet.set_attn_processor( | |
| processor=self.controlnet_attn_proc) | |
| self.pipe.controlnet.set_attn_processor( | |
| processor=self.controlnet_attn_proc) | |
| video_path = gradio_utils.motion_to_video_path( | |
| video_path) if 'Motion' in video_path else video_path | |
| 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=chunk_size, | |
| ) | |
| return utils.create_gif(result, fps, path=save_path, watermark=gradio_utils.logo_name_to_path(watermark)) | |
| def process_controlnet_canny_db(self, | |
| db_path, | |
| video_path, | |
| prompt, | |
| chunk_size=8, | |
| watermark=None, | |
| 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, | |
| use_cf_attn=True, | |
| save_path=None): | |
| 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.states['db_path'] = db_path | |
| if use_cf_attn: | |
| 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' | |
| 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=chunk_size, | |
| ) | |
| return utils.create_gif(result, fps, path=save_path, watermark=gradio_utils.logo_name_to_path(watermark)) | |
| def process_pix2pix(self, | |
| video, | |
| prompt, | |
| resolution=512, | |
| seed=0, | |
| image_guidance_scale=1.0, | |
| start_t=0, | |
| end_t=-1, | |
| out_fps=-1, | |
| chunk_size=8, | |
| watermark=None, | |
| use_cf_attn=True, | |
| save_path=None,): | |
| 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) | |
| if use_cf_attn: | |
| 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=image_guidance_scale, | |
| split_to_chunks=True, | |
| chunk_size=chunk_size, | |
| ) | |
| return utils.create_video(result, fps, path=save_path, watermark=gradio_utils.logo_name_to_path(watermark)) | |
| def process_text2video(self, | |
| prompt, | |
| model_name, | |
| motion_field_strength_x=12, | |
| motion_field_strength_y=12, | |
| t0=44, | |
| t1=47, | |
| n_prompt="", | |
| chunk_size=8, | |
| video_length=8, | |
| watermark=None, | |
| inject_noise_to_warp=False, | |
| resolution=512, | |
| seed=-1, | |
| fps=2, | |
| use_cf_attn=True, | |
| use_motion_field=True, | |
| smooth_bg=False, | |
| smooth_bg_strength=0.4, | |
| path=None): | |
| if self.model_type != ModelType.Text2Video: | |
| unet = UNet2DConditionModel.from_pretrained(model_name, subfolder="unet") | |
| self.set_model(ModelType.Text2Video, model_id=model_name, 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=video_length, | |
| 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, | |
| inject_noise_to_warp=inject_noise_to_warp, | |
| split_to_chunks=True, | |
| chunk_size=chunk_size, | |
| ) | |
| return utils.create_video(result, fps, path=path, watermark=gradio_utils.logo_name_to_path(watermark)) | |