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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
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) |