| """Modified from https://github.com/guoyww/AnimateDiff/blob/main/app.py
|
| """
|
| import base64
|
| import gc
|
| import json
|
| import os
|
| import hashlib
|
| import random
|
| from datetime import datetime
|
| from glob import glob
|
|
|
| import cv2
|
| import gradio as gr
|
| import numpy as np
|
| import pkg_resources
|
| import requests
|
| import torch
|
| from diffusers import (CogVideoXDDIMScheduler, DDIMScheduler,
|
| DPMSolverMultistepScheduler,
|
| EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
|
| FlowMatchEulerDiscreteScheduler, PNDMScheduler)
|
| from omegaconf import OmegaConf
|
| from PIL import Image
|
| from safetensors import safe_open
|
|
|
| from ..data.bucket_sampler import ASPECT_RATIO_512, get_closest_ratio
|
| from ..utils.utils import save_videos_grid
|
| from ..utils.fm_solvers import FlowDPMSolverMultistepScheduler
|
| from ..utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
|
| from ..dist import set_multi_gpus_devices
|
|
|
| gradio_version = pkg_resources.get_distribution("gradio").version
|
| gradio_version_is_above_4 = True if int(gradio_version.split('.')[0]) >= 4 else False
|
|
|
| css = """
|
| .toolbutton {
|
| margin-buttom: 0em 0em 0em 0em;
|
| max-width: 2.5em;
|
| min-width: 2.5em !important;
|
| height: 2.5em;
|
| }
|
| """
|
|
|
| ddpm_scheduler_dict = {
|
| "Euler": EulerDiscreteScheduler,
|
| "Euler A": EulerAncestralDiscreteScheduler,
|
| "DPM++": DPMSolverMultistepScheduler,
|
| "PNDM": PNDMScheduler,
|
| "DDIM": DDIMScheduler,
|
| "DDIM_Origin": DDIMScheduler,
|
| "DDIM_Cog": CogVideoXDDIMScheduler,
|
| }
|
| flow_scheduler_dict = {
|
| "Flow": FlowMatchEulerDiscreteScheduler,
|
| "Flow_Unipc": FlowUniPCMultistepScheduler,
|
| "Flow_DPM++": FlowDPMSolverMultistepScheduler,
|
| }
|
| all_cheduler_dict = {**ddpm_scheduler_dict, **flow_scheduler_dict}
|
|
|
| class Fun_Controller:
|
| def __init__(
|
| self, GPU_memory_mode, scheduler_dict, model_name=None, model_type="Inpaint",
|
| config_path=None, ulysses_degree=1, ring_degree=1,
|
| fsdp_dit=False, fsdp_text_encoder=False, compile_dit=False,
|
| weight_dtype=None, savedir_sample=None,
|
| ):
|
|
|
| self.basedir = os.getcwd()
|
| self.config_dir = os.path.join(self.basedir, "config")
|
| self.diffusion_transformer_dir = os.path.join(self.basedir, "models", "Diffusion_Transformer")
|
| self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
|
| self.personalized_model_dir = os.path.join(self.basedir, "models", "Personalized_Model")
|
| if savedir_sample is None:
|
| self.savedir_sample = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
|
| else:
|
| self.savedir_sample = savedir_sample
|
| os.makedirs(self.savedir_sample, exist_ok=True)
|
|
|
| self.GPU_memory_mode = GPU_memory_mode
|
| self.model_name = model_name
|
| self.diffusion_transformer_dropdown = model_name
|
| self.scheduler_dict = scheduler_dict
|
| self.model_type = model_type
|
| if config_path is not None:
|
| self.config_path = os.path.realpath(config_path)
|
| self.config = OmegaConf.load(config_path)
|
| else:
|
| self.config_path = None
|
| self.ulysses_degree = ulysses_degree
|
| self.ring_degree = ring_degree
|
| self.fsdp_dit = fsdp_dit
|
| self.fsdp_text_encoder = fsdp_text_encoder
|
| self.compile_dit = compile_dit
|
| self.weight_dtype = weight_dtype
|
| self.device = set_multi_gpus_devices(self.ulysses_degree, self.ring_degree)
|
|
|
| self.diffusion_transformer_list = []
|
| self.motion_module_list = []
|
| self.personalized_model_list = []
|
| self.config_list = []
|
|
|
|
|
| self.tokenizer = None
|
| self.text_encoder = None
|
| self.vae = None
|
| self.transformer = None
|
| self.transformer_2 = None
|
| self.pipeline = None
|
| self.base_model_path = "none"
|
| self.base_model_2_path = "none"
|
| self.lora_model_path = "none"
|
| self.lora_model_2_path = "none"
|
|
|
| self.refresh_config()
|
| self.refresh_diffusion_transformer()
|
| self.refresh_personalized_model()
|
| if model_name != None:
|
| self.update_diffusion_transformer(model_name)
|
|
|
| def refresh_config(self):
|
| config_list = []
|
| for root, dirs, files in os.walk(self.config_dir):
|
| for file in files:
|
| if file.endswith(('.yaml', '.yml')):
|
| full_path = os.path.join(root, file)
|
| config_list.append(full_path)
|
| self.config_list = config_list
|
|
|
| def refresh_diffusion_transformer(self):
|
| self.diffusion_transformer_list = sorted(glob(os.path.join(self.diffusion_transformer_dir, "*/")))
|
|
|
| def refresh_personalized_model(self):
|
| personalized_model_list = sorted(glob(os.path.join(self.personalized_model_dir, "*.safetensors")))
|
| self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
|
|
|
| def update_model_type(self, model_type):
|
| self.model_type = model_type
|
|
|
| def update_config(self, config_dropdown):
|
| self.config_path = config_dropdown
|
| self.config = OmegaConf.load(config_dropdown)
|
| print(f"Update config: {config_dropdown}")
|
|
|
| def update_diffusion_transformer(self, diffusion_transformer_dropdown):
|
| pass
|
|
|
| def update_base_model(self, base_model_dropdown, is_checkpoint_2=False):
|
| if not is_checkpoint_2:
|
| self.base_model_path = base_model_dropdown
|
| else:
|
| self.base_model_2_path = base_model_dropdown
|
| print(f"Update base model: {base_model_dropdown}")
|
| if base_model_dropdown == "none":
|
| return gr.update()
|
| if self.transformer is None and not is_checkpoint_2:
|
| gr.Info(f"Please select a pretrained model path.")
|
| print(f"Please select a pretrained model path.")
|
| return gr.update(value=None)
|
| elif self.transformer_2 is None and is_checkpoint_2:
|
| gr.Info(f"Please select a pretrained model path.")
|
| print(f"Please select a pretrained model path.")
|
| return gr.update(value=None)
|
| else:
|
| base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
|
| base_model_state_dict = {}
|
| with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
|
| for key in f.keys():
|
| base_model_state_dict[key] = f.get_tensor(key)
|
| if not is_checkpoint_2:
|
| self.transformer.load_state_dict(base_model_state_dict, strict=False)
|
| else:
|
| self.transformer_2.load_state_dict(base_model_state_dict, strict=False)
|
| print("Update base model done")
|
| return gr.update()
|
|
|
| def update_lora_model(self, lora_model_dropdown, is_checkpoint_2=False):
|
| print(f"Update lora model: {lora_model_dropdown}")
|
| if lora_model_dropdown == "none":
|
| self.lora_model_path = "none"
|
| return gr.update()
|
| lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
|
| if not is_checkpoint_2:
|
| self.lora_model_path = lora_model_dropdown
|
| else:
|
| self.lora_model_2_path = lora_model_dropdown
|
| return gr.update()
|
|
|
| def clear_cache(self,):
|
| gc.collect()
|
| torch.cuda.empty_cache()
|
| torch.cuda.ipc_collect()
|
|
|
| def auto_model_clear_cache(self, model):
|
| origin_device = model.device
|
| model = model.to("cpu")
|
| gc.collect()
|
| torch.cuda.empty_cache()
|
| torch.cuda.ipc_collect()
|
| model = model.to(origin_device)
|
|
|
| def input_check(self,
|
| resize_method,
|
| generation_method,
|
| start_image,
|
| end_image,
|
| validation_video,
|
| control_video,
|
| is_api = False,
|
| ):
|
| if self.transformer is None:
|
| if is_api:
|
| return "", f"Please select a pretrained model path."
|
| else:
|
| raise gr.Error(f"Please select a pretrained model path.")
|
|
|
| if control_video is not None and self.model_type == "Inpaint":
|
| if is_api:
|
| return "", f"If specifying the control video, please set the model_type == \"Control\". "
|
| else:
|
| raise gr.Error(f"If specifying the control video, please set the model_type == \"Control\". ")
|
|
|
| if control_video is None and self.model_type == "Control":
|
| if is_api:
|
| return "", f"If set the model_type == \"Control\", please specifying the control video. "
|
| else:
|
| raise gr.Error(f"If set the model_type == \"Control\", please specifying the control video. ")
|
|
|
| if resize_method == "Resize according to Reference":
|
| if start_image is None and validation_video is None and control_video is None:
|
| if is_api:
|
| return "", f"Please upload an image when using \"Resize according to Reference\"."
|
| else:
|
| raise gr.Error(f"Please upload an image when using \"Resize according to Reference\".")
|
|
|
| if self.transformer.config.in_channels == self.vae.config.latent_channels and start_image is not None:
|
| if is_api:
|
| return "", f"Please select an image to video pretrained model while using image to video."
|
| else:
|
| raise gr.Error(f"Please select an image to video pretrained model while using image to video.")
|
|
|
| if self.transformer.config.in_channels == self.vae.config.latent_channels and generation_method == "Long Video Generation":
|
| if is_api:
|
| return "", f"Please select an image to video pretrained model while using long video generation."
|
| else:
|
| raise gr.Error(f"Please select an image to video pretrained model while using long video generation.")
|
|
|
| if start_image is None and end_image is not None:
|
| if is_api:
|
| return "", f"If specifying the ending image of the video, please specify a starting image of the video."
|
| else:
|
| raise gr.Error(f"If specifying the ending image of the video, please specify a starting image of the video.")
|
| return "", "OK"
|
|
|
| def get_height_width_from_reference(
|
| self,
|
| base_resolution,
|
| start_image,
|
| validation_video,
|
| control_video,
|
| ):
|
| spatial_compression_ratio = self.vae.config.spatial_compression_ratio if hasattr(self.vae.config, "spatial_compression_ratio") else 8
|
| aspect_ratio_sample_size = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
| if self.model_type == "Inpaint":
|
| if validation_video is not None:
|
| original_width, original_height = Image.fromarray(cv2.VideoCapture(validation_video).read()[1]).size
|
| else:
|
| original_width, original_height = start_image[0].size if type(start_image) is list else Image.open(start_image).size
|
| else:
|
| original_width, original_height = Image.fromarray(cv2.VideoCapture(control_video).read()[1]).size
|
| closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
|
| height_slider, width_slider = [int(x / spatial_compression_ratio / 2) * spatial_compression_ratio * 2 for x in closest_size]
|
| return height_slider, width_slider
|
|
|
| def save_outputs(self, is_image, length_slider, sample, fps):
|
| def save_results():
|
| if not os.path.exists(self.savedir_sample):
|
| os.makedirs(self.savedir_sample, exist_ok=True)
|
| index = len([path for path in os.listdir(self.savedir_sample)]) + 1
|
| prefix = str(index).zfill(8)
|
|
|
| md5_hash = hashlib.md5(sample.cpu().numpy().tobytes()).hexdigest()
|
|
|
| if is_image or length_slider == 1:
|
| save_sample_path = os.path.join(self.savedir_sample, prefix + f"-{md5_hash}.png")
|
| print(f"Saving to {save_sample_path}")
|
| image = sample[0, :, 0]
|
| image = image.transpose(0, 1).transpose(1, 2)
|
| image = (image * 255).numpy().astype(np.uint8)
|
| image = Image.fromarray(image)
|
| image.save(save_sample_path)
|
|
|
| else:
|
| save_sample_path = os.path.join(self.savedir_sample, prefix + f"-{md5_hash}.mp4")
|
| print(f"Saving to {save_sample_path}")
|
| save_videos_grid(sample, save_sample_path, fps=fps)
|
| return save_sample_path
|
|
|
| if self.ulysses_degree * self.ring_degree > 1:
|
| import torch.distributed as dist
|
| if dist.get_rank() == 0:
|
| save_sample_path = save_results()
|
| else:
|
| save_sample_path = None
|
| else:
|
| save_sample_path = save_results()
|
| return save_sample_path
|
|
|
| def generate(
|
| self,
|
| diffusion_transformer_dropdown,
|
| base_model_dropdown,
|
| lora_model_dropdown,
|
| lora_alpha_slider,
|
| prompt_textbox,
|
| negative_prompt_textbox,
|
| sampler_dropdown,
|
| sample_step_slider,
|
| resize_method,
|
| width_slider,
|
| height_slider,
|
| base_resolution,
|
| generation_method,
|
| length_slider,
|
| overlap_video_length,
|
| partial_video_length,
|
| cfg_scale_slider,
|
| start_image,
|
| end_image,
|
| validation_video,
|
| validation_video_mask,
|
| control_video,
|
| denoise_strength,
|
| seed_textbox,
|
| enable_teacache = None,
|
| teacache_threshold = None,
|
| num_skip_start_steps = None,
|
| teacache_offload = None,
|
| cfg_skip_ratio = None,
|
| enable_riflex = None,
|
| riflex_k = None,
|
| is_api = False,
|
| ):
|
| pass
|
|
|
| def post_to_host(
|
| diffusion_transformer_dropdown,
|
| base_model_dropdown, lora_model_dropdown, lora_alpha_slider,
|
| prompt_textbox, negative_prompt_textbox,
|
| sampler_dropdown, sample_step_slider, resize_method, width_slider, height_slider,
|
| base_resolution, generation_method, length_slider, cfg_scale_slider,
|
| start_image, end_image, validation_video, validation_video_mask, denoise_strength, seed_textbox,
|
| ref_image = None, enable_teacache = None, teacache_threshold = None, num_skip_start_steps = None,
|
| teacache_offload = None, cfg_skip_ratio = None,enable_riflex = None, riflex_k = None,
|
| ):
|
| if start_image is not None:
|
| with open(start_image, 'rb') as file:
|
| file_content = file.read()
|
| start_image_encoded_content = base64.b64encode(file_content)
|
| start_image = start_image_encoded_content.decode('utf-8')
|
|
|
| if end_image is not None:
|
| with open(end_image, 'rb') as file:
|
| file_content = file.read()
|
| end_image_encoded_content = base64.b64encode(file_content)
|
| end_image = end_image_encoded_content.decode('utf-8')
|
|
|
| if validation_video is not None:
|
| with open(validation_video, 'rb') as file:
|
| file_content = file.read()
|
| validation_video_encoded_content = base64.b64encode(file_content)
|
| validation_video = validation_video_encoded_content.decode('utf-8')
|
|
|
| if validation_video_mask is not None:
|
| with open(validation_video_mask, 'rb') as file:
|
| file_content = file.read()
|
| validation_video_mask_encoded_content = base64.b64encode(file_content)
|
| validation_video_mask = validation_video_mask_encoded_content.decode('utf-8')
|
|
|
| if ref_image is not None:
|
| with open(ref_image, 'rb') as file:
|
| file_content = file.read()
|
| ref_image_encoded_content = base64.b64encode(file_content)
|
| ref_image = ref_image_encoded_content.decode('utf-8')
|
|
|
| datas = {
|
| "base_model_path": base_model_dropdown,
|
| "lora_model_path": lora_model_dropdown,
|
| "lora_alpha_slider": lora_alpha_slider,
|
| "prompt_textbox": prompt_textbox,
|
| "negative_prompt_textbox": negative_prompt_textbox,
|
| "sampler_dropdown": sampler_dropdown,
|
| "sample_step_slider": sample_step_slider,
|
| "resize_method": resize_method,
|
| "width_slider": width_slider,
|
| "height_slider": height_slider,
|
| "base_resolution": base_resolution,
|
| "generation_method": generation_method,
|
| "length_slider": length_slider,
|
| "cfg_scale_slider": cfg_scale_slider,
|
| "start_image": start_image,
|
| "end_image": end_image,
|
| "validation_video": validation_video,
|
| "validation_video_mask": validation_video_mask,
|
| "denoise_strength": denoise_strength,
|
| "seed_textbox": seed_textbox,
|
|
|
| "ref_image": ref_image,
|
| "enable_teacache": enable_teacache,
|
| "teacache_threshold": teacache_threshold,
|
| "num_skip_start_steps": num_skip_start_steps,
|
| "teacache_offload": teacache_offload,
|
| "cfg_skip_ratio": cfg_skip_ratio,
|
| "enable_riflex": enable_riflex,
|
| "riflex_k": riflex_k,
|
| }
|
|
|
| session = requests.session()
|
| session.headers.update({"Authorization": os.environ.get("EAS_TOKEN")})
|
|
|
| response = session.post(url=f'{os.environ.get("EAS_URL")}/videox_fun/infer_forward', json=datas, timeout=300)
|
|
|
| outputs = response.json()
|
| return outputs
|
|
|
|
|
| class Fun_Controller_Client:
|
| def __init__(self, scheduler_dict, savedir_sample):
|
| self.basedir = os.getcwd()
|
| if savedir_sample is None:
|
| self.savedir_sample = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
|
| else:
|
| self.savedir_sample = savedir_sample
|
| os.makedirs(self.savedir_sample, exist_ok=True)
|
|
|
| self.scheduler_dict = scheduler_dict
|
|
|
| def generate(
|
| self,
|
| diffusion_transformer_dropdown,
|
| base_model_dropdown,
|
| lora_model_dropdown,
|
| lora_alpha_slider,
|
| prompt_textbox,
|
| negative_prompt_textbox,
|
| sampler_dropdown,
|
| sample_step_slider,
|
| resize_method,
|
| width_slider,
|
| height_slider,
|
| base_resolution,
|
| generation_method,
|
| length_slider,
|
| cfg_scale_slider,
|
| start_image,
|
| end_image,
|
| validation_video,
|
| validation_video_mask,
|
| denoise_strength,
|
| seed_textbox,
|
| ref_image = None,
|
| enable_teacache = None,
|
| teacache_threshold = None,
|
| num_skip_start_steps = None,
|
| teacache_offload = None,
|
| cfg_skip_ratio = None,
|
| enable_riflex = None,
|
| riflex_k = None,
|
| ):
|
| is_image = True if generation_method == "Image Generation" else False
|
|
|
| outputs = post_to_host(
|
| diffusion_transformer_dropdown,
|
| base_model_dropdown, lora_model_dropdown, lora_alpha_slider,
|
| prompt_textbox, negative_prompt_textbox,
|
| sampler_dropdown, sample_step_slider, resize_method, width_slider, height_slider,
|
| base_resolution, generation_method, length_slider, cfg_scale_slider,
|
| start_image, end_image, validation_video, validation_video_mask, denoise_strength,
|
| seed_textbox, ref_image = ref_image, enable_teacache = enable_teacache, teacache_threshold = teacache_threshold,
|
| num_skip_start_steps = num_skip_start_steps, teacache_offload = teacache_offload,
|
| cfg_skip_ratio = cfg_skip_ratio, enable_riflex = enable_riflex, riflex_k = riflex_k,
|
| )
|
|
|
| try:
|
| base64_encoding = outputs["base64_encoding"]
|
| except Exception:
|
| return gr.Image(visible=False, value=None), gr.Video(None, visible=True), outputs["message"]
|
|
|
| decoded_data = base64.b64decode(base64_encoding)
|
|
|
| if not os.path.exists(self.savedir_sample):
|
| os.makedirs(self.savedir_sample, exist_ok=True)
|
| md5_hash = hashlib.md5(decoded_data).hexdigest()
|
|
|
| index = len([path for path in os.listdir(self.savedir_sample)]) + 1
|
| prefix = str(index).zfill(8)
|
|
|
| if is_image or length_slider == 1:
|
| save_sample_path = os.path.join(self.savedir_sample, prefix + f"-{md5_hash}.png")
|
| print(f"Saving to {save_sample_path}")
|
| with open(save_sample_path, "wb") as file:
|
| file.write(decoded_data)
|
| if gradio_version_is_above_4:
|
| return gr.Image(value=save_sample_path, visible=True), gr.Video(value=None, visible=False), "Success"
|
| else:
|
| return gr.Image.update(value=save_sample_path, visible=True), gr.Video.update(value=None, visible=False), "Success"
|
| else:
|
| save_sample_path = os.path.join(self.savedir_sample, prefix + f"-{md5_hash}.mp4")
|
| print(f"Saving to {save_sample_path}")
|
| with open(save_sample_path, "wb") as file:
|
| file.write(decoded_data)
|
| if gradio_version_is_above_4:
|
| return gr.Image(visible=False, value=None), gr.Video(value=save_sample_path, visible=True), "Success"
|
| else:
|
| return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
|
|
|
