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