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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ByGXyiHZWM_q"
},
"source": [
"# **Deforum Stable Diffusion v0.6**\n",
"[Stable Diffusion](https://github.com/CompVis/stable-diffusion) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bj\u00f6rn Ommer and the [Stability.ai](https://stability.ai/) Team. [K Diffusion](https://github.com/crowsonkb/k-diffusion) by [Katherine Crowson](https://twitter.com/RiversHaveWings).\n",
"\n",
"[Quick Guide](https://docs.google.com/document/d/1RrQv7FntzOuLg4ohjRZPVL7iptIyBhwwbcEYEW2OfcI/edit?usp=sharing) to Deforum v0.6\n",
"\n",
"Notebook by [deforum](https://discord.gg/upmXXsrwZc)"
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "IJjzzkKlWM_s"
},
"source": [
"#@markdown **NVIDIA GPU**\n",
"import subprocess, os, sys\n",
"sub_p_res = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,memory.free', '--format=csv,noheader'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
"print(f\"{sub_p_res[:-1]}\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"id": "UA8-efH-WM_t"
},
"source": [
"# Setup"
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "0D2HQO-PWM_t"
},
"source": [
"\n",
"import subprocess, time, gc, os, sys\n",
"\n",
"def setup_environment():\n",
" print_subprocess = False\n",
" use_xformers_for_colab = True\n",
" try:\n",
" ipy = get_ipython()\n",
" except:\n",
" ipy = 'could not get_ipython'\n",
" if 'google.colab' in str(ipy):\n",
" print(\"..setting up environment\")\n",
" start_time = time.time()\n",
" all_process = [\n",
" ['pip', 'install', 'torch==1.12.1+cu113', 'torchvision==0.13.1+cu113', '--extra-index-url', 'https://download.pytorch.org/whl/cu113'],\n",
" ['pip', 'install', 'omegaconf==2.2.3', 'einops==0.4.1', 'pytorch-lightning==1.7.4', 'torchmetrics==0.9.3', 'torchtext==0.13.1', 'transformers==4.21.2', 'kornia==0.6.7'],\n",
" ['git', 'clone', 'https://github.com/deforum-art/deforum-stable-diffusion'],\n",
" ['pip', 'install', 'accelerate', 'ftfy', 'jsonmerge', 'matplotlib', 'resize-right', 'timm', 'torchdiffeq','scikit-learn'],\n",
" ]\n",
" for process in all_process:\n",
" running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
" if print_subprocess:\n",
" print(running)\n",
" with open('deforum-stable-diffusion/src/k_diffusion/__init__.py', 'w') as f:\n",
" f.write('')\n",
" sys.path.extend([\n",
" 'deforum-stable-diffusion/',\n",
" 'deforum-stable-diffusion/src',\n",
" ])\n",
" end_time = time.time()\n",
"\n",
" if use_xformers_for_colab:\n",
"\n",
" print(\"..installing xformers\")\n",
"\n",
" all_process = [['pip', 'install', 'triton==2.0.0.dev20220701']]\n",
" for process in all_process:\n",
" running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
" if print_subprocess:\n",
" print(running)\n",
" \n",
" v_card_name = subprocess.run(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
" if 't4' in v_card_name.lower():\n",
" name_to_download = 'T4'\n",
" elif 'v100' in v_card_name.lower():\n",
" name_to_download = 'V100'\n",
" elif 'a100' in v_card_name.lower():\n",
" name_to_download = 'A100'\n",
" elif 'p100' in v_card_name.lower():\n",
" name_to_download = 'P100'\n",
" else:\n",
" print(v_card_name + ' is currently not supported with xformers flash attention in deforum!')\n",
"\n",
" x_ver = 'xformers-0.0.13.dev0-py3-none-any.whl'\n",
" x_link = 'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/' + name_to_download + '/' + x_ver\n",
" \n",
" all_process = [\n",
" ['wget', x_link],\n",
" ['pip', 'install', x_ver],\n",
" ['mv', 'deforum-stable-diffusion/src/ldm/modules/attention.py', 'deforum-stable-diffusion/src/ldm/modules/attention_backup.py'],\n",
" ['mv', 'deforum-stable-diffusion/src/ldm/modules/attention_xformers.py', 'deforum-stable-diffusion/src/ldm/modules/attention.py']\n",
" ]\n",
"\n",
" for process in all_process:\n",
" running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
" if print_subprocess:\n",
" print(running)\n",
"\n",
" print(f\"Environment set up in {end_time-start_time:.0f} seconds\")\n",
" else:\n",
" sys.path.extend([\n",
" 'src'\n",
" ])\n",
" return\n",
"\n",
"setup_environment()\n",
"\n",
"import torch\n",
"import random\n",
"import clip\n",
"from IPython import display\n",
"from types import SimpleNamespace\n",
"from helpers.save_images import get_output_folder\n",
"from helpers.settings import load_args\n",
"from helpers.render import render_animation, render_input_video, render_image_batch, render_interpolation\n",
"from helpers.model_load import make_linear_decode, load_model, get_model_output_paths\n",
"from helpers.aesthetics import load_aesthetics_model\n",
"\n",
"#@markdown **Path Setup**\n",
"\n",
"def Root():\n",
" models_path = \"models\" #@param {type:\"string\"}\n",
" configs_path = \"configs\" #@param {type:\"string\"}\n",
" output_path = \"output\" #@param {type:\"string\"}\n",
" mount_google_drive = True #@param {type:\"boolean\"}\n",
" models_path_gdrive = \"/content/drive/MyDrive/AI/models\" #@param {type:\"string\"}\n",
" output_path_gdrive = \"/content/drive/MyDrive/AI/StableDiffusion\" #@param {type:\"string\"}\n",
"\n",
" #@markdown **Model Setup**\n",
" model_config = \"v1-inference.yaml\" #@param [\"custom\",\"v1-inference.yaml\"]\n",
" model_checkpoint = \"v1-5-pruned-emaonly.ckpt\" #@param [\"custom\",\"v1-5-pruned.ckpt\",\"v1-5-pruned-emaonly.ckpt\",\"sd-v1-4-full-ema.ckpt\",\"sd-v1-4.ckpt\",\"sd-v1-3-full-ema.ckpt\",\"sd-v1-3.ckpt\",\"sd-v1-2-full-ema.ckpt\",\"sd-v1-2.ckpt\",\"sd-v1-1-full-ema.ckpt\",\"sd-v1-1.ckpt\", \"robo-diffusion-v1.ckpt\",\"wd-v1-3-float16.ckpt\"]\n",
" custom_config_path = \"\" #@param {type:\"string\"}\n",
" custom_checkpoint_path = \"\" #@param {type:\"string\"}\n",
" half_precision = True\n",
" return locals()\n",
"\n",
"root = Root()\n",
"root = SimpleNamespace(**root)\n",
"\n",
"root.models_path, root.output_path = get_model_output_paths(root)\n",
"root.model, root.device = load_model(root, \n",
" load_on_run_all=True\n",
" , \n",
" check_sha256=True\n",
" )"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"id": "6JxwhBwtWM_t"
},
"source": [
"# Settings"
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "E0tJVYA4WM_u"
},
"source": [
"def DeforumAnimArgs():\n",
"\n",
" #@markdown ####**Animation:**\n",
" animation_mode = 'None' #@param ['None', '2D', '3D', 'Video Input', 'Interpolation'] {type:'string'}\n",
" max_frames = 1000 #@param {type:\"number\"}\n",
" border = 'replicate' #@param ['wrap', 'replicate'] {type:'string'}\n",
"\n",
" #@markdown ####**Motion Parameters:**\n",
" angle = \"0:(0)\"#@param {type:\"string\"}\n",
" zoom = \"0:(1.04)\"#@param {type:\"string\"}\n",
" translation_x = \"0:(10*sin(2*3.14*t/10))\"#@param {type:\"string\"}\n",
" translation_y = \"0:(0)\"#@param {type:\"string\"}\n",
" translation_z = \"0:(10)\"#@param {type:\"string\"}\n",
" rotation_3d_x = \"0:(0)\"#@param {type:\"string\"}\n",
" rotation_3d_y = \"0:(0)\"#@param {type:\"string\"}\n",
" rotation_3d_z = \"0:(0)\"#@param {type:\"string\"}\n",
" flip_2d_perspective = False #@param {type:\"boolean\"}\n",
" perspective_flip_theta = \"0:(0)\"#@param {type:\"string\"}\n",
" perspective_flip_phi = \"0:(t%15)\"#@param {type:\"string\"}\n",
" perspective_flip_gamma = \"0:(0)\"#@param {type:\"string\"}\n",
" perspective_flip_fv = \"0:(53)\"#@param {type:\"string\"}\n",
" noise_schedule = \"0: (0.02)\"#@param {type:\"string\"}\n",
" strength_schedule = \"0: (0.65)\"#@param {type:\"string\"}\n",
" contrast_schedule = \"0: (1.0)\"#@param {type:\"string\"}\n",
"\n",
" #@markdown ####**Coherence:**\n",
" color_coherence = 'Match Frame 0 LAB' #@param ['None', 'Match Frame 0 HSV', 'Match Frame 0 LAB', 'Match Frame 0 RGB'] {type:'string'}\n",
" diffusion_cadence = '1' #@param ['1','2','3','4','5','6','7','8'] {type:'string'}\n",
"\n",
" #@markdown ####**3D Depth Warping:**\n",
" use_depth_warping = True #@param {type:\"boolean\"}\n",
" midas_weight = 0.3#@param {type:\"number\"}\n",
" near_plane = 200\n",
" far_plane = 10000\n",
" fov = 40#@param {type:\"number\"}\n",
" padding_mode = 'border'#@param ['border', 'reflection', 'zeros'] {type:'string'}\n",
" sampling_mode = 'bicubic'#@param ['bicubic', 'bilinear', 'nearest'] {type:'string'}\n",
" save_depth_maps = False #@param {type:\"boolean\"}\n",
"\n",
" #@markdown ####**Video Input:**\n",
" video_init_path ='/content/video_in.mp4'#@param {type:\"string\"}\n",
" extract_nth_frame = 1#@param {type:\"number\"}\n",
" overwrite_extracted_frames = True #@param {type:\"boolean\"}\n",
" use_mask_video = False #@param {type:\"boolean\"}\n",
" video_mask_path ='/content/video_in.mp4'#@param {type:\"string\"}\n",
"\n",
" #@markdown ####**Interpolation:**\n",
" interpolate_key_frames = False #@param {type:\"boolean\"}\n",
" interpolate_x_frames = 4 #@param {type:\"number\"}\n",
" \n",
" #@markdown ####**Resume Animation:**\n",
" resume_from_timestring = False #@param {type:\"boolean\"}\n",
" resume_timestring = \"20220829210106\" #@param {type:\"string\"}\n",
"\n",
" return locals()"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {
"id": "i9fly1RIWM_u"
},
"source": [
"prompts = [\n",
" \"a beautiful lake by Asher Brown Durand, trending on Artstation\", # the first prompt I want\n",
" \"a beautiful portrait of a woman by Artgerm, trending on Artstation\", # the second prompt I want\n",
" #\"this prompt I don't want it I commented it out\",\n",
" #\"a nousr robot, trending on Artstation\", # use \"nousr robot\" with the robot diffusion model (see model_checkpoint setting)\n",
" #\"touhou 1girl komeiji_koishi portrait, green hair\", # waifu diffusion prompts can use danbooru tag groups (see model_checkpoint)\n",
" #\"this prompt has weights if prompt weighting enabled:2 can also do negative:-2\", # (see prompt_weighting)\n",
"]\n",
"\n",
"animation_prompts = {\n",
" 0: \"a beautiful apple, trending on Artstation\",\n",
" 20: \"a beautiful banana, trending on Artstation\",\n",
" 30: \"a beautiful coconut, trending on Artstation\",\n",
" 40: \"a beautiful durian, trending on Artstation\",\n",
"}"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "XVzhbmizWM_u"
},
"source": [
"#@markdown **Load Settings**\n",
"override_settings_with_file = False #@param {type:\"boolean\"}\n",
"settings_file = \"custom\" #@param [\"custom\", \"512x512_aesthetic_0.json\",\"512x512_aesthetic_1.json\",\"512x512_colormatch_0.json\",\"512x512_colormatch_1.json\",\"512x512_colormatch_2.json\",\"512x512_colormatch_3.json\"]\n",
"custom_settings_file = \"/content/drive/MyDrive/Settings.txt\"#@param {type:\"string\"}\n",
"\n",
"def DeforumArgs():\n",
" #@markdown **Image Settings**\n",
" W = 512 #@param\n",
" H = 512 #@param\n",
" W, H = map(lambda x: x - x % 64, (W, H)) # resize to integer multiple of 64\n",
"\n",
" #@markdown **Sampling Settings**\n",
" seed = -1 #@param\n",
" sampler = 'dpmpp_2s_a' #@param [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\",\"plms\", \"ddim\", \"dpm_fast\", \"dpm_adaptive\", \"dpmpp_2s_a\", \"dpmpp_2m\"]\n",
" steps = 80 #@param\n",
" scale = 7 #@param\n",
" ddim_eta = 0.0 #@param\n",
" dynamic_threshold = None\n",
" static_threshold = None \n",
"\n",
" #@markdown **Save & Display Settings**\n",
" save_samples = True #@param {type:\"boolean\"}\n",
" save_settings = True #@param {type:\"boolean\"}\n",
" display_samples = True #@param {type:\"boolean\"}\n",
" save_sample_per_step = False #@param {type:\"boolean\"}\n",
" show_sample_per_step = False #@param {type:\"boolean\"}\n",
"\n",
" #@markdown **Prompt Settings**\n",
" prompt_weighting = True #@param {type:\"boolean\"}\n",
" normalize_prompt_weights = True #@param {type:\"boolean\"}\n",
" log_weighted_subprompts = False #@param {type:\"boolean\"}\n",
"\n",
" #@markdown **Batch Settings**\n",
" n_batch = 1 #@param\n",
" batch_name = \"StableFun\" #@param {type:\"string\"}\n",
" filename_format = \"{timestring}_{index}_{prompt}.png\" #@param [\"{timestring}_{index}_{seed}.png\",\"{timestring}_{index}_{prompt}.png\"]\n",
" seed_behavior = \"iter\" #@param [\"iter\",\"fixed\",\"random\"]\n",
" make_grid = False #@param {type:\"boolean\"}\n",
" grid_rows = 2 #@param \n",
" outdir = get_output_folder(root.output_path, batch_name)\n",
"\n",
" #@markdown **Init Settings**\n",
" use_init = False #@param {type:\"boolean\"}\n",
" strength = 0.0 #@param {type:\"number\"}\n",
" strength_0_no_init = True # Set the strength to 0 automatically when no init image is used\n",
" init_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n",
" # Whiter areas of the mask are areas that change more\n",
" use_mask = False #@param {type:\"boolean\"}\n",
" use_alpha_as_mask = False # use the alpha channel of the init image as the mask\n",
" mask_file = \"https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg\" #@param {type:\"string\"}\n",
" invert_mask = False #@param {type:\"boolean\"}\n",
" # Adjust mask image, 1.0 is no adjustment. Should be positive numbers.\n",
" mask_brightness_adjust = 1.0 #@param {type:\"number\"}\n",
" mask_contrast_adjust = 1.0 #@param {type:\"number\"}\n",
" # Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding\n",
" overlay_mask = True # {type:\"boolean\"}\n",
" # Blur edges of final overlay mask, if used. Minimum = 0 (no blur)\n",
" mask_overlay_blur = 5 # {type:\"number\"}\n",
"\n",
" #@markdown **Exposure/Contrast Conditional Settings**\n",
" mean_scale = 0 #@param {type:\"number\"}\n",
" var_scale = 0 #@param {type:\"number\"}\n",
" exposure_scale = 0 #@param {type:\"number\"}\n",
" exposure_target = 0.5 #@param {type:\"number\"}\n",
"\n",
" #@markdown **Color Match Conditional Settings**\n",
" colormatch_scale = 0 #@param {type:\"number\"}\n",
" colormatch_image = \"https://www.saasdesign.io/wp-content/uploads/2021/02/palette-3-min-980x588.png\" #@param {type:\"string\"}\n",
" colormatch_n_colors = 4 #@param {type:\"number\"}\n",
" ignore_sat_weight = 0 #@param {type:\"number\"}\n",
"\n",
" #@markdown **CLIP\\Aesthetics Conditional Settings**\n",
" clip_name = 'ViT-L/14' #@param ['ViT-L/14', 'ViT-L/14@336px', 'ViT-B/16', 'ViT-B/32']\n",
" clip_scale = 0 #@param {type:\"number\"}\n",
" aesthetics_scale = 0 #@param {type:\"number\"}\n",
" cutn = 1 #@param {type:\"number\"}\n",
" cut_pow = 0.0001 #@param {type:\"number\"}\n",
"\n",
" #@markdown **Other Conditional Settings**\n",
" init_mse_scale = 0 #@param {type:\"number\"}\n",
" init_mse_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n",
"\n",
" blue_scale = 0 #@param {type:\"number\"}\n",
" \n",
" #@markdown **Conditional Gradient Settings**\n",
" gradient_wrt = 'x0_pred' #@param [\"x\", \"x0_pred\"]\n",
" gradient_add_to = 'both' #@param [\"cond\", \"uncond\", \"both\"]\n",
" decode_method = 'linear' #@param [\"autoencoder\",\"linear\"]\n",
" grad_threshold_type = 'dynamic' #@param [\"dynamic\", \"static\", \"mean\", \"schedule\"]\n",
" clamp_grad_threshold = 0.2 #@param {type:\"number\"}\n",
" clamp_start = 0.2 #@param\n",
" clamp_stop = 0.01 #@param\n",
" grad_inject_timing = list(range(1,10)) #@param\n",
"\n",
" #@markdown **Speed vs VRAM Settings**\n",
" cond_uncond_sync = True #@param {type:\"boolean\"}\n",
"\n",
" n_samples = 1 # doesnt do anything\n",
" precision = 'autocast' \n",
" C = 4\n",
" f = 8\n",
"\n",
" prompt = \"\"\n",
" timestring = \"\"\n",
" init_latent = None\n",
" init_sample = None\n",
" init_sample_raw = None\n",
" mask_sample = None\n",
" init_c = None\n",
"\n",
" return locals()\n",
"\n",
"args_dict = DeforumArgs()\n",
"anim_args_dict = DeforumAnimArgs()\n",
"\n",
"if override_settings_with_file:\n",
" load_args(args_dict, anim_args_dict, settings_file, custom_settings_file, verbose=False)\n",
"\n",
"args = SimpleNamespace(**args_dict)\n",
"anim_args = SimpleNamespace(**anim_args_dict)\n",
"\n",
"args.timestring = time.strftime('%Y%m%d%H%M%S')\n",
"args.strength = max(0.0, min(1.0, args.strength))\n",
"\n",
"# Load clip model if using clip guidance\n",
"if (args.clip_scale > 0) or (args.aesthetics_scale > 0):\n",
" root.clip_model = clip.load(args.clip_name, jit=False)[0].eval().requires_grad_(False).to(root.device)\n",
" if (args.aesthetics_scale > 0):\n",
" root.aesthetics_model = load_aesthetics_model(args, root)\n",
"\n",
"if args.seed == -1:\n",
" args.seed = random.randint(0, 2**32 - 1)\n",
"if not args.use_init:\n",
" args.init_image = None\n",
"if args.sampler == 'plms' and (args.use_init or anim_args.animation_mode != 'None'):\n",
" print(f\"Init images aren't supported with PLMS yet, switching to KLMS\")\n",
" args.sampler = 'klms'\n",
"if args.sampler != 'ddim':\n",
" args.ddim_eta = 0\n",
"\n",
"if anim_args.animation_mode == 'None':\n",
" anim_args.max_frames = 1\n",
"elif anim_args.animation_mode == 'Video Input':\n",
" args.use_init = True\n",
"\n",
"# clean up unused memory\n",
"gc.collect()\n",
"torch.cuda.empty_cache()\n",
"\n",
"# dispatch to appropriate renderer\n",
"if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':\n",
" render_animation(args, anim_args, animation_prompts, root)\n",
"elif anim_args.animation_mode == 'Video Input':\n",
" render_input_video(args, anim_args, animation_prompts, root)\n",
"elif anim_args.animation_mode == 'Interpolation':\n",
" render_interpolation(args, anim_args, animation_prompts, root)\n",
"else:\n",
" render_image_batch(args, prompts, root)"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {
"id": "gJ88kZ2-WM_v"
},
"source": [
"# Create Video From Frames"
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "XQGeqaGAWM_v"
},
"source": [
"skip_video_for_run_all = True #@param {type: 'boolean'}\n",
"fps = 12 #@param {type:\"number\"}\n",
"#@markdown **Manual Settings**\n",
"use_manual_settings = False #@param {type:\"boolean\"}\n",
"image_path = \"/content/drive/MyDrive/AI/StableDiffusion/2022-09/20220903000939_%05d.png\" #@param {type:\"string\"}\n",
"mp4_path = \"/content/drive/MyDrive/AI/StableDiffusion/2022-09/20220903000939.mp4\" #@param {type:\"string\"}\n",
"render_steps = False #@param {type: 'boolean'}\n",
"path_name_modifier = \"x0_pred\" #@param [\"x0_pred\",\"x\"]\n",
"make_gif = False\n",
"\n",
"if skip_video_for_run_all == True:\n",
" print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')\n",
"else:\n",
" import os\n",
" import subprocess\n",
" from base64 import b64encode\n",
"\n",
" print(f\"{image_path} -> {mp4_path}\")\n",
"\n",
" if use_manual_settings:\n",
" max_frames = \"200\" #@param {type:\"string\"}\n",
" else:\n",
" if render_steps: # render steps from a single image\n",
" fname = f\"{path_name_modifier}_%05d.png\"\n",
" all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]\n",
" newest_dir = max(all_step_dirs, key=os.path.getmtime)\n",
" image_path = os.path.join(newest_dir, fname)\n",
" print(f\"Reading images from {image_path}\")\n",
" mp4_path = os.path.join(newest_dir, f\"{args.timestring}_{path_name_modifier}.mp4\")\n",
" max_frames = str(args.steps)\n",
" else: # render images for a video\n",
" image_path = os.path.join(args.outdir, f\"{args.timestring}_%05d.png\")\n",
" mp4_path = os.path.join(args.outdir, f\"{args.timestring}.mp4\")\n",
" max_frames = str(anim_args.max_frames)\n",
"\n",
" # make video\n",
" cmd = [\n",
" 'ffmpeg',\n",
" '-y',\n",
" '-vcodec', 'png',\n",
" '-r', str(fps),\n",
" '-start_number', str(0),\n",
" '-i', image_path,\n",
" '-frames:v', max_frames,\n",
" '-c:v', 'libx264',\n",
" '-vf',\n",
" f'fps={fps}',\n",
" '-pix_fmt', 'yuv420p',\n",
" '-crf', '17',\n",
" '-preset', 'veryfast',\n",
" '-pattern_type', 'sequence',\n",
" mp4_path\n",
" ]\n",
" process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
" stdout, stderr = process.communicate()\n",
" if process.returncode != 0:\n",
" print(stderr)\n",
" raise RuntimeError(stderr)\n",
"\n",
" mp4 = open(mp4_path,'rb').read()\n",
" data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
" display.display(display.HTML(f'<video controls loop><source src=\"{data_url}\" type=\"video/mp4\"></video>') )\n",
" \n",
" if make_gif:\n",
" gif_path = os.path.splitext(mp4_path)[0]+'.gif'\n",
" cmd_gif = [\n",
" 'ffmpeg',\n",
" '-y',\n",
" '-i', mp4_path,\n",
" '-r', str(fps),\n",
" gif_path\n",
" ]\n",
" process_gif = subprocess.Popen(cmd_gif, stdout=subprocess.PIPE, stderr=subprocess.PIPE)"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "MMpAcyrYWM_v"
},
"source": [
"skip_disconnect_for_run_all = True #@param {type: 'boolean'}\n",
"\n",
"if skip_disconnect_for_run_all == True:\n",
" print('Skipping disconnect, uncheck skip_disconnect_for_run_all if you want to run it')\n",
"else:\n",
" from google.colab import runtime\n",
" runtime.unassign()"
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.6 ('dsd')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
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"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "b7e04c8a9537645cbc77fa0cbde8069bc94e341b0d5ced104651213865b24e58"
}
},
"colab": {
"provenance": []
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 4
} |