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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jwZ0GT0eObBW",
"tags": []
},
"source": [
"## Install"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hTjD9Ij7Nuh4",
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!git clone https://github.com/FuouM/stable-diffusion-hidamari stable-diffusion\n",
"%cd stable-diffusion\n",
"!git pull\n",
"\n",
"!pip install albumentations==0.4.3\n",
"!pip install opencv-python==4.1.2.30\n",
"!pip install pudb==2019.2\n",
"!pip install imageio==2.9.0\n",
"!pip install imageio-ffmpeg==0.4.2\n",
"#!pip install pytorch-lightning==1.4.2\n",
"!pip install pytorch-lightning \n",
"!pip install omegaconf==2.1.1\n",
"!pip install test-tube>=0.7.5\n",
"!pip install streamlit>=0.73.1\n",
"!pip install einops==0.3.0\n",
"!pip install torch-fidelity==0.3.0\n",
"# !pip install pilmoji\n",
"\n",
"!pip install transformers==4.19.2\n",
"\n",
"!mkdir -p '/notebooks/stable-diffusion/Source'\n",
"!mkdir -p '/notebooks/stable-diffusion/Output'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!mkdir -p /notebooks/stable-diffusion/src/\n",
"%cd /notebooks/stable-diffusion/src/\n",
"!git clone https://github.com/CompVis/taming-transformers.git\n",
"%cd /notebooks/stable-diffusion/src/taming-transformers\n",
"!git pull\n",
"!pip install -e .\n",
"import taming # for some reason these new packages have to be imported here and not later on or else python fails to find them\n",
"\n",
"%cd /notebooks/stable-diffusion/src/\n",
"!git clone https://github.com/openai/CLIP.git\n",
"%cd /notebooks/stable-diffusion/src/CLIP\n",
"!git pull\n",
"!pip install -e .\n",
"import clip\n",
"\n",
"%cd /notebooks/stable-diffusion/src/\n",
"!git clone https://github.com/crowsonkb/k-diffusion.git\n",
"%cd /notebooks/stable-diffusion/src/k-diffusion\n",
"!git pull\n",
"!pip install .\n",
"!pip install kornia\n",
"import kornia"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XvHTXI7KOnu4",
"tags": []
},
"source": [
"## Download the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3BQoPx_8Hj08"
},
"outputs": [],
"source": [
"!wget https://storage.googleapis.com/ws-store2/wd-v1-2-full-ema.ckpt -O /notebooks/stable-diffusion/model.ckpt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CszrKJDe-66T",
"tags": []
},
"source": [
"# Optimized SD + K-diffusion (Updated as of 8/28)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0PXSWiHjOROA",
"tags": []
},
"source": [
"## Prepare"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "L70j4gz6_Aq1",
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"%cd /notebooks/stable-diffusion\n",
"\n",
"import argparse, os, sys, glob, random\n",
"import torch\n",
"import numpy as np\n",
"from random import randint\n",
"import math\n",
"\n",
"import time\n",
"\n",
"from omegaconf import OmegaConf\n",
"from PIL import Image\n",
"from tqdm import tqdm, trange\n",
"from itertools import islice\n",
"\n",
"from einops import rearrange, repeat\n",
"import time\n",
"from pytorch_lightning import seed_everything\n",
"from torch import autocast\n",
"from contextlib import contextmanager, nullcontext\n",
"from ldm.util import instantiate_from_config\n",
"\n",
"def chunk(it, size):\n",
" it = iter(it)\n",
" return iter(lambda: tuple(islice(it, size)), ())\n",
"\n",
"def load_model_from_config(ckpt, verbose=False):\n",
" print(f\"Loading model from {ckpt}\")\n",
" pl_sd = torch.load(ckpt, map_location=\"cpu\")\n",
" if \"global_step\" in pl_sd:\n",
" print(f\"Global Step: {pl_sd['global_step']}\")\n",
" sd = pl_sd[\"state_dict\"]\n",
" return sd\n",
"\n",
"def torch_gc():\n",
" torch.cuda.empty_cache()\n",
" torch.cuda.ipc_collect()\n",
" \n",
"def load_img(init_image, h0, w0):\n",
" \n",
" image = init_image.convert(\"RGB\")\n",
" w, h = image.size\n",
"\n",
" # print(f\"loaded input image of size ({w}, {h}) from {path}\") \n",
" if(h0 is not None and w0 is not None):\n",
" h, w = h0, w0\n",
" \n",
" w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32\n",
"\n",
" print(f\"New image size ({w}, {h})\")\n",
" image = image.resize((w, h), resample = Image.LANCZOS)\n",
" image = np.array(image).astype(np.float32) / 255.0\n",
" image = image[None].transpose(0, 3, 1, 2)\n",
" image = torch.from_numpy(image)\n",
" return 2.*image - 1.\n",
"\n",
"LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)\n",
"invalid_filename_chars = '<>:\"/\\|?*\\n'\n",
"\n",
"def resize_image(resize_mode, im, width, height):\n",
" if resize_mode == 0:\n",
" res = im.resize((width, height), resample=LANCZOS)\n",
" elif resize_mode == 1:\n",
" ratio = width / height\n",
" src_ratio = im.width / im.height\n",
"\n",
" src_w = width if ratio > src_ratio else im.width * height // im.height\n",
" src_h = height if ratio <= src_ratio else im.height * width // im.width\n",
"\n",
" resized = im.resize((src_w, src_h), resample=LANCZOS)\n",
" res = Image.new(\"RGB\", (width, height))\n",
" res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n",
" else:\n",
" if im.width != width or im.height != height:\n",
" ratio = width / height\n",
" src_ratio = im.width / im.height\n",
"\n",
" src_w = width if ratio < src_ratio else im.width * height // im.height\n",
" src_h = height if ratio >= src_ratio else im.height * width // im.width\n",
"\n",
" resized = im.resize((src_w, src_h), resample=LANCZOS)\n",
" res = Image.new(\"RGB\", (width, height))\n",
" res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n",
"\n",
" if ratio < src_ratio:\n",
" fill_height = height // 2 - src_h // 2\n",
" res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))\n",
" res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))\n",
" else:\n",
" fill_width = width // 2 - src_w // 2\n",
" res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))\n",
" res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))\n",
" else:\n",
" return im\n",
"\n",
" return res\n",
"\n",
"\n",
"import PIL\n",
"from PIL import Image, ImageFont, ImageDraw \n",
"\n",
"def add_margin(pil_img, top, right, bottom, left, color):\n",
" width, height = pil_img.size\n",
" new_width = width + right + left\n",
" new_height = height + top + bottom\n",
" result = Image.new(pil_img.mode, (new_width, new_height), color)\n",
" result.paste(pil_img, (left, top))\n",
" return result\n",
"\n",
"def text_wrap(text, font, max_width):\n",
" lines = []\n",
" if font.getsize(text)[0] <= max_width:\n",
" lines.append(text)\n",
" else:\n",
" words = text.split(' ')\n",
" i = 0\n",
" while i < len(words):\n",
" line = ''\n",
" while i < len(words) and font.getsize(line + words[i])[0] <= max_width:\n",
" line = line + words[i]+ \" \"\n",
" i += 1\n",
" if not line:\n",
" line = words[i]\n",
" i += 1\n",
" lines.append(line)\n",
" return lines\n",
"\n",
"def caption(image, prompt, info):\n",
" width, height = image.size\n",
"\n",
" font = ImageFont.truetype(\"/notebooks/stable-diffusion/NotoSansJP-Bold.otf\", 20, encoding='utf-8')\n",
" lines = text_wrap(prompt, font, image.size[0])\n",
" lines.append(f\"{info}\")\n",
" line_height = font.getsize('hg')[1]\n",
" cap_img = add_margin(image, 0, 0, line_height * (len(lines) + 1), 0, (255, 255, 255))\n",
" draw = ImageDraw.Draw(cap_img)\n",
" pad = 2\n",
" x = pad * 2\n",
" y = height + pad\n",
" for line in lines:\n",
" draw.text((x,y), line, fill=(0, 0, 0), font=font)\n",
" y = y + line_height\n",
" return cap_img\n",
"\n",
"def get_concat_h_blank(im1, im2, color=(255, 255, 255)):\n",
" dst = Image.new('RGB', (im1.width + im2.width, max(im1.height, im2.height)), color)\n",
" dst.paste(im1, (0, 0))\n",
" dst.paste(im2, (im1.width, 0))\n",
" return dst\n",
"\n",
"def get_concat_v_blank(im1, im2, color=(255, 255, 255)):\n",
" dst = Image.new('RGB', (max(im1.width, im2.width), im1.height + im2.height), color)\n",
" dst.paste(im1, (0, 0))\n",
" dst.paste(im2, (0, im1.height))\n",
" return dst\n",
"\n",
"def image_grid(imgs, batch_size, n_rows:int):\n",
" if n_rows > 0:\n",
" rows = n_rows\n",
" elif n_rows == 0:\n",
" rows = batch_size\n",
" else:\n",
" rows = math.sqrt(len(imgs))\n",
" rows = round(rows)\n",
"\n",
" cols = math.ceil(len(imgs) / rows)\n",
"\n",
" w, h = imgs[0].size\n",
" grid = Image.new('RGB', size=(cols * w, rows * h), color='black')\n",
"\n",
" for i, img in enumerate(imgs):\n",
" grid.paste(img, box=(i % cols * w, i // cols * h))\n",
"\n",
" return grid\n",
"\n",
"class User_OSD1:\n",
" def __init__(self, prompt: str, seed: int, samples: int, steps: int, scale: float, height:int, width: int,\n",
" rows: int, iter: int, skip_grid: bool, skip_save: bool):\n",
" self.prompt = prompt\n",
" self.seed = seed\n",
" self.n_samples = samples\n",
"\n",
" self.ddim_steps = steps\n",
" self.cfg_scale = scale\n",
" \n",
" self.height = height\n",
" self.width = width\n",
"\n",
" self.n_rows = rows\n",
"\n",
" self.n_iter = iter\n",
"\n",
" self.skip_grid = skip_grid\n",
" self.skip_save = skip_save\n",
" \n",
" \n",
"class User_OSD2:\n",
" def __init__(self, prompt: str, seed: int, samples: int, steps: int, scale: float, strength: float,\n",
" height:int, width: int, rows: int, iter: int, skip_grid: bool, skip_save: bool):\n",
" self.prompt = prompt\n",
" self.seed = seed\n",
"\n",
" self.n_samples = samples\n",
"\n",
" self.ddim_steps = steps\n",
" self.cfg_scale = scale\n",
" self.strength = strength\n",
"\n",
" self.height = height\n",
" self.width = width\n",
"\n",
" self.n_rows = rows\n",
" self.n_iter = iter\n",
"\n",
" self.skip_grid = skip_grid\n",
" self.skip_save = skip_save\n",
"\n",
"config = \"optimizedSD/v1-inference.yaml\"\n",
"ckpt = f\"model.ckpt\"\n",
"device = \"cuda\"\n",
"\n",
"sd = load_model_from_config(f\"{ckpt}\")\n",
"li, lo = [], []\n",
"\n",
"for key, value in sd.items():\n",
" sp = key.split('.')\n",
" if(sp[0]) == 'model':\n",
" if('input_blocks' in sp):\n",
" li.append(key)\n",
" elif('middle_block' in sp):\n",
" li.append(key)\n",
" elif('time_embed' in sp):\n",
" li.append(key)\n",
" else:\n",
" lo.append(key)\n",
" \n",
"for key in li:\n",
" sd['model1.' + key[6:]] = sd.pop(key)\n",
"for key in lo:\n",
" sd['model2.' + key[6:]] = sd.pop(key)\n",
"\n",
"config = OmegaConf.load(f\"{config}\")\n",
"\n",
"\n",
"model = instantiate_from_config(config.modelUNet)\n",
"_, _ = model.load_state_dict(sd, strict=False)\n",
"model.eval()\n",
"\n",
"modelCS = instantiate_from_config(config.modelCondStage)\n",
"_, _ = modelCS.load_state_dict(sd, strict=False)\n",
"modelCS.cond_stage_model.device = device\n",
"modelCS.eval()\n",
" \n",
"modelFS = instantiate_from_config(config.modelFirstStage)\n",
"_, _ = modelFS.load_state_dict(sd, strict=False)\n",
"modelFS.eval()\n",
"\n",
"model.unet_bs = True\n",
"model.cdevice = device\n",
"model.turbo = True\n",
"\n",
"del sd\n",
"\n",
"def txt2img_generate(user: User_OSD1, out_name: str):\n",
" torch_gc()\n",
" \n",
" device = \"cuda\"\n",
" C = 4\n",
" f = 8\n",
" ddim_eta = 0.0\n",
" start_code = None\n",
"\n",
" model.half()\n",
" modelCS.half()\n",
"\n",
" batch_size = user.n_samples\n",
" \n",
" if user.seed == -1:\n",
" user.seed = randint(0, 1000000)\n",
"\n",
" init_seed = user.seed\n",
"\n",
" seed_everything(user.seed)\n",
"\n",
" assert prompt is not None\n",
" data = [batch_size * [prompt]]\n",
"\n",
" precision_scope = autocast\n",
"\n",
" with torch.no_grad():\n",
"\n",
" all_samples = list()\n",
" for _ in trange(user.n_iter, desc=\"Sampling\"):\n",
" for prompts in tqdm(data, desc=\"data\"):\n",
" with precision_scope(\"cuda\"):\n",
" modelCS.to(device)\n",
" uc = None\n",
" if user.cfg_scale != 1.0:\n",
" uc = modelCS.get_learned_conditioning(batch_size * [\"\"])\n",
" if isinstance(prompts, tuple):\n",
" prompts = list(prompts)\n",
" \n",
" c = modelCS.get_learned_conditioning(prompts) \n",
"\n",
" shape = [C, height // f, width // f]\n",
" modelCS.to(\"cpu\") \n",
"\n",
" samples_ddim = model.sample(S=user.ddim_steps,\n",
" conditioning=c,\n",
" batch_size=batch_size,\n",
" seed = user.seed,\n",
" shape=shape,\n",
" verbose=False,\n",
" unconditional_guidance_scale=user.cfg_scale,\n",
" unconditional_conditioning=uc,\n",
" eta=ddim_eta,\n",
" x_T=start_code)\n",
"\n",
" modelFS.to(device)\n",
"\n",
" for i in range(batch_size):\n",
" \n",
" x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))\n",
" x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
" x_sample = 255. * rearrange(x_sample[0].cpu().numpy(), 'c h w -> h w c')\n",
"\n",
" out = Image.fromarray(x_sample.astype(np.uint8))\n",
" if not user.skip_save:\n",
" out.save(f\"/notebooks/stable-diffusion/Output/{out_name}_{init_seed}[{i}].png\")\n",
"\n",
" all_samples.append(out)\n",
" user.seed+=1\n",
"\n",
" modelFS.to(\"cpu\")\n",
"\n",
" del samples_ddim\n",
" del x_sample\n",
" del x_samples_ddim\n",
"\n",
" if not user.skip_grid:\n",
" grid = image_grid(all_samples, batch_size, user.n_rows)\n",
" all_samples.insert(0, grid)\n",
"\n",
" torch_gc()\n",
" return all_samples, init_seed\n",
"\n",
"def img2img_generate(user: User_OSD2, input_image, out_name: str):\n",
" torch_gc()\n",
" device = \"cuda\"\n",
" batch_size = user.n_samples\n",
" model.small_batch = False\n",
" \n",
" \n",
" init_image = load_img(input_image, user.height, user.width).to(device).half()\n",
"\n",
" model.half()\n",
" modelCS.half()\n",
" modelFS.half()\n",
" \n",
" if user.seed == -1:\n",
" user.seed = randint(0, 1000000)\n",
"\n",
" init_seed = user.seed\n",
"\n",
" seed_everything(user.seed)\n",
"\n",
" assert prompt is not None\n",
" data = [batch_size * [prompt]]\n",
"\n",
" modelFS.to(device)\n",
"\n",
" init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)\n",
" init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space\n",
"\n",
" modelFS.to(\"cpu\")\n",
"\n",
" assert 0. <= user.strength <= 1., 'can only work with strength in [0.0, 1.0]'\n",
" t_enc = int(user.strength * user.ddim_steps)\n",
" print(f\"target t_enc is {t_enc} steps\")\n",
"\n",
" precision_scope = autocast\n",
"\n",
" with torch.no_grad():\n",
" all_samples = list()\n",
" for _ in trange(user.n_iter, desc=\"Sampling\"):\n",
" for prompts in tqdm(data, desc=\"data\"):\n",
" with precision_scope(\"cuda\"):\n",
" modelCS.to(device)\n",
" uc = None\n",
" if user.cfg_scale != 1.0:\n",
" uc = modelCS.get_learned_conditioning(batch_size * [\"\"])\n",
" if isinstance(prompts, tuple):\n",
" prompts = list(prompts)\n",
" \n",
" c = modelCS.get_learned_conditioning(prompts)\n",
"\n",
" modelCS.to(\"cpu\")\n",
"\n",
" # encode (scaled latent)\n",
" z_enc = model.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device), user.seed,ddim_steps=user.ddim_steps, ddim_eta=0.0)\n",
" # decode it\n",
" samples_ddim = model.decode(z_enc, c, t_enc, unconditional_guidance_scale=user.cfg_scale,\n",
" unconditional_conditioning=uc,)\n",
"\n",
" modelFS.to(device)\n",
" # print(\"saving images\")\n",
" for i in range(batch_size):\n",
" \n",
" x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))\n",
" x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
" x_sample = 255. * rearrange(x_sample[0].cpu().numpy(), 'c h w -> h w c')\n",
"\n",
" # all_samples.append(x_sample.to(\"cpu\"))\n",
" # all_samples.append(Image.fromarray(x_sample.astype(np.uint8)))\n",
"\n",
" out = Image.fromarray(x_sample.astype(np.uint8))\n",
" if not user.skip_save:\n",
" out.save(f\"/notebooks/stable-diffusion/Output/{out_name}_{init_seed}[{i}].png\")\n",
" all_samples.append(out)\n",
"\n",
" user.seed+=1\n",
"\n",
"\n",
" modelFS.to(\"cpu\")\n",
"\n",
" del samples_ddim\n",
" del x_sample\n",
" del x_samples_ddim\n",
"\n",
" if not user.skip_grid:\n",
" grid = image_grid(all_samples, batch_size, user.n_rows)\n",
" all_samples.insert(0, grid)\n",
" torch_gc()\n",
" return all_samples, init_seed\n",
"\n",
"def txt2img(prompt, seed, samples, steps, scale, height, width, rows, iter, skip_grid, skip_save, out_name: str):\n",
" if(rows > samples):\n",
" rows = samples\n",
" user = User_OSD1(prompt, seed, samples, steps, scale, height, width, rows, iter, skip_grid, skip_save)\n",
" return txt2img_generate(user, out_name)\n",
"\n",
"def img2img(prompt, seed, samples, steps, scale, strength, height, width, rows,\n",
" iter, skip_grid, skip_save, mode, init_image, out_name):\n",
" if mode == \"Just resize\":\n",
" resize_mode = 0\n",
" elif mode == \"Crop and resize\":\n",
" resize_mode = 1\n",
" else:\n",
" resize_mode = 2\n",
" if(rows > samples):\n",
" rows = samples\n",
" user = User_OSD2(prompt, seed, samples, steps, scale, strength, height, width, rows, iter, skip_grid, skip_save)\n",
" init_image = resize_image(resize_mode, init_image, width, height)\n",
"\n",
" return img2img_generate(user, init_image, out_name) + (resize_mode,)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7v-FQVQ9OYtk",
"tags": []
},
"source": [
"# Inference"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9lnxMQBZ_asY"
},
"source": [
"### Text 2 Image\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "ImGOidHaAOZn"
},
"outputs": [],
"source": [
"prompt = \"a cute young girl\"\n",
"samples = 2\n",
"sampler = 'k_dpm_2' # [\"k_euler_a\",\"k-diffusion\", \"k_dpm_2\", \"k_dpm_2_a\", \"k_euler\", \"k_heun\"]\n",
"\n",
"scale = 12 # min:1, max:30, step:0.5\n",
"steps = 120 # min:1, max:150, step:1\n",
"\n",
"seed = -1\n",
"\n",
"# Don't change these if you don't know what you're doing\n",
"width = 512\n",
"height = 512\n",
"\n",
"skip_grid = True \n",
"rows = 2\n",
"\n",
"skip_save = False\n",
"\n",
"out_name = \"out\" + str(int(time.time()))\n",
"\n",
"# ===================================================================================================================\n",
"\n",
"images, seed_new = txt2img(prompt, seed, samples,\n",
" steps, scale, height, width,\n",
" rows, 1, skip_grid, skip_save,\n",
" out_name)\n",
"\n",
"path = \"/notebooks/stable-diffusion/Output/\"\n",
"\n",
"save_all = True\n",
"\n",
"if save_all:\n",
" k = 0\n",
" for i in images:\n",
" i.save(f'{path}{name}_{k}.png')\n",
" k += 1\n",
"else:\n",
" index = 1\n",
" images[index].save(f'{path}{name}_{index}.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L7jnzh7O_vQT",
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"### Image 2 Image\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "xOcjG58T_wzp"
},
"outputs": [],
"source": [
"prompt = \"\" #@param {type:\"string\"}\n",
"sampler = 'k_dpm_2' #@param [\"k_euler_a\",\"k-diffusion\", \"k_dpm_2\", \"k_dpm_2_a\", \"k_euler\", \"k_heun\"] {allow-input: false}\n",
"init_image_path = \"/notebooks/stable-diffusion/Source/794_1000.jpg\" #@param {type: 'string'}\n",
"\n",
"resize_mode = \"Resize and fill\" #@param [\"Just resize\", \"Crop and resize\", \"Resize and fill\"] {allow-input: false}\n",
"\n",
"\n",
"width = 512 #@param {type:\"integer\"}\n",
"height = 512 #@param {type:\"integer\"}\n",
"\n",
"scale = 7.5 #@param {type:\"slider\", min:1, max:30, step:0.5}\n",
"steps = 64 #@param {type:\"slider\", min:1, max:150, step:1}\n",
"strength = 0.7 #@param {type: \"slider\", min:0.00, max:1.00, step:0.01}\n",
"\n",
"samples = 2 #@param {type:'integer'}\n",
"skip_grid = True #@param {type:\"boolean\"}\n",
"rows = 2 #@param {type:'integer'}\n",
"\n",
"seed = -1 #@param {type:'integer'}\n",
"\n",
"init_image = Image.open(init_image_path)\n",
"\n",
"\n",
"images, seed_new, mode = img2img(prompt, init_image_path, seed, sampler, steps, scale, strength, samples, rows, height, width, skip_grid, resize_mode)\n",
"\n",
"path = \"/notebooks/stable-diffusion/Output/\"\n",
"name = \"out\" + str(int(time.time()))\n",
"\n",
"save_all = True #@param {type:\"boolean\"}\n",
"\n",
"if save_all:\n",
" k = 0\n",
" for i in images:\n",
" i.save(f'{path}{name}_{k}.png')\n",
" k += 1\n",
"else:\n",
" index = 1 #@param {type:\"integer\"}\n",
" images[index].save(f'{path}{name}_{index}.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0QJG1W0-fXlI"
},
"outputs": [],
"source": [
"import os\n",
"os.kill(os.getpid(), 9) # Crash colab if runs out of gpu memory / Funny errors (Run from Set up again)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JAlvbY3mxLuc",
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"# Saving"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8m_6DufvxMkF"
},
"outputs": [],
"source": [
"!rm output.zip\n",
"!zip -r ./output.zip ./Output/*.png\n",
"from google.colab import files\n",
"files.download(\"./output.zip\")\n",
"!rm ./Output/*"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
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"private_outputs": true,
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"name": "python",
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