From 0e0fa4c5328c58cfe3c4de22470e5ccfb4f8f29b Mon Sep 17 00:00:00 2001 From: web3pm <71476076+web3pm@users.noreply.github.com> Date: Mon, 21 Feb 2022 22:55:38 -0500 Subject: [PATCH 1/3] Update server.py Test changes from demo --- server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server.py b/server.py index 430d1dc7..08961fe8 100644 --- a/server.py +++ b/server.py @@ -9,7 +9,7 @@ class Handler(http.server.SimpleHTTPRequestHandler): def do_GET(self): self.send_response(HTTPStatus.OK) self.end_headers() - msg = 'Hello! you requested %s' % (self.path) + msg = 'Greetings! you requested %s' % (self.path) self.wfile.write(msg.encode()) From c135e58c36396ccfcb5404b97b6f111f2038ab15 Mon Sep 17 00:00:00 2001 From: web3pm <71476076+web3pm@users.noreply.github.com> Date: Mon, 21 Feb 2022 23:00:48 -0500 Subject: [PATCH 2/3] Add files via upload Test upload of simple colab notebook --- testgan.py | 526 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 526 insertions(+) create mode 100644 testgan.py diff --git a/testgan.py b/testgan.py new file mode 100644 index 00000000..7b7c68df --- /dev/null +++ b/testgan.py @@ -0,0 +1,526 @@ +# -*- coding: utf-8 -*- +"""Copy of Dan's version of VQGAN+CLIP (with pooling).ipynb" + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1FZYlb-tde1Zqidygrc3Ty-ru6hTphCfm + +# Generate images from text prompts with VQGAN and CLIP (z+quantize method). + +Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). The original BigGAN+CLIP method was by https://twitter.com/advadnoun. + Added some explanations and modifications by Eleiber#8347, pooling trick by Crimeacs#8222 (https://twitter.com/EarthML1) and the GUI was made with the help of Abulafia#3734. + +Other resources: +https://www.reddit.com/r/bigsleep/comments/oohmes/tips_and_tricks_for_prompts_and_parameters/ +Prompts: description:weight|description2:weight +Weights can be negative + + +4k / chromatic aberration effect / cinematic effect / diorama / dof / depth of field / field of view / fisheye lens effect / photorealistic / hyperrealistic / raytracing / shaders / stop motion / tilt-shift photography / ultrarealistic / vignetting. cell shading / flat colors / full of color / electric colors. anime / comic / graphic novel / visual novel. + +**Materials**: acrylic painting style / clay / coffee paint / collage / embroidery / glitch / graphite drawing / gouache / illuminated manuscript / ink drawing / medieval parchment / detailed oil painting / tempera / watercolor. isometric / lineart / lofi / lowpoly / photoshop / pixel art / vector art / voxel art. + +**Historical periods**: baroque / German Romanticism / impressionism / Luminism / pointillism / post-impressionism / Vienna Secession. + +You can also use type of painting by region: chinese painting / indian art / tibetan paintings / nordic mythology style / soviet art style, etc. According to movies or video games in Ghost in the Shell style / in Star Wars style / in Ghibli style / in Metropolis film style / Death Stranding style… (the more the entry text is related to the movie or video game, the more it will resemble.) See The 1000 Best Movies in History (2020). According to rendering program Imitate the result, it's not really using those graphics engines[r 5]): + +rendered in Unreal Engine[r 6][r 7], the most used. rendered in vray. rendered in povray. zbrush/cycles. rendered in rtx. rendered in octane. rendered in cinema4d. rendered in autodesk 3ds max. rendered in houdini. etc. They can be combined. Pending: Check: https://www.g2.com/products/octanerender/competitors/alternatives Specific Effects Add brdf / caustics / global-illumination / non-photorealistic / path tracing physically based rendering / raytracing / etc. Effects / lights diamond / fire / fog / glow / incandescent / iridescent / lava / red-hot / shining / etc. Other Trending on modifiers (website): for example "trending on artstation"[r 8] black hole / dismal / grim / liminal / minimalistic / surprising / vibrant + +**Other instructions**: +https://tuscriaturas.miraheze.org/wiki/Ayuda:Generar_im%C3%A1genes_con_VQGAN%2BCLIP +""" + + + + + + + +# @title Licensed under the MIT License + +# Copyright (c) 2021 Katherine Crowson + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. + +!nvidia-smi + +!git clone https://github.com/openai/CLIP +# !pip install taming-transformers +!git clone https://github.com/CompVis/taming-transformers.git +!pip install ftfy regex tqdm omegaconf pytorch-lightning +!pip install kornia +!pip install imageio-ffmpeg +!pip install einops +!mkdir steps + +"""By default, the notebook downloads the 1024 and 16384 models from ImageNet. There are others like COCO-Stuff, WikiArt or S-FLCKR, which are heavy, and if you are not going to use them it would be useless to download them, so if you want to use them, simply remove the numerals at the beginning of the lines depending on the model you want (the model name is at the end of the lines).""" + +#@title Selection of models to download +#@markdown By default, the notebook downloads the 1024 and 16384 models from ImageNet. There are others like COCO-Stuff, WikiArt 1024, WikiArt 16384, FacesHQ or S-FLCKR, which are heavy, and if you are not going to use them it would be pointless to download them, so if you want to use them, simply select the models to download. + +imagenet_1024 = True #@param {type:"boolean"} +imagenet_16384 = True #@param {type:"boolean"} +coco = False #@param {type:"boolean"} +faceshq = False #@param {type:"boolean"} +wikiart_1024 = False #@param {type:"boolean"} +wikiart_16384 = True #@param {type:"boolean"} +sflckr = False #@param {type:"boolean"} +openimages_8192 = False #@param {type:"boolean"} + +if imagenet_1024: + !curl -L -o vqgan_imagenet_f16_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.yaml' #ImageNet 1024 + !curl -L -o vqgan_imagenet_f16_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.ckpt' #ImageNet 1024 +if imagenet_16384: + !curl -L -o vqgan_imagenet_f16_16384.yaml -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384 + !curl -L -o vqgan_imagenet_f16_16384.ckpt -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384 + +if openimages_8192: + !curl -L -o vqgan_openimages_f16_8192.yaml -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384 + !curl -L -o vqgan_openimages_f16_8192.ckpt -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384 + +if coco: + !curl -L -o coco.yaml -C - 'https://dl.nmkd.de/ai/clip/coco/coco.yaml' #COCO + !curl -L -o coco.ckpt -C - 'https://dl.nmkd.de/ai/clip/coco/coco.ckpt' #COCO +if faceshq: + !curl -L -o faceshq.yaml -C - 'https://drive.google.com/uc?export=download&id=1fHwGx_hnBtC8nsq7hesJvs-Klv-P0gzT' #FacesHQ + !curl -L -o faceshq.ckpt -C - 'https://app.koofr.net/content/links/a04deec9-0c59-4673-8b37-3d696fe63a5d/files/get/last.ckpt?path=%2F2020-11-13T21-41-45_faceshq_transformer%2Fcheckpoints%2Flast.ckpt' #FacesHQ +if wikiart_1024: + !curl -L -o wikiart_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/wikiart.yaml' #WikiArt 1024 + !curl -L -o wikiart_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/wikiart.ckpt' #WikiArt 1024 +if wikiart_16384: + !curl -L -o wikiart_16384.yaml -C - 'http://eaidata.bmk.sh/data/Wikiart_16384/wikiart_f16_16384_8145600.yaml' #WikiArt 16384 + !curl -L -o wikiart_16384.ckpt -C - 'http://eaidata.bmk.sh/data/Wikiart_16384/wikiart_f16_16384_8145600.ckpt' #WikiArt 16384 +if sflckr: + !curl -L -o sflckr.yaml -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1' #S-FLCKR + !curl -L -o sflckr.ckpt -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1' #S-FLCKR + +# @title Load libraries and variables + +import argparse +import math +from pathlib import Path +import sys + +sys.path.insert(1, '/content/taming-transformers') +from IPython import display +from base64 import b64encode +from omegaconf import OmegaConf +from PIL import Image +from taming.models import cond_transformer, vqgan +import taming.modules +import torch +from torch import nn, optim +from torch.nn import functional as F +from torchvision import transforms +from torchvision.transforms import functional as TF +from tqdm.notebook import tqdm + +from CLIP import clip +import kornia.augmentation as K +import numpy as np +import imageio +from PIL import ImageFile, Image +ImageFile.LOAD_TRUNCATED_IMAGES = True + +def sinc(x): + return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([])) + + +def lanczos(x, a): + cond = torch.logical_and(-a < x, x < a) + out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([])) + return out / out.sum() + + +def ramp(ratio, width): + n = math.ceil(width / ratio + 1) + out = torch.empty([n]) + cur = 0 + for i in range(out.shape[0]): + out[i] = cur + cur += ratio + return torch.cat([-out[1:].flip([0]), out])[1:-1] + + +def resample(input, size, align_corners=True): + n, c, h, w = input.shape + dh, dw = size + + input = input.view([n * c, 1, h, w]) + + if dh < h: + kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype) + pad_h = (kernel_h.shape[0] - 1) // 2 + input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect') + input = F.conv2d(input, kernel_h[None, None, :, None]) + + if dw < w: + kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype) + pad_w = (kernel_w.shape[0] - 1) // 2 + input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect') + input = F.conv2d(input, kernel_w[None, None, None, :]) + + input = input.view([n, c, h, w]) + return F.interpolate(input, size, mode='bicubic', align_corners=align_corners) + + +class ReplaceGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, x_forward, x_backward): + ctx.shape = x_backward.shape + return x_forward + + @staticmethod + def backward(ctx, grad_in): + return None, grad_in.sum_to_size(ctx.shape) + + +replace_grad = ReplaceGrad.apply + + +class ClampWithGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, input, min, max): + ctx.min = min + ctx.max = max + ctx.save_for_backward(input) + return input.clamp(min, max) + + @staticmethod + def backward(ctx, grad_in): + input, = ctx.saved_tensors + return grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None + + +clamp_with_grad = ClampWithGrad.apply + + +def vector_quantize(x, codebook): + d = x.pow(2).sum(dim=-1, keepdim=True) + codebook.pow(2).sum(dim=1) - 2 * x @ codebook.T + indices = d.argmin(-1) + x_q = F.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook + return replace_grad(x_q, x) + + +class Prompt(nn.Module): + def __init__(self, embed, weight=1., stop=float('-inf')): + super().__init__() + self.register_buffer('embed', embed) + self.register_buffer('weight', torch.as_tensor(weight)) + self.register_buffer('stop', torch.as_tensor(stop)) + + def forward(self, input): + input_normed = F.normalize(input.unsqueeze(1), dim=2) + embed_normed = F.normalize(self.embed.unsqueeze(0), dim=2) + dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2) + dists = dists * self.weight.sign() + return self.weight.abs() * replace_grad(dists, torch.maximum(dists, self.stop)).mean() + + +def parse_prompt(prompt): + vals = prompt.rsplit(':', 2) + vals = vals + ['', '1', '-inf'][len(vals):] + return vals[0], float(vals[1]), float(vals[2]) + + +class MakeCutouts(nn.Module): + def __init__(self, cut_size, cutn, cut_pow=1.): + super().__init__() + self.cut_size = cut_size + self.cutn = cutn + self.cut_pow = cut_pow + + self.augs = nn.Sequential( + # K.RandomHorizontalFlip(p=0.5), + # K.RandomVerticalFlip(p=0.5), + # K.RandomSolarize(0.01, 0.01, p=0.7), + # K.RandomSharpness(0.3,p=0.4), + # K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1), ratio=(0.75,1.333), cropping_mode='resample', p=0.5), + # K.RandomCrop(size=(self.cut_size,self.cut_size), p=0.5), + K.RandomAffine(degrees=15, translate=0.1, p=0.7, padding_mode='border'), + K.RandomPerspective(0.7,p=0.7), + K.ColorJitter(hue=0.1, saturation=0.1, p=0.7), + K.RandomErasing((.1, .4), (.3, 1/.3), same_on_batch=True, p=0.7), + +) + self.noise_fac = 0.1 + self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size)) + self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size)) + + def forward(self, input): + sideY, sideX = input.shape[2:4] + max_size = min(sideX, sideY) + min_size = min(sideX, sideY, self.cut_size) + cutouts = [] + + for _ in range(self.cutn): + + # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size) + # offsetx = torch.randint(0, sideX - size + 1, ()) + # offsety = torch.randint(0, sideY - size + 1, ()) + # cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size] + # cutouts.append(resample(cutout, (self.cut_size, self.cut_size))) + + # cutout = transforms.Resize(size=(self.cut_size, self.cut_size))(input) + + cutout = (self.av_pool(input) + self.max_pool(input))/2 + cutouts.append(cutout) + batch = self.augs(torch.cat(cutouts, dim=0)) + if self.noise_fac: + facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac) + batch = batch + facs * torch.randn_like(batch) + return batch + + +def load_vqgan_model(config_path, checkpoint_path): + config = OmegaConf.load(config_path) + if config.model.target == 'taming.models.vqgan.VQModel': + model = vqgan.VQModel(**config.model.params) + model.eval().requires_grad_(False) + model.init_from_ckpt(checkpoint_path) + elif config.model.target == 'taming.models.vqgan.GumbelVQ': + model = vqgan.GumbelVQ(**config.model.params) + model.eval().requires_grad_(False) + model.init_from_ckpt(checkpoint_path) + elif config.model.target == 'taming.models.cond_transformer.Net2NetTransformer': + parent_model = cond_transformer.Net2NetTransformer(**config.model.params) + parent_model.eval().requires_grad_(False) + parent_model.init_from_ckpt(checkpoint_path) + model = parent_model.first_stage_model + else: + raise ValueError(f'unknown model type: {config.model.target}') + del model.loss + return model + + +def resize_image(image, out_size): + ratio = image.size[0] / image.size[1] + area = min(image.size[0] * image.size[1], out_size[0] * out_size[1]) + size = round((area * ratio)**0.5), round((area / ratio)**0.5) + return image.resize(size, Image.LANCZOS) + +"""## Settings for this run: +Mainly what you will have to modify will be `texts:`, there you can place the text or texts you want to generate (separated with `|`). It is a list because you can put more than one text, and so the AI ​​tries to 'mix' the images, giving the same priority to both texts. + +To use an initial image to the model, you just have to upload a file to the Colab environment (in the section on the left), and then modify `init_image:` putting the exact name of the file. Example: `sample.png` + +You can also modify the model by changing the lines that say `model:`. Currently ImageNet 1024, ImageNet 16384, WikiArt 1024, WikiArt 16384, S-FLCKR and COCO-Stuff are available. To activate them you have to have downloaded them first, and then you can simply select it. + +You can also use `target_images`, which is basically putting one or more images on it that the AI ​​will take as a "target", fulfilling the same function as putting text on it. To put more than one you have to use `|` as a separator. +""" + +#@title Parameters +texts = "moon god trending on artstation" #@param {type:"string"} +width = 400#@param {type:"number"} +height = 400#@param {type:"number"} +model = "vqgan_imagenet_f16_16384" #@param ["vqgan_imagenet_f16_16384", "vqgan_imagenet_f16_1024", "vqgan_openimages_f16_8192", "wikiart_1024", "wikiart_16384", "coco", "faceshq", "sflckr"] +images_interval = 25#@param {type:"number"} +init_image = ""#@param {type:"string"} +target_images = ""#@param {type:"string"} +seed = -1#@param {type:"number"} +max_iterations = 600#@param {type:"number"} + +model_names={"vqgan_imagenet_f16_16384": 'ImageNet 16384',"vqgan_imagenet_f16_1024":"ImageNet 1024", 'vqgan_openimages_f16_8192':'OpenImages 8912', + "wikiart_1024":"WikiArt 1024", "wikiart_16384":"WikiArt 16384", "coco":"COCO-Stuff", "faceshq":"FacesHQ", "sflckr":"S-FLCKR"} +name_model = model_names[model] + +if seed == -1: + seed = None +if init_image == "None": + init_image = None +if target_images == "None" or not target_images: + target_images = [] +else: + target_images = target_images.split("|") + target_images = [image.strip() for image in target_images] + +texts = [phrase.strip() for phrase in texts.split("|")] +if texts == ['']: + texts = [] + + +args = argparse.Namespace( + prompts=texts, + image_prompts=target_images, + noise_prompt_seeds=[], + noise_prompt_weights=[], + size=[width, height], + init_image=init_image, + init_weight=0., + clip_model='ViT-B/32', + vqgan_config=f'{model}.yaml', + vqgan_checkpoint=f'{model}.ckpt', + step_size=0.1, + cutn=32, + cut_pow=1., + display_freq=images_interval, + seed=seed, +) + +#@title Actually do the run... +from urllib.request import urlopen + +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') +print('Using device:', device) +if texts: + print('Using texts:', texts) +if target_images: + print('Using image prompts:', target_images) +if args.seed is None: + seed = torch.seed() +else: + seed = args.seed +torch.manual_seed(seed) +print('Using seed:', seed) + +model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device) +perceptor = clip.load(args.clip_model, jit=False)[0].eval().requires_grad_(False).to(device) +# clock=deepcopy(perceptor.visual.positional_embedding.data) +# perceptor.visual.positional_embedding.data = clock/clock.max() +# perceptor.visual.positional_embedding.data=clamp_with_grad(clock,0,1) + +cut_size = perceptor.visual.input_resolution + +f = 2**(model.decoder.num_resolutions - 1) +make_cutouts = MakeCutouts(cut_size, args.cutn, cut_pow=args.cut_pow) + +toksX, toksY = args.size[0] // f, args.size[1] // f +sideX, sideY = toksX * f, toksY * f + +if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt': + e_dim = 256 + n_toks = model.quantize.n_embed + z_min = model.quantize.embed.weight.min(dim=0).values[None, :, None, None] + z_max = model.quantize.embed.weight.max(dim=0).values[None, :, None, None] +else: + e_dim = model.quantize.e_dim + n_toks = model.quantize.n_e + z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None] + z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None] +# z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None] +# z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None] + +# normalize_imagenet = transforms.Normalize(mean=[0.485, 0.456, 0.406], +# std=[0.229, 0.224, 0.225]) + +if args.init_image: + if 'http' in args.init_image: + img = Image.open(urlopen(args.init_image)) + else: + img = Image.open(args.init_image) + pil_image = img.convert('RGB') + pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS) + pil_tensor = TF.to_tensor(pil_image) + z, *_ = model.encode(pil_tensor.to(device).unsqueeze(0) * 2 - 1) +else: + one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float() + # z = one_hot @ model.quantize.embedding.weight + if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt': + z = one_hot @ model.quantize.embed.weight + else: + z = one_hot @ model.quantize.embedding.weight + z = z.view([-1, toksY, toksX, e_dim]).permute(0, 3, 1, 2) + z = torch.rand_like(z)*2 +z_orig = z.clone() +z.requires_grad_(True) +opt = optim.Adam([z], lr=args.step_size) + +normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], + std=[0.26862954, 0.26130258, 0.27577711]) + + + +pMs = [] + +for prompt in args.prompts: + txt, weight, stop = parse_prompt(prompt) + embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float() + pMs.append(Prompt(embed, weight, stop).to(device)) + +for prompt in args.image_prompts: + path, weight, stop = parse_prompt(prompt) + img = Image.open(path) + pil_image = img.convert('RGB') + img = resize_image(pil_image, (sideX, sideY)) + batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device)) + embed = perceptor.encode_image(normalize(batch)).float() + pMs.append(Prompt(embed, weight, stop).to(device)) + +for seed, weight in zip(args.noise_prompt_seeds, args.noise_prompt_weights): + gen = torch.Generator().manual_seed(seed) + embed = torch.empty([1, perceptor.visual.output_dim]).normal_(generator=gen) + pMs.append(Prompt(embed, weight).to(device)) + +def synth(z): + if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt': + z_q = vector_quantize(z.movedim(1, 3), model.quantize.embed.weight).movedim(3, 1) + else: + z_q = vector_quantize(z.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1) + return clamp_with_grad(model.decode(z_q).add(1).div(2), 0, 1) + +@torch.no_grad() +def checkin(i, losses): + losses_str = ', '.join(f'{loss.item():g}' for loss in losses) + tqdm.write(f'i: {i}, loss: {sum(losses).item():g}, losses: {losses_str}') + out = synth(z) + TF.to_pil_image(out[0].cpu()).save('progress.png') + display.display(display.Image('progress.png')) + +def ascend_txt(): + global i + out = synth(z) + iii = perceptor.encode_image(normalize(make_cutouts(out))).float() + + result = [] + + if args.init_weight: + # result.append(F.mse_loss(z, z_orig) * args.init_weight / 2) + result.append(F.mse_loss(z, torch.zeros_like(z_orig)) * ((1/torch.tensor(i*2 + 1))*args.init_weight) / 2) + for prompt in pMs: + result.append(prompt(iii)) + img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:] + img = np.transpose(img, (1, 2, 0)) + imageio.imwrite('./steps/' + str(i) + '.png', np.array(img)) + + return result + +def train(i): + opt.zero_grad() + lossAll = ascend_txt() + if i % args.display_freq == 0: + checkin(i, lossAll) + + loss = sum(lossAll) + loss.backward() + opt.step() + with torch.no_grad(): + z.copy_(z.maximum(z_min).minimum(z_max)) + +i = 0 +try: + with tqdm() as pbar: + while True: + train(i) + if i == max_iterations: + break + i += 1 + pbar.update() +except KeyboardInterrupt: + pass \ No newline at end of file From b617ad19a107289154bb661336b2c6f9cf84dca8 Mon Sep 17 00:00:00 2001 From: web3pm <71476076+web3pm@users.noreply.github.com> Date: Mon, 21 Feb 2022 23:06:48 -0500 Subject: [PATCH 3/3] Update server.py Test basic import of gan routine --- server.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/server.py b/server.py index 08961fe8..1ec38b2b 100644 --- a/server.py +++ b/server.py @@ -5,6 +5,7 @@ from http import HTTPStatus + class Handler(http.server.SimpleHTTPRequestHandler): def do_GET(self): self.send_response(HTTPStatus.OK) @@ -12,6 +13,8 @@ def do_GET(self): msg = 'Greetings! you requested %s' % (self.path) self.wfile.write(msg.encode()) + import testgan + port = int(os.getenv('PORT', 80)) print('Listening on port %s' % (port))