509 lines
20 KiB
Python
509 lines
20 KiB
Python
import PIL
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import argparse, os, sys, glob
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import torch
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import torch.nn as nn
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import numpy as np
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import gradio as gr
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from omegaconf import OmegaConf
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from PIL import Image
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from itertools import islice
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from einops import rearrange, repeat
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from torch import autocast
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from contextlib import contextmanager, nullcontext
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import mimetypes
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import random
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import math
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import csv
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import k_diffusion as K
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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invalid_filename_chars = '<>:"/\|?*'
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parser = argparse.ArgumentParser()
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parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
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parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
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parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
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parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
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parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default='./GFPGAN')
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opt = parser.parse_args()
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GFPGAN_dir = opt.gfpgan_dir
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def chunk(it, size):
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it = iter(it)
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return iter(lambda: tuple(islice(it, size)), ())
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.cuda()
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model.eval()
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return model
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def load_img_pil(img_pil):
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image = img_pil.convert("RGB")
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w, h = image.size
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print(f"loaded input image of size ({w}, {h})")
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w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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print(f"cropped image to size ({w}, {h})")
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2. * image - 1.
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def load_img(path):
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return load_img_pil(Image.open(path))
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class CFGDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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return uncond + (cond - uncond) * cond_scale
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def load_GFPGAN():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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sys.path.append(os.path.abspath(GFPGAN_dir))
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from gfpgan import GFPGANer
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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GFPGAN = None
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if os.path.exists(GFPGAN_dir):
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try:
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GFPGAN = load_GFPGAN()
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print("Loaded GFPGAN")
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except Exception:
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import traceback
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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
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model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = model.half().to(device)
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def image_grid(imgs, batch_size, round_down=False):
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if opt.n_rows > 0:
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rows = opt.n_rows
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elif opt.n_rows == 0:
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rows = batch_size
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else:
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rows = math.sqrt(len(imgs))
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rows = int(rows) if round_down else round(rows)
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cols = math.ceil(len(imgs) / rows)
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w, h = imgs[0].size
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grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
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for i, img in enumerate(imgs):
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
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torch.cuda.empty_cache()
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outpath = opt.outdir or "outputs/txt2img-samples"
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if seed == -1:
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seed = random.randrange(4294967294)
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seed = int(seed)
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keep_same_seed = False
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is_PLMS = sampler_name == 'PLMS'
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is_DDIM = sampler_name == 'DDIM'
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is_Kdif = sampler_name == 'k-diffusion'
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sampler = None
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if is_PLMS:
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sampler = PLMSSampler(model)
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elif is_DDIM:
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sampler = DDIMSampler(model)
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elif is_Kdif:
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pass
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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model_wrap = K.external.CompVisDenoiser(model)
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os.makedirs(outpath, exist_ok=True)
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batch_size = n_samples
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assert prompt is not None
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prompts = batch_size * [prompt]
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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prompt_matrix_prompts = []
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comment = ""
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if prompt_matrix:
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keep_same_seed = True
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comment = "Image prompts:\n\n"
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items = prompt.split("|")
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combination_count = 2 ** (len(items)-1)
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for combination_num in range(combination_count):
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current = items[0]
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label = 'A'
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for n, text in enumerate(items[1:]):
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if combination_num & (2**n) > 0:
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current += ("" if text.strip().startswith(",") else ", ") + text
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label += chr(ord('B') + n)
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comment += " - " + label + "\n"
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prompt_matrix_prompts.append(current)
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n_iter = math.ceil(len(prompt_matrix_prompts) / batch_size)
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comment += "\nwhere:\n"
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for n, text in enumerate(items):
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comment += " " + chr(ord('A') + n) + " = " + items[n] + "\n"
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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output_images = []
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with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
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for n in range(n_iter):
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if prompt_matrix:
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prompts = prompt_matrix_prompts[n*batch_size:(n+1)*batch_size]
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(len(prompts) * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [opt_C, height // opt_f, width // opt_f]
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batch_seed = seed if keep_same_seed else seed + n * len(prompts)
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# we manually generate all input noises because each one should have a specific seed
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xs = []
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for i in range(len(prompts)):
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current_seed = seed if keep_same_seed else batch_seed + i
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torch.manual_seed(current_seed)
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xs.append(torch.randn(shape, device=device))
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x = torch.stack(xs)
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if is_Kdif:
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sigmas = model_wrap.get_sigmas(ddim_steps)
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x = x * sigmas[0]
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model_wrap_cfg = CFGDenoiser(model_wrap)
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
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elif sampler is not None:
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save or not opt.skip_grid:
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for i, x_sample in enumerate(x_samples_ddim):
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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filename = f"{base_count:05}-{seed if keep_same_seed else batch_seed + i}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
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image.save(os.path.join(sample_path, filename))
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output_images.append(image)
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base_count += 1
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if not opt.skip_grid:
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# additionally, save as grid
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grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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if sampler is not None:
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del sampler
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info = f"""
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{prompt}
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Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip()
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if len(comment) > 0:
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info += "\n\n" + comment
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return output_images, seed, info
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class Flagging(gr.FlaggingCallback):
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def setup(self, components, flagging_dir: str):
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pass
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def flag(self, flag_data, flag_option=None, flag_index=None, username=None) -> int:
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os.makedirs("log/images", exist_ok=True)
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# those must match the "dream" function
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prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data
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filenames = []
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with open("log/log.csv", "a", encoding="utf8", newline='') as file:
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import time
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import base64
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at_start = file.tell() == 0
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writer = csv.writer(file)
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if at_start:
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writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"])
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filename_base = str(int(time.time() * 1000))
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for i, filedata in enumerate(images):
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filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
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if filedata.startswith("data:image/png;base64,"):
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filedata = filedata[len("data:image/png;base64,"):]
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with open(filename, "wb") as imgfile:
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imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
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filenames.append(filename)
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writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]])
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print("Logged:", filenames[0])
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dream_interface = gr.Interface(
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dream,
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inputs=[
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gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=4, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly should the image follow the prompt)', value=7.0),
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gr.Number(label='Seed', value=-1),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
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gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
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],
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outputs=[
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gr.Gallery(label="Images"),
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gr.Number(label='Seed'),
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gr.Textbox(label="Copy-paste generation parameters"),
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],
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title="Stable Diffusion Text-to-Image K",
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description="Generate images from text with Stable Diffusion (using K-LMS)",
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flagging_callback=Flagging()
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)
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def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
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torch.cuda.empty_cache()
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outpath = opt.outdir or "outputs/img2img-samples"
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if seed == -1:
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seed = random.randrange(4294967294)
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model_wrap = K.external.CompVisDenoiser(model)
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os.makedirs(outpath, exist_ok=True)
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batch_size = n_samples
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assert prompt is not None
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data = [batch_size * [prompt]]
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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seedit = 0
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image = init_img.convert("RGB")
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image = image.resize((width, height), resample=PIL.Image.Resampling.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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output_images = []
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
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init_image = 2. * image - 1.
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init_image = init_image.to(device)
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init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
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x0 = init_latent
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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t_enc = int(denoising_strength * ddim_steps)
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for n in range(n_iter):
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for batch_index, prompts in enumerate(data):
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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sigmas = model_wrap.get_sigmas(ddim_steps)
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current_seed = seed + n * len(data) + batch_index
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torch.manual_seed(current_seed)
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noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw
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xi = x0 + noise
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sigma_sched = sigmas[ddim_steps - t_enc - 1:]
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model_wrap_cfg = CFGDenoiser(model_wrap)
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extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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if not opt.skip_save or not opt.skip_grid:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))
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output_images.append(image)
|
|
base_count += 1
|
|
|
|
if not opt.skip_grid:
|
|
# additionally, save as grid
|
|
grid = image_grid(output_images, batch_size)
|
|
grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
|
|
grid_count += 1
|
|
|
|
return output_images, seed
|
|
|
|
|
|
# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed
|
|
|
|
img2img_interface = gr.Interface(
|
|
translation,
|
|
inputs=[
|
|
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
|
|
gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
|
|
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
|
|
gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
|
|
gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
|
|
gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
|
|
gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
|
|
gr.Number(label='Seed', value=-1),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Height", value=512),
|
|
gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Width", value=512),
|
|
],
|
|
outputs=[
|
|
gr.Gallery(),
|
|
gr.Number(label='Seed')
|
|
],
|
|
title="Stable Diffusion Image-to-Image",
|
|
description="Generate images from images with Stable Diffusion",
|
|
)
|
|
|
|
interfaces = [
|
|
(dream_interface, "Dream"),
|
|
(img2img_interface, "Image Translation")
|
|
]
|
|
|
|
def run_GFPGAN(image, strength):
|
|
image = image.convert("RGB")
|
|
|
|
cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
|
|
res = Image.fromarray(restored_img)
|
|
|
|
if strength < 1.0:
|
|
res = PIL.Image.blend(image, res, strength)
|
|
|
|
return res
|
|
|
|
|
|
if GFPGAN is not None:
|
|
interfaces.append((gr.Interface(
|
|
run_GFPGAN,
|
|
inputs=[
|
|
gr.Image(label="Source", source="upload", interactive=True, type="pil"),
|
|
gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
|
|
],
|
|
outputs=[
|
|
gr.Image(label="Result"),
|
|
],
|
|
title="GFPGAN",
|
|
description="Fix faces on images",
|
|
), "GFPGAN"))
|
|
|
|
demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces])
|
|
|
|
demo.launch()
|