Spaces:
Running
Running
File size: 7,863 Bytes
121f6d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 |
import gradio as gr
import numpy as np
import torch
from torchvision.utils import make_grid
from einops import rearrange
import os, re
from PIL import Image
import torch
import pandas as pd
import numpy as np
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import nullcontext
from ldmlib.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging
logging.set_verbosity_error()
import mimetypes
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
return sd
config = "optimizedSD/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"
sd = load_model_from_config(f"{ckpt}")
li, lo = [], []
for key, v_ in sd.items():
sp = key.split(".")
if (sp[0]) == "model":
if "input_blocks" in sp:
li.append(key)
elif "middle_block" in sp:
li.append(key)
elif "time_embed" in sp:
li.append(key)
else:
lo.append(key)
for key in li:
sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
sd["model2." + key[6:]] = sd.pop(key)
config = OmegaConf.load(f"{config}")
model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd
def generate(
prompt,
ddim_steps,
n_iter,
batch_size,
Height,
Width,
scale,
ddim_eta,
unet_bs,
device,
seed,
outdir,
img_format,
turbo,
full_precision,
sampler,
):
C = 4
f = 8
start_code = None
model.unet_bs = unet_bs
model.turbo = turbo
model.cdevice = device
modelCS.cond_stage_model.device = device
if seed == "":
seed = randint(0, 1000000)
seed = int(seed)
seed_everything(seed)
# Logging
logger(locals(), "logs/txt2img_gradio_logs.csv")
if device != "cpu" and full_precision == False:
model.half()
modelFS.half()
modelCS.half()
tic = time.time()
os.makedirs(outdir, exist_ok=True)
outpath = outdir
sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompt)))[:150]
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
# n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
assert prompt is not None
data = [batch_size * [prompt]]
if full_precision == False and device != "cpu":
precision_scope = autocast
else:
precision_scope = nullcontext
all_samples = []
seeds = ""
with torch.no_grad():
all_samples = list()
for _ in trange(n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
with precision_scope("cuda"):
modelCS.to(device)
uc = None
if scale != 1.0:
uc = modelCS.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
subprompts, weights = split_weighted_subprompts(prompts[0])
if len(subprompts) > 1:
c = torch.zeros_like(uc)
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(len(subprompts)):
weight = weights[i]
# if not skip_normalize:
weight = weight / totalWeight
c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
else:
c = modelCS.get_learned_conditioning(prompts)
shape = [batch_size, C, Height // f, Width // f]
if device != "cpu":
mem = torch.cuda.memory_allocated() / 1e6
modelCS.to("cpu")
while torch.cuda.memory_allocated() / 1e6 >= mem:
time.sleep(1)
samples_ddim = model.sample(
S=ddim_steps,
conditioning=c,
seed=seed,
shape=shape,
verbose=False,
unconditional_guidance_scale=scale,
unconditional_conditioning=uc,
eta=ddim_eta,
x_T=start_code,
sampler = sampler,
)
modelFS.to(device)
print("saving images")
for i in range(batch_size):
x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
all_samples.append(x_sample.to("cpu"))
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, "seed_" + str(seed) + "_" + f"{base_count:05}.{img_format}")
)
seeds += str(seed) + ","
seed += 1
base_count += 1
if device != "cpu":
mem = torch.cuda.memory_allocated() / 1e6
modelFS.to("cpu")
while torch.cuda.memory_allocated() / 1e6 >= mem:
time.sleep(1)
del samples_ddim
del x_sample
del x_samples_ddim
print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
toc = time.time()
time_taken = (toc - tic) / 60.0
grid = torch.cat(all_samples, 0)
grid = make_grid(grid, nrow=n_iter)
grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
txt = (
"Samples finished in "
+ str(round(time_taken, 3))
+ " minutes and exported to "
+ sample_path
+ "\nSeeds used = "
+ seeds[:-1]
)
return Image.fromarray(grid.astype(np.uint8)), txt
demo = gr.Interface(
fn=generate,
inputs=[
"text",
gr.Slider(1, 1000, value=50),
gr.Slider(1, 100, step=1),
gr.Slider(1, 100, step=1),
gr.Slider(64, 4096, value=512, step=64),
gr.Slider(64, 4096, value=512, step=64),
gr.Slider(0, 50, value=7.5, step=0.1),
gr.Slider(0, 1, step=0.01),
gr.Slider(1, 2, value=1, step=1),
gr.Text(value="cuda"),
"text",
gr.Text(value="outputs/txt2img-samples"),
gr.Radio(["png", "jpg"], value='png'),
"checkbox",
"checkbox",
gr.Radio(["ddim", "plms","heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"], value="plms"),
],
outputs=["image", "text"],
)
demo.launch()
|