Spaces:
Runtime error
Runtime error
File size: 6,511 Bytes
797142e |
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 |
import sys
import torch
import numpy as np
import streamlit as st
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat, rearrange
from pytorch_lightning import seed_everything
from imwatermark import WatermarkEncoder
from scripts.txt2img import put_watermark
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.data.util import AddMiDaS
torch.set_grad_enabled(False)
@st.cache(allow_output_mutation=True)
def initialize_model(config, ckpt):
config = OmegaConf.load(config)
model = instantiate_from_config(config.model)
model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
return sampler
def make_batch_sd(
image,
txt,
device,
num_samples=1,
model_type="dpt_hybrid"
):
image = np.array(image.convert("RGB"))
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
# sample['jpg'] is tensor hwc in [-1, 1] at this point
midas_trafo = AddMiDaS(model_type=model_type)
batch = {
"jpg": image,
"txt": num_samples * [txt],
}
batch = midas_trafo(batch)
batch["jpg"] = rearrange(batch["jpg"], 'h w c -> 1 c h w')
batch["jpg"] = repeat(batch["jpg"].to(device=device), "1 ... -> n ...", n=num_samples)
batch["midas_in"] = repeat(torch.from_numpy(batch["midas_in"][None, ...]).to(device=device), "1 ... -> n ...", n=num_samples)
return batch
def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=None,
do_full_sample=False):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = sampler.model
seed_everything(seed)
print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
wm = "SDV2"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
with torch.no_grad(),\
torch.autocast("cuda"):
batch = make_batch_sd(image, txt=prompt, device=device, num_samples=num_samples)
z = model.get_first_stage_encoding(model.encode_first_stage(batch[model.first_stage_key])) # move to latent space
c = model.cond_stage_model.encode(batch["txt"])
c_cat = list()
for ck in model.concat_keys:
cc = batch[ck]
cc = model.depth_model(cc)
depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
keepdim=True)
display_depth = (cc - depth_min) / (depth_max - depth_min)
st.image(Image.fromarray((display_depth[0, 0, ...].cpu().numpy() * 255.).astype(np.uint8)))
cc = torch.nn.functional.interpolate(
cc,
size=z.shape[2:],
mode="bicubic",
align_corners=False,
)
depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
keepdim=True)
cc = 2. * (cc - depth_min) / (depth_max - depth_min) - 1.
c_cat.append(cc)
c_cat = torch.cat(c_cat, dim=1)
# cond
cond = {"c_concat": [c_cat], "c_crossattn": [c]}
# uncond cond
uc_cross = model.get_unconditional_conditioning(num_samples, "")
uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
if not do_full_sample:
# encode (scaled latent)
z_enc = sampler.stochastic_encode(z, torch.tensor([t_enc] * num_samples).to(model.device))
else:
z_enc = torch.randn_like(z)
# decode it
samples = sampler.decode(z_enc, cond, t_enc, unconditional_guidance_scale=scale,
unconditional_conditioning=uc_full, callback=callback)
x_samples_ddim = model.decode_first_stage(samples)
result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
def run():
st.title("Stable Diffusion Depth2Img")
# run via streamlit run scripts/demo/depth2img.py <path-tp-config> <path-to-ckpt>
sampler = initialize_model(sys.argv[1], sys.argv[2])
image = st.file_uploader("Image", ["jpg", "png"])
if image:
image = Image.open(image)
w, h = image.size
st.text(f"loaded input image of size ({w}, {h})")
width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
image = image.resize((width, height))
st.text(f"resized input image to size ({width}, {height} (w, h))")
st.image(image)
prompt = st.text_input("Prompt")
seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0)
num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1)
scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=9.0, step=0.1)
steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1)
strength = st.slider("Strength", min_value=0., max_value=1., value=0.9)
eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
t_progress = st.progress(0)
def t_callback(t):
t_progress.progress(min((t + 1) / t_enc, 1.))
assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
do_full_sample = strength == 1.
t_enc = min(int(strength * steps), steps-1)
sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
if st.button("Sample"):
result = paint(
sampler=sampler,
image=image,
prompt=prompt,
t_enc=t_enc,
seed=seed,
scale=scale,
num_samples=num_samples,
callback=t_callback,
do_full_sample=do_full_sample
)
st.write("Result")
for image in result:
st.image(image, output_format='PNG')
if __name__ == "__main__":
run()
|