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Merge pull request #1 from apolinario/main

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Add depth2img Gradio demo
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Robin Rombach 2022-11-24 02:52:01 +01:00 committed by GitHub
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10
README.md
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@ -136,12 +136,18 @@ To augment the well-established [img2img](https://github.com/CompVis/stable-diff
Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image.
If that is not desired, download our [depth-conditional stable diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-depth) model and the `dpt_hybrid` MiDaS [model weights](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), place the latter in a folder `midas_models` and sample via
```
python scripts/streamlit/depth2img.py streamlit run scripts/demo/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
```
or
```
streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
```
This method can be used on the samples of the base model itself.
For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
Using the [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input,
Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input,
and the diffusion model is then conditioned on the (relative) depth output.
<p align="center">

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scripts/gradio/depth2img.py Normal file
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@ -0,0 +1,184 @@
import sys
import torch
import numpy as np
import gradio as gr
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)
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)
depth_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 [depth_image] + [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]
def pad_image(input_image):
pad_w, pad_h = np.max(((2, 2), np.ceil(
np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
im_padded = Image.fromarray(
np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
return im_padded
def predict(input_image, prompt, steps, num_samples, scale, seed, eta, strength):
init_image = input_image.convert("RGB")
image = pad_image(init_image) # resize to integer multiple of 32
sampler.make_schedule(steps, ddim_eta=eta, verbose=True)
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)
result = paint(
sampler=sampler,
image=image,
prompt=prompt,
t_enc=t_enc,
seed=seed,
scale=scale,
num_samples=num_samples,
callback=None,
do_full_sample=do_full_sample
)
return result
sampler = initialize_model(sys.argv[1], sys.argv[2])
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("## Stable Diffusion Depth2Img")
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button(label="Run")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(
label="Images", minimum=1, maximum=4, value=1, step=1)
ddim_steps = gr.Slider(label="Steps", minimum=1,
maximum=50, value=50, step=1)
scale = gr.Slider(
label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
)
strength = gr.Slider(
label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
randomize=True,
)
eta = gr.Number(label="eta (DDIM)", value=0.0)
with gr.Column():
gallery = gr.Gallery(label="Generated images", show_label=False).style(
grid=[2], height="auto")
run_button.click(fn=predict, inputs=[
input_image, prompt, ddim_steps, num_samples, scale, seed, eta, strength], outputs=[gallery])
block.launch()