dreamdrone / app.py
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import gradio as gr
import torch
import numpy as np
import sd.gradio_utils as gradio_utils
import os
import cv2
import argparse
import ipdb
import argparse
from tqdm import tqdm
from diffusers import DDIMScheduler
from diffusers import DDIMScheduler, DDPMScheduler
from sd.core import DDIMBackward, DDPM_forward
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
def slerp(R_target, rotation_speed):
# Compute the angle of rotation from the rotation matrix
angle = np.arccos((np.trace(R_target) - 1) / 2)
# Handle the case where angle is very small (no significant rotation)
if angle < 1e-6:
return np.eye(3)
# Normalize the angle based on rotation_speed
normalized_angle = angle * rotation_speed
# Axis of rotation
axis = np.array([R_target[2, 1] - R_target[1, 2],
R_target[0, 2] - R_target[2, 0],
R_target[1, 0] - R_target[0, 1]])
axis = axis / np.linalg.norm(axis)
# Return the interpolated rotation matrix
return cv2.Rodrigues(axis * normalized_angle)[0]
def compute_extrinsic_parameters(clicked_point, depth, intrinsic_matrix, rotation_speed, step_x=0, step_y=0, step_z=0):
# Normalize the clicked point
x,y = clicked_point
x = int(x)
y = int(y)
x_normalized = (x - intrinsic_matrix[0, 2]) / intrinsic_matrix[0, 0]
y_normalized = (y - intrinsic_matrix[1, 2]) / intrinsic_matrix[1, 1]
# Depth at the clicked point
try:
z = depth[y, x]
except Exception:
ipdb.set_trace()
# Direction vector in camera coordinates
direction_vector = np.array([x_normalized * z, y_normalized * z, z])
# Calculate rotation angles to bring the clicked point to the center
angle_y = -np.arctan2(direction_vector[1], direction_vector[2]) # Rotation about Y-axis
angle_x = np.arctan2(direction_vector[0], direction_vector[2]) # Rotation about X-axis
# Apply rotation speed
angle_y *= rotation_speed
angle_x *= rotation_speed
# Compute rotation matrices
R_x = cv2.Rodrigues(np.array([1, 0, 0]) * angle_x)[0]
R_y = cv2.Rodrigues(np.array([0, 1, 0]) * angle_y)[0]
R = R_y @ R_x
# Compute rotation matrix to align direction vector with principal axis
T = np.array([step_x, -step_y, -step_z])
# Create extrinsic matrix
extrinsic_matrix = np.eye(4)
extrinsic_matrix[:3, :3] = R
extrinsic_matrix[:3, 3] = T
return extrinsic_matrix
@torch.no_grad()
def encode_imgs(imgs):
imgs = 2 * imgs - 1
posterior = pipe.vae.encode(imgs).latent_dist
latents = posterior.mean * 0.18215
return latents
@torch.no_grad()
def decode_latents(latents):
latents = 1 / 0.18215 * latents
imgs = pipe.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
@torch.no_grad()
def ddim_inversion(latent, cond, stop_t=1000, start_t=-1):
timesteps = reversed(pipe.scheduler.timesteps)
pipe.scheduler.set_timesteps(num_inference_steps)
for i, t in enumerate(tqdm(timesteps)):
if t >= stop_t:
break
if t <=start_t:
continue
cond_batch = cond.repeat(latent.shape[0], 1, 1)
alpha_prod_t = pipe.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
pipe.scheduler.alphas_cumprod[timesteps[i - 1]]
if i > 0 else pipe.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = pipe.unet(latent, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (latent - sigma_prev * eps) / mu_prev
latent = mu * pred_x0 + sigma * eps
return latent
@torch.no_grad()
def get_text_embeds(prompt, negative_prompt='', batch_size=1):
text_input = pipe.tokenizer(prompt, padding='max_length', max_length=77, truncation=True, return_tensors='pt')
text_embeddings = pipe.text_encoder(text_input.input_ids.to(device))[0]
uncond_input = pipe.tokenizer(negative_prompt, padding='max_length', max_length=77, truncation=True, return_tensors='pt')
uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(device))[0]
# cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings] * batch_size + [text_embeddings] * batch_size).to(torch_dtype)
return text_embeddings
def save_video(frames, fps=10, out_path='output/output.mp4'):
video_dims = (512, 512)
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
video = cv2.VideoWriter(out_path,fourcc, fps, video_dims)
os.makedirs(os.path.dirname(out_path), exist_ok=True)
for frame in frames:
video.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))
video.release()
def draw_prompt(prompt):
return prompt
def to_image(tensor):
tensor = tensor.squeeze(0).permute(1, 2, 0)
arr = tensor.detach().cpu().numpy()
arr = (arr - arr.min()) / (arr.max() - arr.min())
arr = arr * 255
return arr.astype('uint8')
def add_points_to_image(image, points):
image = gradio_utils.draw_handle_target_points(image, points, 5)
return image
def on_click(state, seed, count, prompt, neg_prompt, speed_r, speed_x, speed_y, speed_z, t1, t2, t3, lr, guidance_weight,attn,threshold, early_stop, evt: gr.SelectData):
end_id = int(t1)
start_id=int(t2)
startstart_id = int(t3)
timesteps = reversed(ddim_scheduler.timesteps)
end_t = timesteps[end_id]
start_t = timesteps[start_id]
startstart_t = timesteps[startstart_id]
attn=float(attn)
cfg_norm=False
cfg_decay=False
guidance_loss_scale = float(guidance_weight)
lr = float(lr)
threshold = int(threshold)
up_ft_indexes = 2
early_stop = int(early_stop)
generator = torch.Generator(device).manual_seed(int(seed)) # 19491001
state['direction_offset'] = [int(evt.index[0]), int(evt.index[1])]
cond = pipe._encode_prompt(prompt, device, 1, True, '')
for _ in range(int(count)):
image = state['img']
img_tensor = torch.from_numpy(np.array(image) / 255.).to(device).to(torch_dtype).permute(2,0,1).unsqueeze(0)
_,_,depth = pipe.midas_model(np.array(image))
centered = is_centered(state['direction_offset'])
if centered:
extrinsic = compute_extrinsic_parameters(state['direction_offset'], depth, intrinsic, rotation_speed=float(0), step_z=float(speed_z), step_x=float(speed_x), step_y=float(speed_y))
state['centered'] = centered
else:
extrinsic = compute_extrinsic_parameters(state['direction_offset'], depth, intrinsic, rotation_speed=float(speed_r), step_z=float(speed_z), step_x=float(speed_x), step_y=float(speed_y))
this_latent = encode_imgs(img_tensor)
this_ddim_inv_noise_end = ddim_inversion(this_latent, cond[1:], stop_t=end_t)
this_ddim_inv_noise_start = ddim_inversion(this_latent, cond[1:], stop_t=startstart_t)
wrapped_this_ddim_inv_noise_end = pipe.midas_model.wrap_img_tensor_w_fft_ext(this_ddim_inv_noise_end.to(torch_dtype),
torch.from_numpy(depth).to(device).to(torch_dtype),
intrinsic,
extrinsic[:3,:3], extrinsic[:3,3], threshold=threshold).to(torch_dtype)
wrapped_this_ddim_inv_noise_start = ddim_inversion(wrapped_this_ddim_inv_noise_end, cond[1:], stop_t=start_t, start_t=end_t,)
wrapped_this_ddim_inv_noise_start = DDPM_forward(wrapped_this_ddim_inv_noise_start, t_start=start_t, delta_t=(startstart_id-start_id)*20,
ddpm_scheduler=ddpm_scheduler, generator=generator)
new_img = pipe.denoise_w_injection(
prompt, generator=generator, num_inference_steps=num_inference_steps,
latents=torch.cat([this_ddim_inv_noise_start, wrapped_this_ddim_inv_noise_start], dim=0), t_start=startstart_t,
latent_mask=torch.ones_like(this_latent[0,0,...], device=device,
).unsqueeze(0),
f=0, attn=attn, guidance_scale=7.5, negative_prompt=neg_prompt,
guidance_loss_scale=guidance_loss_scale, early_stop=early_stop, up_ft_indexes=[up_ft_indexes],
cfg_norm=cfg_norm, cfg_decay=cfg_decay, lr=lr,
intrinsic=intrinsic, extrinsic=extrinsic, threshold=threshold,depth=depth,
).images[1]
new_img = np.array(new_img).astype(np.uint8)
state['img'] = new_img
state['img_his'].append(new_img)
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 1.
state['depth_his'].append(depth)
return new_img, depth, state['img_his'], state
def is_centered(clicked_point, image_dimensions=(512, 512), threshold=5):
image_center = [dim // 2 for dim in image_dimensions]
return all(abs(clicked_point[i] - image_center[i]) <= threshold for i in range(2))
def gen_img(prompt, neg_prompt, state, seed):
generator = torch.Generator(device).manual_seed(int(seed)) # 19491001
img = pipe(
prompt, generator=generator, num_inference_steps=num_inference_steps, negative_prompt=neg_prompt,
).images[0]
img_array = np.array(img)
_,_,depth = pipe.midas_model(img_array)
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 1.
state['img_his'] = [img_array]
state['depth_his'] = [depth]
try:
state['ori_img'] = img_array
state['img'] = img_array
except Exception:
ipdb.set_trace()
return img_array, depth, [img_array], state
def on_undo(state):
if len(state['img_his'])>1:
del state['img_his'][-1]
del state['depth_his'][-1]
image = state['img_his'][-1]
depth = state['depth_his'][-1]
else:
image = state['img_his'][-1]
depth = state['depth_his'][-1]
state['img'] = image
return image, depth, state['img_his'], state
def on_reset(state):
image = state['img_his'][0]
depth = state['depth_his'][0]
state['img'] = image
state['img_his'] = [image]
state['depth_his'] = [depth]
return image, depth, state['img_his'], state
def get_prompt(text):
return text
def on_save(state, video_name):
save_video(state['img_his'], fps=5, out_path=f'output/{video_name}.mp4')
def on_seed(seed):
return int(seed)
def main(args):
with gr.Blocks() as demo:
gr.Markdown(
"""
# DreamDrone
Official implementation of [DreamDrone](https://hyokong.github.io/publications/dreamdrone-page/).
**TL;DR:** Navigate dreamscapes with a ***click*** – your chosen point guides the drone's flight in a thrilling visual journey.
## Tutorial
1. Enter your prompt (and a negative prompt, if necessary) in the textbox, then click the `Generate first image` button.
2. Adjust the camera's moving speed in the `Direction` panel and set hyperparameters in the `Hyper params` panel.
3. Click on the generated image to make the camera fly towards the clicked direction.
4. The generated images will be displayed in the gallery at the bottom. You can view these images by clicking on them in the gallery or by using the left/right arrow buttons.
## Hints
- You can set the number of images to generate after clicking on an image, for convenience.
- Our system uses a right-hand coordinate system, with the Z-axis pointing into the image.
- The rotation speed determines how quickly the camera moves towards the clicked direction (rotation only, no translation). Increase this if you need faster camera pose changes.
- The Speed XYZ-axis controls the camera's movement along the X, Y, and Z axes. Adjust these parameters for different movement styles, similar to a camera arm.
- $t_1$ represents the timestep that wraps the latent code.
- Noise is added from $t_1$ to $t_3$. Between $t_1$ and $t_2$, noise is sourced from a pretrained diffusion U-Net. From $t_2$ to $t_3$, random Gaussian noise is used.
- The `Learning rate` and `Feature Correspondence Guidance` control the feature-correspondence guidance weight during the denoising process (from timestep $t_3$ to $0$).
- The `KV injection` parameter adjusts the extent of key and value injection from the current frame to the next.
> If you encounter any problems, please open an issue. Also, don't forget to star the [Official Github Repo](https://github.com/HyoKong/DreamDrone).
***Without further ado, welcome to DreamDrone – enjoy piloting your virtual drone through imaginative landscapes!***
""",
)
img = np.zeros((512, 512, 3)).astype(np.uint8)
depth_img = np.zeros((512, 512, 3)).astype(np.uint8)
intrinsic_matrix = np.array([[1000, 0, 512/2],
[0, 1000, 512/2],
[0, 0, 1]]) # Example intrinsic matrix
extrinsic_matrix = np.array([[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0]],
dtype=np.float32)
direction_offset = (255, 255)
state = gr.State({
'ori_img': img,
'img': None,
'centered': False,
'img_his': [],
'depth_his': [],
'intrinsic': intrinsic_matrix,
'extrinsic': extrinsic_matrix,
'direction_offset': direction_offset
})
with gr.Row():
with gr.Column(scale=0.2):
with gr.Accordion("Direction"):
speed_r = gr.Number(value=0.1, label='Rotation Speed', step=0.01, minimum=0, maximum=1)
speed_x = gr.Number(value=0, label='Speed X-axis', step=1, minimum=-10, maximum=20.0)
speed_y = gr.Number(value=0, label='Speed Y-axis', step=1, minimum=-10, maximum=20.0)
speed_z = gr.Number(value=5, label='Speed Z-axis', step=1, minimum=-10, maximum=20.0)
with gr.Accordion('Hyper params'):
with gr.Row():
count = gr.Number(value=5, label='Num. of generated images', step=1, minimum=1, maximum=10, precision=0)
seed = gr.Number(value=19491000, label='Seed', precision=0)
t1 = gr.Slider(1, 49, 2, step=1, label='t1')
t2 = gr.Slider(1, 49, 20, step=1, label='t2')
t3 = gr.Slider(1, 49, 27, step=1, label='t3')
lr = gr.Slider(0, 500, 300, step=1, label='Learning rate')
guidance_weight = gr.Slider(0, 10, 0.1, step=0.1, label='Feature correspondance guidance')
attn = gr.Slider(0, 1, 0.5, step=0.1, label='KV injection')
threshold = gr.Slider(0, 31, 20, step=1, label='Threshold of low-pass filter')
early_stop = gr.Slider(0, 50, 48, step=1, label='Early stop timestep for feature-correspondance guidance')
video_name = gr.Textbox(
label="Saved video name", show_label=True, max_lines=1, placeholder='saved video name', value='output',
)
with gr.Column():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
text = gr.Textbox(
label="Enter your prompt", show_label=False, max_lines=1, placeholder='Enter your prompt', value='Backyards of Old Houses in Antwerp in the Snow, van Gogh',
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
with gr.Row().style(mobile_collapse=False, equal_height=True):
with gr.Column(scale=0.8):
neg_text = gr.Textbox(
label="Enter your negative prompt", show_label=False, max_lines=1, value='', placeholder='Enter your negative prompt',
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
with gr.Column(scale=0.2):
gen_btn = gr.Button("Generate first image").style(
margin=False,
rounded=(False, True, True, False),
)
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
with gr.Column():
with gr.Tab('Current view'):
image = gr.Image(img).style(height=600, width=600)
with gr.Column():
with gr.Tab('Depth'):
depth_image = gr.Image(depth_img).style(height=600, width=600)
with gr.Row():
with gr.Column(min_width=100):
reset_btn = gr.Button('Clear All')
with gr.Column(min_width=100):
undo_btn = gr.Button('Undo Last')
with gr.Column(min_width=100):
save_btn = gr.Button('Save Video')
with gr.Row():
with gr.Tab('Generated image gallery'):
gallery = gr.Gallery(
label='Generated images', show_label=False, elem_id='gallery', preview=True, rows=1, height=368,
).style()
image.select(on_click, [state, seed, count, text, neg_text, speed_r, speed_x, speed_y, speed_z, t1, t2, t3, lr, guidance_weight,attn,threshold, early_stop], [image, depth_image, gallery, state])
text.submit(get_prompt, inputs=[text], outputs=[text])
neg_text.submit(get_prompt, inputs=[neg_text], outputs=[neg_text])
gen_btn.click(gen_img, inputs=[text, neg_text, state, seed], outputs=[image, depth_image, gallery, state])
reset_btn.click(on_reset, inputs=[state], outputs=[image, depth_image, gallery, state])
undo_btn.click(on_undo, inputs=[state], outputs=[image, depth_image, gallery, state])
save_btn.click(on_save, inputs=[state, video_name], outputs=[])
global num_inference_steps
global pipe
global intrinsic
global ddim_scheduler
global ddpm_scheduler
global device
global model_id
global torch_dtype
num_inference_steps = 50
device = args.device
model_id = args.model_id
ddim_scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
ddpm_scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
torch_dtype=torch.float16 if 'cuda' in str(device) else torch.float32
pipe = DDIMBackward.from_pretrained(
model_id, scheduler=ddim_scheduler, torch_dtype=torch_dtype,
cache_dir='.', device=str(device), model_id=model_id, depth_model=args.depth_model,
).to(str(device))
if 'cuda' in str(device):
pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention()
intrinsic = np.array([[1000, 0, 256],
[0, 1000., 256],
[0, 0, 1]]) # Example intrinsic matrix
return demo
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='cuda')
parser.add_argument('--model_id', default='stabilityai/stable-diffusion-2-1-base')
parser.add_argument('--depth_model', default='dpt_beit_large_512', choices=['dpt_beit_large_512', 'dpt_swin2_large_384'])
parser.add_argument('--share', action='store_true')
parser.add_argument('-p', '--port', type=int, default=None)
parser.add_argument('--ip', default=None)
args = parser.parse_args()
demo = main(args)
print('Successfully loaded, starting gradio demo')
demo.queue(concurrency_count=1, max_size=20).launch(share=args.share, server_name=args.ip, server_port=args.port)