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, 12, 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', ).style() 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)