Spaces:
Runtime error
Runtime error
| # -*- coding: utf-8 -*- | |
| """Copy of compose_glide.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/19xx6Nu4FeiGj-TzTUFxBf-15IkeuFx_F | |
| """ | |
| import gradio as gr | |
| import torch as th | |
| from composable_diffusion.download import download_model | |
| from composable_diffusion.model_creation import create_model_and_diffusion as create_model_and_diffusion_for_clevr | |
| from composable_diffusion.model_creation import model_and_diffusion_defaults as model_and_diffusion_defaults_for_clevr | |
| from composable_diffusion.composable_stable_diffusion.pipeline_composable_stable_diffusion import \ | |
| ComposableStableDiffusionPipeline | |
| import os | |
| import shutil | |
| import time | |
| import glob | |
| import numpy as np | |
| import open3d as o3d | |
| import open3d.visualization.rendering as rendering | |
| from PIL import Image | |
| from tqdm.auto import tqdm | |
| from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config | |
| from point_e.diffusion.sampler import PointCloudSampler | |
| from point_e.models.download import load_checkpoint | |
| from point_e.models.configs import MODEL_CONFIGS, model_from_config | |
| from point_e.util.pc_to_mesh import marching_cubes_mesh | |
| has_cuda = th.cuda.is_available() | |
| device = th.device('cpu' if not th.cuda.is_available() else 'cuda') | |
| print(has_cuda) | |
| # init stable diffusion model | |
| pipe = ComposableStableDiffusionPipeline.from_pretrained( | |
| "CompVis/stable-diffusion-v1-4", | |
| ).to(device) | |
| pipe.safety_checker = None | |
| # create model for CLEVR Objects | |
| clevr_options = model_and_diffusion_defaults_for_clevr() | |
| flags = { | |
| "image_size": 128, | |
| "num_channels": 192, | |
| "num_res_blocks": 2, | |
| "learn_sigma": True, | |
| "use_scale_shift_norm": False, | |
| "raw_unet": True, | |
| "noise_schedule": "squaredcos_cap_v2", | |
| "rescale_learned_sigmas": False, | |
| "rescale_timesteps": False, | |
| "num_classes": '2', | |
| "dataset": "clevr_pos", | |
| "use_fp16": has_cuda, | |
| "timestep_respacing": '100' | |
| } | |
| for key, val in flags.items(): | |
| clevr_options[key] = val | |
| clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options) | |
| clevr_model.eval() | |
| if has_cuda: | |
| clevr_model.convert_to_fp16() | |
| clevr_model.to(device) | |
| clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device)) | |
| device = th.device('cpu' if not th.cuda.is_available() else 'cuda') | |
| print('creating base model...') | |
| base_name = 'base40M-textvec' | |
| base_model = model_from_config(MODEL_CONFIGS[base_name], device) | |
| base_model.eval() | |
| base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name]) | |
| print('creating upsample model...') | |
| upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device) | |
| upsampler_model.eval() | |
| upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample']) | |
| print('downloading base checkpoint...') | |
| base_model.load_state_dict(load_checkpoint(base_name, device)) | |
| print('downloading upsampler checkpoint...') | |
| upsampler_model.load_state_dict(load_checkpoint('upsample', device)) | |
| print('creating SDF model...') | |
| name = 'sdf' | |
| model = model_from_config(MODEL_CONFIGS[name], device) | |
| model.eval() | |
| print('loading SDF model...') | |
| model.load_state_dict(load_checkpoint(name, device)) | |
| def compose_pointe(prompt, weights, version): | |
| weight_list = [float(x.strip()) for x in weights.split('|')] | |
| sampler = PointCloudSampler( | |
| device=device, | |
| models=[base_model, upsampler_model], | |
| diffusions=[base_diffusion, upsampler_diffusion], | |
| num_points=[1024, 4096 - 1024], | |
| aux_channels=['R', 'G', 'B'], | |
| guidance_scale=[weight_list, 0.0], | |
| model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all | |
| ) | |
| def generate_pcd(prompt_list): | |
| # Produce a sample from the model. | |
| samples = None | |
| for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=prompt_list))): | |
| samples = x | |
| return samples | |
| def generate_fig(samples): | |
| pc = sampler.output_to_point_clouds(samples)[0] | |
| return pc | |
| def generate_mesh(pc): | |
| mesh = marching_cubes_mesh( | |
| pc=pc, | |
| model=model, | |
| batch_size=4096, | |
| grid_size=128, # increase to 128 for resolution used in evals | |
| progress=True, | |
| ) | |
| return mesh | |
| def generate_video(mesh_path): | |
| render = rendering.OffscreenRenderer(640, 480) | |
| mesh = o3d.io.read_triangle_mesh(mesh_path) | |
| mesh.compute_vertex_normals() | |
| mat = o3d.visualization.rendering.MaterialRecord() | |
| mat.shader = 'defaultLit' | |
| render.scene.camera.look_at([0, 0, 0], [1, 1, 1], [0, 0, 1]) | |
| render.scene.add_geometry('mesh', mesh, mat) | |
| timestr = time.strftime("%Y%m%d-%H%M%S") | |
| os.makedirs(timestr, exist_ok=True) | |
| def update_geometry(): | |
| render.scene.clear_geometry() | |
| render.scene.add_geometry('mesh', mesh, mat) | |
| def generate_images(): | |
| for i in range(64): | |
| # Rotation | |
| R = mesh.get_rotation_matrix_from_xyz((0, 0, np.pi / 32)) | |
| mesh.rotate(R, center=(0, 0, 0)) | |
| # Update geometry | |
| update_geometry() | |
| img = render.render_to_image() | |
| o3d.io.write_image(os.path.join(timestr + "/{:05d}.jpg".format(i)), img, quality=100) | |
| time.sleep(0.05) | |
| generate_images() | |
| image_list = [] | |
| for filename in sorted(glob.glob(f'{timestr}/*.jpg')): # assuming gif | |
| im = Image.open(filename) | |
| image_list.append(im) | |
| # remove the folder | |
| shutil.rmtree(timestr) | |
| return image_list | |
| prompt_list = [x.strip() for x in prompt.split("|")] | |
| pcd = generate_pcd(prompt_list) | |
| pc = generate_fig(pcd) | |
| mesh = generate_mesh(pc) | |
| timestr = time.strftime("%Y%m%d-%H%M%S") | |
| mesh_path = os.path.join(f'{timestr}.ply') | |
| with open(mesh_path, 'wb') as f: | |
| mesh.write_ply(f) | |
| image_frames = generate_video(mesh_path) | |
| gif_path = os.path.join(f'{timestr}.gif') | |
| image_frames[0].save(gif_path, save_all=True, optimizer=False, duration=5, append_images=image_frames[1:], loop=0) | |
| return f'{timestr}.gif' | |
| def compose_clevr_objects(prompt, weights, steps): | |
| weights = [float(x.strip()) for x in weights.split('|')] | |
| weights = th.tensor(weights, device=device).reshape(-1, 1, 1, 1) | |
| coordinates = [ | |
| [ | |
| float(x.split(',')[0].strip()), float(x.split(',')[1].strip())] | |
| for x in prompt.split('|') | |
| ] | |
| coordinates += [[-1, -1]] # add unconditional score label | |
| batch_size = 1 | |
| clevr_options['timestep_respacing'] = str(int(steps)) | |
| _, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options) | |
| def model_fn(x_t, ts, **kwargs): | |
| half = x_t[:1] | |
| combined = th.cat([half] * kwargs['y'].size(0), dim=0) | |
| model_out = clevr_model(combined, ts, **kwargs) | |
| eps, rest = model_out[:, :3], model_out[:, 3:] | |
| masks = kwargs.get('masks') | |
| cond_eps = eps[masks] | |
| uncond_eps = eps[~masks] | |
| half_eps = uncond_eps + (weights * (cond_eps - uncond_eps)).sum(dim=0, keepdims=True) | |
| eps = th.cat([half_eps] * x_t.size(0), dim=0) | |
| return th.cat([eps, rest], dim=1) | |
| def sample(coordinates): | |
| masks = [True] * (len(coordinates) - 1) + [False] | |
| model_kwargs = dict( | |
| y=th.tensor(coordinates, dtype=th.float, device=device), | |
| masks=th.tensor(masks, dtype=th.bool, device=device) | |
| ) | |
| samples = clevr_diffusion.p_sample_loop( | |
| model_fn, | |
| (len(coordinates), 3, clevr_options["image_size"], clevr_options["image_size"]), | |
| device=device, | |
| clip_denoised=True, | |
| progress=True, | |
| model_kwargs=model_kwargs, | |
| cond_fn=None, | |
| )[:batch_size] | |
| return samples | |
| samples = sample(coordinates) | |
| out_img = samples[0].permute(1, 2, 0) | |
| out_img = (out_img + 1) / 2 | |
| out_img = (out_img.detach().cpu() * 255.).to(th.uint8) | |
| out_img = out_img.numpy() | |
| return out_img | |
| def stable_diffusion_compose(prompt, steps, weights, seed): | |
| generator = th.Generator("cuda").manual_seed(int(seed)) | |
| image = pipe(prompt, guidance_scale=7.5, num_inference_steps=steps, | |
| weights=weights, generator=generator).images[0] | |
| image.save(f'{"_".join(prompt.split())}.png') | |
| return image | |
| def compose_2D_diffusion(prompt, weights, version, steps, seed): | |
| try: | |
| with th.no_grad(): | |
| if version == 'Stable_Diffusion_1v_4': | |
| res = stable_diffusion_compose(prompt, steps, weights, seed) | |
| return res | |
| else: | |
| return compose_clevr_objects(prompt, weights, steps) | |
| except Exception as e: | |
| return None | |
| examples_1 = "A castle in a forest | grainy, fog" | |
| examples_3 = '0.1, 0.5 | 0.3, 0.5 | 0.5, 0.5 | 0.7, 0.5 | 0.9, 0.5' | |
| examples_5 = 'a white church | lightning in the background' | |
| examples_6 = 'mystical trees | A dark magical pond | dark' | |
| examples_7 = 'A lake | A mountain | Cherry Blossoms next to the lake' | |
| image_examples = [ | |
| [examples_6, "7.5 | 7.5 | -7.5", 'Stable_Diffusion_1v_4', 50, 8], | |
| [examples_6, "7.5 | 7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 8], | |
| [examples_1, "7.5 | -7.5", 'Stable_Diffusion_1v_4', 50, 0], | |
| [examples_7, "7.5 | 7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 3], | |
| [examples_5, "7.5 | 7.5", 'Stable_Diffusion_1v_4', 50, 0], | |
| [examples_3, "7.5 | 7.5 | 7.5 | 7.5 | 7.5", 'CLEVR Objects', 100, 0] | |
| ] | |
| pointe_examples = [["a cake | a house", "7.5 | 7.5", 'Point-E'], | |
| ["a green avocado | a chair", "7.5 | 3", 'Point-E'], | |
| ["a toilet | a chair", "7 | 5", 'Point-E']] | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """<h1 style="text-align: center;"><b>Composable Diffusion Models (ECCV | |
| 2022)</b> - <a href="https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion | |
| -Models/">Project Page</a></h1>""") | |
| gr.Markdown( | |
| """<table style="display: inline-table; table-layout: fixed; width: 100%;"> | |
| <tr> | |
| <td> | |
| <figure> | |
| <img src="https://media.giphy.com/media/gKfDjdXy0lbYNyROKo/giphy.gif" style="text-align:center; width:100%; display:block; margin:auto;"> | |
| <figcaption style="color: black; font-size: 15px; text-align: center;">"Mystical trees" <span style="color: red">AND</span> "A magical pond" <span style="color: red">AND</span> "Dark"</figcaption> | |
| </figure> | |
| </td> | |
| <td> | |
| <figure> | |
| <img src="https://media.giphy.com/media/sf5m1Z5FldemLMatWn/giphy.gif" style="text-align:center; width:100%; display:block; margin:auto;"> | |
| <figcaption style="color: black; font-size: 15px; text-align: center;">"Mystical trees" <span style="color: red">AND</span> "A magical pond" <span style="color: red">AND NOT</span> "Dark"</figcaption> | |
| </figure> | |
| </td> | |
| <td> | |
| <figure> | |
| <img src="https://media.giphy.com/media/lTzdW41bFnrD8AYa0K/giphy.gif" style="text-align:center; width:100%; display:block; margin:auto;"> | |
| <figcaption style="color: black; font-size: 15px; text-align: center;">"A toilet" <span style="color: red">AND</span> "A chair"</figcaption> | |
| </figure> | |
| </td> | |
| <td> | |
| <figure> | |
| <img src="https://media.giphy.com/media/nFkMh70kzZCwjbRrx5/giphy.gif" style="text-align:center; width:100%; display:block; margin:auto;"> | |
| <figcaption style="color: black; font-size: 15px; text-align: center;">"A monitor" <span style="color: red">AND</span> "A brown couch"</figcaption> | |
| </figure> | |
| </td> | |
| </tr> | |
| </table> | |
| """ | |
| ) | |
| gr.Markdown( | |
| """<p style="font-size: 18px;">Compositional visual generation by composing pre-trained diffusion models | |
| using compositional operators, <b>AND</b> and <b>NOT</b>.</p>""") | |
| gr.Markdown( | |
| """<p style="font-size: 18px;">When composing multiple inputs, please use <b>“|”</b> to separate them </p>""") | |
| gr.Markdown( | |
| """<p>( <b>Note</b>: For composing CLEVR objects, we recommend using <b><i>x</i></b> in range <b><i>[0.1, | |
| 0.9]</i></b> and <b><i>y</i></b> in range <b><i>[0.25, 0.7]</i></b>, since the training dataset labels are in | |
| given ranges.)</p><hr>""") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| """<h4>Composing natural language descriptions / objects for 2D image | |
| generation</h4>""") | |
| with gr.Row(): | |
| text_input = gr.Textbox(value="mystical trees | A dark magical pond | dark", label="Text to image prompt") | |
| weights_input = gr.Textbox(value="7.5 | 7.5 | 7.5", label="Weights") | |
| with gr.Row(): | |
| seed_input = gr.Number(0, label="Seed") | |
| steps_input = gr.Slider(10, 200, value=50, label="Steps") | |
| with gr.Row(): | |
| model_input = gr.Radio( | |
| ['Stable_Diffusion_1v_4', 'CLEVR Objects'], type="value", label='Text to image model', | |
| value='Stable_Diffusion_1v_4') | |
| image_output = gr.Image() | |
| image_button = gr.Button("Generate") | |
| img_examples = gr.Examples( | |
| examples=image_examples, | |
| inputs=[text_input, weights_input, model_input, steps_input, seed_input] | |
| ) | |
| with gr.Column(): | |
| gr.Markdown( | |
| """<h4>Composing natural language descriptions for 3D asset generation</h4>""") | |
| with gr.Row(): | |
| asset_input = gr.Textbox(value="a cake | a house", label="Text to 3D prompt") | |
| with gr.Row(): | |
| asset_weights = gr.Textbox(value="7.5 | 7.5", label="Weights") | |
| with gr.Row(): | |
| asset_model = gr.Radio(['Point-E'], type="value", label='Text to 3D model', value='Point-E') | |
| asset_output = gr.Image(label='GIF') | |
| asset_button = gr.Button("Generate") | |
| asset_examples = gr.Examples(examples=pointe_examples, inputs=[asset_input, asset_weights, asset_model]) | |
| image_button.click(compose_2D_diffusion, | |
| inputs=[text_input, weights_input, model_input, steps_input, seed_input], | |
| outputs=image_output) | |
| asset_button.click(compose_pointe, inputs=[asset_input, asset_weights, asset_model], outputs=asset_output) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=5) | |
| demo.launch(debug=True) | |