Delete app.py

app.py

@@ -1,219 +0,0 @@
-import math
-import fire
-import gradio as gr
-import numpy as np
-import rich
-import torch
-from contextlib import nullcontext
-from einops import rearrange
-from functools import partial
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.util import load_and_preprocess, instantiate_from_config
-from omegaconf import OmegaConf
-from PIL import Image
-from rich import print
-from torch import autocast
-from torchvision import transforms
-
-
-_SHOW_INTERMEDIATE = True
-_GPU_INDEX = 0
-# _GPU_INDEX = 2
-
-
-def load_model_from_config(config, ckpt, device, verbose=False):
-    print(f'Loading model from {ckpt}')
-    pl_sd = torch.load(ckpt, map_location=device)
-    if 'global_step' in pl_sd:
-        print(f'Global Step: {pl_sd["global_step"]}')
-    sd = pl_sd['state_dict']
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print('missing keys:')
-        print(m)
-    if len(u) > 0 and verbose:
-        print('unexpected keys:')
-        print(u)
-
-    model.to(device)
-    model.eval()
-    return model
-
-
-@torch.no_grad()
-def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale,
-                 ddim_eta, x, y, z):
-    precision_scope = autocast if precision == 'autocast' else nullcontext
-    with precision_scope('cuda'):
-        with model.ema_scope():
-            c = model.get_learned_conditioning(input_im).tile(n_samples, 1, 1)
-            T = torch.tensor([math.radians(x), math.sin(
-                math.radians(y)), math.cos(math.radians(y)), z])
-            T = T[None, None, :].repeat(n_samples, 1, 1).to(c.device)
-            c = torch.cat([c, T], dim=-1)
-            c = model.cc_projection(c)
-            cond = {}
-            cond['c_crossattn'] = [c]
-            c_concat = model.encode_first_stage((input_im.to(c.device))).mode().detach()
-            cond['c_concat'] = [model.encode_first_stage((input_im.to(c.device))).mode().detach()
-                                .repeat(n_samples, 1, 1, 1)]
-            if scale != 1.0:
-                uc = {}
-                uc['c_concat'] = [torch.zeros(n_samples, 4, h // 8, w // 8).to(c.device)]
-                uc['c_crossattn'] = [torch.zeros_like(c).to(c.device)]
-            else:
-                uc = None
-
-            shape = [4, h // 8, w // 8]
-            samples_ddim, _ = sampler.sample(S=ddim_steps,
-                                             conditioning=cond,
-                                             batch_size=n_samples,
-                                             shape=shape,
-                                             verbose=False,
-                                             unconditional_guidance_scale=scale,
-                                             unconditional_conditioning=uc,
-                                             eta=ddim_eta,
-                                             x_T=None)
-            print(samples_ddim.shape)
-            # samples_ddim = torch.nn.functional.interpolate(samples_ddim, 64, mode='nearest', antialias=False)
-            x_samples_ddim = model.decode_first_stage(samples_ddim)
-            return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
-
-
-def main(
-    model,
-    device,
-    input_im,
-    preprocess=True,
-    x=0.,
-    y=0.,
-    z=0.,
-    scale=3.0,
-    n_samples=4,
-    ddim_steps=50,
-    ddim_eta=1.0,
-    precision='fp32',
-    h=256,
-    w=256,
-):
-    # input_im[input_im == [0., 0., 0.]] = [1., 1., 1., 1.]
-    print('old input_im:', input_im.size)
-
-    if preprocess:
-        input_im = load_and_preprocess(input_im)
-        input_im = (input_im / 255.0).astype(np.float32)
-        # (H, W, 3) array in [0, 1].
-
-    else:
-        input_im = input_im.resize([256, 256], Image.Resampling.LANCZOS)
-        input_im = np.asarray(input_im, dtype=np.float32) / 255.0
-        # (H, W, 4) array in [0, 1].
-        
-        # old method: very important, thresholding background
-        # input_im[input_im[:, :, -1] <= 0.9] = [1., 1., 1., 1.]
-
-        # new method: apply correct method of compositing to avoid sudden transitions / thresholding
-        # (smoothly transition foreground to white background based on alpha values)
-        alpha = input_im[:, :, 3:4]
-        white_im = np.ones_like(input_im)
-        input_im = alpha * input_im + (1.0 - alpha) * white_im
-        
-        input_im = input_im[:, :, 0:3]
-        # (H, W, 3) array in [0, 1].
-
-    print('new input_im:', input_im.shape, input_im.dtype, input_im.min(), input_im.max())
-    show_in_im = Image.fromarray((input_im * 255).astype(np.uint8))
-
-    input_im = transforms.ToTensor()(input_im).unsqueeze(0).to(device)
-    input_im = input_im * 2 - 1
-    input_im = transforms.functional.resize(input_im, [h, w])
-
-    sampler = DDIMSampler(model)
-    x_samples_ddim = sample_model(input_im, model, sampler, precision, h, w,
-                                  ddim_steps, n_samples, scale, ddim_eta, x, y, z)
-
-    output_ims = []
-    for x_sample in x_samples_ddim:
-        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-        output_ims.append(Image.fromarray(x_sample.astype(np.uint8)))
-    
-    if _SHOW_INTERMEDIATE:
-        return (output_ims, show_in_im)
-    else:
-        return output_ims
-
-
-description = '''
-Generate novel viewpoints of an object depicted in one input image using a fine-tuned version of Stable Diffusion.
-'''
-
-article = '''
-## How to use this?
-TBD
-## How does this work?
-TBD
-'''
-
-
-def run_demo(
-    device_idx=_GPU_INDEX,
-    ckpt='last.ckpt',
-    config='configs/sd-objaverse-finetune-c_concat-256.yaml',
-):
-
-    device = f'cuda:{device_idx}'
-    config = OmegaConf.load(config)
-    model = load_model_from_config(config, ckpt, device=device)
-
-    inputs = [
-        gr.Image(type='pil', image_mode='RGBA'),  # shape=[512, 512]
-        gr.Checkbox(True, label='Preprocess image (remove background and center)',
-        info='If enabled, the uploaded image will be preprocessed to remove the background and center the object by cropping and/or padding as necessary. '
-        'If disabled, the image will be used as-is, *BUT* a fully transparent or white background is required.'),
-        # gr.Number(label='polar (between axis z+)'),
-        # gr.Number(label='azimuth (between axis x+)'),
-        # gr.Number(label='z (distance from center)'),
-        gr.Slider(-90, 90, value=0, step=5, label='Polar angle (vertical rotation in degrees)',
-        info='Positive values move the camera down, while negative values move the camera up.'),
-        gr.Slider(-90, 90, value=0, step=5, label='Azimuth angle (horizontal rotation in degrees)',
-        info='Positive values move the camera right, while negative values move the camera left.'),
-        gr.Slider(-2, 2, value=0, step=0.5, label='Radius (distance from center)',
-        info='Positive values move the camera further away, while negative values move the camera closer.'),
-        gr.Slider(0, 30, value=3, step=1, label='cfg scale'),
-        gr.Slider(1, 8, value=4, step=1, label='Number of samples to generate'),
-        gr.Slider(5, 200, value=100, step=5, label='Number of steps'),
-    ]
-    output = [gr.Gallery(label='Generated images from specified new viewpoint')]
-    output[0].style(grid=2)
-    
-    if _SHOW_INTERMEDIATE:
-        output += [gr.Image(type='pil', image_mode='RGB', label='Preprocessed input image')]
-
-    fn_with_model = partial(main, model, device)
-    fn_with_model.__name__ = 'fn_with_model'
-
-    examples = [
-        # ['assets/zero-shot/bear.png', 0, 0, 0, 3, 4, 100],
-        # ['assets/zero-shot/car.png', 0, 0, 0, 3, 4, 100],
-        # ['assets/zero-shot/elephant.png', 0, 0, 0, 3, 4, 100],
-        # ['assets/zero-shot/pikachu.png', 0, 0, 0, 3, 4, 100],
-        # ['assets/zero-shot/spyro.png', 0, 0, 0, 3, 4, 100],
-        # ['assets/zero-shot/taxi.png', 0, 0, 0, 3, 4, 100],
-    ]
-
-    demo = gr.Interface(
-        fn=fn_with_model,
-        title='Demo for Zero-Shot Control of Camera Viewpoints within a Single Image',
-        description=description,
-        article=article,
-        inputs=inputs,
-        outputs=output,
-        examples=examples,
-        allow_flagging='never',
-    )
-    demo.launch(enable_queue=True, share=True)
-
-
-if __name__ == '__main__':
-    fire.Fire(run_demo)