start f16-higher res config
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rromb
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2 changed files with 161 additions and 6 deletions
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@ -0,0 +1,129 @@
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.001
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linear_end: 0.015
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 48
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channels: 16
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.22765929 # magic number
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ckpt_path: # TODO: add
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 48 # not really needed
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in_channels: 16
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out_channels: 16
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model_channels: 320 # TODO: scale model here
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 16
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monitor: val/rec_loss
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ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
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ddconfig:
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double_z: True
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z_channels: 16
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
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num_res_blocks: 2
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attn_resolutions: [ 16 ]
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
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batch_size: 10
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num_workers: 4
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multinode: True
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min_size: 384 # TODO: experiment. Note: for 2B, images are stored at max 384 resolution
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train:
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shards: '{000000..231317}.tar -'
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shuffle: 10000
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 768
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 768
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# NOTE use enough shards to avoid empty validation loops in workers
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validation:
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shards: '{231318..231349}.tar -'
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shuffle: 0
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 768
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interpolation: 3
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- target: torchvision.transforms.CenterCrop
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params:
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size: 768
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lightning:
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callbacks:
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image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 5000
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max_images: 4
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increase_log_steps: False
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log_first_step: False
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log_images_kwargs:
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use_ema_scope: False
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inpaint: False
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plot_progressive_rows: False
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plot_diffusion_rows: False
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N: 4
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unconditional_guidance_scale: 3.0
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unconditional_guidance_label: [""]
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trainer:
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benchmark: True
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val_check_interval: 5000000
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num_sanity_val_steps: 0
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accumulate_grad_batches: 2
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@ -7,6 +7,7 @@ from tqdm import tqdm, trange
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from itertools import islice
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from einops import rearrange
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from torchvision.utils import make_grid
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import time
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from ldm.util import instantiate_from_config
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from ldm.models.diffusion.ddim import DDIMSampler
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@ -63,6 +64,12 @@ if __name__ == "__main__":
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help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
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)
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parser.add_argument(
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"--skip_save",
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action='store_true',
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help="do not save indiviual samples. For speed measurements.",
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)
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parser.add_argument(
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"--ddim_steps",
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type=int,
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@ -103,6 +110,19 @@ if __name__ == "__main__":
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help="image width, in pixel space",
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)
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parser.add_argument(
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"--C",
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type=int,
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default=4,
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help="latent channels",
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)
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parser.add_argument(
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"--f",
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type=int,
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default=8,
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help="downsampling factor, most often 8 or 16",
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)
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parser.add_argument(
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"--n_samples",
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type=int,
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@ -184,6 +204,7 @@ if __name__ == "__main__":
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with torch.no_grad():
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with model.ema_scope():
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tic = time.time()
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for n in trange(opt.n_iter, desc="Sampling"):
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all_samples = list()
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for prompts in tqdm(data, desc="data"):
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@ -193,7 +214,7 @@ if __name__ == "__main__":
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [4, opt.H//8, opt.W//8]
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shape = [opt.C, opt.H//opt.f, opt.W//opt.f]
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samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
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conditioning=c,
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batch_size=opt.n_samples,
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@ -207,6 +228,7 @@ if __name__ == "__main__":
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
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if not opt.skip_save:
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for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
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@ -224,4 +246,8 @@ if __name__ == "__main__":
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
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toc = time.time()
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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f"Sampling took {toc-tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
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f" \nEnjoy.")
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