[example] add diffusion inference (#1986)

examples/images/diffusion/README.md

@@ -96,11 +96,55 @@ We provide the finetuning example on CIFAR10 dataset
 
 You can run by config `train_colossalai_cifar10.yaml`
 ```
-python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai_cifar10.yaml
+python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai_cifar10.yaml 
+```
+
+## Inference
+you can get yout training last.ckpt and train config.yaml in your `--logdir`, and run by
+```
+python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 
+    --outdir ./output \
+    --config path/to/logdir/checkpoints/last.ckpt \
+    --ckpt /path/to/logdir/configs/project.yaml  \
+```
+
+
+```commandline
+usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA]
+                  [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
+                  [--seed SEED] [--precision {full,autocast}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --prompt [PROMPT]     the prompt to render
+  --outdir [OUTDIR]     dir to write results to
+  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
+  --skip_save           do not save individual samples. For speed measurements.
+  --ddim_steps DDIM_STEPS
+                        number of ddim sampling steps
+  --plms                use plms sampling
+  --laion400m           uses the LAION400M model
+  --fixed_code          if enabled, uses the same starting code across samples
+  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
+  --n_iter N_ITER       sample this often
+  --H H                 image height, in pixel space
+  --W W                 image width, in pixel space
+  --C C                 latent channels
+  --f F                 downsampling factor
+  --n_samples N_SAMPLES
+                        how many samples to produce for each given prompt. A.k.a. batch size
+  --n_rows N_ROWS       rows in the grid (default: n_samples)
+  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
+  --from-file FROM_FILE
+                        if specified, load prompts from this file
+  --config CONFIG       path to config which constructs model
+  --ckpt CKPT           path to checkpoint of model
+  --seed SEED           the seed (for reproducible sampling)
+  --precision {full,autocast}
+                        evaluate at this precision
 ```
 
 
-
 ## Comments
 
 - Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)

examples/images/diffusion/configs/train_colossalai_teyvat.yaml

+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: txt
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1.e-4 ]
+        f_min: [ 1.e-10 ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/unet/diffusion_pytorch_model.bin'
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: False
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/vae/diffusion_pytorch_model.bin'
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        use_fp16: True
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 16
+    num_workers: 4
+    train:
+      target: ldm.data.teyvat.hf_dataset
+      params:
+        path: Fazzie/Teyvat
+        image_transforms:
+        - target: torchvision.transforms.Resize
+          params:
+            size: 512
+        # - target: torchvision.transforms.RandomCrop
+        #   params:
+        #     size: 256
+        # - target: torchvision.transforms.RandomHorizontalFlip
+
+lightning:
+  trainer:
+    accelerator: 'gpu' 
+    devices: 2
+    log_gpu_memory: all
+    max_epochs: 10
+    precision: 16
+    auto_select_gpus: False
+    strategy:
+      target: lightning.pytorch.strategies.ColossalAIStrategy
+      params:
+        use_chunk: False
+        enable_distributed_storage: True,
+        placement_policy: cuda
+        force_outputs_fp32: False
+
+    log_every_n_steps: 2
+    logger: True
+    default_root_dir: "/tmp/diff_log/"
+    profiler: pytorch
+
+  logger_config:
+    wandb:
+      target: lightning.pytorch.loggers.WandbLogger
+      params:
+          name: nowname
+          save_dir: "/tmp/diff_log/"
+          offline: opt.debug
+          id: nowname
\ No newline at end of file

examples/images/diffusion/ldm/data/teyvat.py

+from typing import Dict
+import numpy as np
+from omegaconf import DictConfig, ListConfig
+import torch
+from torch.utils.data import Dataset
+from pathlib import Path
+import json
+from PIL import Image
+from torchvision import transforms
+from einops import rearrange
+from ldm.util import instantiate_from_config
+from datasets import load_dataset
+
+def make_multi_folder_data(paths, caption_files=None, **kwargs):
+    """Make a concat dataset from multiple folders
+    Don't suport captions yet
+    If paths is a list, that's ok, if it's a Dict interpret it as:
+    k=folder v=n_times to repeat that
+    """
+    list_of_paths = []
+    if isinstance(paths, (Dict, DictConfig)):
+        assert caption_files is None, \
+            "Caption files not yet supported for repeats"
+        for folder_path, repeats in paths.items():
+            list_of_paths.extend([folder_path]*repeats)
+        paths = list_of_paths
+
+    if caption_files is not None:
+        datasets = [FolderData(p, caption_file=c, **kwargs) for (p, c) in zip(paths, caption_files)]
+    else:
+        datasets = [FolderData(p, **kwargs) for p in paths]
+    return torch.utils.data.ConcatDataset(datasets)
+
+class FolderData(Dataset):
+    def __init__(self,
+        root_dir,
+        caption_file=None,
+        image_transforms=[],
+        ext="jpg",
+        default_caption="",
+        postprocess=None,
+        return_paths=False,
+        ) -> None:
+        """Create a dataset from a folder of images.
+        If you pass in a root directory it will be searched for images
+        ending in ext (ext can be a list)
+        """
+        self.root_dir = Path(root_dir)
+        self.default_caption = default_caption
+        self.return_paths = return_paths
+        if isinstance(postprocess, DictConfig):
+            postprocess = instantiate_from_config(postprocess)
+        self.postprocess = postprocess
+        if caption_file is not None:
+            with open(caption_file, "rt") as f:
+                ext = Path(caption_file).suffix.lower()
+                if ext == ".json":
+                    captions = json.load(f)
+                elif ext == ".jsonl":
+                    lines = f.readlines()
+                    lines = [json.loads(x) for x in lines]
+                    captions = {x["file_name"]: x["text"].strip("\n") for x in lines}
+                else:
+                    raise ValueError(f"Unrecognised format: {ext}")
+            self.captions = captions
+        else:
+            self.captions = None
+
+        if not isinstance(ext, (tuple, list, ListConfig)):
+            ext = [ext]
+
+        # Only used if there is no caption file
+        self.paths = []
+        for e in ext:
+            self.paths.extend(list(self.root_dir.rglob(f"*.{e}")))
+        if isinstance(image_transforms, ListConfig):
+            image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
+        image_transforms.extend([transforms.ToTensor(),
+                                 transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
+        image_transforms = transforms.Compose(image_transforms)
+        self.tform = image_transforms
+
+
+    def __len__(self):
+        if self.captions is not None:
+            return len(self.captions.keys())
+        else:
+            return len(self.paths)
+
+    def __getitem__(self, index):
+        data = {}
+        if self.captions is not None:
+            chosen = list(self.captions.keys())[index]
+            caption = self.captions.get(chosen, None)
+            if caption is None:
+                caption = self.default_caption
+            filename = self.root_dir/chosen
+        else:
+            filename = self.paths[index]
+
+        if self.return_paths:
+            data["path"] = str(filename)
+
+        im = Image.open(filename)
+        im = self.process_im(im)
+        data["image"] = im
+
+        if self.captions is not None:
+            data["txt"] = caption
+        else:
+            data["txt"] = self.default_caption
+
+        if self.postprocess is not None:
+            data = self.postprocess(data)
+
+        return data
+
+    def process_im(self, im):
+        im = im.convert("RGB")
+        return self.tform(im)
+
+def hf_dataset(
+    path = "Fazzie/Teyvat",
+    image_transforms=[],
+    image_column="image",
+    text_column="text",
+    image_key='image',
+    caption_key='txt',
+    ):
+    """Make huggingface dataset with appropriate list of transforms applied
+    """
+    ds = load_dataset(path, name="train")
+    ds = ds["train"]
+    image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
+    image_transforms.extend([transforms.Resize((256, 256)),
+                            transforms.ToTensor(),
+                            transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))]
+                        )
+    tform = transforms.Compose(image_transforms)
+
+    assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
+    assert text_column in ds.column_names, f"Didn't find column {text_column} in {ds.column_names}"
+
+    def pre_process(examples):
+        processed = {}
+        processed[image_key] = [tform(im) for im in examples[image_column]]
+        processed[caption_key] = examples[text_column]
+
+        return processed
+
+    ds.set_transform(pre_process)
+    return ds
\ No newline at end of file

examples/images/diffusion/ldm/models/diffusion/ddpm.py

@@ -99,12 +99,12 @@ class DDPM(pl.LightningModule):
         self.use_positional_encodings = use_positional_encodings
         self.unet_config = unet_config
         self.conditioning_key = conditioning_key
-        # self.model = DiffusionWrapper(unet_config, conditioning_key)
-        # count_params(self.model, verbose=True)
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
         self.use_ema = use_ema
-        # if self.use_ema:
-        #     self.model_ema = LitEma(self.model)
-        #     print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         self.use_scheduler = scheduler_config is not None
         if self.use_scheduler:
@@ -125,20 +125,20 @@ class DDPM(pl.LightningModule):
         self.linear_start = linear_start
         self.linear_end = linear_end
         self.cosine_s = cosine_s
-        # if ckpt_path is not None:
-        #     self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-        #
-        # self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-        #                        linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+        
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
 
         self.loss_type = loss_type
 
         self.learn_logvar = learn_logvar
         self.logvar_init = logvar_init
-        # self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        # if self.learn_logvar:
-        #     self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-        #     self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
 
         self.use_fp16 = use_fp16
         if use_fp16:
@@ -312,14 +312,6 @@ class DDPM(pl.LightningModule):
 
     def get_loss(self, pred, target, mean=True):
 
-        if pred.isnan().any():
-            print("Warning: Prediction has nan values")
-            lr = self.optimizers().param_groups[0]['lr']
-            # self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-            print(f"lr: {lr}")
-        if pred.isinf().any():
-            print("Warning: Prediction has inf values")
-
         if self.use_fp16:
             target = target.half()
 
@@ -334,15 +326,6 @@ class DDPM(pl.LightningModule):
                 loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
         else:
             raise NotImplementedError("unknown loss type '{loss_type}'")
-                   
-        if loss.isnan().any():
-            print("Warning: loss has nan values")
-            print("loss: ", loss[0][0][0])
-            raise ValueError("loss has nan values")
-        if loss.isinf().any():
-            print("Warning: loss has inf values")
-            print("loss: ", loss)
-            raise ValueError("loss has inf values")
 
         return loss
 
@@ -382,11 +365,7 @@ class DDPM(pl.LightningModule):
         return self.p_losses(x, t, *args, **kwargs)
 
     def get_input(self, batch, k):
-        # print("+" * 30)
-        # print(batch['jpg'].shape)
-        # print(len(batch['txt']))
-        # print(k)
-        # print("=" * 30)
+
         if not isinstance(batch, torch.Tensor):
             x = batch[k]
         else:
@@ -534,8 +513,8 @@ class LatentDiffusion(DDPM):
         else:
             self.cond_stage_config["params"].update({"use_fp16": False})
             rank_zero_info("Using fp16 for conditioning stage = {}".format(self.cond_stage_config["params"]["use_fp16"]))
-        # self.instantiate_first_stage(first_stage_config)
-        # self.instantiate_cond_stage(cond_stage_config)
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
         self.cond_stage_forward = cond_stage_forward
         self.clip_denoised = False
         self.bbox_tokenizer = None  
@@ -561,16 +540,11 @@ class LatentDiffusion(DDPM):
         self.logvar = torch.full(fill_value=self.logvar_init, size=(self.num_timesteps,))
         if self.learn_logvar:
             self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-            # self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
         if self.ckpt_path is not None:
             self.init_from_ckpt(self.ckpt_path, self.ignore_keys)
             self.restarted_from_ckpt = True
 
-        # TODO()
-        # for p in self.model.modules():
-        #     if not p.parameters().data.is_contiguous:
-        #     p.data = p.data.contiguous()
-    
         self.instantiate_first_stage(self.first_stage_config)
         self.instantiate_cond_stage(self.cond_stage_config)
 

examples/images/diffusion/scripts/txt2img.sh

+python scripts/txt2img.py --prompt "Teyvat, Name:Layla, Element: Cryo, Weapon:Sword, Region:Sumeru, Model type:Medium Female, Description:a woman in a blue outfit holding a sword" --plms \
+    --outdir ./output \
+    --config /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/checkpoints/last.ckpt \
+    --ckpt /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/configs/2022-11-18T16-38-46-project.yaml  \
+    --n_samples 4
+