1 files changed, 75 insertions, 0 deletions

diff --git a/training/common.py b/training/common.py
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index 0000000..99a6e67
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+++ b/training/common.py
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+import torch
+import torch.nn.functional as F
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+
+
+def run_model(
+    vae: AutoencoderKL,
+    noise_scheduler: DDPMScheduler,
+    unet: UNet2DConditionModel,
+    prompt_processor,
+    num_class_images: int,
+    prior_loss_weight: float,
+    seed: int,
+    step: int,
+    batch,
+    eval: bool = False
+):
+    # Convert images to latent space
+    latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
+    latents = latents * 0.18215
+
+    # Sample noise that we'll add to the latents
+    noise = torch.randn_like(latents)
+    bsz = latents.shape[0]
+    # Sample a random timestep for each image
+    timesteps_gen = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None
+    timesteps = torch.randint(
+        0,
+        noise_scheduler.config.num_train_timesteps,
+        (bsz,),
+        generator=timesteps_gen,
+        device=latents.device,
+    )
+    timesteps = timesteps.long()
+
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
+    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+    noisy_latents = noisy_latents.to(dtype=unet.dtype)
+
+    # Get the text embedding for conditioning
+    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])
+    encoder_hidden_states = encoder_hidden_states.to(dtype=unet.dtype)
+
+    # Predict the noise residual
+    model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+    # Get the target for loss depending on the prediction type
+    if noise_scheduler.config.prediction_type == "epsilon":
+        target = noise
+    elif noise_scheduler.config.prediction_type == "v_prediction":
+        target = noise_scheduler.get_velocity(latents, noise, timesteps)
+    else:
+        raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+    if num_class_images != 0:
+        # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
+        model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
+        target, target_prior = torch.chunk(target, 2, dim=0)
+
+        # Compute instance loss
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+        # Compute prior loss
+        prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
+
+        # Add the prior loss to the instance loss.
+        loss = loss + prior_loss_weight * prior_loss
+    else:
+        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+    acc = (model_pred == target).float().mean()
+
+    return loss, acc, bsz