ディフューザーの DPMScheduler/AutoencoderKL/UNet2DConditionModel/CLIPTextModel コードの詳細な説明

拡散モデルのトレーニングは比較的簡単です

上の図からわかるように、unet は eps であり、θ は unet です。ノイズと予測ノイズは mse 損失として使用されます。

トレーニングの日常的なプロセス:

latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist_sample()
latents = latents*vae.config.scaling_factor
noise = torch.randn_like(latents)
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
            
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
 
target = noise
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

具体分析：

ディフューザー/モデル/autoencoder_kl.py

AutoencoderKL.encode->
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
AutoencoderKLOutput(posterior)

ディフューザー/スケジューラー/scheduing_ddpm.py

add_noise(original_samples,noise,timesteps)->
noisy_samples = sqrt_alpha_prod*original_samples+sqrt_one_inus_alpha_prod*noise

トランスフォーマー/モデル/クリップ/modeling_clip.py

CLIPTextModel.forward->
self.text_model()->

hidden_states = self.embedding(input_ids,position_ids)->
causal_attention_mask = self._build_causal_attention_mask(bsz,seq_len,hidden_states)
encoder_outputs = self.encoder(hidden_states,attention_mask,causal_attention_mask,output_attention,output_hidden_states)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), input_ids.argmax(dim=-1)]

ディフューザー/モデル/unet_2d_condition.py

{
  "_class_name": "UNet2DConditionModel",
  "_diffusers_version": "0.19.3",
  "act_fn": "silu",
  "addition_embed_type": null,
  "addition_embed_type_num_heads": 64,
  "addition_time_embed_dim": null,
  "attention_head_dim": 8,
  "block_out_channels": [
    320,
    640,
    1280,
    1280
  ],
  "center_input_sample": false,
  "class_embed_type": null,
  "class_embeddings_concat": false,
  "conv_in_kernel": 3,
  "conv_out_kernel": 3,
  "cross_attention_dim": 768,
  "cross_attention_norm": null,
  "down_block_types": [
    "CrossAttnDownBlock2D",
    "CrossAttnDownBlock2D",
    "CrossAttnDownBlock2D",
    "DownBlock2D"
  ],
  "downsample_padding": 1,
  "dual_cross_attention": false,
  "encoder_hid_dim": null,
  "encoder_hid_dim_type": null,
  "flip_sin_to_cos": true,
  "freq_shift": 0,
  "in_channels": 4,
  "layers_per_block": 2,
  "mid_block_only_cross_attention": null,
  "mid_block_scale_factor": 1,
  "mid_block_type": "UNetMidBlock2DCrossAttn",
  "norm_eps": 1e-05,
  "norm_num_groups": 32,
  "num_attention_heads": null,
  "num_class_embeds": null,
  "only_cross_attention": false,
  "out_channels": 4,
  "projection_class_embeddings_input_dim": null,
  "resnet_out_scale_factor": 1.0,
  "resnet_skip_time_act": false,
  "resnet_time_scale_shift": "default",
  "sample_size": 64,
  "time_cond_proj_dim": null,
  "time_embedding_act_fn": null,
  "time_embedding_dim": null,
  "time_embedding_type": "positional",
  "timestep_post_act": null,
  "transformer_layers_per_block": 1,
  "up_block_types": [
    "UpBlock2D",
    "CrossAttnUpBlock2D",
    "CrossAttnUpBlock2D",
    "CrossAttnUpBlock2D"
  ],
  "upcast_attention": false,
  "use_linear_projection": false
}

model_pred = unet(noisy_latents,timesteps,encoder_hidden_states).sample

0.center input
sample = 2*sample-1

1.time
t_emb = self.time_proj(timesteps)
emb = self.time_embedding(t_emb,timestep_cond)

2.pre-process
sample = self.conv_in(sample)

3.down
for downsample_block in self.down_blocks:
    sample,res_samples = downsample_block(sample,emb)
    down_block_res_samples += res_samples

4.mid
sample = self.mid_block(sample,emb)

5.up
for i,upsample_block in enumerate(self.up_blocks):
    sample = upsample_block(hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size)

6.post-process
sample = self.conv_out(sample)

普及モデルの推論:

ディフューザー/パイプライン/stable_diffusion/pipeline_stable_diffusion.py

StableDiffusionPipeline->

0.default height and width to unet

1.check inputs.
self.check_inputs(prompt,height,width,callback_steps,negative_prompt,prompt_embeds,negative_embeds)

2.define call parameters
batch
do_classifier_free_guidance

3.encode input prompt
prompt_embeds = self._encode_prompt(prompt,negative_prompt)

4.prepare timesteps
self.scheduler.set_timesteps(num_inference_steps)
timesteps = self.scheduler.timesteps

5.prepare latent variables
latents = self.prepare_latents(batch_size * num_images_per_prompt,num_channels_latents,height,width,prompt_embeds.dtype,device,generator,latents)

6.prepare extra step kwargs
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

7.denosing loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
for i,t in enumerate(timesteps):
    latent_model_input = torch.cat([latents]*2)
    latent_model_input = self.scheduler.scale_model_input(latent_model_input,t)
    
    # predict the noise residual
    noise_pred = self.unet(latent_model_input,t...)[0]
    
    if do_classifer_free_guidance:
        noise_pred_uncond,noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + guidance_scale*(noise_pred_text-noise_pred_uncond)
    
    # compute the previous noisy sample x_t->x_t-1
    latents = self.scheduler.step(noise_pred,t,latents,..)[0] # xt

image = self.image_processor.postprocess()

ディフューザー/スケジューラー/scheduling_ddpm.py

step->
t = timesteps
prev_t = self.previous_timestep(t)
- prev_t = timestep-self.config.num_train_timesteps//num_inference_steps

1.compute alpha,betas 
# 认为设置超参数beta,满足beta随着t的增大而增大，根据beta计算alpha
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
current_alpha_t = alpha_prod_t / alpha_prod_t_prev
current_beta_t = 1 - current_alpha_t

2.compute predicted original sample from predicted noise also called predicted_x0
pred_original_sample = (sample-beta_prod_t**(0.5)*model_output)/alpha_prod_t**(0.5)

3.clip or threshold predicted x0
pred_original_sample = pred_original_sample.clamp(-self.config.clip_sample_range,self.config.clip_sample_range)

4.compute coefficients for pred_original_sample x0 and current sample xt
pred_original_sample_coeff = (alpha_prod_t_prev**0.5*current_beta_t)/beta_prod_t
current_sample_coeff = current_alpha_t**0.5*beta_prod_t_prev/beta_prod_t

5.compute predicted previous sample 
pred_prev_sample = pred_original_sample_coeff*pred_original_sample+current_sample_coeff*sample

6.add noise
variance_noise = randn_tensor()
variance = self._get_variance(t,predicted_variance)*variance_noise
pred_prev_sample = pred_prev_sample+variance

return pred_prev_sample,pred_original_sample

xt = pred_prev_sample、x0 = pred_original_sample、xt は次の予測結果に簡略化されます。