SDXL Turbo support and example launch (#6473)

* support and example launch for sdxl turbo

* White space fixes

* Trailing whitespace character

* ruff format

* fix guidance_scale and steps for turbo mode

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Radames Ajna <radamajna@gmail.com>

examples/research_projects/diffusion_dpo/README.md

@@ -61,6 +61,34 @@ accelerate launch train_diffusion_dpo_sdxl.py \
   --push_to_hub
 ```
 
+## SDXL Turbo training command
+
+```bash
+accelerate launch train_diffusion_dpo_sdxl.py \
+  --pretrained_model_name_or_path=stabilityai/sdxl-turbo \
+  --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
+  --output_dir="diffusion-sdxl-turbo-dpo" \
+  --mixed_precision="fp16" \
+  --dataset_name=kashif/pickascore \
+  --train_batch_size=8 \
+  --gradient_accumulation_steps=2 \
+  --gradient_checkpointing \
+  --use_8bit_adam \
+  --rank=8 \
+  --learning_rate=1e-5 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=2000 \
+  --checkpointing_steps=500 \
+  --run_validation --validation_steps=50 \
+  --seed="0" \
+  --report_to="wandb" \
+  --is_turbo --resolution 512 \
+  --push_to_hub
+```
+
+
 ## Acknowledgements
 
 This is based on the amazing work done by [Bram](https://github.com/bram-w) here for Diffusion DPO: https://github.com/bram-w/trl/blob/dpo/. 

examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py

@@ -118,9 +118,16 @@ def log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_v
     images = []
     context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()
 
+    guidance_scale = 5.0
+    num_inference_steps = 25
+    if args.is_turbo:
+        guidance_scale = 0.0
+        num_inference_steps = 4
     for prompt in VALIDATION_PROMPTS:
         with context:
-            image = pipeline(prompt, num_inference_steps=25, generator=generator).images[0]
+            image = pipeline(
+                prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator
+            ).images[0]
             images.append(image)
 
     tracker_key = "test" if is_final_validation else "validation"
@@ -141,7 +148,10 @@ def log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_v
     if is_final_validation:
         pipeline.disable_lora()
         no_lora_images = [
-            pipeline(prompt, num_inference_steps=25, generator=generator).images[0] for prompt in VALIDATION_PROMPTS
+            pipeline(
+                prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator
+            ).images[0]
+            for prompt in VALIDATION_PROMPTS
         ]
 
         for tracker in accelerator.trackers:
@@ -423,6 +433,11 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
     )
+    parser.add_argument(
+        "--is_turbo",
+        action="store_true",
+        help=("Use if tuning SDXL Turbo instead of SDXL"),
+    )
     parser.add_argument(
         "--rank",
         type=int,
@@ -444,6 +459,9 @@ def parse_args(input_args=None):
     if args.dataset_name is None:
         raise ValueError("Must provide a `dataset_name`.")
 
+    if args.is_turbo:
+        assert "turbo" in args.pretrained_model_name_or_path
+
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
     if env_local_rank != -1 and env_local_rank != args.local_rank:
         args.local_rank = env_local_rank
@@ -560,6 +578,36 @@ def main(args):
 
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+
+    def enforce_zero_terminal_snr(scheduler):
+        # Modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py#L93
+        # Original implementation https://arxiv.org/pdf/2305.08891.pdf
+        # Turbo needs zero terminal SNR
+        # Turbo: https://static1.squarespace.com/static/6213c340453c3f502425776e/t/65663480a92fba51d0e1023f/1701197769659/adversarial_diffusion_distillation.pdf
+        # Convert betas to alphas_bar_sqrt
+        alphas = 1 - scheduler.betas
+        alphas_bar = alphas.cumprod(0)
+        alphas_bar_sqrt = alphas_bar.sqrt()
+
+        # Store old values.
+        alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+        alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+        # Shift so last timestep is zero.
+        alphas_bar_sqrt -= alphas_bar_sqrt_T
+        # Scale so first timestep is back to old value.
+        alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+        alphas_bar = alphas_bar_sqrt**2
+        alphas = alphas_bar[1:] / alphas_bar[:-1]
+        alphas = torch.cat([alphas_bar[0:1], alphas])
+
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
+        scheduler.alphas_cumprod = alphas_cumprod
+        return
+
+    if args.is_turbo:
+        enforce_zero_terminal_snr(noise_scheduler)
+
     text_encoder_one = text_encoder_cls_one.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
     )
@@ -909,6 +957,10 @@ def main(args):
                 timesteps = torch.randint(
                     0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long
                 ).repeat(2)
+                if args.is_turbo:
+                    # Learn a 4 timestep schedule
+                    timesteps_0_to_3 = timesteps % 4
+                    timesteps = 250 * timesteps_0_to_3 + 249
 
                 # Add noise to the model input according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)