[CLIPGuidedStableDiffusion] support DDIM scheduler (#1190)

add ddim in clip guided

Author: patil-suraj

examples/community/clip_guided_stable_diffusion.py

@@ -5,7 +5,14 @@
 from torch import nn
 from torch.nn import functional as F
 
-from diffusers import AutoencoderKL, DiffusionPipeline, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
 from torchvision import transforms
 from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
@@ -56,7 +63,7 @@ def __init__(
         clip_model: CLIPModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler],
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
         feature_extractor: CLIPFeatureExtractor,
     ):
         super().__init__()
@@ -123,7 +130,7 @@ def cond_fn(
         # predict the noise residual
         noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
 
-        if isinstance(self.scheduler, PNDMScheduler):
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)):
             alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
             beta_prod_t = 1 - alpha_prod_t
             # compute predicted original sample from predicted noise also called
@@ -176,6 +183,7 @@ def __call__(
         num_inference_steps: Optional[int] = 50,
         guidance_scale: Optional[float] = 7.5,
         num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
         clip_guidance_scale: Optional[float] = 100,
         clip_prompt: Optional[Union[str, List[str]]] = None,
         num_cutouts: Optional[int] = 4,
@@ -275,6 +283,20 @@ def __call__(
         # scale the initial noise by the standard deviation required by the scheduler
         latents = latents * self.scheduler.init_noise_sigma
 
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
         for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
@@ -306,7 +328,7 @@ def __call__(
                 )
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents