finalize

README.md

@@ -7,7 +7,7 @@
 
 ![model_diff_1_50](https://user-images.githubusercontent.com/23423619/171610307-dab0cd8b-75da-4d4e-9f5a-5922072e2bb5.png)
 
-**Schedulers**: Algorithm to sample noise schedule for both *training* and *inference*. Defines alpha and beta schedule, timesteps, etc..
+**Schedulers**: Algorithm to compute previous image according to alpha, beta schedule and to sample noise. Should be used for both *training* and *inference*.
 *Example: Gaussian DDPM, DDIM, PMLS, DEIN*
 
 ![sampling](https://user-images.githubusercontent.com/23423619/171608981-3ad05953-a684-4c82-89f8-62a459147a07.png)
@@ -18,13 +18,15 @@
 
 ![imagen](https://user-images.githubusercontent.com/23423619/171609001-c3f2c1c9-f597-4a16-9843-749bf3f9431c.png)
 
-## 1. `diffusers` as a central modular diffusion and sampler library
+## Quickstart
+
+### 1. `diffusers` as a central modular diffusion and sampler library
 
 `diffusers` is more modularized than `transformers`. The idea is that researchers and engineers can use only parts of the library easily for the own use cases.
 It could become a central place for all kinds of models, schedulers, training utils and processors that one can mix and match for one's own use case.
 Both models and schedulers should be load- and saveable from the Hub.
 
-Example for [DDPM](https://arxiv.org/abs/2006.11239):
+**Example for [DDPM](https://arxiv.org/abs/2006.11239):**
 
 ```python
 import torch
@@ -50,7 +52,7 @@ for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_s
 				residual = self.unet(image, t)
 
 		# predict previous mean of image x_t-1
-		pred_prev_image = noise_scheduler.get_prev_image_step(residual, image, t)
+		pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t)
 
 		# optionally sample variance
 		variance = 0
@@ -71,7 +73,7 @@ image_pil = PIL.Image.fromarray(image_processed[0])
 image_pil.save("test.png")
 ```
 
-Example for [DDIM](https://arxiv.org/abs/2010.02502):
+**Example for [DDIM](https://arxiv.org/abs/2010.02502):**
 
 ```python
 import torch
@@ -99,7 +101,7 @@ for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_ste
 				residual = self.unet(image, inference_step_times[t])
 
 		# 2. predict previous mean of image x_t-1
-		pred_prev_image = noise_scheduler.get_prev_image_step(residual, image, t, num_inference_steps, eta)
+		pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t, num_inference_steps, eta)
 
 		# 3. optionally sample variance
 		variance = 0
@@ -120,10 +122,10 @@ image_pil = PIL.Image.fromarray(image_processed[0])
 image_pil.save("test.png")
 ```
 
-## 2. `diffusers` as a collection of most important Diffusion systems (GLIDE, Dalle, ...)
+### 2. `diffusers` as a collection of most important Diffusion systems (GLIDE, Dalle, ...)
 `models` directory in repository hosts the complete code necessary for running a diffusion system as well as to train it. A `DiffusionPipeline` class allows to easily run the diffusion model in inference:
 
-Example:
+**Example image generation with DDPM**
 
 ```python
 from diffusers import DiffusionPipeline

models/vision/ddim/modeling_ddim.py

@@ -58,7 +58,7 @@ class DDIM(DiffusionPipeline):
                 residual = self.unet(image, inference_step_times[t])
 
             # 2. predict previous mean of image x_t-1
-            pred_prev_image = self.noise_scheduler.get_prev_image_step(residual, image, t, num_inference_steps, eta)
+            pred_prev_image = self.noise_scheduler.compute_prev_image_step(residual, image, t, num_inference_steps, eta)
 
             # 3. optionally sample variance
             variance = 0

models/vision/ddpm/modeling_ddpm.py

@@ -45,7 +45,7 @@ class DDPM(DiffusionPipeline):
                 residual = self.unet(image, t)
 
             # 2. predict previous mean of image x_t-1
-            pred_prev_image = self.noise_scheduler.get_prev_image_step(residual, image, t)
+            pred_prev_image = self.noise_scheduler.compute_prev_image_step(residual, image, t)
 
             # 3. optionally sample variance
             variance = 0

src/diffusers/schedulers/ddim.py

@@ -100,7 +100,7 @@ class DDIMScheduler(nn.Module, ConfigMixin):
 
         return variance
 
-    def get_prev_image_step(self, residual, image, t, num_inference_steps, eta, output_pred_x_0=False):
+    def compute_prev_image_step(self, residual, image, t, num_inference_steps, eta, output_pred_x_0=False):
         # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding
 

src/diffusers/schedulers/gaussian_ddpm.py

@@ -108,7 +108,7 @@ class GaussianDDPMScheduler(nn.Module, ConfigMixin):
 
         return variance
 
-    def get_prev_image_step(self, residual, image, t, output_pred_x_0=False):
+    def compute_prev_image_step(self, residual, image, t, output_pred_x_0=False):
         # 1. compute alphas, betas
         alpha_prod_t = self.get_alpha_prod(t)
         alpha_prod_t_prev = self.get_alpha_prod(t - 1)