From 8e020677ad5f4c45a7e8dd89e952cad50a13fb49 Mon Sep 17 00:00:00 2001
From: Patrick von Platen <patrick.v.platen@gmail.com>
Date: Wed, 15 Jun 2022 12:17:17 +0200
Subject: [PATCH 1/2] Update README.md

---
 README.md | 58 -------------------------------------------------------
 1 file changed, 58 deletions(-)

diff --git a/README.md b/README.md
index 35986bb620..d8ff34e7ee 100644
--- a/README.md
+++ b/README.md
@@ -255,61 +255,3 @@ from scipy.io.wavfile import write as wavwrite
 sampling_rate = 22050
 wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
 ```
-
-## Library structure:
-
-```
-├── LICENSE
-├── Makefile
-├── README.md
-├── pyproject.toml
-├── setup.cfg
-├── setup.py
-├── src
-│   ├── diffusers
-│       ├── __init__.py
-│       ├── configuration_utils.py
-│       ├── dependency_versions_check.py
-│       ├── dependency_versions_table.py
-│       ├── dynamic_modules_utils.py
-│       ├── modeling_utils.py
-│       ├── models
-│       │   ├── __init__.py
-│       │   ├── unet.py
-│       │   ├── unet_glide.py
-│       │   └── unet_ldm.py
-│       ├── pipeline_utils.py
-│       ├── pipelines
-│       │   ├── __init__.py
-│       │   ├── configuration_ldmbert.py
-│       │   ├── conversion_glide.py
-│       │   ├── modeling_vae.py
-│       │   ├── pipeline_bddm.py
-│       │   ├── pipeline_ddim.py
-│       │   ├── pipeline_ddpm.py
-│       │   ├── pipeline_glide.py
-│       │   └── pipeline_latent_diffusion.py
-│       ├── schedulers
-│       │   ├── __init__.py
-│       │   ├── classifier_free_guidance.py
-│       │   ├── scheduling_ddim.py
-│       │   ├── scheduling_ddpm.py
-│       │   ├── scheduling_plms.py
-│       │   └── scheduling_utils.py
-│       ├── testing_utils.py
-│       └── utils
-│           ├── __init__.py
-│           └── logging.py
-├── tests
-│   ├── __init__.py
-│   ├── test_modeling_utils.py
-│   └── test_scheduler.py
-└── utils
-    ├── check_config_docstrings.py
-    ├── check_copies.py
-    ├── check_dummies.py
-    ├── check_inits.py
-    ├── check_repo.py
-    ├── check_table.py
-    └── check_tf_ops.py
-```

From f8cd3a20e47be25e96929ed2929f2e402fc2d074 Mon Sep 17 00:00:00 2001
From: Patrick von Platen <patrick.v.platen@gmail.com>
Date: Wed, 15 Jun 2022 12:25:48 +0200
Subject: [PATCH 2/2] Update README.md

---
 README.md | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index d8ff34e7ee..f97838df89 100644
--- a/README.md
+++ b/README.md
@@ -58,12 +58,14 @@ git clone https://github.com/huggingface/diffusers.git
 cd diffusers && pip install -e .
 ```
 
-### 1. `diffusers` as a central modular diffusion and sampler library
+### 1. `diffusers` as a toolbox for schedulers and models.
 
 `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.
 
+For more examples see [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) and [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)
+
 #### **Example for [DDPM](https://arxiv.org/abs/2006.11239):**
 
 ```python
@@ -171,25 +173,35 @@ 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, ...)
-`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:
+### 2. `diffusers` as a collection of popula Diffusion systems (GLIDE, Dalle, ...)
 
-#### **Example image generation with DDPM**
+For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
+
+#### **Example image generation with PNDM**
 
 ```python
-from diffusers import DiffusionPipeline
+from diffusers import PNDM, UNetModel, PNDMScheduler
 import PIL.Image
 import numpy as np
+import torch
+
+model_id = "fusing/ddim-celeba-hq"
+
+model = UNetModel.from_pretrained(model_id)
+scheduler = PNDMScheduler()
 
 # load model and scheduler
-ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom")
+ddpm = PNDM(unet=model, noise_scheduler=scheduler)
 
 # run pipeline in inference (sample random noise and denoise)
-image = ddpm()
+with torch.no_grad():
+    image = ddpm()
 
 # process image to PIL
 image_processed = image.cpu().permute(0, 2, 3, 1)
-image_processed = (image_processed + 1.0) * 127.5
+image_processed = (image_processed + 1.0) / 2
+image_processed = torch.clamp(image_processed, 0.0, 1.0)
+image_processed = image_processed * 255
 image_processed = image_processed.numpy().astype(np.uint8)
 image_pil = PIL.Image.fromarray(image_processed[0])