v1-5 docs updates (#921)

* Update README.md

Additionally add FLAX so the model card can be slimmer and point to this page

* Find and replace all

* v-1-5 -> v1-5

* revert test changes

* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update docs/source/quicktour.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update docs/source/quicktour.mdx
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Revert certain references to v1-5

* Docs changes

* Apply suggestions from code review
Co-authored-by: apolinario <joaopaulo.passos+multimodal@gmail.com>
Co-authored-by: anton-l <anton@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

README.md

@@ -64,44 +64,54 @@ In order to get started, we recommend taking a look at two notebooks:
 - The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your
   diffusion models on an image dataset, with explanatory graphics. 
   
-## **New** Stable Diffusion is now fully compatible with `diffusers`!  
+## Stable Diffusion is fully compatible with `diffusers`!  
 
-Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
+Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM.
 See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
 
-You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
+You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license carefully and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
 
 
 ### Text-to-Image generation with Stable Diffusion
 
+First let's install
+```bash
+pip install --upgrade diffusers transformers scipy
+```
+
+Run this command to log in with your HF Hub token if you haven't before (you can skip this step if you prefer to run the model locally, follow [this](#running-the-model-locally) instead)
+```bash
+huggingface-cli login
+```
+
 We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost always the same results as full
 precision while being roughly twice as fast and requiring half the amount of GPU RAM.
 
 ```python
-# make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_type=torch.float16, revision="fp16")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_type=torch.float16, revision="fp16")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
 image = pipe(prompt).images[0]  
 ```
 
-**Note**: If you don't want to use the token, you can also simply download the model weights
-(after having [accepted the license](https://huggingface.co/CompVis/stable-diffusion-v1-4)) and pass
+#### Running the model locally
+If you don't want to login to Hugging Face, you can also simply download the model folder
+(after having [accepted the license](https://huggingface.co/runwayml/stable-diffusion-v1-5)) and pass
 the path to the local folder to the `StableDiffusionPipeline`.
 
 ```
 git lfs install
-git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
-Assuming the folder is stored locally under `./stable-diffusion-v1-4`, you can also run stable diffusion
+Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can also run stable diffusion
 without requiring an authentication token:
 
 ```python
-pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -114,7 +124,7 @@ The following snippet should result in less than 4GB VRAM.
 
 ```python
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", 
+    "runwayml/stable-diffusion-v1-5", 
     revision="fp16", 
     torch_dtype=torch.float16,
 )
@@ -125,7 +135,7 @@ pipe.enable_attention_slicing()
 image = pipe(prompt).images[0]  
 ```
 
-If you wish to use a different scheduler, you can simply instantiate
+If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate
 it before the pipeline and pass it to `from_pretrained`.
     
 ```python
@@ -138,7 +148,7 @@ lms = LMSDiscreteScheduler(
 )
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", 
+    "runwayml/stable-diffusion-v1-5", 
     revision="fp16", 
     torch_dtype=torch.float16,
     scheduler=lms,
@@ -158,7 +168,7 @@ please run the model in the default *full-precision* setting:
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 
 # disable the following line if you run on CPU
 pipe = pipe.to("cuda")
@@ -169,6 +179,75 @@ image = pipe(prompt).images[0]
 image.save("astronaut_rides_horse.png")
 ```
 
+### JAX/Flax
+
+To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax.
+
+Running the pipeline with default PNDMScheduler
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="flax", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+**Note**:
+If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
 ### Image-to-Image text-guided generation with Stable Diffusion
 
 The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.
@@ -183,14 +262,14 @@ from diffusers import StableDiffusionImg2ImgPipeline
 
 # load the pipeline
 device = "cuda"
-model_id_or_path = "CompVis/stable-diffusion-v1-4"
+model_id_or_path = "runwayml/stable-diffusion-v1-5"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     model_id_or_path,
     revision="fp16", 
     torch_dtype=torch.float16,
 )
-# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
-# and pass `model_id_or_path="./stable-diffusion-v1-4"`.
+# or download via git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+# and pass `model_id_or_path="./stable-diffusion-v1-5"`.
 pipe = pipe.to(device)
 
 # let's download an initial image

docs/source/api/pipelines/overview.mdx

@@ -67,8 +67,8 @@ Diffusion models often consist of multiple independently-trained models or other
 Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one. 
 During inference, we however want to be able to easily load all components and use them in inference - even if one component, *e.g.* CLIP's text encoder, originates from a different library, such as [Transformers](https://github.com/huggingface/transformers). To that end, all pipelines provide the following functionality:
 
-- [`from_pretrained` method](../diffusion_pipeline) that accepts a Hugging Face Hub repository id, *e.g.* [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) or a path to a local directory, *e.g.*
-"./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [CompVis/stable-diffusion-v1-4/model_index.json](https://huggingface.co/CompVis/stable-diffusion-v1-4/blob/main/model_index.json), which defines all components that should be 
+- [`from_pretrained` method](../diffusion_pipeline) that accepts a Hugging Face Hub repository id, *e.g.* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) or a path to a local directory, *e.g.*
+"./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [runwayml/stable-diffusion-v1-5/model_index.json](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), which defines all components that should be
 loaded into the pipelines. More specifically, for each model/component one needs to define the format `<name>: ["<library>", "<class name>"]`. `<name>` is the attribute name given to the loaded instance of `<class name>` which can be found in the library or pipeline folder called `"<library>"`.
 - [`save_pretrained`](../diffusion_pipeline) that accepts a local path, *e.g.* `./stable-diffusion` under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, *e.g.* `./stable_diffusion/unet`. 
 In addition, a `model_index.json` file is created at the root of the local path, *e.g.* `./stable_diffusion/model_index.json` so that the complete pipeline can again be instantiated 
@@ -100,7 +100,7 @@ logic including pre-processing, an unrolled diffusion loop, and post-processing
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -123,7 +123,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 # let's download an initial image

docs/source/optimization/fp16.mdx

@@ -56,7 +56,7 @@ If you use a CUDA GPU, you can take advantage of `torch.autocast` to perform inf
 from torch import autocast
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -72,7 +72,7 @@ To save more GPU memory and get even more speed, you can load and run the model
 
 ```Python
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="fp16",
     torch_dtype=torch.float16,
 )
@@ -97,7 +97,7 @@ import torch
 from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="fp16",
     torch_dtype=torch.float16,
 )
@@ -152,7 +152,7 @@ def generate_inputs():
 
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")
@@ -216,7 +216,7 @@ class UNet2DConditionOutput:
 
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")

docs/source/optimization/mps.mdx

@@ -31,7 +31,7 @@ We recommend to "prime" the pipeline using an additional one-time pass through i
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("mps")
 
 prompt = "a photo of an astronaut riding a horse on mars"

docs/source/optimization/onnx.mdx

@@ -28,7 +28,7 @@ The snippet below demonstrates how to use the ONNX runtime. You need to use `Sta
 from diffusers import StableDiffusionOnnxPipeline
 
 pipe = StableDiffusionOnnxPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="onnx",
     provider="CUDAExecutionProvider",
 )

docs/source/quicktour.mdx

@@ -68,8 +68,7 @@ You can save the image by simply calling:
 
 More advanced models, like [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) require you to accept a [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) before running the model.
 This is due to the improved image generation capabilities of the model and the potentially harmful content that could be produced with it.
-Long story short: Head over to your stable diffusion model of choice, *e.g.* [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4), read through the license and click-accept to get 
-access to the model. 
+Please, head over to your stable diffusion model of choice, *e.g.*  [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license carefully and tick the checkbox if you agree.
 You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
 Having "click-accepted" the license, you can save your token:
 
@@ -77,13 +76,13 @@ Having "click-accepted" the license, you can save your token:
 AUTH_TOKEN = "<please-fill-with-your-token>"
 ```
 
-You can then load [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 
+You can then load [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)
 just like we did before only that now you need to pass your `AUTH_TOKEN`:
 
 ```python
 >>> from diffusers import DiffusionPipeline
 
->>> generator = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=AUTH_TOKEN)
+>>> generator = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=AUTH_TOKEN)
 ```
 
 If you do not pass your authentication token you will see that the diffusion system will not be correctly 
@@ -95,15 +94,15 @@ the weights locally via:
 
 ```
 git lfs install
-git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
 and then load locally saved weights into the pipeline. This way, you do not need to pass an authentication
-token. Assuming that `"./stable-diffusion-v1-4"` is the local path to the cloned stable-diffusion-v1-4 repo,
+token. Assuming that `"./stable-diffusion-v1-5"` is the local path to the cloned stable-diffusion-v1-5 repo,
 you can also load the pipeline as follows:
 
 ```python
->>> generator = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
+>>> generator = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
 ```
 
 Running the pipeline is then identical to the code above as it's the same model architecture.
@@ -125,7 +124,7 @@ you could use it as follows:
 >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
 
 >>> generator = StableDiffusionPipeline.from_pretrained(
-...     "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=AUTH_TOKEN
+...     "runwayml/stable-diffusion-v1-5", scheduler=scheduler, use_auth_token=AUTH_TOKEN
 ... )
 ```
 

docs/source/training/text_inversion.mdx

@@ -83,7 +83,7 @@ Now let's get our dataset.Download 3-4 images from [here](https://drive.google.c
 And launch the training using
 
 ```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export DATA_DIR="path-to-dir-containing-images"
 
 accelerate launch textual_inversion.py \

docs/source/using-diffusers/custom_pipelines.mdx

@@ -58,7 +58,7 @@ feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
 clip_model = CLIPModel.from_pretrained(clip_model_id)
 
 pipeline = DiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,

docs/source/using-diffusers/img2img.mdx

@@ -25,7 +25,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
 ).to(device)
 
 # let's download an initial image

examples/community/README.md

@@ -18,7 +18,7 @@ If a community doesn't work as expected, please open an issue and ping the autho
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 ```py
-pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="filename_in_the_community_folder")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
 ```
 
 ## Example usages
@@ -41,7 +41,7 @@ clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K",
 
 
 guided_pipeline = DiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
@@ -141,7 +141,7 @@ def download_image(url):
     response = requests.get(url)
     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
 
-pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
 pipe.to("cuda")
 pipe.enable_attention_slicing()
 

examples/community/stable_diffusion_mega.py

@@ -46,7 +46,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionMegaSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """

examples/test_examples.py

@@ -101,7 +101,7 @@ class ExamplesTestsAccelerate(unittest.TestCase):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""
                 examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path CompVis/stable-diffusion-v1-4
+                --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5
                 --train_data_dir docs/source/imgs
                 --learnable_property object
                 --placeholder_token <cat-toy>

examples/textual_inversion/README.md

@@ -48,7 +48,7 @@ Now let's get our dataset.Download 3-4 images from [here](https://drive.google.c
 And launch the training using
 
 ```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
 export DATA_DIR="path-to-dir-containing-images"
 
 accelerate launch textual_inversion.py \

src/diffusers/modeling_flax_utils.py

@@ -105,14 +105,14 @@ class FlaxModelMixin:
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # load model
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
         >>> params = model.to_bf16(params)
         >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
         >>> # then pass the mask as follows
         >>> from flax import traverse_util
 
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> flat_params = traverse_util.flatten_dict(params)
         >>> mask = {
         ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
@@ -141,7 +141,7 @@ class FlaxModelMixin:
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # Download model and configuration from huggingface.co
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> # By default, the model params will be in fp32, to illustrate the use of this method,
         >>> # we'll first cast to fp16 and back to fp32
         >>> params = model.to_f16(params)
@@ -171,14 +171,14 @@ class FlaxModelMixin:
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # load model
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> # By default, the model params will be in fp32, to cast these to float16
         >>> params = model.to_fp16(params)
         >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
         >>> # then pass the mask as follows
         >>> from flax import traverse_util
 
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> flat_params = traverse_util.flatten_dict(params)
         >>> mask = {
         ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
@@ -216,7 +216,7 @@ class FlaxModelMixin:
 
                     - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                       Valid model ids are namespaced under a user or organization name, like
-                      `CompVis/stable-diffusion-v1-4`.
+                      `runwayml/stable-diffusion-v1-5`.
                     - A path to a *directory* containing model weights saved using [`~ModelMixin.save_pretrained`],
                       e.g., `./my_model_directory/`.
             dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
@@ -273,7 +273,7 @@ class FlaxModelMixin:
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # Download model and configuration from huggingface.co and cache.
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
         >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")
         ```"""

src/diffusers/pipeline_flax_utils.py

@@ -244,7 +244,7 @@ class FlaxDiffusionPipeline(ConfigMixin):
         <Tip>
 
          It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
-         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"CompVis/stable-diffusion-v1-4"`
+         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"`
 
         </Tip>
 
@@ -266,13 +266,13 @@ class FlaxDiffusionPipeline(ConfigMixin):
         >>> # Download pipeline that requires an authorization token
         >>> # For more information on access tokens, please refer to this section
         >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
         >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
+        >>> pipeline = FlaxDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler)
         ```
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

src/diffusers/pipeline_utils.py

@@ -317,7 +317,7 @@ class DiffusionPipeline(ConfigMixin):
         <Tip>
 
          It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
-         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"CompVis/stable-diffusion-v1-4"`
+         models](https://huggingface.co/docs/hub/models-gated#gated-models), *e.g.* `"runwayml/stable-diffusion-v1-5"`
 
         </Tip>
 
@@ -339,13 +339,13 @@ class DiffusionPipeline(ConfigMixin):
         >>> # Download pipeline that requires an authorization token
         >>> # For more information on access tokens, please refer to this section
         >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
-        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
         >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-        >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
+        >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler)
         ```
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)

src/diffusers/pipelines/README.md

@@ -55,8 +55,8 @@ Diffusion models often consist of multiple independently-trained models or other
 Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one. 
 During inference, we however want to be able to easily load all components and use them in inference - even if one component, *e.g.* CLIP's text encoder, originates from a different library, such as [Transformers](https://github.com/huggingface/transformers). To that end, all pipelines provide the following functionality:
 
-- [`from_pretrained` method](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L139) that accepts a Hugging Face Hub repository id, *e.g.* [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) or a path to a local directory, *e.g.*
-"./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [CompVis/stable-diffusion-v1-4/model_index.json](https://huggingface.co/CompVis/stable-diffusion-v1-4/blob/main/model_index.json), which defines all components that should be 
+- [`from_pretrained` method](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L139) that accepts a Hugging Face Hub repository id, *e.g.* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) or a path to a local directory, *e.g.*
+"./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [runwayml/stable-diffusion-v1-5/model_index.json](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), which defines all components that should be 
 loaded into the pipelines. More specifically, for each model/component one needs to define the format `<name>: ["<library>", "<class name>"]`. `<name>` is the attribute name given to the loaded instance of `<class name>` which can be found in the library or pipeline folder called `"<library>"`.
 - [`save_pretrained`](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L90) that accepts a local path, *e.g.* `./stable-diffusion` under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, *e.g.* `./stable_diffusion/unet`. 
 In addition, a `model_index.json` file is created at the root of the local path, *e.g.* `./stable_diffusion/model_index.json` so that the complete pipeline can again be instantiated 
@@ -88,7 +88,7 @@ logic including pre-processing, an unrolled diffusion loop, and post-processing
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -111,7 +111,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4",
+    "runwayml/stable-diffusion-v1-5",
     revision="fp16", 
     torch_dtype=torch.float16,
 ).to(device)

src/diffusers/pipelines/stable_diffusion/README.md

@@ -13,7 +13,7 @@ The summary of the model is the following:
 - Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model.
 - An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion).
 - If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can 
-download the weights with `git lfs install; git clone https://huggingface.co/CompVis/stable-diffusion-v1-4` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.
+download the weights with `git lfs install; git clone https://huggingface.co/runwayml/stable-diffusion-v1-5` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.
 - Stable Diffusion can work with a variety of different samplers as is shown below.
 
 ## Available Pipelines:
@@ -33,14 +33,14 @@ If you want to download the model weights using a single Python line, you need t
 ```python
 from diffusers import DiffusionPipeline
 
-pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 ```
 
-This however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `"./stable-diffusion-v1-4"`:
+This however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `"./stable-diffusion-v1-5"`:
 
 ```
 git lfs install
-git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
 and simply passing the local path to `from_pretrained`:
@@ -48,7 +48,7 @@ and simply passing the local path to `from_pretrained`:
 ```python
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
 ```
 
 ### Text-to-Image with default PLMS scheduler
@@ -57,7 +57,7 @@ pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -75,7 +75,7 @@ from diffusers import StableDiffusionPipeline, DDIMScheduler
 scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", 
+    "runwayml/stable-diffusion-v1-5", 
     scheduler=scheduler,
 ).to("cuda")
 
@@ -98,7 +98,7 @@ lms = LMSDiscreteScheduler(
 )
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", 
+    "runwayml/stable-diffusion-v1-5", 
     scheduler=lms,
 ).to("cuda")
 

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -45,7 +45,7 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
             [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], or [`FlaxPNDMScheduler`].
         safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -50,7 +50,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """