[docs] Replace runwayml/stable-diffusion-v1-5 with Lykon/dreamshaper-8 (#9428)

* [docs] Replace runwayml/stable-diffusion-v1-5 with Lykon/dreamshaper-8

Updated documentation as runwayml/stable-diffusion-v1-5 has been removed from Huggingface.

* Update docs/source/en/using-diffusers/inpaint.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Replace with stable-diffusion-v1-5/stable-diffusion-v1-5

* Update inpaint.md

---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/zh/stable_diffusion.md

@@ -22,12 +22,12 @@ specific language governing permissions and limitations under the License.
 本教程将指导您如何通过 [`DiffusionPipeline`]  更快、更好地生成图像。
 
 
-首先，加载 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 模型:
+首先，加载 [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) 模型:
 
 ```python
 from diffusers import DiffusionPipeline
 
-model_id = "runwayml/stable-diffusion-v1-5"
+model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 pipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
 ```
 

examples/community/README.md

@@ -76,7 +76,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 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("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
+pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
 ```
 
 ## Example usages
@@ -363,7 +363,7 @@ clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K",
 
 
 guided_pipeline = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
@@ -462,7 +462,7 @@ def download_image(url):
     response = requests.get(url)
     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
 
-pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, variant="fp16")
+pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, variant="fp16")
 pipe.to("cuda")
 pipe.enable_attention_slicing()
 
@@ -921,7 +921,7 @@ if __name__ == "__main__":
     tensor_norm = torch.Tensor([[43.8203],[28.3668],[27.9345],[28.0084],[28.2958],[28.2576],[28.3373],[28.2695],[28.4097],[28.2790],[28.2825],[28.2807],[28.2775],[28.2708],[28.2682],[28.2624],[28.2589],[28.2611],[28.2616],[28.2639],[28.2613],[28.2566],[28.2615],[28.2665],[28.2799],[28.2885],[28.2852],[28.2863],[28.2780],[28.2818],[28.2764],[28.2532],[28.2412],[28.2336],[28.2514],[28.2734],[28.2763],[28.2977],[28.2971],[28.2948],[28.2818],[28.2676],[28.2831],[28.2890],[28.2979],[28.2999],[28.3117],[28.3363],[28.3554],[28.3626],[28.3589],[28.3597],[28.3543],[28.3660],[28.3731],[28.3717],[28.3812],[28.3753],[28.3810],[28.3777],[28.3693],[28.3713],[28.3670],[28.3691],[28.3679],[28.3624],[28.3703],[28.3703],[28.3720],[28.3594],[28.3576],[28.3562],[28.3438],[28.3376],[28.3389],[28.3433],[28.3191]])
 
     pipeline = DiffusionPipeline.from_pretrained(
-        "runwayml/stable-diffusion-v1-5",
+        "stable-diffusion-v1-5/stable-diffusion-v1-5",
         text_encoder=text_encoder,
         tokenizer=tokenizer,
         custom_pipeline="gluegen"
@@ -1744,7 +1744,7 @@ from diffusers.utils import load_image
 input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
 
 pipe = StableDiffusionReferencePipeline.from_pretrained(
-       "runwayml/stable-diffusion-v1-5",
+       "stable-diffusion-v1-5/stable-diffusion-v1-5",
        safety_checker=None,
        torch_dtype=torch.float16
        ).to('cuda:0')
@@ -1801,7 +1801,7 @@ canny_image = Image.fromarray(image)
 
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
 pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
-       "runwayml/stable-diffusion-v1-5",
+       "stable-diffusion-v1-5/stable-diffusion-v1-5",
        controlnet=controlnet,
        safety_checker=None,
        torch_dtype=torch.float16
@@ -1857,7 +1857,7 @@ python -m pip install intel_extension_for_pytorch==<version_name> -f https://dev
 **Note:** The setting of generated image height/width for `prepare_for_ipex()` should be same as the setting of pipeline inference.
 
 ```python
-pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
+pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
 # For Float32
 pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) # value of image height/width should be consistent with the pipeline inference
 # For BFloat16
@@ -1883,7 +1883,7 @@ from diffusers import StableDiffusionPipeline
 import time
 
 prompt = "sailing ship in storm by Rembrandt"
-model_id = "runwayml/stable-diffusion-v1-5"
+model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 # Helper function for time evaluation
 def elapsed_time(pipeline, nb_pass=3, num_inference_steps=20):
     # warmup
@@ -2507,7 +2507,7 @@ from diffusers import DiffusionPipeline
 
 # load the pipeline
 # make sure you're logged in with `huggingface-cli login`
-model_id_or_path = "runwayml/stable-diffusion-v1-5"
+model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 # can also be used with dreamlike-art/dreamlike-photoreal-2.0
 pipe = DiffusionPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16, custom_pipeline="pipeline_fabric").to("cuda")
 
@@ -2548,7 +2548,7 @@ image.save("black_to_blue.png")
 
 *With enough feedbacks you can create very similar high quality images.*
 
-The original codebase can be found at [sd-fabric/fabric](https://github.com/sd-fabric/fabric), and available checkpoints are [dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0), [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5), and [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) (may give unexpected results).
+The original codebase can be found at [sd-fabric/fabric](https://github.com/sd-fabric/fabric), and available checkpoints are [dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0), [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), and [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) (may give unexpected results).
 
 Let's have a look at the images (_512X512_)
 
@@ -3579,7 +3579,7 @@ import torch
 from diffusers import DDIMScheduler, DiffusionPipeline
 
 # Load the pipeline
-model_path = "runwayml/stable-diffusion-v1-5"
+model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 scheduler = DDIMScheduler.from_pretrained(model_path, subfolder="scheduler")
 pipe = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, custom_pipeline="sde_drag")
 pipe.to('cuda')
@@ -3674,7 +3674,7 @@ prompt = "A lying cat"
 prompt = "A lying dog"
 
 # Float32 is essential to a well optimization
-model_path = "runwayml/stable-diffusion-v1-5"
+model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 scheduler = DDIMScheduler(num_train_timesteps=1000, beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear")
 pipeline = NullTextPipeline.from_pretrained(model_path, scheduler=scheduler, torch_dtype=torch.float32).to(device)
 
@@ -3738,7 +3738,7 @@ controlnet = ControlNetModel.from_pretrained(
 
 # You can use any finetuned SD here
 pipe = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, custom_pipeline='rerender_a_video').to('cuda')
+    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, custom_pipeline='rerender_a_video').to('cuda')
 
 # Optional: you can download vae-ft-mse-840000-ema-pruned.ckpt to enhance the results
 # pipe.vae = AutoencoderKL.from_single_file(
@@ -4274,7 +4274,7 @@ from diffusers.utils import load_image, make_image_grid
 from diffusers.utils.torch_utils import randn_tensor
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
     custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
     torch_dtype=torch.float16
 )

examples/community/README_community_scripts.md

@@ -209,7 +209,7 @@ def seamless_tiling(pipeline, x_axis, y_axis):
         layer._conv_forward = asymmetric_conv2d_convforward.__get__(layer, torch.nn.Conv2d)
     return pipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True)
 pipeline.enable_model_cpu_offload()
 prompt = ["texture of a red brick wall"]
 seed = 123456

examples/consistency_distillation/README.md

@@ -51,7 +51,7 @@ When running `accelerate config`, if we specify torch compile mode to True there
 The following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example, and for illustrative purposes only. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/). You may also need to search the hyperparameter space according to the dataset you use.
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path/to/saved/model"
 
 accelerate launch train_lcm_distill_sd_wds.py \
@@ -85,7 +85,7 @@ Instead of fine-tuning the full model, we can also just train a LoRA that can be
 The following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/).
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path/to/saved/model"
 
 accelerate launch train_lcm_distill_lora_sd_wds.py \

examples/controlnet/README.md

@@ -45,7 +45,7 @@ write_basic_config()
 
 The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.
 
-Our training examples use [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).
+Our training examples use [Stable Diffusion 1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).
 
 ## Training
 
@@ -59,7 +59,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet.py \
@@ -81,7 +81,7 @@ biases.
 Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet.py \
@@ -102,7 +102,7 @@ accelerate launch train_controlnet.py \
 for running distributed training with `accelerate`. Here is an example command:
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
@@ -149,7 +149,7 @@ Optimizations:
 [bitandbytes install instructions](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet.py \
@@ -175,7 +175,7 @@ Optimizations:
 - set grads to none
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet.py \
@@ -235,7 +235,7 @@ it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer
 does not seem to be compatible with DeepSpeed at the moment.
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet.py \
@@ -365,7 +365,7 @@ huggingface-cli login
 Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="runs/fill-circle-{timestamp}"
 export HUB_MODEL_ID="controlnet-fill-circle"
 ```
@@ -397,7 +397,7 @@ Since we passed the `--push_to_hub` flag, it will automatically create a model r
 Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`.  Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)):
 
 ```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export MODEL_DIR="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export OUTPUT_DIR="runs/uncanny-faces-{timestamp}"
 export HUB_MODEL_ID="controlnet-uncanny-faces"
 

examples/dreambooth/README.md

@@ -313,7 +313,7 @@ Let's get started with a simple example. We will re-use the dog example of the [
 First, you need to set-up your dreambooth training example as is explained in the [installation section](#Installing-the-dependencies).
 Next, let's download the dog dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. Make sure to set `INSTANCE_DIR` to the name of your directory further below. This will be our training data.
 
-Now, you can launch the training. Here we will use [Stable Diffusion 1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
+Now, you can launch the training. Here we will use [Stable Diffusion 1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
 
 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
 
@@ -321,7 +321,7 @@ Now, you can launch the training. Here we will use [Stable Diffusion 1-5](https:
 
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export INSTANCE_DIR="dog"
 export OUTPUT_DIR="path-to-save-model"
 ```

examples/instruct_pix2pix/README.md

@@ -65,7 +65,7 @@ Configure environment variables such as the dataset identifier and the Stable Di
 checkpoint:
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export DATASET_ID="fusing/instructpix2pix-1000-samples"
 ```
 
@@ -122,7 +122,7 @@ for running distributed training with `accelerate`. Here is an example command:
 
 ```bash
 accelerate launch --mixed_precision="fp16" --multi_gpu train_instruct_pix2pix.py \
- --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \
+ --pretrained_model_name_or_path=stable-diffusion-v1-5/stable-diffusion-v1-5 \
  --dataset_name=sayakpaul/instructpix2pix-1000-samples \
  --use_ema \
  --enable_xformers_memory_efficient_attention \

examples/instruct_pix2pix/README_sdxl.md

@@ -155,7 +155,7 @@ If you're looking for some interesting ways to use the InstructPix2Pix training
 We aim to understand the differences resulting from the use of SD-1.5 and SDXL-0.9 as pretrained models. To achieve this, we trained on the [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) using both of these pretrained models. The training script is as follows:
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5" or "stabilityai/stable-diffusion-xl-base-0.9"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5" or "stabilityai/stable-diffusion-xl-base-0.9"
 export DATASET_ID="fusing/instructpix2pix-1000-samples"
 
 accelerate launch train_instruct_pix2pix.py \

examples/research_projects/diffusion_dpo/README.md

@@ -13,7 +13,7 @@ We provide implementations for both Stable Diffusion (SD) and Stable Diffusion X
 
 ```bash
 accelerate launch train_diffusion_dpo.py \
-  --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5  \
+  --pretrained_model_name_or_path=stable-diffusion-v1-5/stable-diffusion-v1-5  \
   --output_dir="diffusion-dpo" \
   --mixed_precision="fp16" \
   --dataset_name=kashif/pickascore \

examples/research_projects/multi_token_textual_inversion/README.md

@@ -53,7 +53,7 @@ accelerate config
 
 ### Cat toy example
 
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.
+You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), 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 of the documentation](https://huggingface.co/docs/hub/security-tokens).
 
@@ -74,7 +74,7 @@ And launch the training using
 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export DATA_DIR="path-to-dir-containing-images"
 
 accelerate launch textual_inversion.py \

examples/research_projects/onnxruntime/textual_inversion/README.md

@@ -39,7 +39,7 @@ accelerate config
 
 ### Cat toy example
 
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.
+You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5), 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 of the documentation](https://huggingface.co/docs/hub/security-tokens).
 
@@ -73,7 +73,7 @@ In order to leverage onnxruntime to accelerate training, please use textual_inve
 The command to train on custom data with onnxruntime:
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export DATA_DIR="path-to-dir-containing-images"
 
 accelerate launch textual_inversion.py \

examples/research_projects/vae/README.md

@@ -5,7 +5,7 @@
 ```
 cd examples/research_projects/vae
 python vae_roundtrip.py \
-    --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
+    --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5" \
     --subfolder="vae" \
     --input_image="/path/to/your/input.png"
 ```

examples/textual_inversion/README.md

@@ -63,7 +63,7 @@ Now we can launch the training using:
 **___Note: Please follow the [README_sdxl.md](./README_sdxl.md) if you are using the [stable-diffusion-xl](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).___**
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-v1-5"
 export DATA_DIR="./cat"
 
 accelerate launch textual_inversion.py \

src/diffusers/pipelines/README.md

@@ -56,8 +56,8 @@ 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.* [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
+- [`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.* [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/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.* [stable-diffusion-v1-5/stable-diffusion-v1-5/model_index.json](https://huggingface.co/stable-diffusion-v1-5/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
@@ -89,7 +89,7 @@ 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("runwayml/stable-diffusion-v1-5")
+pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -112,7 +112,7 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
     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/runwayml/stable-diffusion-v1-5` 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/stable-diffusion-v1-5/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("runwayml/stable-diffusion-v1-5")
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/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-v1-5"`:
 
 ```
 git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5
 ```
 
 and simply passing the local path to `from_pretrained`:
@@ -57,7 +57,7 @@ pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/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.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
     scheduler=scheduler,
 ).to("cuda")
 
@@ -94,7 +94,7 @@ from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 lms = LMSDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
     scheduler=lms,
 ).to("cuda")